Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Data Architecture for Blockchain Deployment of Digital Assets: LPBI IP Asset Classes I,II,III,V

Author: Aviva Lev-Ari, PhD, RN

UPDATED on 9/8/2021

LPBI’s DATA LEGACY

Top Ten Articles of N = 6,085 ranked by Views in 2012 of the 633 articles published in 2012.

	Article Name	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
0	Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?	851	3441	3701	4512	2458	1118	580	382	245	89
1	‘Gamifying’ Drug R&D: Boehringer Ingelheim, Sanofi, Eli Lilly	727	218	89	62	30	25	26	26	31	9
2	Biosimilars: Intellectual Property Creation and Protection by Pioneer and by Biosimilar Manufacturers	658	340	236	167	58	53	22	12	25	18
3	Treatment of Refractory Hypertension via Percutaneous Renal Denervation	631	385	37	21	5	6	3	2	5	12
4	Treatment of Refractory Hypertension via Percutaneous Renal Denervation	631	385	37	21	5	6	3	2	5	12
5	Future of Calcitonin…?	594	401	103	61	35	11	5	1	5	3
6	The mechanism of action of the drug ‘Acthar’ for Systemic Lupus Erythematosus (SLE)	546	685	681	568	262	193	76	44	28	27
7	Zithromax – likely to ‘max’ Heart Attack	535	33	6	0	1	0	0	1	0	0
8	Zithromax – likely to ‘max’ Heart Attack	535	33	6	0	1	0	0	1	0	0
9	Nitric Oxide has a Ubiquitous Role in the Regulation of Glycolysis – with a Concomitant Influence on Mitochondrial Function	525	286	113	111	54	54	78	62	45	16
10	Closing the Mammography gap	497	323	229	185	44	44	41	18	26	11

1. Article #
2. Article Name
3. Number views per year since publication date

• We need to add from each article in the journal:

1. Author(s) Name
2. Tags
3. Research Categories
4. Biological Images
5. WordCloud
6. Date of Publication
7. Is the article a Curation of a Scientific Report
8. Is the article REAL TIME content created at a conference
9. Is the article a Collection of article forming a a REAL TIME e-Proceeding
10. Is the article a Tweet Collection

• From Books eTOCs we need to add:

1. Is the article in the eTOCs of the 18 Book
2. In which Series
3. In which Volume
4. In which Chapter [all articles in One chapter in a Book form a Collection for NLP]
5. NLP – generates for each article the following GRAPHS:

5.1       One WordCloud5

5.2       One Bar Diagram Plot for 25 Keywords

5.3       For ONE Chapter Number of WOrdClouds and Number of Bar Diagram plot is identical to number of Articles in the Chapter

5.4       From the MERGED Text file of ALL the articles in ONE chapter in A book – ONE Hypergraph plot is created

5.5       From the MERGED Text file of ALL the articles in ONE chapter in A book – ONE Tree Diagram Plot is created with 3 sections because the graph is too large

5.6       All Biological Images with Text legend – the text can be subjected to NLP

THEN

Blockchain IT Infrastructure Design

1. Blockchain Transactions System: Using Fluree Blockchain Open Source – Design the Features and functionality including interface with Wolfram’s Deep Learning Natural Language Processing (DL-NLP)
2. https://www.shopify.com/website GUI Design (Front-end) to manage outside queries to the system (B2C) using Fluree Blockchain Back-end
3. Design Recommendation Engine Response (RER): for a Knowledge Graph Database type of Information System (IS): Design a smart Query system using indexing, Journal ontology and predicated node-edge relations
4. The Interface between Fluree and Wolfram enables execution of DL-NLP and storing the results in Fluree Knowledge Graph Database

UPDATED on 8/5/2021

Smart use of customizable software in conjunction with 1.0 LPBI IP assets and competencies:

Mission #1:  Natural Language Processing (NLP) – Team in USA & India – Medical Text Analysis with NLP – on LPBI 3.3 Giga Bytes of Content. Two NLP types: (a) Statistical NLP and (b) Deep Learning by Machine Learning using Wolfram Language for Biological Sciences

https://pharmaceuticalintelligence.com/2021-medical-text-analysis-nlp/

In Mission #1: Using Machine Learning (ML) algorithms for Text Analysis of our 3.3 Giga Bytes of English Text

Statistical Natural Language Processing (NLP). This yields

• WordClouds, Bar Diagrams for each article and Tree Diagrams for collection of articles

Deep Learning (DL) for Semantic Analysis of the Text. This yields

• Hyper-graphs for collections of articles using knowledge graphs in knowledge graph databases.

Mission #2:  Blockchain IT and NLP Processing API generating NLP visualization Products used by Knowledge Graphs stored in Graph Databases – Content monetization infrastructure B2B and B2C.

https://pharmaceuticalintelligence.com/blockchain-transactions-network/

In Mission #2: The Transactions-enabled blockchain platform for Content Monetization of IP assets embodies the  development of a Blockchain information technology infrastructure that is transactions-enabled allowing payments for content digital products. On the blockchain we will store all the following Digital products:

1.0 LPBI four IP Asset Classes:

IP Asset Class I: Journal articles +6,070

IP Asset Class II: electronic Books in Medicine

IIa.     18 e-books in English

IIb.     18 Bi-Lingual electronic Table of Contents (eTOCs): Spanish & English

IIc..    18 e-Books – NLP visualization products

IId.     18 e-Books Expert written NLP results Interpretations: Spanish & English

IIe.     For 18 books all audio Podcasts in Spanish & English – selective content

IIf.     For each e-Series, A,B,C,D,E we plan to publish a volume containing the Bi-Lingual Spanish-English electronic Table of Contents in each e-Series for all the e-Books in the e-Series – an additional 5 Bi-Lingual e-Books.

IP Asset Class III.      e-Proceedings: 100 volumes

IP Asset Class V:       Gallery of Biological Images 6,200 to grow if NLP yields 10 graphs 📊 per article

2.0 LPBI all products of NLP, see Yields for Mission #1, above PLUS IP Asset Classes XI, XII, XIII, mentioned, below serve as a compelling justification for the selection of Blockchain Transactions Network architecture as our IS/IT platform.= among other alternatives.

• IP Asset Class XI: New Digital Products as a result of Discovery 💡 of new digital products derived from and created for the new queries by users to be generated on the fly
• IP Asset Class XII: All digital products of Mission #3, below and
• P Asset Class XIII: All digital products of Mission #4, below

Mission #3:  New GENRE of Multimedia Scientific Books: These 18 LPBI e-Books will be the first on the Medical Books Market to contain Text Analysis with NLP of the original e-Books. BioMed e-Books – Book Republishing in new GENRE – Bi-Lingual and Multimedia Audio Podcast for Books in the 18-e-Books in five e-Series: A,B,C,D,E . The New book architecture for each Book:

• Part A: Spanish and English electronic Table of Contents in Text and in Audio Podcast.
• Part B: NLP & Expert Interpretation of the visualizations in Text and Podcast: English and Spanish,NLP results for the content of the e-Book
  • Hyper-graphs for each Chapter
  • Domain Knowledge Expert Interpretation of all NLP results:

  TO BE CREATED – English Text and Spanish Text

  TO BE CREATED – English Audio Podcast and Spanish Audio Podcast

• Part C: Editorial of original book (Preface, Volume Introduction, Volume Summary and Epilogue) -English Audio Podcast

https://pharmaceuticalintelligence.com/2021/07/24/proposal-for-new-e-book-architecture-combining-a-bi-lingual-etocs-english-spanish-with-nlps-results-of-medical-text-analysis-series-b-genomics-volume-1-2-and-series-c-cancer-volume-1/

Mission #4: Synthetic Biology Software for Drug Discovery 💡 targeting Galectins. See details in the link, below 

https://pharmaceuticalintelligence.com/synthetic-biology-in-drug-discovery/

UPDATED on 7/28/2021

From: Erich Greenebaum <erich@prosperci.com>
Date: Wednesday, July 28, 2021 at 10:24 AM
To: Aviva Lev-Ari <avivalev-ari@alum.berkeley.edu>
Cc: “Dr. Stephen J. Williams” <sjwilliamspa@comcast.net>
Subject: Re: FLUREE: Reminder: LeadSemantics: Automatic Knowledge Graph generation from secure NLP pipelines with Fluree. starts in 1 hour

Hope you enjoyed the presentation.

It is interesting how similar Fluree and BurstIQ are from an architectural perspective, notably:

• They both layer a graph onto of a underlying blockchain persistence layer
• They both embed “smart contracts” into the data to manage permissions and other interactions with the data

Meanwhile, in this presentation, we saw a directly analogous application of graph+blockchain+NLP in practice. Here we saw it realized using LeadSemantics, but one could just as easily use Linguamatics or Wolfram. I think I mentioned before that Wolfram has a new SPARQL interface available, which can connect directly to Fluree for mutating the graph.

There are a couple pluses and minuses to consider:

• Fluree is an open source product, so there is zero cost to use it. They do have an optional hosting model where they run the system for you, and that obviously comes at a cost. Otherwise you would run it yourself at i.e. AWS. The big advantage here is that there is no licensed technology dependency going forward, and the LPBI technology solution can be marketed AND SOLD as its own product free of that encumbrance.
• On the other hand, BurstIQ has HIPAA compliance out oft he box. That’s not to say one couldn’t create a HIPAA compliant solution using Fluree, just that there would be additional hoops to jump through for certification. In the event LPBI envisions the corpus+knowledge graph being tied to patient research, this could be a benefit of BurstIQ.

Hope this finds you well!

/eg

UPDATED ON 7/26/2021

From: Erich Greenebaum <erich@prosperci.com>
Date: Monday, July 26, 2021 at 2:09 PM
To: Aviva Lev-Ari <avivalev-ari@alum.berkeley.edu>
Subject: Re: Who wish to write the story of LPBI for valuation referring to the stream of innovations, below?

On Jul 26, 2021, at 12:54 PM, Aviva Lev-Ari <avivalev-ari@alum.berkeley.edu> wrote:

6. LPBI and BurstIQ are architecting NOW the first NLP-Blockchain IT infrastructure in existence

Hi Aviva,

I have tried to explain this many times, and am getting a little fatigued. The powerful aspect is hydrating a knowledge graph with NLP inferences so that they can be used for DISCOVERY. It is not just storing those inferences on a blockchain. I agree there is also value to committing the NLP inferences to a blockchain for purposes of provenance, but this is the very important part I want you to understand – The INFRASTRUCTURE behind BurstIQ is NOT JUST A BLOCKCHAIN. Continuing to repeat this phrase that you use “NLP-BLcockchain IT Infrastructure” is minimizing and naive. The NLP inferences are USELESS as a practical matter if they are not part of a graph database (in this case LifeGraph), and so it is critical that you begin thinking in more complete terms about the larger architecture of the system.

Hope this helps.

/eg

UPDATED on 6/26/2021

From: Erich Greenebaum <erich@prosperci.com>
Date: Saturday, June 26, 2021 at 9:54 AM
To: Aviva Lev-Ari <aviva.lev-ari@comcast.net>, Stephen Williams <sjwilliamspa@comcast.net>, John McCarthy <jfmccarthy2@gmail.com>, “George N. Gamota Jr” <gngamotajr@gmail.com>
Cc: David DiPerri <david@prosperci.com>
Subject: Blockchain Indexing

Greetings all,

I hope everyone is having a nice weekend.

On one of our calls I mentioned “The Graph” and wanted to provide the link here as background.

Based on information I’ve reviewed, BurstIQ appears to be a combination of a blockchain ledger with a graph database overlay for indexing the data (with smart contracts embedded on-chain for access control, etc) – this is the same basic architecture as Fluree. Meanwhile, The Graph is essentially a GraphDB index across multiple blockchains, IPFS, and other similar decentralized networks; a meta-index of decentralized data, exposed via APIs (actually one big meta-API). In theory, decoupling the indexing layer from underlying data stores can have substantial utility. One of the great promises of “Web3” is the ability to search across distributed data, and The Graph is a great example of the principle in action.

I’ll look forward to learning from Amber how data in their system can be linked to external data on other blockchains and networks, and then for LPBI, whether that would be an important feature, i.e. NLP inferences between LPBI content and external data; see i.e. BioFed.  Likewise it will be good to learn if data in BurstIQ can be discovered from external systems such as the graph, to the extent that is also a desirable feature for LPBI.

Regards,

/eg

UPDATED on 6/23/2021

On June 18, 2021 Erich Greenebaum <erich@prosperci.com> wrote:

I did want to share a little detail about the open source semantic graph database project called “Fluree.” Critically, when it comes to hydrating a knowledge graph using NLP, Fluree supports SPARQL queries directly, and so I believe you would be able to interact against it directly from Wolfram. As graph databases are finding currency in NLP/ML applications, this struck me as potentially powerful tool in your work.

An interesting property of Fluree is that its state is persisted on a blockchain style database, which facilitates what they refer to as “time travel” across the history of the graph. This comes along with providing cryptographically provable provenance of the data. Finally, they build a “smart contract” approach into their data model to handle access control and other rule based logic within the graph, which opens up a lot of possibilities of exposing datasets publicly while still protecting proprietary data at a very fine grained level, i.e. one might want to provide search facilities while not actually exposing the content without some licensing agreement.

Again, I want to avoid speculating too far before I have a better sense of the BurstIQ architecture, but I mention Fluree mostly because you might find the technology interesting in your NLP work in general. If it proves of interest to you,  I’d be happy to chat about it more.

Hope this finds you well!

/eg

On Jun 11, 2021, at 11:51 AM, Erich Greenebaum <erich@prosperci.com> wrote:

Hi Aviva,

If the objective is to enrich LPBI’s existing “data network space” within the BurstIQ ecosystem, intuitively it seems better that the students would work in that same space. It might also make sense for them to have their own space for prototyping purposes. Perhaps these spaces can be federated in some way to facilitate interoperability, but again I’m too far down the rabbit hole of speculation already.

On the BurstIQ website there are references to “data on chain” using a trademarked “BurstChain” technology. Similarly, there are references to “Data in Another Dimension” realized as “immutable longitudinal profiles of people, places, and things” creating “multi-dimensional blockchains, called LifeGraphs(tm).” From this I speculate there is some Graph DB based overlay of  “on chain” data, and yet they offer no clear publicly available statements that a computer scientist could make literal sense of. As this relates to the prospective student NLP work, perhaps there are ways for the DB layer in one “space” to reference “on chain” data in another… but, who knows.

/eg

UPDATED on 6/21/2021

From: Erich Greenebaum <erich@prosperci.com>

Date: Monday, June 21, 2021 at 9:38 AM

To: “Aviva Lev-Ari, PhD, RN” <AvivaLev-Ari@alum.berkeley.edu>, “Stephen Williams, PhD” <sjwilliamspa@comcast.net>

Subject: Upcoming presentation on NLP and Knowledge Graphs with Fluree

Greetings,

This came across the wire this morning and given the coincidence, I thought I’d share in case you’d like to attend. Note the first item below, “Automatic Knowledge Graph Generation from Secure NLP Pipelines with Fluree.

Depending on architecture and cost considerations, one approach might be using this sort of open source pipeline (i.e. Wolfram->Fluree) for generating the NLP data, then exporting to BurstIQ for dissemination.

All the best,

/eg

From: Erich Greenebaum <erich@prosperci.com>

Date: Monday, June 21, 2021 at 9:38 AM

To: “Aviva Lev-Ari, PhD, RN” <AvivaLev-Ari@alum.berkeley.edu>, “Stephen Williams, PhD” <sjwilliamspa@comcast.net>

Subject: Upcoming presentation on NLP and Knowledge Graphs with Fluree

Greetings,

This came across the wire this morning and given the coincidence, I thought I’d share in case you’d like to attend. Note the first item below, “Automatic Knowledge Graph Generation from Secure NLP Pipelines with Fluree.

Depending on architecture and cost considerations, one approach might be using this sort of open source pipeline (i.e. Wolfram->Fluree) for generating the NLP data, then exporting to BurstIQ for dissemination.

All the best,

/eg

From: Erich Greenebaum <erich@prosperci.com>

Date: Saturday, June 19, 2021 at 4:48 PM

To: “Aviva Lev-Ari, PhD, RN” <AvivaLev-Ari@alum.berkeley.edu>

Cc: “David DiPerri, CPA” <david@dealdonebb.com>, “Dr. Larry Bernstein” <larry.bernstein@gmail.com>, “Prof. Marcus W Feldman” <mfeldman@stanford.edu>, “Stephen Williams, PhD” <sjwilliamspa@comcast.net>, “John F. McCarthy” <jfmccarthy2@gmail.com>, “Ofer Markman, PhD” <oferm2020@gmail.com>, “George N. Gamota Jr” <gngamotajr@gmail.com>

Subject: Re: IMPORTANT e-mail: Please Review and REPLY —>>> SEE YOUUR NAME BELOW —>>> SLIDE Decks presented on June 8, 2021 at LPBI’s Global Monthly Zoom Meeting

Aviva,

I did not intend my slides to be shared with Amber, as they were meant to provide strategic perspective for your internal deliberations. At this point, I fear she will see me as an adversary, which is both inaccurate and strategically counterproductive framing for your goals.

While I didn’t find it on their website, I did manage to track down a white paper from 2017 which is elucidating. If you haven’t read it, I encourage you to review. In short, as of 2017, it’s not entirely clear how your business model fit into their strategy. That’s not to say it can’t, or that their business model hasn’t evolved since 2017, but as of that writing it’s clear they have a patient centered goal of empowering individuals to control access to their protected medical information while enabling a marketplace for i.e. discovering participants for clinical trials, building networks of people suffering from similar medical conditions, etc. 

Meanwhile, I was able to gain a much better sense of their architecture. Based on that review, at least at a cursory level, it looks like the key ingredients are in place. Nevertheless, if you look at the middle tier, it looks as if their access model is about managing access to personal  information for clinical studies, etc. The “data grid” layer appears to have all the core components necessary for your goals, but at least in this snapshot in time, it’s clear your use case wasn’t considered as a target application.

<PastedGraphic-1.png>

As I have said, the plusses to something like BurstIQ are that they manage all of the development and hosting of the platform, compared to LPBI being responsible for establishing and maintaining a solution. Whereas the downside is that it is not part of your own intellectual property, is a commercial dependency in any exit deal you might consider, and is a downside risk if they go out of business or change direction as part of M&A activity or other eventualities when compared to well established open standards. Meanwhile, you have mentioned several times that no one had ever asked about combining NLP processes with BurstIQ – perhaps there are good reasons for that; maybe BurstIQ is not a good representational model for those kinds of data. We will endeavor to find out.

My open questions around BurstIQ are:

• Is the data model rich enough to exploit relationships NLP generates for purposes of search/discovery/ML? The white paper suggests the underlying “data grid” is, but the other layers of the architecture suggest that’s not their intended use case. We’ll see more in discovery.
• How does their “marketplace” feature actually apply to your use case? How are financial transactions handled? How would NLP results fit into their market model as a distinct product? Can they provide examples of the sort of market relationships your application requires, where customers are paying for access to a corpus and related NLP results.
• Can data in BurstIQ be correlated against external datasets? As an example, see The Linked Open Data Cloud. Does it make sense for LPBI NLP processes to generate links to such external data to further enrich discovery?
• Does BurstIQ’s brand awareness/momentum in the target markets of LPBI represent a significant market advantage for LPBI in its own right? Do LPBI’s market goals align with those of BurstIQ.
• How do the proprietary BurstIQ data-space and features compare with truly distributed approaches to this problem?
• My impression from reviewing their site is HIPPA compliance for sensitive study data is a key differentiator for their product, whereas these may not be key requirements for your purposes. To what degree are compliance features in the context of sharing data across organizational boundaries the key commercial advantage of BurstIQ?

In short, there are both technological and business strategy assessments to be made, and I continue to reserve any judgement whatsoever about the fit of BurstIQ until after an initial discovery meeting with Amber.

Hope this helps.

/eg

UPDATED on 6/21/2021

Product Price List Itemized for 1.0 LPBI & 2.0 LPBI

B2B & B2C will access 1.0 LPBI & 2.0 LPBI Products

 

Price List below represents B2C

Market installations in B2B will have a different Pricing structure based on Point-of-Research (POR)

 

• 0 LPBI – Digital Products
• 0 LPBI – Visualization (Graphical) Products & Multi-Lingual Interpretations

Product Price List Itemized for 1.0 LPBI Digital Published Products

 

• One Article Download $30
• Book Purchase on Amazon.com

https://lnkd.in/ekWGNqA

 

(1) Price List of Books (Price range $75 to $135 per book, Six volumes on Cardiovascular bundled for $515

(2) Page per View

• Book Page Download on Amazon.com – Price determined by Amazon.com

TO BE AVAILABLE for download and purchase on Blockchain

Downloads of 1.0 LPBI’s Digital Printed Products on 2.0 LPBI Infrastructure

• One eProceedings or One Tweet Collections         $100
• One Biological Image $30
• Spanish eTOCs – One volume $15
• Spanish eTOCs all 18 Volume $125

 

Product Price List Itemized for 2.0 LPBI Visualization Artifacts produced by

AI/ML/NLP & Interpretation Text Products

 

A PowerPoint Presentation based on a Proof-of-Concept of 33 articles in Cancer, including examples for each Visualization Artifact is available. Currently, these products are not YET available for sale – to download digital content following payment requires a BLOCKCHAIN platform with the features mentioned above – it is under design – Work-in-Progress

• WordCloudsr  epresenting Article abstracts                                 $20
• Bar Diagrams  representing Word Frequencies                                    $20
• Hyper-graphs  representing Semantic relationships                     $20
• Tree Diagrams  representing hierarchical clustering of conceptual similarities          $20
• Interpretation of Visualization Artifacts

English                $20

Spanish              $30

Japanese            $30

Russian               $30

UPDATED on 6/18/2021

From: Erich Greenebaum <erich@prosperci.com>

Date: Friday, June 18, 2021 at 10:16 AM

To: “Stephen Williams, PhD” <sjwilliamspa@comcast.net>

Cc: “Aviva Lev-Ari, PhD, RN” <aviva.lev-ari@comcast.net>

Subject: Re: Exploration of Collaboration on Medical Text Analysis using Machine Learning (ML) and Natural Language Processing (NLP)

In most enterprise computing projects, it is very typical to have “development” vs” production” environments. In this context, it seems that you are in the “development” mode, and so it makes good sense to do that work in a separate environment in my view.

I will be learning more about BurstIQ next month, but I did want to share a little detail about the open source semantic graph database project called “Fluree.”

https://flur.ee/2020/02/25/wake-forest-school-of-medicine-partners-with-fluree-to-improve-research-analysis/

Critically, when it comes to hydrating a knowledge graph using NLP, Fluree supports SPARQL queries directly, and so I believe you would be able to interact against it directly from Wolfram. As graph databases are finding currency in NLP/ML applications, this struck me as potentially powerful tool in your work.

An interesting property of Fluree is that its state is persisted on a blockchain style database, which facilitates what they refer to as “time travel” across the history of the graph. This comes along with providing cryptographically provable provenance of the data. Finally, they build a “smart contract” approach into their data model to handle access control and other rule based logic within the graph, which opens up a lot of possibilities of exposing datasets publicly while still protecting proprietary data at a very fine grained level, i.e. one might want to provide search facilities while not actually exposing the content without some licensing agreement.

Again, I want to avoid speculating too far before I have a better sense of the BurstIQ architecture, but I mention Fluree mostly because you might find the technology interesting in your NLP work in general. If it proves of interest to you,  I’d be happy to chat about it more.

Hope this finds you well!

/eg

On Jun 14, 2021, at 8:21 PM, Stephen Williams <sjwilliamspa@comcast.net> wrote:

Dear Dr. Greenebaum,

I was referred on this email as I am working, along with Aviva, on NLP strategies with a few fellows and interns.  We are currently using the environment on Wolfram to host data as well as algorithms to conduct text cleanup and analysis.  This platform has the ability to integrate Python scripts.  As such I feel it might be more useful to have students use their UTA platform to test Python scripts for NLP use eventually on LPBI’s Wolfram account and space.  I look forward to getting your opinion on the matter and hopefully early next week we could get together on a Zoom meeting to discuss this further.

Sincerely

Stephen J Williams, PhD

LPBI Group, CSO

Assistant Professor

Temple University, CST Biology

UPDATED on 6/7/2021

Review Graphics DB:

https://flur.ee

UPDATED on 5/8/2021

Discussion on the Number of Relations EXPECTED to be revealed by NLP by Linguamatics

I used the ratio of 673 found relations in 33 articles to say about 20 relations in one article

• Thus, in 600 articles x 20 = 12,000 Relations – In the 4 volumes: 2 Cancer, @ Genomics – Together ~600 articles
• Thus, 6,000 articles as Posts in the ENTIRE Journal Corpus (Plus ~400 Pages) x 20 = 120,000 Relations

Blockchain Infrastructure will be designed for On demand Analytics of LPBI Stored Content:

The Data Science functionality of the Blockchain IT Infrastructure will enable to perform NLP, TEXT MINING and Analytics on article collections.

Content Consumer Specifies preference/selection of the topic CONTEXT from the following three Collection Types

• B2C – Independent Scientists select topic context
• B2B – inside an organization, Knowledge workers select topic context

Suggested are the following Article Collection Types for CONTEXT of Semantic Analysis:

Article Collection Type 1: All Articles in a Chapter in a Book

• In Book x  [x = 1,2,3,…,18]
• An Article Collection is defined as = All Articles in a Chapter in a Book for Book x  [x=1,2,3,…,18]

Article Collection Type 2: The Research Category attribution assignment made by authors/curators at Publishing time

• Type 2 is defined as = any subset of articles in a given RESEARCH CATEGORY (RC) 
• Dynamic Journal Ontology [RC = 1,2,3…, 733]
• For Article Collection Type 2, it is suggested to rank all articles in a given RC by Number of Views, selection top 12, from top to 12th by Views

Article Collection Type 3:  Keywords in the Article Title 

• Search for all articles by a keyword or keywords in the Article Title 
• Select by either Number of Views, or by
• Most recent published

HYPOTHESES :

#1:

Highest Number of Relationships EXPECTED to be found, in ranked order

1. Article Collection Type 1

2. Article Collection Type 2 

3. Article Collection Type 3

#2:

Strength of relationship suggested by Dr. John McCarthy. 

A STRENGTH Measure for semantic relationship needs to be developed, it is like an analogy for Affinity or Similarity

THEN 

Highest STRENGTH of relationships EXPECTED to be found, in ranked order

1. Article Collection Type 2

2. Article Collection Type 1 

3. Article Collection Type 3

UPDATED on 4/30/2021

Spanish Edition

of LPBI Group’s BioMed e-Series

18 Books in Medicine 

https://pharmaceuticalintelligence.com/biomed-e-books/

All books are available for Sale and Page Downloads on Amazon.com

https://lnkd.in/ekWGNqA

The Context:

Montero’s partner, known as Leaders in Pharmaceutical Business Intelligence (LPBI) Group, HQS in Boston, MA, USA is planning the launch of its Digital Store in a Healthcare Digital Marketplace designed and operated by BurstIQ. The Digital Store is using a Blockchain Transactions Network as its IT platform for B2C and B2B transactions for their digital content.

The available digital content in Life Sciences, Pharmaceutical, Healthcare, Medicine, Medical Devices, Medical equipment, Biotech and Bioscience includes the 1.0 LPBI IP Portfolio of an e-Scientific Publisher – 3.3 Giga bytes of English text and Biological images. The portfolio contains four IP asset classes:

• 6,000 scientific Journal articles – curations of peer reviewed scientific findings – with clinical interpretation written by experts.
• 18 Books in Medicine and Pharmaceutics
• 100 e-Proceedings of the most impoetant Medical and Biotech Global Conferences that we covered in real time using PRESS passes and Tweet Collections from 36 events
• 5,100 Biological images used in the articles mentioned above

The Blockchain design of the IT platform for Content Transactions will include, in addition to the 1.0 LPBI IP Portfolio (2012-2020) described above (the four IP asset classes), the 2.0 LPBI IP Portfolio of visualization artifacts currently under development. 

2.0 LPBI IP Portfolio (2021-2025) consists of expert Interpretation of the visualization products resulting from Medical Text Analysis and Text Mining of all its Digital Published Products. The Text Analysis and Text Mining is performed by advanced algorithms from Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP).

• Montero is currently translating from English into Spanish the cover pages and the electronic Table of Contents of 18 Books in Medicine and Pharmaceutics
• This project originator is Dr. Aviva Lev-Ari, PhD, RN, who is the Founder of 1.0 LPBI and 2.0 LPBI and Editor-in-Chief of the Journal [com] and of the BioMed e-Series [https://lnkd.in/ekWGNqA]

In 2021 LPBI Group began the transition from:

A nine years young profile of being

• A very dynamic and cutting age electronic Scientific Publisher,

Known as 1.0 LPBI during 2012 – 2020

toward designing its new image while becoming a new Company with a new profile,

Known as LPBI, in 2021-2025

• A Medical Text Analysis company using (NLP-ML-AI) – Software as a Service (SaaS) and
• Content Monetization (on a Blockchain Transactions Network) – Blockchain as a Service (BaaS).

The Blockchain platform design includes the following five features:

1. Recommendation Engine residing on a blockchain
2. Permissions,
3. Immutable LEDGER,
4. Smart contracts and
5. Cyber-security for protecting the IP

 

Economies of scale will be achieved by:

• Development of one content promotion system
• Unified IT cloud-based infrastructure
• Maintenance of B2C IT transaction system in a Digital Store at a Healthcare Marketplace [monthly fee paid for the use of the network and
• Installations of B2B Point-of-Research (PORs) at institution – pay per use vs subscription base – type of contracts not specified yet.

 

The Competitive Landscape

 

2.0 LPBI is a Very Unique Organization

https://pharmaceuticalintelligence.com/2021/03/02/2-0-lpbi-is-a-very-unique-organization/

 

The uniqueness and the competitive space is addressed at length in 1.0 LPBI Prospectus, a 300 page document

• It Is sent as an attachment separately
• List of competitors using Blockchain are telemedicine companies not scientific e-publishers

https://pharmaceuticalintelligence.com/blockchain-transactions-network/

• NO other e-Scientific Publisher is Using NLP on a Blockchain platform.
• LPBI has the FIRST MOVER ADVANTAGE over all other e-Scientific Publishers
• LPBI had the FIRST MOVER ADVANTAGE in curation of scientific findings in 2012.
• Our NLP Partner, Linguamatics said: No client ever asked us about Blockchain
• Our Blockchain IT Partner, BurstIQ said: No client ever asked us about NLP
• LPBI is now working with both on an entirely solution.
• All the Text Analysis with NLP currently done converns Literature in the Public Domain: PubMed, MedLIne, Ontologies and Formularies
• Peer reviewed articles in PubMed, MedLine publish content only on EXPERIMENTS and on Clinical Trials
• LPBI content is CURATIONS by Experts, secondary research on the clinical interpretation of primary research using ONLY peer reviewed published articles as sources.

1.0 LPBI Products versus 2.0 LPBI Products

1. 1.0 LPBI – Blockchain LEDGER for Content Monetization of IP Asset Classes I, II, III, V

LPBI 1.0, 2012-2020 is the creator and the custodian of the Portfolio of ten IP Assets Classes. For content monetization, we identified four of the ten assets that are related to the curation methodology and process:

Class I: Journal articles,

Class II: 18 Books,

Class III: 100 e-Proceedings & Tweet Collections,

Class V: +5,100 Biological Images

The Use Case for data entity design and meta data architecture is a Journal article. It has the following Profile:

• Article ID – IP Asset Class I
• Author
• URL
• Date of Publication
• Research Categories assigned by Author(s)
• Is this Article a Curation or a Scientific Report
• Is this Article included in a Book? If yes, in which Books – IP Asset Class II
• Is this Article a component of an e-Proceedings? If Yes, What is the Conference Title, Date, Location – IP Asset Class III
• List of Biological Images included in this article – IP Asset Class V

 

2. 2.0 LPBI Digital Products:

ALL the content of 3.3 Giga Bytes is to be subjected to Text Analysis with AI/ML/NLP. The Products of this Machine Learning analysis of text are in the format of visualization artifacts (Graph Files). They represent the SEMANTIC relationships between concepts and keywords ACROSS (A) all articles in One Chapter of the book or (B) in several main Categories of Research.

• This aggregation of content, i.e., 20 articles making up a Chapter in a book or
• 20 articles were all written by different authors/curators, yet all have been assigned the same research categories. This means that semantically these two collections of articles represent a common theme or similar location on the Tree of knowledge, represented by spatial proximity to a similarity graph (Hyper-graph) or on an hierarchical clustering graph (Tree Diagram).

The Benefits of Text Analysis performed by Machine Learning Algorithms

• All articles are in one Chapter in the book
• Some Articles in several main Categories of Research are assigned to the article by the Author/Curator
• Some research categories have +1,000 articles assigned, i.e., Cancer Biology & Therapies

These attributes: Assignment of an article a Chapter in a book or a research category represent the thematic context of the article.

The context reveals INSIGHTS needed for understanding relationships among articles vs each of the 6,000 articles to stand alone as a singular point in knowledge space. Thus these two affiliation criteria serve as classifiers.

LPBI’s Journal has an ontology of 670 categories of research. In theory one could run NLP on all the articles in each of these 670 categories and reach a semantic map for the entire universe of the Journal Corpus.

Current offering from LPBI are four corpuses, Text Analysis with NLP done by Machine Learning software is the ENGINE for identification of conceptual relationship in context.

The Suite of Products – A Portfolio of Intellectual Properties (IP):

Four Corpuses in details:

1. 6,000 scientific Journal articles – curations of peer reviewed scientific findings – with clinical interpretation written by experts.
2. 18 Books in Medicine and Pharmaceutics
3. 100 e-Proceedings of the most important Medical and Biotech Global Conferences covered in real time owithPRESS passes and Tweet Collections from 36 events
4. 5,100 Biological images used in the articles above

The Journal consists of

• Posts (6,037 on 4/23/2021),
• Pages (393 on 4/23/2021)

Posts consist of four Article Types:

• Type A: Authored article by an Expert, Author, Writer (EAW) or more then one – all are PhD, MD, MD/PhD, PharmD level
• Type B: Curated article by an EAW or more then one – all are PhD, MD, MD/PhD, PharmD level
• Type C: Scientific Reporting by an EAW, by a PostDoc level or by a Masters Level
• Type D: e-Proceedings of Conferences and Tweet Collections. Namely, all e-Proceedings are Posts not Pages

Pages consist of three Page Types

• Public Published Page
• Password Protected Page
• Public Published Page that is a Book

Example of Recently Published Posts, Live links

• Apr 19th, 10:22 AM Identification of novel genes in humans that fight COVID-19 infection
• Apr 14th, 4:42 PM The top 10 medtech M&A deals of 2020
• Apr 14th, 12:56 PM Mechanism of Thrombosis with AstraZeneca and J & J Vaccines: Expert Opinion by Kate Chander Chiang & Ajay Gupta, MD
• Apr 13th, 3:37 PM Fighting chaos with care, community trust, engagement must be cornerstones of pandemic response
• Apr 12th, 3:34 PM Linking Thrombotic Thrombocytopenia to ChAdOx1 nCov-19 Vaccination, AstraZeneca

18 Books in Medicine and Pharmaceutics

The BioMed e-Series, 18 volumes consist of five multi volume series.

BioMed e-Series

• Series A: e-Books on Cardiovascular Diseases – Six volumes
• Series B: Frontiers in Genomics Research – Two volumes
• Series C: e-Books on Cancer & Oncology – Two volumes
• Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases and Genomic Endocrinology – Four volumes
• Series E: Patient-Centered Medicine – Four volumes

Of Note,

• The majority of the articles in these books are CURATIONS
• Curation of Scientific Findings is a unique methodology for creation of Posts which are Journal articles of com.
• The Curation process is explained in Chapter 1 in Series A, Volume 2

Cardiovascular Diseases, Volume Two: Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation. On Amazon.com since 11/30/2015

http://www.amazon.com/dp/B018Q5MCN8

 

• These 18 Books consist of application of the Curation Methodology for the creation of electronic Table of Contents (eTOCs) for each of the 18 books
• This Methodology allowed our Expert Editors to produce systematic classification of all eTOCs by culling articles from the journals’ research categories to create a one of a kind eTOCs for each volume
• Each eTOCs represents a Non Fungible Token (NFT)
• An Update to existing Journal articles represents a Non Fungible Token (NFT)
• Dr. Aviva Lev-Ari was involved in the creation of 14 of the eTOCS of the BioMed e-Series books
• Except for the following four volumes: Series B, Volume 1 (Dr. Williams & 3 Editors); Series D, Volume 1 and Series E, Volume 2 & 3 (Single Author/Editor, Dr. LHB]

In 2021, 2.0 LPBI is planning to launch a Blockchain Transactions Network Ecosystem to sell Journal Articles, e-Books, e–Proceedings & Tweet Collections and Biological Images

Regarding Selling books and the Blockchain IT Platform:

The current plan is to promote the books and refer the interested Content Consumer/End-User to purchase the Books on Amazon.com which grants 35% of books Sales to Authors. Amazon.com does not allow selling the book on any other platform, per contract signed by authors under KDP.

However, the Transfer of Ownership of the LPBI IP Portfolio can include a condition for removal of the Books from the Amazon.com platform, Kindle Store and the permission to republish the book under a New Publisher Title, keeping all contents and authors as currently listed on the Amazon platform. Under that condition, a book priced $135 may remain at the same price or the price may change; in either case 100% of the Price upon a book sale will be recorded and credited to the new Publisher.

 

This scenario may be favorable to a Scientific publisher with a Global distribution of Books infrastructure in place.

The Process of Content Purchase and Monetization – How will it work on the Blockchain Transactions Network?

• The content will be downloaded into a Digital Cart subsequent to Content Customer conducting a query to interrogate the Knowledge repositories of our four corpuses stored on a Blockchain IT infrastructure, which represents the back-end of a Digital Store and executes the data and transaction processing functionality on the Healthcare Digital Marketplace network
• The Recommendation Engines (one for Text) and (one for Biological Images) present the Content Customer with selection choices and a Price Tag associated with all selection options
• Content Customer performs selections on a FORM after reviewing all recommendations – The Front-end of the transaction GUI.
• Form submission generates an Invoice
• Invoice is Paid
• Permission is authorized by the blockchain system
• All contents selected is downloaded in Content Customer’s cart and become available for use instantaneously
• On the back-end, the transaction is recorded on the LEDGER and funds are transferred from the Content Consumer to LPBI Account Receivable

Content Customer/End-user interact with a computer screen or a mobile device for submission of queries to DBs in the Digital Store:

 

Options for selection include:

• Knowledge repositories [1.0 LPBI IP Asset Classes I, II, III, V]
• Content Customer/End-user will submit a query and will Specify

Current, Choices for the search:

• Original articles,
• Books,
• e-Proceedings and Tweet Collections and
• Biological Images or
• All of the above

 

A.  The current choices for the search are NOW in Read Only mode since the content in the WordPress.com Cloud is not connected to a Transactions Network.

We design the Blockchain and the digital store to enable transactions for our current and future digital content.

LPBI’s new Content will continue to be added to the WordPress.com Cloud and migrated to the Blockchain system

• This process has not yet been specified since the indexing and the current content migration of 3.3 Giga Bytes has not yet started. The Blockchain is under DESIGN. BETA testing, Launch will follow

B.  Work-in-Progress – Future Digital Products for Content Customer/End-user to specify during interaction with the System

 

• Spanish Translated eTOCs of 18 Books [Montero current involvement]

 

C.  Work-in-Progress – Content Customer/End-user will specify during interaction with the Text Analysis by AI/ML/NLP

 

• Specifying Visualization artifacts to be generated by AI/ML/NLP as a result of Text Analysis and Text Mining
• Specifying the Foreign Language for the Interpretation of Visualization: Spanish, Japanese, Russian [Montero potential future involvement]

 

The Objective: Content Monetization & Global Dissemination of Life Sciences Scientific Innovations

The transformative work done by LPBI Group allows cutting-edge biomedical research innovation to be widely disseminated and accessible to the global research and non-research communities

The Blockchain Transactions Network enables Selling Content on the INTERNET to B2C and to B2B

• LPBI’s method of curation represents a mode of scientific communication including synthesis, analysis, and interpretation done by experts in +6,000 Journal Posts and ~400 Pages
• Experts, authors, and writers add their knowledge and expertise in re-thinking and conceptualizing subjects selected in their domain of expertise, to form new curations and update existing ones.
• The books are transformative in their capacity to accelerate diffusion of scientific innovations. They represent the frontier of life sciences research.
• The curation is done by experts with a perspective within each field, allowing for the creation of scientific content that combines conceptual evolution within the scientific breakthroughs analyzed together with their anticipated future implications.

The Content is offered to the Content Consumer: B2B and B2C

 

LPBI content is in Life Sciences, Pharmaceutical, Healthcare, Medicine, Medical Devices. Thus, it would attract institutions active in several verticals

 

1.0 LPBI IP Portfolio of an e-Scientific Publisher

Present 3.3 Giga bytes of English text and Biological Images

Intellectual property of LPBI is of four types:

A corpus of curated articles,

• 6,000 scientific Journal articles – curations of peer reviewed scientific findings – the clinical interpretation written by experts.

A corpus of e-books

• 18 Books in Medicine and Pharmaceutics

A corpus of e-proceedings

• 100 e-Proceedings of the most important Medical and Biotech Global Conferences covered in real time using PRESS passes and Tweet Collections from 36 events

A Gallery of Biological Images

• 5,100 Biological images used in the articles above

Future 2.0 LPBI Suite of Visualization Artifacts created by Text Analysis with AI/ML/NLP

Will be available on the Blockchain platform and will be produced on the fly per distinct queries submitted by the Content Consumer to the Content Databases:

Visualization artifacts produced by AI/ML/NLP include the following files: As scaling up takes place, these artifacts will become available for download and monetized at a

1. per Article basis in the +6,000 corpus
2. Collections of articles in Books’ chapters
3. Main research categories assigned to articles by authors/curators
4. On demand, collections specified by end-users produced on the fly on the Blockchain platform enriched with Data Science & Analytics features [some are currently done in the NLP environment; more can be done on the Blockchain when all the four corpuses become live for transactions and for Analytics]

 

List of New digital products to be produced by LPBI Team working on Medical Text Analysis using NLP strategy:

 

Graph Files

• WordClouds representing Article abstracts
• Bar Diagrams representing Word Frequencies
• Hyper-graphs representing Semantic relationships
• Tree Diagrams representing hierarchical clustering of conceptual similarities

Text Files

Interpretations of the visualization artifacts generated by AI/ML/NLP are included in the plan 

Multi-Lingual Translation of the Text Files produced by Domain Knowledge Experts.

1. Spanish
2. Japanese
3. Russian

The Volume of Content Consumed to date:

• Books published on Amazon.com – To date: +135,000 pages have been downloaded from the 18 Volumes.
• Journal articles (Posts and Pages): To date: ~2MM Views
• We used data on Actual Article Views since date of publication (2012-2020) for projection of Article Views (2021-2025)
• Assumption: One view is a download of a $30 article
• Projection of Revenues: 2021-2025 based on actual ~2MM views, 2012-2020

PharmaceuticalIntelligence.com Journal – Projecting the Annual Rate of Article Views

https://pharmaceuticalintelligence.com/vision/pharmaceuticalintelligence-com-journal-projecting-the-annual-rate-of-article-views/

See explanations in 1.0 LPBI Prospectus

Content Availability by Access Mode

 

Read Only

• Present – All content Is hosted on

https://pharmaceuticalintelligence.com/

• 2021 – New Website is under construction
• New URL for 2.0 LPBI, Medical Text Analysis with AI/ML/NLP and Blockchain for Content Monetization – Work-in-Progress
• See two alternative Site Maps for new website design – Work-in-Progress

https://pharmaceuticalintelligence.com/2020/12/02/two-site-map-proposals-for-lpbis-new-web-site/

Transactions enabled Website

for Books on Amazon.com – Kindle Store, Bookshelf: Life Sciences & Medicine – 18 Books in Medicine & Pharmaceutics

https://lnkd.in/ekWGNqA

http://www.amazon.com/dp/B00DINFFYC

http://www.amazon.com/dp/B018Q5MCN8

http://www.amazon.com/dp/B018PNHJ84

http://www.amazon.com/dp/B018DHBUO6

http://www.amazon.com/dp/B013RVYR2K

http://www.amazon.com/dp/B012BB0ZF0

http://www.amazon.com/dp/B019UM909A

http://www.amazon.com/dp/B019VH97LU

http://www.amazon.com/dp/B071VQ6YYK

https://www.amazon.com/dp/B075CXHY1B

https://www.amazon.com/dp/B076HGB6MZ

https://www.amazon.com/dp/B078313281

https://www.amazon.com/dp/B078QVDV2W

https://www.amazon.com/dp/B07MGSFDWR

https://www.amazon.com/dp/B07MKHDBHF

https://www.amazon.com/dp/B08385KF87

http://www.amazon.com/dp/B08VTFWVKM

Aviva Lev-Ari, the Editor-in-Chief that had uploaded all these books to Amazon.com, is the only person that can remove them from Amazon.com and transfer ownership of these 18 books to another Publisher.

LPBI Digital Store in Healthcare Marketplace – Ecosystem for content downloads and content monetization – Transactions enabled interface

• Design of Blockchain IT Transactions Network – Work-in-Progress

 

Marketing Communication Needs: 1 – 7

 

1. Spanish Edition – Content promotion of 18 Medical books in Spanish speaking Countries
2. LPBI has needs in Marketing Communication, Media & PR for the venture’s potential M&A by a 3^rd party: i.e., Scientific Publisher, Healthcare NGO, Ministry of Education in Country x,y,z, Research Institute, i.e., National Institute of Health in Country x,y,z
3. 0 LPBI is producing new digital media: Priority #1: Audio Podcasts. Future plans under new ownership: Audio Articles, Audio Books,
4. 0 LPBI is producing new Visualization artifacts as outcomes of Text Analysis with AI/ML/NLP
5. 0 LPBI is Planning Advertisement for Amazon Books using Amazon Advertising in different countries for different book volumes, i.e., Genomics Volume 2 in the UK, Cancer Volume 1 & 2 in Latin America – This is a case of promotion of Books – expertise in auctions used in experimental design of advertisement running Ads is needed.
6. NEW documentation on IT Architecture for Content Monetization of Journal articles on the Blockchain IT infrastructure – Work-in-Progress
7. NEW documentation for content promotion and Monetization of other IP Asset Classes: Biological Images, e-Proceedings – Work-in-Progress

The Targets: END-USERS are the Life Sciences Content Consumers: including physicians, biotech knowledge worker, big pharma R&D and Medical Affairs Departments, Investment community in Healthcare

MedCity SPOTLIGHT Video – Healthcare Trends and Venture Capital Outlook

https://www.youtube.com/watch?v=YEfNWan0l5Q

 

For the Transfer of Ownership – Global Scope

Business Model for Blockchain Platform:

 

Product Price List Itemized for 1.0 LPBI & 2.0 LPBI

 

B2B & B2C will access 1.0 LPBI & 2.0 LPBI Products

 

Price List below represents B2C. Market installations in B2B will have a different Pricing structure based on Point-of-Research (POR)

 

• 1.0 LPBI – Digital Products
• 2.0 LPBI – Visualization (Graphical) Products & Multi-Lingual Interpretations

Product Price List Itemized for 1.0 LPBI Digital Published Products

 

• Article Download                   $30
• Book Purchase                   Amazon.com

(1) Price List of Books

(Price range $75 to $135 per book)

https://lnkd.in/ekWGNqA

• Book Page Download – price set by Amazon.com

(2) Page per View LPBI Digital Products

DOWNLOADS of 1.0 LPBI Other Digital Products

• eProceedings/Tweet Collections $100
• One Biological Image                    $30
• Spanish eTOCs – One volume      $15
• Spanish eTOCs 18 Volume          $125

 

Product Price List Itemized for 2.0 LPBI Visualization Artifacts produced by AI/ML/NLP & Interpretation Text Products

 

A PowerPoint Presentation based on a Proof-of-Concept of 33 articles in Cancer, including examples for each Visualization Artifact is available

Currently, these products are not YET available for sale – to download digital content following payment requires a BLOCKCHAIN platform with the features mentioned above – it is under design – Work-in-Progress

• WordClouds representing Article abstracts             $20
• Bar Diagrams representing Word Frequencies        $20
• Hyper-graphs representing Semantic relationships $20
• Tree Diagrams representing hierarchical clustering of conceptual similarities  $20

• Expert Interpretation of Visualization Artifacts

English                                                    $20

Spanish                                                  $30

Japanese                                                $30

Russian                                                   $30

The Transition from e-Publishing to Text Analysis by ML and Content Monetization

Phase I: Transformation and Transition

Phase I requires for the following projects:

1. Global content promotion using Amazon Advertising that provides Analytics on $ spent and sales gained
2. Marketing Communication projects
3. Blockchain infrastructure design and implementation
4. Data indexing and data migration to blockchain platform: +6,000articles and ~400 pages
5. Scaling up the NLP phase to 3.3 Giga bytes of data
6. Translations to Foreign languages: Spanish, Japanese, Russian
7. Decisions on Audio articles and Audio Books and estimating the cost involved
8. Management of the Digital Store Shelves beyond the Network management provided with the monthly fee by the host of the Digital Marketplace
9. Subletting shelves in the Digital Store to cover the monthly fees of network usage would require Recruitment of Content Creators to host and transact their content in LPBI’s Digital Store.
10. Enabling a content marketplace for 3^rd party content creators to contribute and monetize their own content (was discussed as a future phase after the foundational marketplace is created using LPBI content).

Phase II: Pursuit of Conceptualization for the pipelines leading to the transition to 2.0 LPBI.

Phase II is paving the way to

• A new organization
• Need for new ownership
• Need for new management

Phase III: Preparation for M&A and Exit.

See Elevator Pitches by all team members:

Versions of LPBI Group’s Elevator Pitch: 2.0 LPBI Group’s Team – In Our Own Words

https://pharmaceuticalintelligence.com/2020/10/20/versions-of-lpbi-groups-elevator-pitch-2-0-lpbi-groups-team-in-our-own-words/

In light of Phase I, II, III – LPBI’s Founder is fully engaged and is running in parallel three strategic courses:

1. The transition plan and new technologies emergence: NLP and Blockchain
2. The recruitment of External Business Relation, External Scientific Business relations, NLP team members, New Domain Knowledge Experts
3. The prospecting process in the event of Technology Transfer of Ownership: M&A talent

List of IP Assets for Technology Transfer of Ownership – DIGITAL PUBLISHED PRODUCTS:

• IP Asset Class I: The Journal +6,000 Scientific articles https://lnkd.in/erfbayJ
• IP Asset Class II: 18 Volumes in BioMed e-Series https://lnkd.in/ekWGNqA
• IP Asset Class III: +100 eProceedings of BioTech & Medical Conference and Tweet Collections

https://pharmaceuticalintelligence.com/press-coverage/part-three-conference-eproceedings-deliverables-social-media-analytics/

• IP Asset Class V: A Gallery of 5,100 Biological Images

https://pharmaceuticalintelligence.com/

See below considerations for Venture Valuation addressing IP Asset Classes: IV, VI, VII, VIII, IX, X, which are NOT related to the curation methodology

 

1.0 LPBI – Inventory of Digital Products – a VAST portfolio of IP developed by 1.0 LPBI since inception

2012-2020

• +6,000 articles and 5,100 biological images,
• 18 books in Medicine
• 100 e-Proceedings & Tweet Collections
• +3.3 Giga Bytes of IP
• Translation of 18 books in Medicine: Title page and electronic Table of Contents to Spanish for 22 Counties speaking Spanish

 

2.0 LPBI – Technology and Marketing Strategies

2021-2025

• Working with BurstIQ, a leader in Blockchain, on architecture of a platform for LPBI’s Content Monetization

1. A Digital Store on BurstIQ HealthCare Digital Marketplace
2. Features of the Blockchain IT infrastructure defined
3. Transactions Network: Recommendation Engine, Permissions, Smart Contracts, Immutable LEDGER, CyberSecurity, Content Promotions
4. We co-design the architecture to include NLP features to compute on Demand visualization artifacts

• Working with Linguamatics/IQVIA on NLPscaling up from a Proof-of-concept to +6,000 articles, all books all e-Proceedings and Tweet collections and Biological images

1. Will get a quote for Licensing Linguamatics NLP Platformto LPBI 
2. Or Licensing Linguamatics NLP Platform to BurstIQ

• Working with Montero LS, Madrid, Spain on a Marketing Campaign for the SPANISH Edition resulting from translation of 18 books in Medicine: Title page and electronic Table of Contents to SPANISH for 22 Counties speaking Spanish

BioMed e-Series: 18 Volumes – electronic Table of Contents (eTOCs) of each Volume

https://pharmaceuticalintelligence.com/2017/12/12/biomed-e-series-16-volumes-electronic-table-of-contents-of-each-volume/

1. Accepted a quote for the translation job [Translation of 18 books in Medicine: Title page and electronic Table of Contents to SPANISH for 22 Counties speaking Spanish]
2. Will review a quote for the joint Marketing Campaign for Latin America with a focus on Mexico, Spain, Argentina
3. Will review a quote for Marketing Communications projects

UPDATED on 2/5/2020

Decision RULES:

1. IF an article is in an e-Book THEN context for NLP is defined to be All articles in its Chapter in the Book
2. IF an article is NOT in an e-Book THEN context for NLP is defined to be Articles in Main Research Category Top 12 by Views

Pending estimation of:

1. Investment needed for Text Analysis with NLP 
2. Investment needed for Content Monetization on Blockchain IT Infrastructure by vendor
3. Investment needed for Text to Audio conversion
4. Investment needed for Translation to Foreign languages
5. Cost of translation of (e), below to several Foreign Languages
6. Pricing EACH OUTPUT of NLP process: 

(a) WordCloud 

(b) Bar diagram 

(c) Hyper-graph

(d) Tree Diagram

(e) Expert Interpretation of (a) to (d)

UPDATED on 2/1/2021

At present, I see the following:

LPBI 1.0 – Blockchain LEDGER for Monetization of Class I, II, III, V

• Custodian of the LPBI 1.0, 2012-2020 Portfolio of IP ten Assets Classes
• For content monetization, we identified four of the ten assets: 

Class I: Journal articles, 

Class II: 18 Books, 

Class III: 100 e-Proceedings & Tweet Collections, 

Class V: +5,100 Biological Images

• Content monetization requires a Blockchain Transaction Networks: Immutable ledger, permissions, smart contracts, recommendation engine

LPBI 2.0 – Blockchain LEDGER for Monetization of Graphics generated by ML and Experts interpretation in several Foreign languages

• NLP, Machine Learning-AI applied for Text Analysis of Class I, II, III, V
• Content monetization requires a Blockchain Transaction Networks

Economies of scale will be achieved by:

• Development of one Content Promotion System
• Unified IT Cloud-based infrastructure
• Maintenance of B2C IT transaction system in a Digital Store at a Healthcare Marketplace [monthly fee paid for the use of the network and hosting content]
• Installations of B2B at institution – pay per use vs subscription base

UPDATED on 1/28/2021

REST API Tutorial

UPDATED on 1/27/2021 – Additional Observation

From: Amber 

Date: Thursday, January 28, 2021 at 11:21 AM

To: “Aviva Lev-Ari, PhD, RN” <AvivaLev-Ari@alum.berkeley.edu>

Subject: Re: Data Architecture for Blockchain Deployment of Digital Assets: LPBI IP Asset Classes I,II,III,V | Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Thank you, Aviva. This is consistent with my understanding as well. A couple of notes:

1. We can build the analytics that you described directly on the BurstIQ Platform; you do not need NLP to render these visuals (although you can certainly use NLP if you want to). The visuals can be presented in the marketplace either as a static image, or as a dynamic visual that changes based on how the user filters the data.

2. With respect to your note re: using one block for NLP: one block equals one piece of data, like a word cloud image or an author’s name. To incorporate NLP, we would integrate with the NLP services via a REST integration, so that the platform can both present data to the NLP service and ingest processed data from the NLP.  Then the output files from the NLP service would be stored in one or more blocks on the platform.

I hope that additional info helps.

Cheers,

Amber

We are still working to produce the 

• INPUT two TEXT files for LINGUAMATICS to run their NLP
• We will run on SAME Text our access to Wolfram’s NLP
• On BurstIQ end: 
• FOR OUR PROJECT – may be it is worth exploring having ONE block in the blockchain to be the processor of NLP – this is OUR IDEA for our own needs

We will get back to you as soon as we clarify which one runs supreme Linguamatics vs Wolfram 

We are to meet with CS CMU experts to clarify our specs about that interface that will be best:

• Static Graphic files vs 
• Graphic production on the fly by ONE NLP block on your Blockchain [That will need to be tested???? 

Observations:

1. Advantage of static files – Graphics produced by NLP exist for Content Promotion and are available to the Recommendation Engine to display as a result of a query
2. Advantage of compute on the fly – done on subset of article collection ON DEMAND not in existence in the statics files generated on 2 article sets: All articles in one Chapter and Same number of articles form the Main category of research
3. BOTH MAY BE NEEDED TO EXIST ?????
4. I assume each MODE of implementation has a difference I/O and overhead performance numbers and if Both exists these numbers may be x2 ????

PS

• The first Quote was for Existing IP – 1.0 LPBI
• The amended Quote [PENDING] – will be addition to consider the NLP Graphical output been ingress or created on fly or both (reasons, above, why both are needed). Graphical output from NLP are Content Products to be available on the Transaction infrastructure for download and monetizing of the IP involved

We are now designing the requirement for the Data Architecture for the blockchain Transaction Network for Content Monetization.

https://pharmaceuticalintelligence.com/2020/11/16/data-architecture-for-blockchain-deployment-of-digital-assets-lpbi-ip-asset-classes-iiiiii/

• The unit case is an “Article” – a Longitudinal Profile of Classifiers

1. Article has date of publication, 
2. Author(s) Name, 
3. Title, 
4. Length, 
5. URL 
6. is it in a Book? 
7. Series, Volume, Chapter; 
8. Views end of each year since published; 
9. is the article a Conference output or not; 
10. if yes Name of Conference, date, location, 
11. is it part of e-Proceedings? 
12. If yes Title & URL; 
13. Does a Tweet Collection for this Conference exist? 
14. If yes Title & URL

• Each of the is a columns added in an Excel file FOR the same article in one row A to Z
• Same is repeated for Row 2 – A to Z for article #2 
• End of Rows is +6,000
• End of Columns is Last Classifier, 1 to n
• The Views per article times length of article # Words = Score for Authors contribution times all article by same Author = Total score for potential compensation AFTER Exit.

Currently, for performing NLP:

• The content – is an MS Word file of the article 
• It is INGRESS to a platform that has Natural Language Processing [NLP] Algorithms on it
• Semantic Text Analysis is Performed
• NLP system generate Graphical OUTPUT 

1. WordCloud, 
2. Bar Diagram for Word frequency, 
3. Hyper-graph for concept relations, 
4. Tree Diagram for hierarchical affinity translated into distance proximity among words; 
5. Domain Knowledge Expert writes Interpretation of the Graphs

FUTURE

• These Graphical OUTPUTS EGRESS the NLP platform
• These Graphical OUTPUTS will INGRESS the Blockchain Transaction infrastructure
• That interface NEED to be design on several layers. For our ability to declare our SPECS on that we will meet with experts from CS @CMU 
• LPBI does not have enough expertise onboard at that level of data engineering, data workflow & system design to be able to submit specs.

UPDATED on 1/27/2021 – This update deals with Integration of NLP Graphical output on a Blockcahin transaction network IT infrastructure

Our content is in Life Sciences, Pharmaceutical, Healthcare, Medicine, Medical Devices

1.0 LPBI IP Portfolio of an e-Scientific Publisher – 3.3 Giga bites of English text and Biological graphics

• 6,000 scientific Journal articles – curations of peer reviewed scientific findings – the clinical interpretation written by experts.
• 18 Books in Medicine and Pharmaceutics
• 100 e-Proceedings of Medical and Biotech top Global Conferences we covered in realtime on PRESS passes and Tweet Collections from 36 events
• 5100 Biological images used in the articles above

2.0 LPBI IP Portfolio of a Medical Text Analysis w/ Machine Learning-AI (SaaS) and Content Monetization Blockchain company: BaaS.

We plan to apply Natural Language Processing, ML-AI on that content for Semantic Medical Text Analysis on 1.0 LPBI IP portfolio, listed above and generate graphical representation of the semantic relations:

• WordClouds
• Hyper-graphs
• Tree Diagrams
• Domain Knowledge Interpretation of Graphical output of NLP, ML-AI

Our Proof-of-Concept is on–going 

• Interested party in NLP on our content in Genomics & Cancer is a Healthcare Insurer in UT.
• We are interested in NLP on ALL our content: Cardiovascular, Genomics, Cancer, Immunology, Metabolomics, Infectious Diseases, Genomic Endocrinology and Precision Medicine – our 18 books in medicine, average book size 2400 pages ~ 1800 articles in the entire BioMed e-Series and the 4200 articles in the Journal not in Books
• We are interested in content monetization of the

1. Content in Text format, and of the
2. Digital graphical products generated by NLP 
3. Domain Knowledge Experts interpretations of the Graphical output of NLP
4. These Interpretations of the digital graphical products generated by NLP are and will be a fundamental resource for consultancy of drug discovery, drug repurposing , drug substitution. Team of 10.

External Relations:

NLP 

• LINGUAMATICS / IQVIA will run on their NLP system our test sample TEXT files and we are using internally Wolfram for Biological Sciences 
• We will compare the two graphical outputs: theirs and ours 

Blockchain

We work with a leader in Blockchain IT vendor in Colorado on the design of a cloud-based Transaction Network IT infrastructure for content monetization taking place on an IT system with Blockchain features: Permissions, Smart Contracts, Immutable Ledger, Recommendation Engine

Two types of markets will be served: 

• B2C – a digital store in a Healthcare Digital Marketplace for 1.0 LPBI IP Portfolio and 2.0 LPBI IP Portfolio
• B2B – Special installations at Big Pharma R&D and at Healthcare Insurers

2.0 LPBI IP Portfolio and strategy represent the first implementation ever done of

NLP on a Blockchain backbone

[we were told so by the leader in NLP and by the leader in Blockchain]

We explore to discuss our plans with with additional experts from CS at CMU 

• Experts on NLP 
• Experts on Blockchain Transaction Network
• We need to decide on between two designs considered for the interface between NLP & Blockchain
• The interface is related to two methods of input graphic data processing: (a) ingress NLP outputs to the blockchain system from a DB vs creation of NLP graphic products on the fly
• We need to discuss the System design and the data architecture with CMU experts in both fields: NLP & Blockchain
• We will need expert assistance in defining each of the Blockchain features: Permissions, Smart Contracts, Immutable Ledger, Recommendation Engine Rule Base

Business Side

• We are seeking new ownership
• We are seeking new management
• Scaling up from the proof-of-concept to commercialization and content monetization represents a scale of operation that is beyond us. 
• We have a VAST IP Portfolio and a Team of Experts N=10
• We are the creators of the IP portfolio of 1.0 LPBI – 3.3 Giga bites
• We are the creators of the Vision for 2.0 LPBI IP 

Strategy #1: NLP for Text analysis of 1.0 LPBI content and 

Strategy #1: Content monetization on Blockchain IT Transaction network: Original Content and NLP digital graphical products

• All the content is in the Cloud hosted by Wordpress.com
• PharmaceuticalIntelligence.com is the Domain Name – it is listed on my own name. Formula for post-Exit compensation of Experts, Authors, Writers of the 6,000 articles is in place.

UPDATED on 1/26/2021 – This Update is on “The unit case is an “Article” – a Longitudinal Profile of Classifiers”

The unit case is an “Article” – a Longitudinal Profile of Classifiers

• It has date of publication, Author(s) Name, Title, Length, URL is it in a Book? Series, Volume, Chapter; Views end of each year since published; is it a Conference or not; if yes Name of Conference, date, location, is it part of e-Proceedings; is there a Tweet Collection for that Conference?
• The content – an MS Word file of the article is INGRESS by a platform that has Natural Language Processing [NLP] Algorithms on
• Semantic Text Analysis is Performed
• Graphical OUT is created and EGRESS:

1. WordCloud,
2. Bar Diagram for Word frequency,
3. Hyper-graph for concept relations,
4. Tree Diagram for hierarchical affinity translated into distance proximity among words;
5. Domain Knowledge Expert writes Interpretation of the Graphs

• Each of the is a column added in an Excel file FOR the same article on one row in (i to n) columns
• Same is repeated for Row 2 – (i to n) columns for article #2 
• End of Rows is +6,000
• End of Columns is Last Classifier, n
• The Views per article times article length = Score for Authors contribution times all article by same Author = key score for potential compensation AFTER Exit.
• ORIGINAL Excel file on Article Views has the VIEWS data organized as a Classifier in a LONGITUDINAL Article profile

UPDATED on 1/18/2021 – adding data fields or DBs for Content monetization

The hyper-graphs and the Tree Word are including all words – that does not affect the revealed SIGNIFICANT words.

1. We include all of the NEW runs in the POWERPOINT Presentation

We need to present YOUR PowerPoint on 

• 1/20 Zoom with NLP Vendor
• 1/22 Zoom with Blockchain Vendor

All the iterations are needed for as to test the concepts of the 16 articles – ALSO on

A. One article and all the OTHER articles in ONE CHAPTER in ONE Book, I.e., Genomics Volume 1, Chapter 1

B. One article and other articles included in the MAIN Research Category this article was assigned to by the Author

We will need Hyper-graphs and Tree Diagrams for A and for B, above – THEN

• we will decide on 2.0 LPBI standard: Hyper-graphs or Tree Diagrams as the INPUT for Domain Knowledge Expert’s Interpretation.

C. Announcing Proof-of-Concept for Genomics and Cancer is COMPLETE and CLOSED.

D. Enumeration of all artifacts in one “STANDARD 2.0 LPBI Medical Text Analysis OPERATION” [by Code Author: Madison Davis]

1. WordCloud
2. Bar graph
3. Hyper-graph or Tree Diagram – ONE to be decided to make to the Standard
4. Text – Interpretation by Domain Knowledge Expert for 1,2,3, above

E. Announcement of Scaling up Project by BioMed e-series: A, B,C, D, E

• using the “STANDARD 2.0 LPBI Medical Text Analysis OPERATION” [Standard was developed by the Proof-of-Concept.

UPDATED on 1/18/2021 – adding features to Content monetization

We are 2.0 LPBI

1. Medical Text Analysis

2. Content monetization

IF

3rd party requests services we did in 1.0 LPBI

THEN

We offer the service for a fee and the monetization will be held by the Blockchain transaction system

Thus, we need to guide our IT Vendor designer of our Blockchain features platform to DESIGN the LEDGER to include few additional categories such as:

1. Consulting Services – Fee for Service

Types of Service:

1.1 Implementation of Medical Text Analysis for Pharma

1.2 Implementation of Medical Text Analysis for Healthcare Insurers

2. Response by 2.0 LPBI to Requests to promote content by 3rd party: 

2.1 Co-marketing of a Conference organized by 3rd Parties – promotion on LPBI Channels

2.2 LPBI to Publish 3rd Party contents, i.e., Articles by guest authors: Payment based on # of views every 90 days at $30 per view

3. Consulting on Media development

3.1 Conference organization

3.2 Book content development

3.3 Real time Press coverage

UPDATED on 1/13/2021

• We will have from our IT Vendor a BLUEPRINTS for the content monetization system design with all the components laid out in a workflow for a production process to incorporate two sources of data:

1.0 LPBI four IP Asset classes: I, II, III, V will be available for monetization 

The Design include all monetization Features to incorporate the 2.0 LPBI NEWLY TO BE CREATED PRODUCTS by NLP integrated at the article level with the 1.0 LPBI IP.

We will generate four Text Analysis products, like the FOUR outcomes of NLP included in the Proof-of-Concept: 

NLP Products: Will be available for monetization as 2.0 LPBI IP: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

PHASE I: All Articles in ALL Books at the Chapter Level – THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

For:

Series A:  6 volumes, 

Series B:  2 volumes

Series C:  2 volumes

Series D:  4 volumes – 1, 2&3 in one Book, 4

Series E:  4 volumes

Total 17 Books for 18 Volumes

PHASE II: All Articles Not in Books and Not as e-Proceedings – – THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

PHASE III: 60 e-Proceedings + 36 Tweet Collections – – THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

PHASE IV: 5,100 Biological Images -– THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

UPDATED on 1/5/2021

• WE ARE ARE DOING THE PROOF-OF-CONCEPT in house with INTERNS on a one year Internship on a volunteer basis.
• My intent was to TEAM UP with AWS and one of their PARTNERS to REDO the POC on the VERSION that XXX has in the NLP Software and with that Partner jointly to Present to the INSURER and secure a contract for that PARTNER that will scale up from
• (a) 16 articles on Genomics to Volume 1 and Volume 2 Genomics Books and
• (b) 16 articles on Cancer to Volume 1 and Volume 2 Cancer Books.
• Hoping in the following phase of the relations with the INSURER –
• they will be interested in all medical indications covered in our 16 Books (#17 due 1/11/2021) – Namely, they have Patients with Heart problems – LPBI has 6 Volumes in Cardiovascular, books on Immunology, Infectious disease, Metabolic, Endocrine and 4 volumes on Precision Medicine.
  • We mean to use the POC as a Lead toward having the INSURER involved in performing Medical Text Analysis on our 17 books
  • Since they will be the first to get access to the outcomes of such a massive NLP, ML-AI on 17 books
  • They will get access to Hyper-graphs and Domain Expert Interpretations for their INTEREST in Drug substitution and Cost containment and access to our TEAM for ad hoc genomics challenges.
• The full scale implementation of the POC on all the content in the books requires a PARTNER with expertise and a platform for NLP 
• It was my intent to find that PARTNER at XXX and its system of Partnerships
• Our alternative is to Team up with another player in the NLP arena that is not AWS – in the case that XXX can’t team us up with their NLP capabilities
• WE have approached XXX because our architecture REQUIRES INTEGRATIONS OF THE RESULTS on Medical Text Analysis

1. WordCloud (Images files),
2. Hyper-graphs (graph files),
3. Interpretation of Hyper-graphs (Text file in English and in several Foreign Languages)

• WITH A CONTENT MONETIZATION SYSTEM that is to be designed for our journal articles, Books, e-Proceedings, Tweet Collections, Biological images

• Such an Integration will allowing for a

Customer to be able to request to review

(a) articles on Topic x

(b) receive from the system 12 top articles

(c) select one or more

(d) pay for them

(e) download the articles they paid for

Expand (a) to (e) to Books, e-Proceedings, Tweet Collections, Biological images

(a) to (e) represents 1.0 LPBI IP

• Such an Integration will allowing for a

Customer to be able to request to review 

(f) WordClouds = Article ABSTRACTS

(g) Hyper-graphs

(h) Domain Expert Interpretations

(I) Interpretations in Few Foreign Languages

Customer will receive from the RECOMMENDATION engine 12 WordClouds of related top articles

Customer will receive from the RECOMMENDATION engine 12 Hyper-graphs of related top articles or one or more research categories

(j) Customer will select one or more

(k) pay for them

(l) download the WordClouds they paid for

(m) Download the HyperGraph they paid for

(n)  Download the Domain Expert Interpretations for the hyper-graph(s)

(o)  Select for the Interpretations to be in one of Few Foreign Languages the system offer

 (j) to (o) represents 2.0 LPBI IP

THE NEEDS OF LPBI IS for ONE INTEGRATED SYSTEM THAT CONTAINS:

(a) to (e) represents 1.0 LPBI IP

AND

(j) to (o) represents 2.0 LPBI IP

AND

CONTENT MONETIZATION SYSTEM with features such as:

PERMISSIONS, LEDGER, RECOMMENDATION ENGINE

It may be the case that YYY has competence in monetization system design BUT DOES NOT currently have what LPBI needs in the Text Analysis with NLP, ML-AI

• As a result XXX needs to pair us up with one additional XXX-Partner in the space of Text Analysis with NLP, ML-AI – to understand our requirements and to enable scaling up from POC to all the 17 Volumes in Medicine
• YYY’s Monetization design needs to be INTEGRATED with the the system design for Text Analysis with NLP, ML-AI done by a second AWS partner
• THEN
• Hosting on XXX needs to be discussed
• LPBI’s IP Asset Classes: I,II,III,V –  journal articles, Books, e-Proceedings, Tweet Collections, Biological images – FIT very well AWS Marketplace
• Please introduce us to the XXX contact for discussion on LPBI and XXX Market place
• See, Priority #3, Below and due to Priority #1 & #2
• It seems to be the case that the DEVELOPMENT efforts are expansive for a venture like LPBI, therefore I requested to receive a POINTER to the XXX Venture Acquisition department/team/one person

• Aviva: We need a Partner to Use our Content and use NLP, ML-AI to execute the SEMANTIC Medical Text Analysis to convert TEST to WordClouds and to Hyper-Graphs
• if YYY can declare expertise in the Medical Text Analysis with NLP, ML-AI
• If not, XXX may introduce us to another XXX Partner that can handle for LPBI Priority #1, below

 

• Aviva: We need a Partner to design CONTENT MONETIZATION for existing content AND for the RESULTS of the Medical Text Analysis

EXPLANATIONS:

All of the above MUST bring all parties to an understanding of the NEEDS that LPBI has:

PRIORITY #1:

Medical Text Analysis using NLP, ML-AI

1. LPBI has a Proof-of-Concept in Medical Text Analysis using NLP, ML-AI – will be completed mid Feb. 2021
2. LPBI has a Client – a Healthcare Insurer interested in Genomics and Cancer and potentially, because they are also a HMO, in all other medical indications covered in LPBI BioMed e-Series – 17 BOOKS
3. To present to this client (and to other Healthcare Insurers) – LPBI needs one  IT Partner in Medical Text Analysis using NLP, ML-AI able to GET a contract from the INSURER for using the POC to SCALE UP to 2 books in Genomics and 2 books in Cancer – desirable – to be followed up by the remaining (17 – 4) = 13 Books

PRIORITY #2 and PRIORITY #3: need to be running in parallel

PRIORITY #2

DESIGN and ENABLEMENT of Content Monetization for

(a) EXISTING digital products and

(b) the results of PRIORITY #1, above: Medical Text Analysis using NLP, ML-AI

1. LPBI needs a Content Monetization System (CMS) that we believe YYY has the competences to design
2. Continuing of progress on this design need to take place
3. LPBI needs a Proposal and costs of monetization system design for presentation to IB and other funding sources
4. LPBI is anticipating 3rd parties that will invest in IT infrastructure development.
5. LPBI created a e-Scientific Publishing venture second to none – based on ~2MM Views has projected revenues to $ZZZ MM
6. The Content Monetization Cloud-based IT System DESIGN needs to satisfy the following:
7. THE NEEDS OF LPBI are of ONE INTEGRATED SYSTEM THAT CONTAINS:

[(a) to (e) represents 1.0 LPBI IP] – existing products 
AND 
[(j) to (o) represents 2.0 LPBI IP] – to be developed by NLP, ML-AI of the existing products
AND 
ENABLES CONTENT MONETIZATION of the two sources with features such as:
PERMISSIONS, LEDGER, RECOMMENDATION ENGINE

PRIORITY #3

DESIGN of CONTENT PROMOTION campaigns

1. XXX Advertising is a company of XXX.com
2. We need to be teamed up with a Partner or an inside Group to XXX for the DESIGN of CONTENT PROMOTION campaigns for (a) to (e) represents 1.0 LPBI IP [digital products: journal articles, e-Proceedings, Tweet Collections, Biological images]
3. Upon progress with (j) to (o) represents 2.0 LPBI IP = the results of Text Analysis with NLP, ML-AI 
4. We need to be teamed up with a Partner or an inside Group to XXX for the DESIGN of CONTENT PROMOTION campaign for WordClouds, Hyper-graphs and Domain Expert Interpretation of the Hyper-graphs in foreign languages

UPDATED on 1/4/2021

SPECIFICATION for the Road Map toward an Architecture for Monetization of Content at LPBI

1 – Data entry done by 2.0 LPBI Team of Interns 

2 – Data entry done by IT Vendor

3 – Architecture will be for monetization of 1.0 LPBI IP Asset Classes I,II,III,V

and for

4 – Architecture will also include the infrastructure for the data generated by Medical Text Analysis with NLP, ML, AI done on 1.0 LPBI IP Asset Classes I,II,III,V – called Results of Text Analysis

5. Results of Medical Text Analysis with NLP, ML, AI will include the following Databases (DB):

PHASE I: 

IP Asset Class II – e–Books

• WordClouds for all articles in 17 BioMed e-Series BOOKS – [Image file – DB]
• Number of words of which each WordCloud was built on [Text file – DB]
• Hyper-grapah for articles in each Chapter in the book [Graph file – DB]
• DomainExpert interpretation of the Hyper-graphs  [English Text file – DB]

1. TITLES of each article in the eTOCs of a Book across all books will be TRANSLATED into Spanish, Japanese, Russian [Text file – DBs, one per language]

2. One page of Domain Expert interpretation of the Hyper-graphs will be TRANSLATED into Spanish, Japanese, Russian  [Text file – DBs, one per language]

PHASE II:

Scale up PHASE I – from IP Asset Class II [all articles in 17 Books]  TO all the articles in the Journal = IP Asset Class I

PHASE III:

Scale up from PHASE I: from All Books (IP Asset Class II) and PHASE II: all the articles in the Journal (IP Asset Class I)

TO 

• IP Asset Class III (e-Proceedings/Tweet Collections), 

PHASE IV:

• IP Asset Class V (Biological Images)

UPDATED on 1/2/2021

Announcing Strategic Transition from 1.0 LPBI to 2.0 LPBI on 1/1/2021: New Management, Marketing Communication and New Scientific/Technical Opportunities

Author: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/01/01/announcing-strategic-transition-from-1-0-lpbi-to-2-0-lpbi-on-1-1-2021-new-management-and-new-technical-opportunities/

We have transitioned from

• 1.0 LPBI was an electronic Scientific Publisher, 2012 – 2020

to

• 2.0 LPBI a Medical Text Analysis (NLP-ML-AI) – SaaS and Content Monetization (Blockchain) – BaaS.

• A new company profile, 2021 – 2025

Content Monetization has TWO distinct parts:

2.1   Belongs to 1.0 LPBI: exist in WordPress.com cloud EXISTING digital IP asset classes: Articles, Books, e-Proceedings/Tweet Collections, biological images

2.2   Belongs to 2.0 LPBI: will be created by Text Analysis with NLP. ALL NEW TO BE CREATED digital IP asset classes by 2.0 LPBI as a result of Strategy #1: Text Analysis using NLP, ML, AI:

2.2.1 WordClouds – a DB of all images created by NLP one per article. This will be IP Asset Class 11, will belong to 2.0 LPBI (1 to 10, exist and belong to 1.0 LPBI)

2.2.2 Hyper-graphs – a DB of all graphs,  the hyper-graphs created by NLP. This will be IP Asset Class 12, will belong to 2.0 LPBI.

Examples:

• One hyper-graph for articles in a Book Chapter x 20 Chapter per one book x 17 books
• One hyper-graph for articles in Categories on the Journal ontology
• N=730 categories

2.2.3 English Text interpretation of each Hyper-graph – a DB of text Interpretations linked to DB of graphs and DB of Images. This will be IP asset Class 13, belongs to 2.0 LPBI

These Text interpretations of hyper-graphs will be translated to foreign languages. Example, Spanish, Japanese

ONE DB of Text interpretations per one language

2.0 LPBI had several IT infrastructure needs:

A.  Infrastructure for Text Analysis with NLP of all IP assets in 2.1

B.  Monetization infrastructure for IP Assets of 2.1, above

C.  Monetization infrastructure for IP Assets of 2.2, above

 

System integration of A, B, C

My understanding is that you wish to address B.

Leaving A and C for later.

My view is:

• B and C are one project because a USE CASE called A Journal Article Profile needs to have all the data fields I covered, in the e-mail, below 2.1 plus 2.2, as above. The architecture for B and C are inseparable – Meta data needs to be comprehensive
• A – Infrastructure for Text Analysis needs to be developed in parallel to the Content Monetization B and C.

If All what 2.0 LPBI will do will be

• monetization of Content generated, 2012-2020 – it’s valuation will be x

Versus

2.0 LPBI

(a) A Medical Text Analysis Company – SaaS and a

(b) Content Monetization Company – Blockchain as a Service (BaaS)

2.0 LPBI distinct competitive advantages are:

1. we created content we own it vs applying NLP on PubMed.
2. we create Value-Add by NLP with Expert INTERPRETATION in multi languages
3. We monetize digital content
4. We monetize WordClouds “image files and Hyper-graphs “graph files”

System Integration job needed for 2.0 LPBI includes the following:

1. Our IP on WordPress needs to be migrated into a Cloud Computing environment of an INTEGRATOR i.e.,

• AWS
• DELL
• Other

2. That integrator needs to have the two technologies we need:

Strategy #1: Text Analysis by ML

• Medical Text Analysis SW: NLP, ML, AI

This is Strategy #1 for 2.0 LPBI, namely

Conversion of 3.2 Giga bites of English Text into Hyper-graphs of Semantic content relationships for applications such as:

– drug discovery (needed by Big Pharma)

– drug repurposing (needed by Big Pharma)

– drug substitution & cost containment (needed by Healthcare Insurers)

Strategy #2: Content Monetization by Blockchain IT infrastructure features:

• Permission granting to download content on a cyber-secure IT platform
• immutable LEDGER – recording payments
• Recommendation Engine: choose one or more article from this list of 12
• Blockchain SW: Transaction network for Ledger, Immutability, Recommendation engine and Permission to download

This is Strategy #2 for 2.0 LPBI, namely

Content monetization requires IT infrastructure

We understand that 2.0 LPBI need to

• partner

or

• be acquired by a 3^rd party

(a) to invest in the IT needed for content monetization of

1.0 LPBI IP asset classes: I, II, III, IV

2.0 LPBI IP novel asset classes such as

IP Asset Class 11: WordClouds

(image file DB)

IP Asset Class 12: Hyper-graphs

(graph file DB)

IP Asset Class 13: Domain Expert interpretation of Hyper-graphs

(text file DB, one DB for a Language, expert interpretation translated in several languages)

2. 2.0 LPBI Strategy #1: Medical Text Analysis (NLP, ML, AI) (SaaS)

and

2. 2.0 LPBI Strategy #2:  Monetization of Text Analysis Results as Products (Blockchain as a Service (BaaS))

and

1. LPBI & A2C-AWS regarding Strategy #1: NLP
2. LPBI & A2C-AWS regarding Strategy #2: Monetization

I believe that the definition for the Profile of an Article I am providing below will clarify matters more and your feedback will be helpful.

1.0 LPBI had created 6,000 articles in need for monetization

2.0 LPBI is launching Six new initiatives the relations of four of the six are tied with the definition of an Article PROFILE, as below.

• The monetization INFRASTRUCTURE needs to accommodate TWO types of Digital Products:

(a) The existing Journal articles

(b) The RESULTS generated from Journal articles being subjected to TEXT ANALYSIS with NLP, ML, AI

Therefore we need to address:

C.  LPBI & A2C-AWS regarding Strategy #1: NLP on 1.0 LPBI Text

D.  LPBI & A2C-AWS regarding Strategy #2: Monetization

Let’s start with C.

LPBI & A2C-AWS regarding Strategy #1: NLP on 1.0 LPBI Text

It seems that AWS has technologies in place for A2C to use for performing Medical Text Analysis using AWS NLP, ML, AI on 1.0 LPBI’s 6,000 articles

– Thus, we need to explore HOW we can use AWS NLP, ML, AI technologies and produce for 2.0 LPBI the following Text Analysis features:

[they are derived from our Proof-of-Concept is on–going]

5.3 Does the article have the Text Analysis features which are obtained by performing text analysis with NLP:

5.3.1.  a WordCloud – needs to be stored in graph file of WordClouds

5.3.2.  # words used

5.3.3.  Hyper-graphs – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4.  Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – performed by 2.0 LPBI Experts generating Text files

5.3.4.2 Domain Expert interpretation for 5.3.3.2 – performed by 2.0 LPBI Experts generating Text files

Let’s continue with D.

LPBI & A2C-AWS regarding Strategy #2: Monetization

A2C will design a Cloud-based IT Infrastructure that will enable monetization of two types of products:

Type One: 1.0 LPBI Asset Classed I, II, III, V

• Below is the Profile Definition for the Unit Case: A Journal Article (1.0 LPBI Asset Classed I) – See below
• Same Profile Definitions needs to be done for 1.0 LPBI Asset Classed II (books), III (e-Proceedings/Tweet Collections), V (Gallery of 5,100 Images) – PENDING

Type Two: POST Medical Text Analysis using NLP, ML, AI – the following NEW PRODUCTS are created and NEED TO BE MONETIZED

Text Analysis features to be produced by NLP, ML, AI:

5.3.1. a WordCloud – needs to be stored in graph file of WordClouds

5.3.2. # words used

5.3.3. Hyper-graphs  – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4. Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – performed by 2.0 LPBI Experts generating Text files

5.3.4.2 Domain Expert interpretation for 5.3.3.2 – performed by 2.0 LPBI Experts generating Text files

BASED on the definition provided, below, suggested steps by 2.0 LPBi are the following:

• A2C-AWS and 2.0 LPBI will generate a PROPOSAL for AWS to fund that effort for future placement in AWS Marketplace
• A2C-AWS and 2.0 LPBI will develop Plans and Cost Structures of the infrastructure needed for CONTENT monetization – to be presented to Investment Banker in NYC
• A2C-AWS and 2.0 LPBI will take the LPBI Proof-of-Concept on Medical Text Analysis with NLP in Genomic and Cancer and will create jointly TWO skeleton IT Structures

#1 Skeleton IT Structure: 

Reproduce the Proof-of-Concept using AWS – NLP–ML-AI technology and Scale up to One Chapter in Genomics Volume 1 and One Chapter in Cancer Volume 2

That will be JOINTLY presented at a Healthcare Insurer [LPBI’s Contact] by LPBI AND A2C-AWS – with the scope of getting a Contract that A2C-AWS will define, execute and manage the Statement Of Work (SOW) and submit Costs to the Healthcare Insurer. Prospects of expansion to Cardiovascular and Immunology, beyond Genomics and Cancer are strong.

#2 Skeleton IT Structure: 

Produce a Skeleton for Monetization of 

• 0 LPBI – Journal articles AND 
• 0 LPBI — the Results of #1 Skeleton IT Structure: PRODUCTION OF FEATURES of TEXT ANALYSIS using AWS NLP technologies

That will be presented at 

• an Investment Banker in NYC [LPBI’s Contact], 

and 

• by LPBI 

to other funding sources, and 

• by A2C-AWS to other funding sources, Chiefly, AWS – internally.

             

The Opportunities MAP written on 2/2019 for LPBI M&A or Exit include

Twelve Economic Segments for LPBI Group’s IP – Prospects for Transfer of Ownership

1.     Holding Companies, Investment Bankers and Private Equity
2.     Information Technology Companies – Health Care
3.     Scientific Publishers
4.     Big Pharma
5.     Internet Health Care Media & Digital Health
6.     Online Education
7.     Health Insurance Companies & HMOs
8.     Medical and Pharma Associations
9.     Medical Education
10.     Information Syndicators
11.     Global Biotech & Pharmaceutical Conference Organizer
12.     CRO & CRA 

Information Technology Sector: Cloud-based – 

Amazon Web Services (AWS),  Alphabet – Verily, Apple-Health,  IBM Watson

Information Technology Sector: Cloud & Server-based – 

Microsoft-Health, Dell Boomi, Oracle-Health, SAP, Intel-Health

 

Please review this LINK:

https://pharmaceuticalintelligence.com/2019-vista/opportunities-map-in-the-acquisition-arena/

For the DESIGN of IT Infrastructure for Monetization, the following is an essential 

DEFINITION of a USE CASE for “PROFILE of an Article”: 

1.0 LPBI BEGINS 

Monetization of 6,000 Digital Products – USE CASE: A Journal Article

5.0 Article Title

5.0.1 Article URL

5.0.2 Author 1: Name

5.0.2.1 Author 2: Name

5.0.2.2 Author 3: Name

5.0.2.3 Author 4: Name

5.0.3  Date of Publication

5.0.4  # Words

5.0.5  # Views since Published to DATE

5.1 Is the article in a Book?

5.1.1 Article is not in a Book only in the Journal

5.2 Article is in a Book – In which one(s)?

5.2.1 LPBI Series A

5.2.1.1 Volume 1

5.2.1.2 Volume 2

5.2.1.3 Volume 3

5.2.1.4 Volume 4

5.2.1.5 Volume 5

5.2.1.6 Volume 6

5.2.2   LPBI Series B

5.2.2.1 Volume 1

5.2.2.2  Volume 2

5.2.3   LPBI Series C

5.2.3.1 Volume 1

5.2.3.2 Volume 2

5.2.4   LPBI Series D

5.2.4.1 Volume 1

5.2.4.2 Volume 2

5.2.4.3 Volume 3

5.2.4.4 Volume 4 [Dr. Williams and Dr. Irina are adding editorials, NOW]

5.2.5   LPBI Series E

5.2.5.1 Volume 1

5.2.5.2 Volume 2

5.2.5.3 Volume 3

5.2.5.4 Volume 4

1.0 LPBI ENDS

2.0 LPBI BEGINS

Strategy #1: Medical Text Analysis (NLP, ML, AI) (SaaS)

and 

Strategy #2: Monetization of Text Analysis Results as Products (Blockchain as a Service (BaaS)

5.3 Does article have the Text Analysis features:

5.3.1.a WordCloud – needs to be stored in graph file of WordClouds

5.3.2. # words used

5.3.3. Hyper-graphs  – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4.  Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – Translated into few other languages

5.3.4.2 Domain Expert interpretation for 5.3.3.2 -– Translated into few other languages

5.4 Audio File added to Article

5.4.1 In place – Audio file type [Text to Audio]

5.4.2 SoundCloud file

5.  Article Titles was translated to

5.5.1   Spanish

5.5.2   Japanese

5.5.3   Russian

6.   Article Interpretation of Hyper-graphs was translated to

5.6.1   Spanish

5.6.2   Japanese

5.6.3   Russian

The content below was not Updated on 1/2/2021

Distinction between A and B, below

• A.  1.0 LPBI – 2012–2020 – IP Assets available for sale

• B.  2.0 LPBI – 2021–2025 – IP Assets under construction – WILL BE AVAILABLE FOR SALE

A.  1.0 LPBI – 2012–2020 – IP Assets available for sale

A.1 A List of Scientific articles N=6,000

STORED in Excel file run on 6/30/2020 and 12/31/2020

They need to be Indexed by several keys:

A.1.1  Author Name

A.1.2  Article Title

A.1.3  Category of Research, see article example , below

For the Cancer category

• we have the following tree structure
• System had data on how many articles are in each category

•  Cancer – General
•  Cancer and Current Therapeutics
  •  interventional oncology
    •  Breast Cancer – impalpable breast lesions
    •  Prostate Cancer: Monitoring vs Treatment
•  CANCER BIOLOGY & Innovations in Cancer Therapy
  •  Anaerobic Glycolysis
  •  Cachexia
  •  Cancer Genomics
    •  Circulating Tumor Cells (CTC)
      •  Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood
        •  mRNA
      •  MagSifter chip
    •  KRAS Mutation
    •  Li-fraumeni syndrome.
    •  TP53 – Germline mutations
  •  cancer metabolism
  •  Funding Opportunities for Cancer Research
  •  Genomic Expression
  •  Glioblastoma
  •  Hexokinase
  •  Loss of function gene
  •  Metabolic Immuno-Oncology
  •  Metastasis Process
  •  Methylation
  •  Microbiome and Responses to Cancer Therapy
  •  Monoclonal Immunotherapy
  •  mtDNA
  •  Oxidative phosphorylation
  •  Pancreatic cancer
  •  Pyruvate Kinase
  •  The NCI Formulary
  •  tumor microenvironment
  •  Warburg effect
•  Cancer Informatics
•  Cancer Prevention: Research & Programs
•  Cancer Screening
•  Cancer Vaccines: Targeting Cancer Genes for Immunotherapy
  •  Engineering Enhanced Cancer Vaccines

A.1.4  Type of article: by the role of the author: 

• If the Author is Curator THAN this article is a curation
• If the Author is Reporter THEN this article is a Scientific reporting article

A.1.5  Article Abstract will be a WordCloud created by ML – one image per article

Example

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?  <<<<<<<<< Article Title

Author: Larry H. Bernstein, MD, FCAP  <<<<<<<<< Author’s Name

https://pharmaceuticalintelligence.com/2012/10/17/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view/   <<<<<<< URL

• The system provides: “Related” what you named associated, see below – will need to be placed in the article description
• The system provides: “Posted in” – meaning – ALL the categories of research checked off by the author that this article belong to by the SUBJECT MATTER of the article

EXAMPLE for “Related” what you named associated

Related

What can we expect of tumor therapeutic response?

In “Biological Networks, Gene Regulation and Evolution”

WordCloud Visualization of LPBI’s Top Twelve Articles by Views at All Time and their Research Categories in the Ontology of PharmaceuticalIntelligence.com

In “Academic Publishing”

AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

In “Biological Networks, Gene Regulation and Evolution”

Examples for >>>>>>>> Category of Research – live links listing in parenthesis number of articles in one category

Posted in Biological Networks, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Disease Biology, Genome Biology, Imaging-based Cancer Patient Management, International Global Work in Pharmaceutical, Liver & Digestive Diseases Research, Metabolomics, Molecular Genetics & Pharmaceutical, Nutrition, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Population Health Management, Proteomics, Stem Cells for Regenerative Medicine, Technology Transfer: Biotech and Pharmaceutical | Tagged Adenosine triphosphate, ATP, Glycolysis, Hypoxia-inducible factors, Kreb, Lactate dehydrogenase, Mammalian target of rapamycin, Mitochondrion, Warburg Effect | 40 Comments

Below, an excerpt from the 6,000 LIST: Top Posts by VIEWS for all days ending 2020-06-02 (Summarized)

All Time
Title	Views	Author Name	Type of Article
Home page / Archives	676,690	Internet Access	Tabulation
Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?	17,117	Larry H. Bernstein, MD, FACP	Investigator Initiated Research
Recent comprehensive review on the role of ultrasound in breast cancer management	14,242	Dr. D. Nir	Commission by Aviva Lev-Ari, PhD, RN
Do Novel Anticoagulants Affect the PT/INR? The Cases of XARELTO (rivaroxaban) and PRADAXA (dabigatran)	13,839	Dr. Pearlman, MD, PhD, FACC & Aviva Lev-Ari, PhD, RN	Commission by Aviva Lev-Ari, PhD, RN
Paclitaxel vs Abraxane (albumin-bound paclitaxel)	13,709	Tilda Barliya, PhD	Investigator Initiated Research
Apixaban (Eliquis): Mechanism of Action, Drug Comparison and Additional Indications	8,230	Aviva Lev-Ari, PhD, RN	Investigator Initiated Research
Clinical Indications for Use of Inhaled Nitric Oxide (iNO) in the Adult Patient Market: Clinical Outcomes after Use, Therapy Demand and Cost of Care	7,903	Dr. Pearlman, MD, PhD, FACC & Aviva Lev-Ari, PhD, RN	Investigator Initiated Research
Mesothelin: An early detection biomarker for cancer (By Jack Andraka)	6,540	Tilda Barliya, PhD	Investigator Initiated Research
Our TEAM	6,505	Internet Access	Tabulation
Biochemistry of the Coagulation Cascade and Platelet Aggregation: Nitric Oxide: Platelets, Circulatory Disorders, and Coagulation Effects	5,221	Larry H. Bernstein, MD, FACP	Investigator Initiated Research
Interaction of enzymes and hormones	4,901	Larry H. Bernstein, MD, FACP	Commission by Aviva Lev-Ari, PhD, RN
Akt inhibition for cancer treatment, where do we stand today?	4,852	Ziv Raviv, PhD	Investigator Initiated Research
AstraZeneca’s WEE1 protein inhibitor AZD1775 Shows Success Against Tumors with a SETD2 mutation	4,535	Stephen J. Williams, PhD	Investigator Initiated Research
The History and Creators of Total Parenteral Nutrition	4,511	Larry H. Bernstein, MD, FACP	Commission by Aviva Lev-Ari, PhD, RN
Newer Treatments for Depression: Monoamine, Neurotrophic Factor & Pharmacokinetic Hypotheses	4,365	Zohi Sternberg, PhD	Investigator Initiated Research
FDA Guidelines For Developmental and Reproductive Toxicology (DART) Studies for Small Molecules	4,188	Stephen J. Williams, PhD	Investigator Initiated Research
The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets	4,038	Dr. Pearlman, MD, PhD, FACC, Larry H. Bernstein, MD, FACP & Aviva Lev-Ari, PhD, RN	Commission by Aviva Lev-Ari, PhD, RN
Founder	3,895	Aviva Lev-Ari, PhD, RN	Investigator Initiated Research

EndFragment

A.2 A List of 16 e-BOOKS

https://lnkd.in/ekWGNqA

A.2.1   Each book is made of articles included in the N=6,000

A.2.2 Books will list the URL of each book

http://www.amazon.com/dp/B00DINFFYC

http://www.amazon.com/dp/B018Q5MCN8

http://www.amazon.com/dp/B018PNHJ84

http://www.amazon.com/dp/B018DHBUO6

http://www.amazon.com/dp/B013RVYR2K

http://www.amazon.com/dp/B012BB0ZF0

http://www.amazon.com/dp/B019UM909A

http://www.amazon.com/dp/B019VH97LU

http://www.amazon.com/dp/B071VQ6YYK

https://www.amazon.com/dp/B075CXHY1B

https://www.amazon.com/dp/B076HGB6MZ

https://www.amazon.com/dp/B078313281

https://www.amazon.com/dp/B078QVDV2W

https://www.amazon.com/dp/B07MGSFDWR

https://www.amazon.com/dp/B07MKHDBHF

https://www.amazon.com/dp/B08385KF87

A.3 A List of e-Proceedings and Tweet Collections

• Part Two: List of BioTech Conferences 2013 to Present
• Part Three: Conference eProceedings DELIVERABLES & Social Media Analytics

A.3.1 each entry is an article included in N=6,000

B.   2.0 LPBI – 2021–2025 –

IP Assets under construction –

WILL BE AVAILABLE FOR SALE

B.1 Journal articles

• Will be subjected to ML and a NEW product will be created
• Instead of N=6,000 article – we will have N= 6,000 Medical INSIGHTS

B.2 16 e-Books

• Will be subjected to ML and a NEW product will be created
• Instead of 16 Books – we will have 16 COLLECTIONS of Medical INSIGHTS derived from Text Analysis of ONLY the articles included on each Volume
• 16 e-Books will become 16 AUDIO BOOKS
• 16 e-Books will become 16 Books in Japanese, Spanish and Russians

B.3 eProceedings & Tweet collections

• Will be subjected to ML and a NEW product will be created
• Instead of 60 e-Proceedings and 30 Tweet collections we will get 100 Business INSIGHTS Collections in the domain of each conference

We believe that Blockchain will enable STORAGE of each item that will be available for sale

• LPBI will have team members Bundling items per customer needs 
• Promotion can be done OUTSIDE the Blockchain system – STIRRING Customers to the Blockchain transaction system for TRADE and recording of transactions
• That is true for A and for B, below

A.   1.0 LPBI – 2012–2020 – IP Assets available for sale

B.   2.0 LPBI – 2021–2025 – IP Assets under construction – WILL BE AVAILABLE FOR SALE

Data Architecture Questions

1. In what data format is the content stored? In other words, is the content in image pdfs, searchable document pdfs, html, xls, word documents, text files, or some other form?

Example: TEXT

Versions of LPBI Group’s Elevator Pitch: 2.0 LPBI Group’s Team – In Our Own Words

• Aviva Lev-Ari, PhD, RN, Founder, LPBI Group: 1.0 & 2.0

My proposed Elevator Pitch

For the first time in the ten years of our private ownership, the opportunity to acquire the Inventor of Scientific curation has become a reality, Available for Transfer of ownership.

You can own a portfolio of Intellectual Property Assets that commands ~2MM e-Readers and offers ~6,000 of the best interpretive articles in five specialties of Medicine and Life Sciences. Pages of our 16 books have been downloaded ~125,000 times and over 100 of the top biotech and medical conferences were covered in real time and recorded in writing and Tweets. New strategies in AI and Blockchain are now applied on LPBI’s content for INSIGHT searches and pattern recognition by automated Machine Learning algorithms for use in drug discovery and drug repurposing. All of LPBI’s content was created by our Experts, Authors, Writers (EAWs).

• • We UPLOAD MS Word file NOT PDF
  • INVENTORY is stored in Excel
  • Top Posts for all days ending 2020-11-16 (Summarized)
    1. 7 Days |30 Days |Quarter |Year |All time
  • All Time

Title		Views
Home page / Archives		716,030
Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?		17,263
Recent comprehensive review on the role of ultrasound in breast cancer management		15,300
Paclitaxel vs Abraxane (albumin-bound paclitaxel)		14,341
Do Novel Anticoagulants Affect the PT/INR? The Cases of XARELTO (rivaroxaban) and PRADAXA (dabigatran)		14,006
Apixaban (Eliquis): Mechanism of Action, Drug Comparison and Additional Indications		8,770
Clinical Indications for Use of Inhaled Nitric Oxide (iNO) in the Adult Patient Market: Clinical Outcomes after Use, Therapy Demand and Cost of Care		8,398
Our TEAM		6,632
Mesothelin: An early detection biomarker for cancer (By Jack Andraka)		6,580
Interaction of enzymes and hormones		5,536
Biochemistry of the Coagulation Cascade and Platelet Aggregation: Nitric Oxide: Platelets, Circulatory Disorders, and Coagulation Effects		5,304
The History and Creators of Total Parenteral Nutrition		5,056
Akt inhibition for cancer treatment, where do we stand today?		4,899
Pyrroloquinoline quinone (PQQ) – an unproved supplement		4,712
AstraZeneca’s WEE1 protein inhibitor AZD1775 Shows Success Against Tumors with a SETD2 mutation		4,665
Newer Treatments for Depression: Monoamine, Neurotrophic Factor & Pharmacokinetic Hypotheses		4,453
FDA Guidelines For Developmental and Reproductive Toxicology (DART) Studies for Small Molecules		4,416
Evolution of Myoglobin and Hemoglobin		4,335
Isoenzymes in cell metabolic pathways		4,206
The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets		4,126
Founder		4,118

2. Within each content file or dataset, is the content metadata already defined, or would we need to parse the file to pull out the metadata? In other words, in the file for a journal article, do you already have the author, date, abstract, keywords, etc. defined as discrete pieces of data, or is all of this information embedded within the overall file?

YES

They need to be Indexed by several keys:

A.1.1  Author Name

A.1.2  Article Title

A.1.3  Category of Research

3. Do you expect to use a single type of subscription (such as a monthly subscription), or will different types of data have different types 

of subscription options (similar to how journals offer both one-time 24-hour subscriptions to a single article as well as monthly ongoing subscriptions)?

We wish to SELL ARTICLE DOWNLOAD vs Subscriptions

4. Does the marketplace need to include fuzzy search (i.e., the ability to find content based on “similar to” criteria, instead of just exact match searches)? Does it need to present the user with related content, or only the content that was searched for?

Our system attaches to each article RELATED content

5. We assume that the marketplace is not intended to replace your current LPBI company website? We are not scoping the quote to include a full website rebuild; it is assumed that the marketplace is separate (and your users would access the marketplace via the LPBI website).

YES – the digital store will connect to our newly to be designed web site for 2.0 LPBI on WordPress.com

• The digital store is the FORUM to buy goods by digital download of content
• $30 for One digital article or Audio article
• REFERRAL to Amazon Website to buy a book or the book in AUDIO format or a book in Japanese and Spanish – Russia is not served by Amazon – we can sell directly to consumers
• $100 download of an e-Proceedings for a Conference or the Tweet collection

For 2.0 LPBI Products

Bundles of Insights for Targeted Industries – B–to-B

• Tier #1:  Insights for drug discovery embedded in consulting engagements
• Tier #2:  Insights for drug repurposing embedded in consulting engagements
• Tier #3:  Insights for Health Care Insurers embedded in consulting engagements

Bundles of insights for theScientific Community – B–to-C

UPDATED on 6/18/2021

From: Erich Greenebaum <erich@prosperci.com>

Date: Friday, June 18, 2021 at 10:16 AM

To: “Stephen Williams, PhD” <sjwilliamspa@comcast.net>

Cc: “Aviva Lev-Ari, PhD, RN” <aviva.lev-ari@comcast.net>

Subject: Re: Exploration of Collaboration on Medical Text Analysis using Machine Learning (ML) and Natural Language Processing (NLP)

In most enterprise computing projects, it is very typical to have “development” vs” production” environments. In this context, it seems that you are in the “development” mode, and so it makes good sense to do that work in a separate environment in my view.

I will be learning more about BurstIQ next month, but I did want to share a little detail about the open source semantic graph database project called “Fluree.”

https://flur.ee/2020/02/25/wake-forest-school-of-medicine-partners-with-fluree-to-improve-research-analysis/

Critically, when it comes to hydrating a knowledge graph using NLP, Fluree supports SPARQL queries directly, and so I believe you would be able to interact against it directly from Wolfram. As graph databases are finding currency in NLP/ML applications, this struck me as potentially powerful tool in your work.

An interesting property of Fluree is that its state is persisted on a blockchain style database, which facilitates what they refer to as “time travel” across the history of the graph. This comes along with providing cryptographically provable provenance of the data. Finally, they build a “smart contract” approach into their data model to handle access control and other rule based logic within the graph, which opens up a lot of possibilities of exposing datasets publicly while still protecting proprietary data at a very fine grained level, i.e. one might want to provide search facilities while not actually exposing the content without some licensing agreement.

Again, I want to avoid speculating too far before I have a better sense of the BurstIQ architecture, but I mention Fluree mostly because you might find the technology interesting in your NLP work in general. If it proves of interest to you,  I’d be happy to chat about it more.

Hope this finds you well!

/eg

On Jun 14, 2021, at 8:21 PM, Stephen Williams <sjwilliamspa@comcast.net> wrote:

Dear Dr. Greenebaum,

I was referred on this email as I am working, along with Aviva, on NLP strategies with a few fellows and interns.  We are currently using the environment on Wolfram to host data as well as algorithms to conduct text cleanup and analysis.  This platform has the ability to integrate Python scripts.  As such I feel it might be more useful to have students use their UTA platform to test Python scripts for NLP use eventually on LPBI’s Wolfram account and space.  I look forward to getting your opinion on the matter and hopefully early next week we could get together on a Zoom meeting to discuss this further.

Sincerely

Stephen J Williams, PhD

LPBI Group, CSO

Assistant Professor

Temple University, CST Biology

UPDATED on 6/7/2021

Review Graphics DB:

https://flur.ee

UPDATED on 5/8/2021

Discussion on the Number of Relations EXPECTED to be revealed by NLP by Linguamatics

I used the ratio of 673 found relations in 33 articles to say about 20 relations in one article

• Thus, in 600 articles x 20 = 12,000 Relations – In the 4 volumes: 2 Cancer, @ Genomics – Together ~600 articles
• Thus, 6,000 articles as Posts in the ENTIRE Journal Corpus (Plus ~400 Pages) x 20 = 120,000 Relations

Blockchain Infrastructure will be designed for On demand Analytics of LPBI Stored Content:

The Data Science functionality of the Blockchain IT Infrastructure will enable to perform NLP, TEXT MINING and Analytics on article collections.

Content Consumer Specifies preference/selection of the topic CONTEXT from the following three Collection Types

• B2C – Independent Scientists select topic context
• B2B – inside an organization, Knowledge workers select topic context

Suggested are the following Article Collection Types for CONTEXT of Semantic Analysis:

Article Collection Type 1: All Articles in a Chapter in a Book

• In Book x  [x = 1,2,3,…,18]
• An Article Collection is defined as = All Articles in a Chapter in a Book for Book x  [x=1,2,3,…,18]

Article Collection Type 2: The Research Category attribution assignment made by authors/curators at Publishing time

• Type 2 is defined as = any subset of articles in a given RESEARCH CATEGORY (RC) 
• Dynamic Journal Ontology [RC = 1,2,3…, 733]
• For Article Collection Type 2, it is suggested to rank all articles in a given RC by Number of Views, selection top 12, from top to 12th by Views

Article Collection Type 3:  Keywords in the Article Title 

• Search for all articles by a keyword or keywords in the Article Title 
• Select by either Number of Views, or by
• Most recent published

HYPOTHESES :

#1:

Highest Number of Relationships EXPECTED to be found, in ranked order

1. Article Collection Type 1

2. Article Collection Type 2 

3. Article Collection Type 3

#2:

Strength of relationship suggested by Dr. John McCarthy. 

A STRENGTH Measure for semantic relationship needs to be developed, it is like an analogy for Affinity or Similarity

THEN 

Highest STRENGTH of relationships EXPECTED to be found, in ranked order

1. Article Collection Type 2

2. Article Collection Type 1 

3. Article Collection Type 3

UPDATED on 4/30/2021

Spanish Edition

of LPBI Group’s BioMed e-Series

18 Books in Medicine 

https://pharmaceuticalintelligence.com/biomed-e-books/

All books are available for Sale and Page Downloads on Amazon.com

https://lnkd.in/ekWGNqA

The Context:

Montero’s partner, known as Leaders in Pharmaceutical Business Intelligence (LPBI) Group, HQS in Boston, MA, USA is planning the launch of its Digital Store in a Healthcare Digital Marketplace designed and operated by BurstIQ. The Digital Store is using a Blockchain Transactions Network as its IT platform for B2C and B2B transactions for their digital content.

The available digital content in Life Sciences, Pharmaceutical, Healthcare, Medicine, Medical Devices, Medical equipment, Biotech and Bioscience includes the 1.0 LPBI IP Portfolio of an e-Scientific Publisher – 3.3 Giga bytes of English text and Biological images. The portfolio contains four IP asset classes:

• 6,000 scientific Journal articles – curations of peer reviewed scientific findings – with clinical interpretation written by experts.
• 18 Books in Medicine and Pharmaceutics
• 100 e-Proceedings of the most impoetant Medical and Biotech Global Conferences that we covered in real time using PRESS passes and Tweet Collections from 36 events
• 5,100 Biological images used in the articles mentioned above

The Blockchain design of the IT platform for Content Transactions will include, in addition to the 1.0 LPBI IP Portfolio (2012-2020) described above (the four IP asset classes), the 2.0 LPBI IP Portfolio of visualization artifacts currently under development. 

2.0 LPBI IP Portfolio (2021-2025) consists of expert Interpretation of the visualization products resulting from Medical Text Analysis and Text Mining of all its Digital Published Products. The Text Analysis and Text Mining is performed by advanced algorithms from Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP).

• Montero is currently translating from English into Spanish the cover pages and the electronic Table of Contents of 18 Books in Medicine and Pharmaceutics
• This project originator is Dr. Aviva Lev-Ari, PhD, RN, who is the Founder of 1.0 LPBI and 2.0 LPBI and Editor-in-Chief of the Journal [com] and of the BioMed e-Series [https://lnkd.in/ekWGNqA]

In 2021 LPBI Group began the transition from:

A nine years young profile of being

• A very dynamic and cutting age electronic Scientific Publisher,

Known as 1.0 LPBI during 2012 – 2020

toward designing its new image while becoming a new Company with a new profile,

Known as LPBI, in 2021-2025

• A Medical Text Analysis company using (NLP-ML-AI) – Software as a Service (SaaS) and
• Content Monetization (on a Blockchain Transactions Network) – Blockchain as a Service (BaaS).

The Blockchain platform design includes the following five features:

1. Recommendation Engine residing on a blockchain
2. Permissions,
3. Immutable LEDGER,
4. Smart contracts and
5. Cyber-security for protecting the IP

 

Economies of scale will be achieved by:

• Development of one content promotion system
• Unified IT cloud-based infrastructure
• Maintenance of B2C IT transaction system in a Digital Store at a Healthcare Marketplace [monthly fee paid for the use of the network and
• Installations of B2B Point-of-Research (PORs) at institution – pay per use vs subscription base – type of contracts not specified yet.

 

The Competitive Landscape

 

2.0 LPBI is a Very Unique Organization

https://pharmaceuticalintelligence.com/2021/03/02/2-0-lpbi-is-a-very-unique-organization/

 

The uniqueness and the competitive space is addressed at length in 1.0 LPBI Prospectus, a 300 page document

• It Is sent as an attachment separately
• List of competitors using Blockchain are telemedicine companies not scientific e-publishers

https://pharmaceuticalintelligence.com/blockchain-transactions-network/

• NO other e-Scientific Publisher is Using NLP on a Blockchain platform.
• LPBI has the FIRST MOVER ADVANTAGE over all other e-Scientific Publishers
• LPBI had the FIRST MOVER ADVANTAGE in curation of scientific findings in 2012.
• Our NLP Partner, Linguamatics said: No client ever asked us about Blockchain
• Our Blockchain IT Partner, BurstIQ said: No client ever asked us about NLP
• LPBI is now working with both on an entirely solution.
• All the Text Analysis with NLP currently done converns Literature in the Public Domain: PubMed, MedLIne, Ontologies and Formularies
• Peer reviewed articles in PubMed, MedLine publish content only on EXPERIMENTS and on Clinical Trials
• LPBI content is CURATIONS by Experts, secondary research on the clinical interpretation of primary research using ONLY peer reviewed published articles as sources.

1.0 LPBI Products versus 2.0 LPBI Products

1. 1.0 LPBI – Blockchain LEDGER for Content Monetization of IP Asset Classes I, II, III, V

LPBI 1.0, 2012-2020 is the creator and the custodian of the Portfolio of ten IP Assets Classes. For content monetization, we identified four of the ten assets that are related to the curation methodology and process:

Class I: Journal articles,

Class II: 18 Books,

Class III: 100 e-Proceedings & Tweet Collections,

Class V: +5,100 Biological Images

The Use Case for data entity design and meta data architecture is a Journal article. It has the following Profile:

• Article ID – IP Asset Class I
• Author
• URL
• Date of Publication
• Research Categories assigned by Author(s)
• Is this Article a Curation or a Scientific Report
• Is this Article included in a Book? If yes, in which Books – IP Asset Class II
• Is this Article a component of an e-Proceedings? If Yes, What is the Conference Title, Date, Location – IP Asset Class III
• List of Biological Images included in this article – IP Asset Class V

 

2. 2.0 LPBI Digital Products:

ALL the content of 3.3 Giga Bytes is to be subjected to Text Analysis with AI/ML/NLP. The Products of this Machine Learning analysis of text are in the format of visualization artifacts (Graph Files). They represent the SEMANTIC relationships between concepts and keywords ACROSS (A) all articles in One Chapter of the book or (B) in several main Categories of Research.

• This aggregation of content, i.e., 20 articles making up a Chapter in a book or
• 20 articles were all written by different authors/curators, yet all have been assigned the same research categories. This means that semantically these two collections of articles represent a common theme or similar location on the Tree of knowledge, represented by spatial proximity to a similarity graph (Hyper-graph) or on an hierarchical clustering graph (Tree Diagram).

The Benefits of Text Analysis performed by Machine Learning Algorithms

• All articles are in one Chapter in the book
• Some Articles in several main Categories of Research are assigned to the article by the Author/Curator
• Some research categories have +1,000 articles assigned, i.e., Cancer Biology & Therapies

These attributes: Assignment of an article a Chapter in a book or a research category represent the thematic context of the article.

The context reveals INSIGHTS needed for understanding relationships among articles vs each of the 6,000 articles to stand alone as a singular point in knowledge space. Thus these two affiliation criteria serve as classifiers.

LPBI’s Journal has an ontology of 670 categories of research. In theory one could run NLP on all the articles in each of these 670 categories and reach a semantic map for the entire universe of the Journal Corpus.

Current offering from LPBI are four corpuses, Text Analysis with NLP done by Machine Learning software is the ENGINE for identification of conceptual relationship in context.

The Suite of Products – A Portfolio of Intellectual Properties (IP):

Four Corpuses in details:

1. 6,000 scientific Journal articles – curations of peer reviewed scientific findings – with clinical interpretation written by experts.
2. 18 Books in Medicine and Pharmaceutics
3. 100 e-Proceedings of the most important Medical and Biotech Global Conferences covered in real time owithPRESS passes and Tweet Collections from 36 events
4. 5,100 Biological images used in the articles above

The Journal consists of

• Posts (6,037 on 4/23/2021),
• Pages (393 on 4/23/2021)

Posts consist of four Article Types:

• Type A: Authored article by an Expert, Author, Writer (EAW) or more then one – all are PhD, MD, MD/PhD, PharmD level
• Type B: Curated article by an EAW or more then one – all are PhD, MD, MD/PhD, PharmD level
• Type C: Scientific Reporting by an EAW, by a PostDoc level or by a Masters Level
• Type D: e-Proceedings of Conferences and Tweet Collections. Namely, all e-Proceedings are Posts not Pages

Pages consist of three Page Types

• Public Published Page
• Password Protected Page
• Public Published Page that is a Book

Example of Recently Published Posts, Live links

• Apr 19th, 10:22 AM Identification of novel genes in humans that fight COVID-19 infection
• Apr 14th, 4:42 PM The top 10 medtech M&A deals of 2020
• Apr 14th, 12:56 PM Mechanism of Thrombosis with AstraZeneca and J & J Vaccines: Expert Opinion by Kate Chander Chiang & Ajay Gupta, MD
• Apr 13th, 3:37 PM Fighting chaos with care, community trust, engagement must be cornerstones of pandemic response
• Apr 12th, 3:34 PM Linking Thrombotic Thrombocytopenia to ChAdOx1 nCov-19 Vaccination, AstraZeneca

18 Books in Medicine and Pharmaceutics

The BioMed e-Series, 18 volumes consist of five multi volume series.

BioMed e-Series

• Series A: e-Books on Cardiovascular Diseases – Six volumes
• Series B: Frontiers in Genomics Research – Two volumes
• Series C: e-Books on Cancer & Oncology – Two volumes
• Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases and Genomic Endocrinology – Four volumes
• Series E: Patient-Centered Medicine – Four volumes

Of Note,

• The majority of the articles in these books are CURATIONS
• Curation of Scientific Findings is a unique methodology for creation of Posts which are Journal articles of com.
• The Curation process is explained in Chapter 1 in Series A, Volume 2

Cardiovascular Diseases, Volume Two: Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation. On Amazon.com since 11/30/2015

http://www.amazon.com/dp/B018Q5MCN8

 

• These 18 Books consist of application of the Curation Methodology for the creation of electronic Table of Contents (eTOCs) for each of the 18 books
• This Methodology allowed our Expert Editors to produce systematic classification of all eTOCs by culling articles from the journals’ research categories to create a one of a kind eTOCs for each volume
• Each eTOCs represents a Non Fungible Token (NFT)
• An Update to existing Journal articles represents a Non Fungible Token (NFT)
• Dr. Aviva Lev-Ari was involved in the creation of 14 of the eTOCS of the BioMed e-Series books
• Except for the following four volumes: Series B, Volume 1 (Dr. Williams & 3 Editors); Series D, Volume 1 and Series E, Volume 2 & 3 (Single Author/Editor, Dr. LHB]

In 2021, 2.0 LPBI is planning to launch a Blockchain Transactions Network Ecosystem to sell Journal Articles, e-Books, e–Proceedings & Tweet Collections and Biological Images

Regarding Selling books and the Blockchain IT Platform:

The current plan is to promote the books and refer the interested Content Consumer/End-User to purchase the Books on Amazon.com which grants 35% of books Sales to Authors. Amazon.com does not allow selling the book on any other platform, per contract signed by authors under KDP.

However, the Transfer of Ownership of the LPBI IP Portfolio can include a condition for removal of the Books from the Amazon.com platform, Kindle Store and the permission to republish the book under a New Publisher Title, keeping all contents and authors as currently listed on the Amazon platform. Under that condition, a book priced $135 may remain at the same price or the price may change; in either case 100% of the Price upon a book sale will be recorded and credited to the new Publisher.

 

This scenario may be favorable to a Scientific publisher with a Global distribution of Books infrastructure in place.

The Process of Content Purchase and Monetization – How will it work on the Blockchain Transactions Network?

• The content will be downloaded into a Digital Cart subsequent to Content Customer conducting a query to interrogate the Knowledge repositories of our four corpuses stored on a Blockchain IT infrastructure, which represents the back-end of a Digital Store and executes the data and transaction processing functionality on the Healthcare Digital Marketplace network
• The Recommendation Engines (one for Text) and (one for Biological Images) present the Content Customer with selection choices and a Price Tag associated with all selection options
• Content Customer performs selections on a FORM after reviewing all recommendations – The Front-end of the transaction GUI.
• Form submission generates an Invoice
• Invoice is Paid
• Permission is authorized by the blockchain system
• All contents selected is downloaded in Content Customer’s cart and become available for use instantaneously
• On the back-end, the transaction is recorded on the LEDGER and funds are transferred from the Content Consumer to LPBI Account Receivable

Content Customer/End-user interact with a computer screen or a mobile device for submission of queries to DBs in the Digital Store:

 

Options for selection include:

• Knowledge repositories [1.0 LPBI IP Asset Classes I, II, III, V]
• Content Customer/End-user will submit a query and will Specify

Current, Choices for the search:

• Original articles,
• Books,
• e-Proceedings and Tweet Collections and
• Biological Images or
• All of the above

 

A.  The current choices for the search are NOW in Read Only mode since the content in the WordPress.com Cloud is not connected to a Transactions Network.

We design the Blockchain and the digital store to enable transactions for our current and future digital content.

LPBI’s new Content will continue to be added to the WordPress.com Cloud and migrated to the Blockchain system

• This process has not yet been specified since the indexing and the current content migration of 3.3 Giga Bytes has not yet started. The Blockchain is under DESIGN. BETA testing, Launch will follow

B.  Work-in-Progress – Future Digital Products for Content Customer/End-user to specify during interaction with the System

 

• Spanish Translated eTOCs of 18 Books [Montero current involvement]

 

C.  Work-in-Progress – Content Customer/End-user will specify during interaction with the Text Analysis by AI/ML/NLP

 

• Specifying Visualization artifacts to be generated by AI/ML/NLP as a result of Text Analysis and Text Mining
• Specifying the Foreign Language for the Interpretation of Visualization: Spanish, Japanese, Russian [Montero potential future involvement]

 

The Objective: Content Monetization & Global Dissemination of Life Sciences Scientific Innovations

The transformative work done by LPBI Group allows cutting-edge biomedical research innovation to be widely disseminated and accessible to the global research and non-research communities

The Blockchain Transactions Network enables Selling Content on the INTERNET to B2C and to B2B

• LPBI’s method of curation represents a mode of scientific communication including synthesis, analysis, and interpretation done by experts in +6,000 Journal Posts and ~400 Pages
• Experts, authors, and writers add their knowledge and expertise in re-thinking and conceptualizing subjects selected in their domain of expertise, to form new curations and update existing ones.
• The books are transformative in their capacity to accelerate diffusion of scientific innovations. They represent the frontier of life sciences research.
• The curation is done by experts with a perspective within each field, allowing for the creation of scientific content that combines conceptual evolution within the scientific breakthroughs analyzed together with their anticipated future implications.

The Content is offered to the Content Consumer: B2B and B2C

 

LPBI content is in Life Sciences, Pharmaceutical, Healthcare, Medicine, Medical Devices. Thus, it would attract institutions active in several verticals

 

1.0 LPBI IP Portfolio of an e-Scientific Publisher

Present 3.3 Giga bytes of English text and Biological Images

Intellectual property of LPBI is of four types:

A corpus of curated articles,

• 6,000 scientific Journal articles – curations of peer reviewed scientific findings – the clinical interpretation written by experts.

A corpus of e-books

• 18 Books in Medicine and Pharmaceutics

A corpus of e-proceedings

• 100 e-Proceedings of the most important Medical and Biotech Global Conferences covered in real time using PRESS passes and Tweet Collections from 36 events

A Gallery of Biological Images

• 5,100 Biological images used in the articles above

Future 2.0 LPBI Suite of Visualization Artifacts created by Text Analysis with AI/ML/NLP

Will be available on the Blockchain platform and will be produced on the fly per distinct queries submitted by the Content Consumer to the Content Databases:

Visualization artifacts produced by AI/ML/NLP include the following files: As scaling up takes place, these artifacts will become available for download and monetized at a

1. per Article basis in the +6,000 corpus
2. Collections of articles in Books’ chapters
3. Main research categories assigned to articles by authors/curators
4. On demand, collections specified by end-users produced on the fly on the Blockchain platform enriched with Data Science & Analytics features [some are currently done in the NLP environment; more can be done on the Blockchain when all the four corpuses become live for transactions and for Analytics]

 

List of New digital products to be produced by LPBI Team working on Medical Text Analysis using NLP strategy:

 

Graph Files

• WordClouds representing Article abstracts
• Bar Diagrams representing Word Frequencies
• Hyper-graphs representing Semantic relationships
• Tree Diagrams representing hierarchical clustering of conceptual similarities

Text Files

Interpretations of the visualization artifacts generated by AI/ML/NLP are included in the plan 

Multi-Lingual Translation of the Text Files produced by Domain Knowledge Experts.

1. Spanish
2. Japanese
3. Russian

The Volume of Content Consumed to date:

• Books published on Amazon.com – To date: +135,000 pages have been downloaded from the 18 Volumes.
• Journal articles (Posts and Pages): To date: ~2MM Views
• We used data on Actual Article Views since date of publication (2012-2020) for projection of Article Views (2021-2025)
• Assumption: One view is a download of a $30 article
• Projection of Revenues: 2021-2025 based on actual ~2MM views, 2012-2020

PharmaceuticalIntelligence.com Journal – Projecting the Annual Rate of Article Views

https://pharmaceuticalintelligence.com/vision/pharmaceuticalintelligence-com-journal-projecting-the-annual-rate-of-article-views/

See explanations in 1.0 LPBI Prospectus

Content Availability by Access Mode

 

Read Only

• Present – All content Is hosted on

https://pharmaceuticalintelligence.com/

• 2021 – New Website is under construction
• New URL for 2.0 LPBI, Medical Text Analysis with AI/ML/NLP and Blockchain for Content Monetization – Work-in-Progress
• See two alternative Site Maps for new website design – Work-in-Progress

https://pharmaceuticalintelligence.com/2020/12/02/two-site-map-proposals-for-lpbis-new-web-site/

Transactions enabled Website

for Books on Amazon.com – Kindle Store, Bookshelf: Life Sciences & Medicine – 18 Books in Medicine & Pharmaceutics

https://lnkd.in/ekWGNqA

http://www.amazon.com/dp/B00DINFFYC

http://www.amazon.com/dp/B018Q5MCN8

http://www.amazon.com/dp/B018PNHJ84

http://www.amazon.com/dp/B018DHBUO6

http://www.amazon.com/dp/B013RVYR2K

http://www.amazon.com/dp/B012BB0ZF0

http://www.amazon.com/dp/B019UM909A

http://www.amazon.com/dp/B019VH97LU

http://www.amazon.com/dp/B071VQ6YYK

https://www.amazon.com/dp/B075CXHY1B

https://www.amazon.com/dp/B076HGB6MZ

https://www.amazon.com/dp/B078313281

https://www.amazon.com/dp/B078QVDV2W

https://www.amazon.com/dp/B07MGSFDWR

https://www.amazon.com/dp/B07MKHDBHF

https://www.amazon.com/dp/B08385KF87

http://www.amazon.com/dp/B08VTFWVKM

Aviva Lev-Ari, the Editor-in-Chief that had uploaded all these books to Amazon.com, is the only person that can remove them from Amazon.com and transfer ownership of these 18 books to another Publisher.

LPBI Digital Store in Healthcare Marketplace – Ecosystem for content downloads and content monetization – Transactions enabled interface

• Design of Blockchain IT Transactions Network – Work-in-Progress

 

Marketing Communication Needs: 1 – 7

 

1. Spanish Edition – Content promotion of 18 Medical books in Spanish speaking Countries
2. LPBI has needs in Marketing Communication, Media & PR for the venture’s potential M&A by a 3^rd party: i.e., Scientific Publisher, Healthcare NGO, Ministry of Education in Country x,y,z, Research Institute, i.e., National Institute of Health in Country x,y,z
3. 0 LPBI is producing new digital media: Priority #1: Audio Podcasts. Future plans under new ownership: Audio Articles, Audio Books,
4. 0 LPBI is producing new Visualization artifacts as outcomes of Text Analysis with AI/ML/NLP
5. 0 LPBI is Planning Advertisement for Amazon Books using Amazon Advertising in different countries for different book volumes, i.e., Genomics Volume 2 in the UK, Cancer Volume 1 & 2 in Latin America – This is a case of promotion of Books – expertise in auctions used in experimental design of advertisement running Ads is needed.
6. NEW documentation on IT Architecture for Content Monetization of Journal articles on the Blockchain IT infrastructure – Work-in-Progress
7. NEW documentation for content promotion and Monetization of other IP Asset Classes: Biological Images, e-Proceedings – Work-in-Progress

The Targets: END-USERS are the Life Sciences Content Consumers: including physicians, biotech knowledge worker, big pharma R&D and Medical Affairs Departments, Investment community in Healthcare

MedCity SPOTLIGHT Video – Healthcare Trends and Venture Capital Outlook

https://www.youtube.com/watch?v=YEfNWan0l5Q

 

For the Transfer of Ownership – Global Scope

Business Model for Blockchain Platform:

 

Product Price List Itemized for 1.0 LPBI & 2.0 LPBI

 

B2B & B2C will access 1.0 LPBI & 2.0 LPBI Products

 

Price List below represents B2C. Market installations in B2B will have a different Pricing structure based on Point-of-Research (POR)

 

• 1.0 LPBI – Digital Products
• 2.0 LPBI – Visualization (Graphical) Products & Multi-Lingual Interpretations

Product Price List Itemized for 1.0 LPBI Digital Published Products

 

• Article Download                   $30
• Book Purchase                   Amazon.com

(1) Price List of Books

(Price range $75 to $135 per book)

https://lnkd.in/ekWGNqA

• Book Page Download – price set by Amazon.com

(2) Page per View LPBI Digital Products

DOWNLOADS of 1.0 LPBI Other Digital Products

• eProceedings/Tweet Collections $100
• One Biological Image                    $30
• Spanish eTOCs – One volume      $15
• Spanish eTOCs 18 Volume          $125

 

Product Price List Itemized for 2.0 LPBI Visualization Artifacts produced by AI/ML/NLP & Interpretation Text Products

 

A PowerPoint Presentation based on a Proof-of-Concept of 33 articles in Cancer, including examples for each Visualization Artifact is available

Currently, these products are not YET available for sale – to download digital content following payment requires a BLOCKCHAIN platform with the features mentioned above – it is under design – Work-in-Progress

• WordClouds representing Article abstracts             $20
• Bar Diagrams representing Word Frequencies        $20
• Hyper-graphs representing Semantic relationships $20
• Tree Diagrams representing hierarchical clustering of conceptual similarities  $20

• Expert Interpretation of Visualization Artifacts

English                                                    $20

Spanish                                                  $30

Japanese                                                $30

Russian                                                   $30

The Transition from e-Publishing to Text Analysis by ML and Content Monetization

Phase I: Transformation and Transition

Phase I requires for the following projects:

1. Global content promotion using Amazon Advertising that provides Analytics on $ spent and sales gained
2. Marketing Communication projects
3. Blockchain infrastructure design and implementation
4. Data indexing and data migration to blockchain platform: +6,000articles and ~400 pages
5. Scaling up the NLP phase to 3.3 Giga bytes of data
6. Translations to Foreign languages: Spanish, Japanese, Russian
7. Decisions on Audio articles and Audio Books and estimating the cost involved
8. Management of the Digital Store Shelves beyond the Network management provided with the monthly fee by the host of the Digital Marketplace
9. Subletting shelves in the Digital Store to cover the monthly fees of network usage would require Recruitment of Content Creators to host and transact their content in LPBI’s Digital Store.
10. Enabling a content marketplace for 3^rd party content creators to contribute and monetize their own content (was discussed as a future phase after the foundational marketplace is created using LPBI content).

Phase II: Pursuit of Conceptualization for the pipelines leading to the transition to 2.0 LPBI.

Phase II is paving the way to

• A new organization
• Need for new ownership
• Need for new management

Phase III: Preparation for M&A and Exit.

See Elevator Pitches by all team members:

Versions of LPBI Group’s Elevator Pitch: 2.0 LPBI Group’s Team – In Our Own Words

https://pharmaceuticalintelligence.com/2020/10/20/versions-of-lpbi-groups-elevator-pitch-2-0-lpbi-groups-team-in-our-own-words/

In light of Phase I, II, III – LPBI’s Founder is fully engaged and is running in parallel three strategic courses:

1. The transition plan and new technologies emergence: NLP and Blockchain
2. The recruitment of External Business Relation, External Scientific Business relations, NLP team members, New Domain Knowledge Experts
3. The prospecting process in the event of Technology Transfer of Ownership: M&A talent

List of IP Assets for Technology Transfer of Ownership – DIGITAL PUBLISHED PRODUCTS:

• IP Asset Class I: The Journal +6,000 Scientific articles https://lnkd.in/erfbayJ
• IP Asset Class II: 18 Volumes in BioMed e-Series https://lnkd.in/ekWGNqA
• IP Asset Class III: +100 eProceedings of BioTech & Medical Conference and Tweet Collections

https://pharmaceuticalintelligence.com/press-coverage/part-three-conference-eproceedings-deliverables-social-media-analytics/

• IP Asset Class V: A Gallery of 5,100 Biological Images

https://pharmaceuticalintelligence.com/

See below considerations for Venture Valuation addressing IP Asset Classes: IV, VI, VII, VIII, IX, X, which are NOT related to the curation methodology

 

1.0 LPBI – Inventory of Digital Products – a VAST portfolio of IP developed by 1.0 LPBI since inception

2012-2020

• +6,000 articles and 5,100 biological images,
• 18 books in Medicine
• 100 e-Proceedings & Tweet Collections
• +3.3 Giga Bytes of IP
• Translation of 18 books in Medicine: Title page and electronic Table of Contents to Spanish for 22 Counties speaking Spanish

 

2.0 LPBI – Technology and Marketing Strategies

2021-2025

• Working with BurstIQ, a leader in Blockchain, on architecture of a platform for LPBI’s Content Monetization

1. A Digital Store on BurstIQ HealthCare Digital Marketplace
2. Features of the Blockchain IT infrastructure defined
3. Transactions Network: Recommendation Engine, Permissions, Smart Contracts, Immutable LEDGER, CyberSecurity, Content Promotions
4. We co-design the architecture to include NLP features to compute on Demand visualization artifacts

• Working with Linguamatics/IQVIA on NLPscaling up from a Proof-of-concept to +6,000 articles, all books all e-Proceedings and Tweet collections and Biological images

1. Will get a quote for Licensing Linguamatics NLP Platformto LPBI 
2. Or Licensing Linguamatics NLP Platform to BurstIQ

• Working with Montero LS, Madrid, Spain on a Marketing Campaign for the SPANISH Edition resulting from translation of 18 books in Medicine: Title page and electronic Table of Contents to SPANISH for 22 Counties speaking Spanish

BioMed e-Series: 18 Volumes – electronic Table of Contents (eTOCs) of each Volume

https://pharmaceuticalintelligence.com/2017/12/12/biomed-e-series-16-volumes-electronic-table-of-contents-of-each-volume/

1. Accepted a quote for the translation job [Translation of 18 books in Medicine: Title page and electronic Table of Contents to SPANISH for 22 Counties speaking Spanish]
2. Will review a quote for the joint Marketing Campaign for Latin America with a focus on Mexico, Spain, Argentina
3. Will review a quote for Marketing Communications projects

UPDATED on 2/5/2020

Decision RULES:

1. IF an article is in an e-Book THEN context for NLP is defined to be All articles in its Chapter in the Book
2. IF an article is NOT in an e-Book THEN context for NLP is defined to be Articles in Main Research Category Top 12 by Views

Pending estimation of:

1. Investment needed for Text Analysis with NLP 
2. Investment needed for Content Monetization on Blockchain IT Infrastructure by vendor
3. Investment needed for Text to Audio conversion
4. Investment needed for Translation to Foreign languages
5. Cost of translation of (e), below to several Foreign Languages
6. Pricing EACH OUTPUT of NLP process: 

(a) WordCloud 

(b) Bar diagram 

(c) Hyper-graph

(d) Tree Diagram

(e) Expert Interpretation of (a) to (d)

UPDATED on 2/1/2021

At present, I see the following:

LPBI 1.0 – Blockchain LEDGER for Monetization of Class I, II, III, V

• Custodian of the LPBI 1.0, 2012-2020 Portfolio of IP ten Assets Classes
• For content monetization, we identified four of the ten assets: 

Class I: Journal articles, 

Class II: 18 Books, 

Class III: 100 e-Proceedings & Tweet Collections, 

Class V: +5,100 Biological Images

• Content monetization requires a Blockchain Transaction Networks: Immutable ledger, permissions, smart contracts, recommendation engine

LPBI 2.0 – Blockchain LEDGER for Monetization of Graphics generated by ML and Experts interpretation in several Foreign languages

• NLP, Machine Learning-AI applied for Text Analysis of Class I, II, III, V
• Content monetization requires a Blockchain Transaction Networks

Economies of scale will be achieved by:

• Development of one Content Promotion System
• Unified IT Cloud-based infrastructure
• Maintenance of B2C IT transaction system in a Digital Store at a Healthcare Marketplace [monthly fee paid for the use of the network and hosting content]
• Installations of B2B at institution – pay per use vs subscription base

UPDATED on 1/28/2021

REST API Tutorial

UPDATED on 1/27/2021 – Additional Observation

From: Amber 

Date: Thursday, January 28, 2021 at 11:21 AM

To: “Aviva Lev-Ari, PhD, RN” <AvivaLev-Ari@alum.berkeley.edu>

Subject: Re: Data Architecture for Blockchain Deployment of Digital Assets: LPBI IP Asset Classes I,II,III,V | Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Thank you, Aviva. This is consistent with my understanding as well. A couple of notes:

1. We can build the analytics that you described directly on the BurstIQ Platform; you do not need NLP to render these visuals (although you can certainly use NLP if you want to). The visuals can be presented in the marketplace either as a static image, or as a dynamic visual that changes based on how the user filters the data.

2. With respect to your note re: using one block for NLP: one block equals one piece of data, like a word cloud image or an author’s name. To incorporate NLP, we would integrate with the NLP services via a REST integration, so that the platform can both present data to the NLP service and ingest processed data from the NLP.  Then the output files from the NLP service would be stored in one or more blocks on the platform.

I hope that additional info helps.

Cheers,

Amber

We are still working to produce the 

• INPUT two TEXT files for LINGUAMATICS to run their NLP
• We will run on SAME Text our access to Wolfram’s NLP
• On BurstIQ end: 
• FOR OUR PROJECT – may be it is worth exploring having ONE block in the blockchain to be the processor of NLP – this is OUR IDEA for our own needs

We will get back to you as soon as we clarify which one runs supreme Linguamatics vs Wolfram 

We are to meet with CS CMU experts to clarify our specs about that interface that will be best:

• Static Graphic files vs 
• Graphic production on the fly by ONE NLP block on your Blockchain [That will need to be tested???? 

Observations:

1. Advantage of static files – Graphics produced by NLP exist for Content Promotion and are available to the Recommendation Engine to display as a result of a query
2. Advantage of compute on the fly – done on subset of article collection ON DEMAND not in existence in the statics files generated on 2 article sets: All articles in one Chapter and Same number of articles form the Main category of research
3. BOTH MAY BE NEEDED TO EXIST ?????
4. I assume each MODE of implementation has a difference I/O and overhead performance numbers and if Both exists these numbers may be x2 ????

PS

• The first Quote was for Existing IP – 1.0 LPBI
• The amended Quote [PENDING] – will be addition to consider the NLP Graphical output been ingress or created on fly or both (reasons, above, why both are needed). Graphical output from NLP are Content Products to be available on the Transaction infrastructure for download and monetizing of the IP involved

We are now designing the requirement for the Data Architecture for the blockchain Transaction Network for Content Monetization.

https://pharmaceuticalintelligence.com/2020/11/16/data-architecture-for-blockchain-deployment-of-digital-assets-lpbi-ip-asset-classes-iiiiii/

• The unit case is an “Article” – a Longitudinal Profile of Classifiers

1. Article has date of publication, 
2. Author(s) Name, 
3. Title, 
4. Length, 
5. URL 
6. is it in a Book? 
7. Series, Volume, Chapter; 
8. Views end of each year since published; 
9. is the article a Conference output or not; 
10. if yes Name of Conference, date, location, 
11. is it part of e-Proceedings? 
12. If yes Title & URL; 
13. Does a Tweet Collection for this Conference exist? 
14. If yes Title & URL

• Each of the is a columns added in an Excel file FOR the same article in one row A to Z
• Same is repeated for Row 2 – A to Z for article #2 
• End of Rows is +6,000
• End of Columns is Last Classifier, 1 to n
• The Views per article times length of article # Words = Score for Authors contribution times all article by same Author = Total score for potential compensation AFTER Exit.

Currently, for performing NLP:

• The content – is an MS Word file of the article 
• It is INGRESS to a platform that has Natural Language Processing [NLP] Algorithms on it
• Semantic Text Analysis is Performed
• NLP system generate Graphical OUTPUT 

1. WordCloud, 
2. Bar Diagram for Word frequency, 
3. Hyper-graph for concept relations, 
4. Tree Diagram for hierarchical affinity translated into distance proximity among words; 
5. Domain Knowledge Expert writes Interpretation of the Graphs

FUTURE

• These Graphical OUTPUTS EGRESS the NLP platform
• These Graphical OUTPUTS will INGRESS the Blockchain Transaction infrastructure
• That interface NEED to be design on several layers. For our ability to declare our SPECS on that we will meet with experts from CS @CMU 
• LPBI does not have enough expertise onboard at that level of data engineering, data workflow & system design to be able to submit specs.

UPDATED on 1/27/2021 – This update deals with Integration of NLP Graphical output on a Blockcahin transaction network IT infrastructure

Our content is in Life Sciences, Pharmaceutical, Healthcare, Medicine, Medical Devices

1.0 LPBI IP Portfolio of an e-Scientific Publisher – 3.3 Giga bites of English text and Biological graphics

• 6,000 scientific Journal articles – curations of peer reviewed scientific findings – the clinical interpretation written by experts.
• 18 Books in Medicine and Pharmaceutics
• 100 e-Proceedings of Medical and Biotech top Global Conferences we covered in realtime on PRESS passes and Tweet Collections from 36 events
• 5100 Biological images used in the articles above

2.0 LPBI IP Portfolio of a Medical Text Analysis w/ Machine Learning-AI (SaaS) and Content Monetization Blockchain company: BaaS.

We plan to apply Natural Language Processing, ML-AI on that content for Semantic Medical Text Analysis on 1.0 LPBI IP portfolio, listed above and generate graphical representation of the semantic relations:

• WordClouds
• Hyper-graphs
• Tree Diagrams
• Domain Knowledge Interpretation of Graphical output of NLP, ML-AI

Our Proof-of-Concept is on–going 

• Interested party in NLP on our content in Genomics & Cancer is a Healthcare Insurer in UT.
• We are interested in NLP on ALL our content: Cardiovascular, Genomics, Cancer, Immunology, Metabolomics, Infectious Diseases, Genomic Endocrinology and Precision Medicine – our 18 books in medicine, average book size 2400 pages ~ 1800 articles in the entire BioMed e-Series and the 4200 articles in the Journal not in Books
• We are interested in content monetization of the

1. Content in Text format, and of the
2. Digital graphical products generated by NLP 
3. Domain Knowledge Experts interpretations of the Graphical output of NLP
4. These Interpretations of the digital graphical products generated by NLP are and will be a fundamental resource for consultancy of drug discovery, drug repurposing , drug substitution. Team of 10.

External Relations:

NLP 

• LINGUAMATICS / IQVIA will run on their NLP system our test sample TEXT files and we are using internally Wolfram for Biological Sciences 
• We will compare the two graphical outputs: theirs and ours 

Blockchain

We work with a leader in Blockchain IT vendor in Colorado on the design of a cloud-based Transaction Network IT infrastructure for content monetization taking place on an IT system with Blockchain features: Permissions, Smart Contracts, Immutable Ledger, Recommendation Engine

Two types of markets will be served: 

• B2C – a digital store in a Healthcare Digital Marketplace for 1.0 LPBI IP Portfolio and 2.0 LPBI IP Portfolio
• B2B – Special installations at Big Pharma R&D and at Healthcare Insurers

2.0 LPBI IP Portfolio and strategy represent the first implementation ever done of

NLP on a Blockchain backbone

[we were told so by the leader in NLP and by the leader in Blockchain]

We explore to discuss our plans with with additional experts from CS at CMU 

• Experts on NLP 
• Experts on Blockchain Transaction Network
• We need to decide on between two designs considered for the interface between NLP & Blockchain
• The interface is related to two methods of input graphic data processing: (a) ingress NLP outputs to the blockchain system from a DB vs creation of NLP graphic products on the fly
• We need to discuss the System design and the data architecture with CMU experts in both fields: NLP & Blockchain
• We will need expert assistance in defining each of the Blockchain features: Permissions, Smart Contracts, Immutable Ledger, Recommendation Engine Rule Base

Business Side

• We are seeking new ownership
• We are seeking new management
• Scaling up from the proof-of-concept to commercialization and content monetization represents a scale of operation that is beyond us. 
• We have a VAST IP Portfolio and a Team of Experts N=10
• We are the creators of the IP portfolio of 1.0 LPBI – 3.3 Giga bites
• We are the creators of the Vision for 2.0 LPBI IP 

Strategy #1: NLP for Text analysis of 1.0 LPBI content and 

Strategy #1: Content monetization on Blockchain IT Transaction network: Original Content and NLP digital graphical products

• All the content is in the Cloud hosted by Wordpress.com
• PharmaceuticalIntelligence.com is the Domain Name – it is listed on my own name. Formula for post-Exit compensation of Experts, Authors, Writers of the 6,000 articles is in place.

UPDATED on 1/26/2021 – This Update is on “The unit case is an “Article” – a Longitudinal Profile of Classifiers”

The unit case is an “Article” – a Longitudinal Profile of Classifiers

• It has date of publication, Author(s) Name, Title, Length, URL is it in a Book? Series, Volume, Chapter; Views end of each year since published; is it a Conference or not; if yes Name of Conference, date, location, is it part of e-Proceedings; is there a Tweet Collection for that Conference?
• The content – an MS Word file of the article is INGRESS by a platform that has Natural Language Processing [NLP] Algorithms on
• Semantic Text Analysis is Performed
• Graphical OUT is created and EGRESS:

1. WordCloud,
2. Bar Diagram for Word frequency,
3. Hyper-graph for concept relations,
4. Tree Diagram for hierarchical affinity translated into distance proximity among words;
5. Domain Knowledge Expert writes Interpretation of the Graphs

• Each of the is a column added in an Excel file FOR the same article on one row in (i to n) columns
• Same is repeated for Row 2 – (i to n) columns for article #2 
• End of Rows is +6,000
• End of Columns is Last Classifier, n
• The Views per article times article length = Score for Authors contribution times all article by same Author = key score for potential compensation AFTER Exit.
• ORIGINAL Excel file on Article Views has the VIEWS data organized as a Classifier in a LONGITUDINAL Article profile

UPDATED on 1/18/2021 – adding data fields or DBs for Content monetization

The hyper-graphs and the Tree Word are including all words – that does not affect the revealed SIGNIFICANT words.

1. We include all of the NEW runs in the POWERPOINT Presentation

We need to present YOUR PowerPoint on 

• 1/20 Zoom with NLP Vendor
• 1/22 Zoom with Blockchain Vendor

All the iterations are needed for as to test the concepts of the 16 articles – ALSO on

A. One article and all the OTHER articles in ONE CHAPTER in ONE Book, I.e., Genomics Volume 1, Chapter 1

B. One article and other articles included in the MAIN Research Category this article was assigned to by the Author

We will need Hyper-graphs and Tree Diagrams for A and for B, above – THEN

• we will decide on 2.0 LPBI standard: Hyper-graphs or Tree Diagrams as the INPUT for Domain Knowledge Expert’s Interpretation.

C. Announcing Proof-of-Concept for Genomics and Cancer is COMPLETE and CLOSED.

D. Enumeration of all artifacts in one “STANDARD 2.0 LPBI Medical Text Analysis OPERATION” [by Code Author: Madison Davis]

1. WordCloud
2. Bar graph
3. Hyper-graph or Tree Diagram – ONE to be decided to make to the Standard
4. Text – Interpretation by Domain Knowledge Expert for 1,2,3, above

E. Announcement of Scaling up Project by BioMed e-series: A, B,C, D, E

• using the “STANDARD 2.0 LPBI Medical Text Analysis OPERATION” [Standard was developed by the Proof-of-Concept.

UPDATED on 1/18/2021 – adding features to Content monetization

We are 2.0 LPBI

1. Medical Text Analysis

2. Content monetization

IF

3rd party requests services we did in 1.0 LPBI

THEN

We offer the service for a fee and the monetization will be held by the Blockchain transaction system

Thus, we need to guide our IT Vendor designer of our Blockchain features platform to DESIGN the LEDGER to include few additional categories such as:

1. Consulting Services – Fee for Service

Types of Service:

1.1 Implementation of Medical Text Analysis for Pharma

1.2 Implementation of Medical Text Analysis for Healthcare Insurers

2. Response by 2.0 LPBI to Requests to promote content by 3rd party: 

2.1 Co-marketing of a Conference organized by 3rd Parties – promotion on LPBI Channels

2.2 LPBI to Publish 3rd Party contents, i.e., Articles by guest authors: Payment based on # of views every 90 days at $30 per view

3. Consulting on Media development

3.1 Conference organization

3.2 Book content development

3.3 Real time Press coverage

UPDATED on 1/13/2021

• We will have from our IT Vendor a BLUEPRINTS for the content monetization system design with all the components laid out in a workflow for a production process to incorporate two sources of data:

1.0 LPBI four IP Asset classes: I, II, III, V will be available for monetization 

The Design include all monetization Features to incorporate the 2.0 LPBI NEWLY TO BE CREATED PRODUCTS by NLP integrated at the article level with the 1.0 LPBI IP.

We will generate four Text Analysis products, like the FOUR outcomes of NLP included in the Proof-of-Concept: 

NLP Products: Will be available for monetization as 2.0 LPBI IP: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

PHASE I: All Articles in ALL Books at the Chapter Level – THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

For:

Series A:  6 volumes, 

Series B:  2 volumes

Series C:  2 volumes

Series D:  4 volumes – 1, 2&3 in one Book, 4

Series E:  4 volumes

Total 17 Books for 18 Volumes

PHASE II: All Articles Not in Books and Not as e-Proceedings – – THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

PHASE III: 60 e-Proceedings + 36 Tweet Collections – – THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

PHASE IV: 5,100 Biological Images -– THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

UPDATED on 1/5/2021

• WE ARE ARE DOING THE PROOF-OF-CONCEPT in house with INTERNS on a one year Internship on a volunteer basis.
• My intent was to TEAM UP with AWS and one of their PARTNERS to REDO the POC on the VERSION that XXX has in the NLP Software and with that Partner jointly to Present to the INSURER and secure a contract for that PARTNER that will scale up from
• (a) 16 articles on Genomics to Volume 1 and Volume 2 Genomics Books and
• (b) 16 articles on Cancer to Volume 1 and Volume 2 Cancer Books.
• Hoping in the following phase of the relations with the INSURER –
• they will be interested in all medical indications covered in our 16 Books (#17 due 1/11/2021) – Namely, they have Patients with Heart problems – LPBI has 6 Volumes in Cardiovascular, books on Immunology, Infectious disease, Metabolic, Endocrine and 4 volumes on Precision Medicine.
  • We mean to use the POC as a Lead toward having the INSURER involved in performing Medical Text Analysis on our 17 books
  • Since they will be the first to get access to the outcomes of such a massive NLP, ML-AI on 17 books
  • They will get access to Hyper-graphs and Domain Expert Interpretations for their INTEREST in Drug substitution and Cost containment and access to our TEAM for ad hoc genomics challenges.
• The full scale implementation of the POC on all the content in the books requires a PARTNER with expertise and a platform for NLP 
• It was my intent to find that PARTNER at XXX and its system of Partnerships
• Our alternative is to Team up with another player in the NLP arena that is not AWS – in the case that XXX can’t team us up with their NLP capabilities
• WE have approached XXX because our architecture REQUIRES INTEGRATIONS OF THE RESULTS on Medical Text Analysis

1. WordCloud (Images files),
2. Hyper-graphs (graph files),
3. Interpretation of Hyper-graphs (Text file in English and in several Foreign Languages)

• WITH A CONTENT MONETIZATION SYSTEM that is to be designed for our journal articles, Books, e-Proceedings, Tweet Collections, Biological images

• Such an Integration will allowing for a

Customer to be able to request to review

(a) articles on Topic x

(b) receive from the system 12 top articles

(c) select one or more

(d) pay for them

(e) download the articles they paid for

Expand (a) to (e) to Books, e-Proceedings, Tweet Collections, Biological images

(a) to (e) represents 1.0 LPBI IP

• Such an Integration will allowing for a

Customer to be able to request to review 

(f) WordClouds = Article ABSTRACTS

(g) Hyper-graphs

(h) Domain Expert Interpretations

(I) Interpretations in Few Foreign Languages

Customer will receive from the RECOMMENDATION engine 12 WordClouds of related top articles

Customer will receive from the RECOMMENDATION engine 12 Hyper-graphs of related top articles or one or more research categories

(j) Customer will select one or more

(k) pay for them

(l) download the WordClouds they paid for

(m) Download the HyperGraph they paid for

(n)  Download the Domain Expert Interpretations for the hyper-graph(s)

(o)  Select for the Interpretations to be in one of Few Foreign Languages the system offer

 (j) to (o) represents 2.0 LPBI IP

THE NEEDS OF LPBI IS for ONE INTEGRATED SYSTEM THAT CONTAINS:

(a) to (e) represents 1.0 LPBI IP

AND

(j) to (o) represents 2.0 LPBI IP

AND

CONTENT MONETIZATION SYSTEM with features such as:

PERMISSIONS, LEDGER, RECOMMENDATION ENGINE

It may be the case that YYY has competence in monetization system design BUT DOES NOT currently have what LPBI needs in the Text Analysis with NLP, ML-AI

• As a result XXX needs to pair us up with one additional XXX-Partner in the space of Text Analysis with NLP, ML-AI – to understand our requirements and to enable scaling up from POC to all the 17 Volumes in Medicine
• YYY’s Monetization design needs to be INTEGRATED with the the system design for Text Analysis with NLP, ML-AI done by a second AWS partner
• THEN
• Hosting on XXX needs to be discussed
• LPBI’s IP Asset Classes: I,II,III,V –  journal articles, Books, e-Proceedings, Tweet Collections, Biological images – FIT very well AWS Marketplace
• Please introduce us to the XXX contact for discussion on LPBI and XXX Market place
• See, Priority #3, Below and due to Priority #1 & #2
• It seems to be the case that the DEVELOPMENT efforts are expansive for a venture like LPBI, therefore I requested to receive a POINTER to the XXX Venture Acquisition department/team/one person

• Aviva: We need a Partner to Use our Content and use NLP, ML-AI to execute the SEMANTIC Medical Text Analysis to convert TEST to WordClouds and to Hyper-Graphs
• if YYY can declare expertise in the Medical Text Analysis with NLP, ML-AI
• If not, XXX may introduce us to another XXX Partner that can handle for LPBI Priority #1, below

 

• Aviva: We need a Partner to design CONTENT MONETIZATION for existing content AND for the RESULTS of the Medical Text Analysis

EXPLANATIONS:

All of the above MUST bring all parties to an understanding of the NEEDS that LPBI has:

PRIORITY #1:

Medical Text Analysis using NLP, ML-AI

1. LPBI has a Proof-of-Concept in Medical Text Analysis using NLP, ML-AI – will be completed mid Feb. 2021
2. LPBI has a Client – a Healthcare Insurer interested in Genomics and Cancer and potentially, because they are also a HMO, in all other medical indications covered in LPBI BioMed e-Series – 17 BOOKS
3. To present to this client (and to other Healthcare Insurers) – LPBI needs one  IT Partner in Medical Text Analysis using NLP, ML-AI able to GET a contract from the INSURER for using the POC to SCALE UP to 2 books in Genomics and 2 books in Cancer – desirable – to be followed up by the remaining (17 – 4) = 13 Books

PRIORITY #2 and PRIORITY #3: need to be running in parallel

PRIORITY #2

DESIGN and ENABLEMENT of Content Monetization for

(a) EXISTING digital products and

(b) the results of PRIORITY #1, above: Medical Text Analysis using NLP, ML-AI

1. LPBI needs a Content Monetization System (CMS) that we believe YYY has the competences to design
2. Continuing of progress on this design need to take place
3. LPBI needs a Proposal and costs of monetization system design for presentation to IB and other funding sources
4. LPBI is anticipating 3rd parties that will invest in IT infrastructure development.
5. LPBI created a e-Scientific Publishing venture second to none – based on ~2MM Views has projected revenues to $ZZZ MM
6. The Content Monetization Cloud-based IT System DESIGN needs to satisfy the following:
7. THE NEEDS OF LPBI are of ONE INTEGRATED SYSTEM THAT CONTAINS:

[(a) to (e) represents 1.0 LPBI IP] – existing products 
AND 
[(j) to (o) represents 2.0 LPBI IP] – to be developed by NLP, ML-AI of the existing products
AND 
ENABLES CONTENT MONETIZATION of the two sources with features such as:
PERMISSIONS, LEDGER, RECOMMENDATION ENGINE

PRIORITY #3

DESIGN of CONTENT PROMOTION campaigns

1. XXX Advertising is a company of XXX.com
2. We need to be teamed up with a Partner or an inside Group to XXX for the DESIGN of CONTENT PROMOTION campaigns for (a) to (e) represents 1.0 LPBI IP [digital products: journal articles, e-Proceedings, Tweet Collections, Biological images]
3. Upon progress with (j) to (o) represents 2.0 LPBI IP = the results of Text Analysis with NLP, ML-AI 
4. We need to be teamed up with a Partner or an inside Group to XXX for the DESIGN of CONTENT PROMOTION campaign for WordClouds, Hyper-graphs and Domain Expert Interpretation of the Hyper-graphs in foreign languages

UPDATED on 1/4/2021

SPECIFICATION for the Road Map toward an Architecture for Monetization of Content at LPBI

1 – Data entry done by 2.0 LPBI Team of Interns 

2 – Data entry done by IT Vendor

3 – Architecture will be for monetization of 1.0 LPBI IP Asset Classes I,II,III,V

and for

4 – Architecture will also include the infrastructure for the data generated by Medical Text Analysis with NLP, ML, AI done on 1.0 LPBI IP Asset Classes I,II,III,V – called Results of Text Analysis

5. Results of Medical Text Analysis with NLP, ML, AI will include the following Databases (DB):

PHASE I: 

IP Asset Class II – e–Books

• WordClouds for all articles in 17 BioMed e-Series BOOKS – [Image file – DB]
• Number of words of which each WordCloud was built on [Text file – DB]
• Hyper-grapah for articles in each Chapter in the book [Graph file – DB]
• DomainExpert interpretation of the Hyper-graphs  [English Text file – DB]

1. TITLES of each article in the eTOCs of a Book across all books will be TRANSLATED into Spanish, Japanese, Russian [Text file – DBs, one per language]

2. One page of Domain Expert interpretation of the Hyper-graphs will be TRANSLATED into Spanish, Japanese, Russian  [Text file – DBs, one per language]

PHASE II:

Scale up PHASE I – from IP Asset Class II [all articles in 17 Books]  TO all the articles in the Journal = IP Asset Class I

PHASE III:

Scale up from PHASE I: from All Books (IP Asset Class II) and PHASE II: all the articles in the Journal (IP Asset Class I)

TO 

• IP Asset Class III (e-Proceedings/Tweet Collections), 

PHASE IV:

• IP Asset Class V (Biological Images)

UPDATED on 1/2/2021

Announcing Strategic Transition from 1.0 LPBI to 2.0 LPBI on 1/1/2021: New Management, Marketing Communication and New Scientific/Technical Opportunities

Author: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/01/01/announcing-strategic-transition-from-1-0-lpbi-to-2-0-lpbi-on-1-1-2021-new-management-and-new-technical-opportunities/

We have transitioned from

• 1.0 LPBI was an electronic Scientific Publisher, 2012 – 2020

to

• 2.0 LPBI a Medical Text Analysis (NLP-ML-AI) – SaaS and Content Monetization (Blockchain) – BaaS.

• A new company profile, 2021 – 2025

Content Monetization has TWO distinct parts:

2.1   Belongs to 1.0 LPBI: exist in WordPress.com cloud EXISTING digital IP asset classes: Articles, Books, e-Proceedings/Tweet Collections, biological images

2.2   Belongs to 2.0 LPBI: will be created by Text Analysis with NLP. ALL NEW TO BE CREATED digital IP asset classes by 2.0 LPBI as a result of Strategy #1: Text Analysis using NLP, ML, AI:

2.2.1 WordClouds – a DB of all images created by NLP one per article. This will be IP Asset Class 11, will belong to 2.0 LPBI (1 to 10, exist and belong to 1.0 LPBI)

2.2.2 Hyper-graphs – a DB of all graphs,  the hyper-graphs created by NLP. This will be IP Asset Class 12, will belong to 2.0 LPBI.

Examples:

• One hyper-graph for articles in a Book Chapter x 20 Chapter per one book x 17 books
• One hyper-graph for articles in Categories on the Journal ontology
• N=730 categories

2.2.3 English Text interpretation of each Hyper-graph – a DB of text Interpretations linked to DB of graphs and DB of Images. This will be IP asset Class 13, belongs to 2.0 LPBI

These Text interpretations of hyper-graphs will be translated to foreign languages. Example, Spanish, Japanese

ONE DB of Text interpretations per one language

2.0 LPBI had several IT infrastructure needs:

A.  Infrastructure for Text Analysis with NLP of all IP assets in 2.1

B.  Monetization infrastructure for IP Assets of 2.1, above

C.  Monetization infrastructure for IP Assets of 2.2, above

 

System integration of A, B, C

My understanding is that you wish to address B.

Leaving A and C for later.

My view is:

• B and C are one project because a USE CASE called A Journal Article Profile needs to have all the data fields I covered, in the e-mail, below 2.1 plus 2.2, as above. The architecture for B and C are inseparable – Meta data needs to be comprehensive
• A – Infrastructure for Text Analysis needs to be developed in parallel to the Content Monetization B and C.

If All what 2.0 LPBI will do will be

• monetization of Content generated, 2012-2020 – it’s valuation will be x

Versus

2.0 LPBI

(a) A Medical Text Analysis Company – SaaS and a

(b) Content Monetization Company – Blockchain as a Service (BaaS)

2.0 LPBI distinct competitive advantages are:

1. we created content we own it vs applying NLP on PubMed.
2. we create Value-Add by NLP with Expert INTERPRETATION in multi languages
3. We monetize digital content
4. We monetize WordClouds “image files and Hyper-graphs “graph files”

System Integration job needed for 2.0 LPBI includes the following:

1. Our IP on WordPress needs to be migrated into a Cloud Computing environment of an INTEGRATOR i.e.,

• AWS
• DELL
• Other

2. That integrator needs to have the two technologies we need:

Strategy #1: Text Analysis by ML

• Medical Text Analysis SW: NLP, ML, AI

This is Strategy #1 for 2.0 LPBI, namely

Conversion of 3.2 Giga bites of English Text into Hyper-graphs of Semantic content relationships for applications such as:

– drug discovery (needed by Big Pharma)

– drug repurposing (needed by Big Pharma)

– drug substitution & cost containment (needed by Healthcare Insurers)

Strategy #2: Content Monetization by Blockchain IT infrastructure features:

• Permission granting to download content on a cyber-secure IT platform
• immutable LEDGER – recording payments
• Recommendation Engine: choose one or more article from this list of 12
• Blockchain SW: Transaction network for Ledger, Immutability, Recommendation engine and Permission to download

This is Strategy #2 for 2.0 LPBI, namely

Content monetization requires IT infrastructure

We understand that 2.0 LPBI need to

• partner

or

• be acquired by a 3^rd party

(a) to invest in the IT needed for content monetization of

1.0 LPBI IP asset classes: I, II, III, IV

2.0 LPBI IP novel asset classes such as

IP Asset Class 11: WordClouds

(image file DB)

IP Asset Class 12: Hyper-graphs

(graph file DB)

IP Asset Class 13: Domain Expert interpretation of Hyper-graphs

(text file DB, one DB for a Language, expert interpretation translated in several languages)

2. 2.0 LPBI Strategy #1: Medical Text Analysis (NLP, ML, AI) (SaaS)

and

2. 2.0 LPBI Strategy #2:  Monetization of Text Analysis Results as Products (Blockchain as a Service (BaaS))

and

1. LPBI & A2C-AWS regarding Strategy #1: NLP
2. LPBI & A2C-AWS regarding Strategy #2: Monetization

I believe that the definition for the Profile of an Article I am providing below will clarify matters more and your feedback will be helpful.

1.0 LPBI had created 6,000 articles in need for monetization

2.0 LPBI is launching Six new initiatives the relations of four of the six are tied with the definition of an Article PROFILE, as below.

• The monetization INFRASTRUCTURE needs to accommodate TWO types of Digital Products:

(a) The existing Journal articles

(b) The RESULTS generated from Journal articles being subjected to TEXT ANALYSIS with NLP, ML, AI

Therefore we need to address:

C.  LPBI & A2C-AWS regarding Strategy #1: NLP on 1.0 LPBI Text

D.  LPBI & A2C-AWS regarding Strategy #2: Monetization

Let’s start with C.

LPBI & A2C-AWS regarding Strategy #1: NLP on 1.0 LPBI Text

It seems that AWS has technologies in place for A2C to use for performing Medical Text Analysis using AWS NLP, ML, AI on 1.0 LPBI’s 6,000 articles

– Thus, we need to explore HOW we can use AWS NLP, ML, AI technologies and produce for 2.0 LPBI the following Text Analysis features:

[they are derived from our Proof-of-Concept is on–going]

5.3 Does the article have the Text Analysis features which are obtained by performing text analysis with NLP:

5.3.1.  a WordCloud – needs to be stored in graph file of WordClouds

5.3.2.  # words used

5.3.3.  Hyper-graphs – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4.  Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – performed by 2.0 LPBI Experts generating Text files

5.3.4.2 Domain Expert interpretation for 5.3.3.2 – performed by 2.0 LPBI Experts generating Text files

Let’s continue with D.

LPBI & A2C-AWS regarding Strategy #2: Monetization

A2C will design a Cloud-based IT Infrastructure that will enable monetization of two types of products:

Type One: 1.0 LPBI Asset Classed I, II, III, V

• Below is the Profile Definition for the Unit Case: A Journal Article (1.0 LPBI Asset Classed I) – See below
• Same Profile Definitions needs to be done for 1.0 LPBI Asset Classed II (books), III (e-Proceedings/Tweet Collections), V (Gallery of 5,100 Images) – PENDING

Type Two: POST Medical Text Analysis using NLP, ML, AI – the following NEW PRODUCTS are created and NEED TO BE MONETIZED

Text Analysis features to be produced by NLP, ML, AI:

5.3.1. a WordCloud – needs to be stored in graph file of WordClouds

5.3.2. # words used

5.3.3. Hyper-graphs  – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4. Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – performed by 2.0 LPBI Experts generating Text files

5.3.4.2 Domain Expert interpretation for 5.3.3.2 – performed by 2.0 LPBI Experts generating Text files

BASED on the definition provided, below, suggested steps by 2.0 LPBi are the following:

• A2C-AWS and 2.0 LPBI will generate a PROPOSAL for AWS to fund that effort for future placement in AWS Marketplace
• A2C-AWS and 2.0 LPBI will develop Plans and Cost Structures of the infrastructure needed for CONTENT monetization – to be presented to Investment Banker in NYC
• A2C-AWS and 2.0 LPBI will take the LPBI Proof-of-Concept on Medical Text Analysis with NLP in Genomic and Cancer and will create jointly TWO skeleton IT Structures

#1 Skeleton IT Structure: 

Reproduce the Proof-of-Concept using AWS – NLP–ML-AI technology and Scale up to One Chapter in Genomics Volume 1 and One Chapter in Cancer Volume 2

That will be JOINTLY presented at a Healthcare Insurer [LPBI’s Contact] by LPBI AND A2C-AWS – with the scope of getting a Contract that A2C-AWS will define, execute and manage the Statement Of Work (SOW) and submit Costs to the Healthcare Insurer. Prospects of expansion to Cardiovascular and Immunology, beyond Genomics and Cancer are strong.

#2 Skeleton IT Structure: 

Produce a Skeleton for Monetization of 

• 0 LPBI – Journal articles AND 
• 0 LPBI — the Results of #1 Skeleton IT Structure: PRODUCTION OF FEATURES of TEXT ANALYSIS using AWS NLP technologies

That will be presented at 

• an Investment Banker in NYC [LPBI’s Contact], 

and 

• by LPBI 

to other funding sources, and 

• by A2C-AWS to other funding sources, Chiefly, AWS – internally.

             

The Opportunities MAP written on 2/2019 for LPBI M&A or Exit include

Twelve Economic Segments for LPBI Group’s IP – Prospects for Transfer of Ownership

1.     Holding Companies, Investment Bankers and Private Equity
2.     Information Technology Companies – Health Care
3.     Scientific Publishers
4.     Big Pharma
5.     Internet Health Care Media & Digital Health
6.     Online Education
7.     Health Insurance Companies & HMOs
8.     Medical and Pharma Associations
9.     Medical Education
10.     Information Syndicators
11.     Global Biotech & Pharmaceutical Conference Organizer
12.     CRO & CRA 

Information Technology Sector: Cloud-based – 

Amazon Web Services (AWS),  Alphabet – Verily, Apple-Health,  IBM Watson

Information Technology Sector: Cloud & Server-based – 

Microsoft-Health, Dell Boomi, Oracle-Health, SAP, Intel-Health

 

Please review this LINK:

https://pharmaceuticalintelligence.com/2019-vista/opportunities-map-in-the-acquisition-arena/

For the DESIGN of IT Infrastructure for Monetization, the following is an essential 

DEFINITION of a USE CASE for “PROFILE of an Article”: 

1.0 LPBI BEGINS 

Monetization of 6,000 Digital Products – USE CASE: A Journal Article

5.0 Article Title

5.0.1 Article URL

5.0.2 Author 1: Name

5.0.2.1 Author 2: Name

5.0.2.2 Author 3: Name

5.0.2.3 Author 4: Name

5.0.3  Date of Publication

5.0.4  # Words

5.0.5  # Views since Published to DATE

5.1 Is the article in a Book?

5.1.1 Article is not in a Book only in the Journal

5.2 Article is in a Book – In which one(s)?

5.2.1 LPBI Series A

5.2.1.1 Volume 1

5.2.1.2 Volume 2

5.2.1.3 Volume 3

5.2.1.4 Volume 4

5.2.1.5 Volume 5

5.2.1.6 Volume 6

5.2.2   LPBI Series B

5.2.2.1 Volume 1

5.2.2.2  Volume 2

5.2.3   LPBI Series C

5.2.3.1 Volume 1

5.2.3.2 Volume 2

5.2.4   LPBI Series D

5.2.4.1 Volume 1

5.2.4.2 Volume 2

5.2.4.3 Volume 3

5.2.4.4 Volume 4 [Dr. Williams and Dr. Irina are adding editorials, NOW]

5.2.5   LPBI Series E

5.2.5.1 Volume 1

5.2.5.2 Volume 2

5.2.5.3 Volume 3

5.2.5.4 Volume 4

1.0 LPBI ENDS

2.0 LPBI BEGINS

Strategy #1: Medical Text Analysis (NLP, ML, AI) (SaaS)

and 

Strategy #2: Monetization of Text Analysis Results as Products (Blockchain as a Service (BaaS)

5.3 Does article have the Text Analysis features:

5.3.1.a WordCloud – needs to be stored in graph file of WordClouds

5.3.2. # words used

5.3.3. Hyper-graphs  – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4.  Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – Translated into few other languages

5.3.4.2 Domain Expert interpretation for 5.3.3.2 -– Translated into few other languages

5.4 Audio File added to Article

5.4.1 In place – Audio file type [Text to Audio]

5.4.2 SoundCloud file

5.  Article Titles was translated to

5.5.1   Spanish

5.5.2   Japanese

5.5.3   Russian

6.   Article Interpretation of Hyper-graphs was translated to

5.6.1   Spanish

5.6.2   Japanese

5.6.3   Russian

The content below was not Updated on 1/2/2021

Distinction between A and B, below

• A.  1.0 LPBI – 2012–2020 – IP Assets available for sale

• B.  2.0 LPBI – 2021–2025 – IP Assets under construction – WILL BE AVAILABLE FOR SALE

A.  1.0 LPBI – 2012–2020 – IP Assets available for sale

A.1 A List of Scientific articles N=6,000

STORED in Excel file run on 6/30/2020 and 12/31/2020

They need to be Indexed by several keys:

A.1.1  Author Name

A.1.2  Article Title

A.1.3  Category of Research, see article example , below

For the Cancer category

• we have the following tree structure
• System had data on how many articles are in each category

•  Cancer – General
•  Cancer and Current Therapeutics
  •  interventional oncology
    •  Breast Cancer – impalpable breast lesions
    •  Prostate Cancer: Monitoring vs Treatment
•  CANCER BIOLOGY & Innovations in Cancer Therapy
  •  Anaerobic Glycolysis
  •  Cachexia
  •  Cancer Genomics
    •  Circulating Tumor Cells (CTC)
      •  Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood
        •  mRNA
      •  MagSifter chip
    •  KRAS Mutation
    •  Li-fraumeni syndrome.
    •  TP53 – Germline mutations
  •  cancer metabolism
  •  Funding Opportunities for Cancer Research
  •  Genomic Expression
  •  Glioblastoma
  •  Hexokinase
  •  Loss of function gene
  •  Metabolic Immuno-Oncology
  •  Metastasis Process
  •  Methylation
  •  Microbiome and Responses to Cancer Therapy
  •  Monoclonal Immunotherapy
  •  mtDNA
  •  Oxidative phosphorylation
  •  Pancreatic cancer
  •  Pyruvate Kinase
  •  The NCI Formulary
  •  tumor microenvironment
  •  Warburg effect
•  Cancer Informatics
•  Cancer Prevention: Research & Programs
•  Cancer Screening
•  Cancer Vaccines: Targeting Cancer Genes for Immunotherapy
  •  Engineering Enhanced Cancer Vaccines

A.1.4  Type of article: by the role of the author: 

• If the Author is Curator THAN this article is a curation
• If the Author is Reporter THEN this article is a Scientific reporting article

A.1.5  Article Abstract will be a WordCloud created by ML – one image per article

Example

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?  <<<<<<<<< Article Title

Author: Larry H. Bernstein, MD, FCAP  <<<<<<<<< Author’s Name

https://pharmaceuticalintelligence.com/2012/10/17/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view/   <<<<<<< URL

• The system provides: “Related” what you named associated, see below – will need to be placed in the article description
• The system provides: “Posted in” – meaning – ALL the categories of research checked off by the author that this article belong to by the SUBJECT MATTER of the article

EXAMPLE for “Related” what you named associated

Related

What can we expect of tumor therapeutic response?

In “Biological Networks, Gene Regulation and Evolution”

WordCloud Visualization of LPBI’s Top Twelve Articles by Views at All Time and their Research Categories in the Ontology of PharmaceuticalIntelligence.com

In “Academic Publishing”

AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

In “Biological Networks, Gene Regulation and Evolution”

Examples for >>>>>>>> Category of Research – live links listing in parenthesis number of articles in one category

Posted in Biological Networks, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Disease Biology, Genome Biology, Imaging-based Cancer Patient Management, International Global Work in Pharmaceutical, Liver & Digestive Diseases Research, Metabolomics, Molecular Genetics & Pharmaceutical, Nutrition, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Population Health Management, Proteomics, Stem Cells for Regenerative Medicine, Technology Transfer: Biotech and Pharmaceutical | Tagged Adenosine triphosphate, ATP, Glycolysis, Hypoxia-inducible factors, Kreb, Lactate dehydrogenase, Mammalian target of rapamycin, Mitochondrion, Warburg Effect | 40 Comments

Below, an excerpt from the 6,000 LIST: Top Posts by VIEWS for all days ending 2020-06-02 (Summarized)

All Time
Title	Views	Author Name	Type of Article
Home page / Archives	676,690	Internet Access	Tabulation
Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?	17,117	Larry H. Bernstein, MD, FACP	Investigator Initiated Research
Recent comprehensive review on the role of ultrasound in breast cancer management	14,242	Dr. D. Nir	Commission by Aviva Lev-Ari, PhD, RN
Do Novel Anticoagulants Affect the PT/INR? The Cases of XARELTO (rivaroxaban) and PRADAXA (dabigatran)	13,839	Dr. Pearlman, MD, PhD, FACC & Aviva Lev-Ari, PhD, RN	Commission by Aviva Lev-Ari, PhD, RN
Paclitaxel vs Abraxane (albumin-bound paclitaxel)	13,709	Tilda Barliya, PhD	Investigator Initiated Research
Apixaban (Eliquis): Mechanism of Action, Drug Comparison and Additional Indications	8,230	Aviva Lev-Ari, PhD, RN	Investigator Initiated Research
Clinical Indications for Use of Inhaled Nitric Oxide (iNO) in the Adult Patient Market: Clinical Outcomes after Use, Therapy Demand and Cost of Care	7,903	Dr. Pearlman, MD, PhD, FACC & Aviva Lev-Ari, PhD, RN	Investigator Initiated Research
Mesothelin: An early detection biomarker for cancer (By Jack Andraka)	6,540	Tilda Barliya, PhD	Investigator Initiated Research
Our TEAM	6,505	Internet Access	Tabulation
Biochemistry of the Coagulation Cascade and Platelet Aggregation: Nitric Oxide: Platelets, Circulatory Disorders, and Coagulation Effects	5,221	Larry H. Bernstein, MD, FACP	Investigator Initiated Research
Interaction of enzymes and hormones	4,901	Larry H. Bernstein, MD, FACP	Commission by Aviva Lev-Ari, PhD, RN
Akt inhibition for cancer treatment, where do we stand today?	4,852	Ziv Raviv, PhD	Investigator Initiated Research
AstraZeneca’s WEE1 protein inhibitor AZD1775 Shows Success Against Tumors with a SETD2 mutation	4,535	Stephen J. Williams, PhD	Investigator Initiated Research
The History and Creators of Total Parenteral Nutrition	4,511	Larry H. Bernstein, MD, FACP	Commission by Aviva Lev-Ari, PhD, RN
Newer Treatments for Depression: Monoamine, Neurotrophic Factor & Pharmacokinetic Hypotheses	4,365	Zohi Sternberg, PhD	Investigator Initiated Research
FDA Guidelines For Developmental and Reproductive Toxicology (DART) Studies for Small Molecules	4,188	Stephen J. Williams, PhD	Investigator Initiated Research
The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets	4,038	Dr. Pearlman, MD, PhD, FACC, Larry H. Bernstein, MD, FACP & Aviva Lev-Ari, PhD, RN	Commission by Aviva Lev-Ari, PhD, RN
Founder	3,895	Aviva Lev-Ari, PhD, RN	Investigator Initiated Research

EndFragment

A.2 A List of 16 e-BOOKS

https://lnkd.in/ekWGNqA

A.2.1   Each book is made of articles included in the N=6,000

A.2.2 Books will list the URL of each book

http://www.amazon.com/dp/B00DINFFYC

http://www.amazon.com/dp/B018Q5MCN8

http://www.amazon.com/dp/B018PNHJ84

http://www.amazon.com/dp/B018DHBUO6

http://www.amazon.com/dp/B013RVYR2K

http://www.amazon.com/dp/B012BB0ZF0

http://www.amazon.com/dp/B019UM909A

http://www.amazon.com/dp/B019VH97LU

http://www.amazon.com/dp/B071VQ6YYK

https://www.amazon.com/dp/B075CXHY1B

https://www.amazon.com/dp/B076HGB6MZ

https://www.amazon.com/dp/B078313281

https://www.amazon.com/dp/B078QVDV2W

https://www.amazon.com/dp/B07MGSFDWR

https://www.amazon.com/dp/B07MKHDBHF

https://www.amazon.com/dp/B08385KF87

A.3 A List of e-Proceedings and Tweet Collections

• Part Two: List of BioTech Conferences 2013 to Present
• Part Three: Conference eProceedings DELIVERABLES & Social Media Analytics

A.3.1 each entry is an article included in N=6,000

B.   2.0 LPBI – 2021–2025 –

IP Assets under construction –

WILL BE AVAILABLE FOR SALE

B.1 Journal articles

• Will be subjected to ML and a NEW product will be created
• Instead of N=6,000 article – we will have N= 6,000 Medical INSIGHTS

B.2 16 e-Books

• Will be subjected to ML and a NEW product will be created
• Instead of 16 Books – we will have 16 COLLECTIONS of Medical INSIGHTS derived from Text Analysis of ONLY the articles included on each Volume
• 16 e-Books will become 16 AUDIO BOOKS
• 16 e-Books will become 16 Books in Japanese, Spanish and Russians

B.3 eProceedings & Tweet collections

• Will be subjected to ML and a NEW product will be created
• Instead of 60 e-Proceedings and 30 Tweet collections we will get 100 Business INSIGHTS Collections in the domain of each conference

We believe that Blockchain will enable STORAGE of each item that will be available for sale

• LPBI will have team members Bundling items per customer needs 
• Promotion can be done OUTSIDE the Blockchain system – STIRRING Customers to the Blockchain transaction system for TRADE and recording of transactions
• That is true for A and for B, below

A.   1.0 LPBI – 2012–2020 – IP Assets available for sale

B.   2.0 LPBI – 2021–2025 – IP Assets under construction – WILL BE AVAILABLE FOR SALE

Data Architecture Questions

1. In what data format is the content stored? In other words, is the content in image pdfs, searchable document pdfs, html, xls, word documents, text files, or some other form?

Example: TEXT

Versions of LPBI Group’s Elevator Pitch: 2.0 LPBI Group’s Team – In Our Own Words

• Aviva Lev-Ari, PhD, RN, Founder, LPBI Group: 1.0 & 2.0

My proposed Elevator Pitch

For the first time in the ten years of our private ownership, the opportunity to acquire the Inventor of Scientific curation has become a reality, Available for Transfer of ownership.

You can own a portfolio of Intellectual Property Assets that commands ~2MM e-Readers and offers ~6,000 of the best interpretive articles in five specialties of Medicine and Life Sciences. Pages of our 16 books have been downloaded ~125,000 times and over 100 of the top biotech and medical conferences were covered in real time and recorded in writing and Tweets. New strategies in AI and Blockchain are now applied on LPBI’s content for INSIGHT searches and pattern recognition by automated Machine Learning algorithms for use in drug discovery and drug repurposing. All of LPBI’s content was created by our Experts, Authors, Writers (EAWs).

• • We UPLOAD MS Word file NOT PDF
  • INVENTORY is stored in Excel
  • Top Posts for all days ending 2020-11-16 (Summarized)
    1. 7 Days |30 Days |Quarter |Year |All time
  • All Time

Title		Views
Home page / Archives		716,030
Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?		17,263
Recent comprehensive review on the role of ultrasound in breast cancer management		15,300
Paclitaxel vs Abraxane (albumin-bound paclitaxel)		14,341
Do Novel Anticoagulants Affect the PT/INR? The Cases of XARELTO (rivaroxaban) and PRADAXA (dabigatran)		14,006
Apixaban (Eliquis): Mechanism of Action, Drug Comparison and Additional Indications		8,770
Clinical Indications for Use of Inhaled Nitric Oxide (iNO) in the Adult Patient Market: Clinical Outcomes after Use, Therapy Demand and Cost of Care		8,398
Our TEAM		6,632
Mesothelin: An early detection biomarker for cancer (By Jack Andraka)		6,580
Interaction of enzymes and hormones		5,536
Biochemistry of the Coagulation Cascade and Platelet Aggregation: Nitric Oxide: Platelets, Circulatory Disorders, and Coagulation Effects		5,304
The History and Creators of Total Parenteral Nutrition		5,056
Akt inhibition for cancer treatment, where do we stand today?		4,899
Pyrroloquinoline quinone (PQQ) – an unproved supplement		4,712
AstraZeneca’s WEE1 protein inhibitor AZD1775 Shows Success Against Tumors with a SETD2 mutation		4,665
Newer Treatments for Depression: Monoamine, Neurotrophic Factor & Pharmacokinetic Hypotheses		4,453
FDA Guidelines For Developmental and Reproductive Toxicology (DART) Studies for Small Molecules		4,416
Evolution of Myoglobin and Hemoglobin		4,335
Isoenzymes in cell metabolic pathways		4,206
The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets		4,126
Founder		4,118

2. Within each content file or dataset, is the content metadata already defined, or would we need to parse the file to pull out the metadata? In other words, in the file for a journal article, do you already have the author, date, abstract, keywords, etc. defined as discrete pieces of data, or is all of this information embedded within the overall file?

YES

They need to be Indexed by several keys:

A.1.1  Author Name

A.1.2  Article Title

A.1.3  Category of Research

3. Do you expect to use a single type of subscription (such as a monthly subscription), or will different types of data have different types 

of subscription options (similar to how journals offer both one-time 24-hour subscriptions to a single article as well as monthly ongoing subscriptions)?

We wish to SELL ARTICLE DOWNLOAD vs Subscriptions

4. Does the marketplace need to include fuzzy search (i.e., the ability to find content based on “similar to” criteria, instead of just exact match searches)? Does it need to present the user with related content, or only the content that was searched for?

Our system attaches to each article RELATED content

5. We assume that the marketplace is not intended to replace your current LPBI company website? We are not scoping the quote to include a full website rebuild; it is assumed that the marketplace is separate (and your users would access the marketplace via the LPBI website).

YES – the digital store will connect to our newly to be designed web site for 2.0 LPBI on WordPress.com

• The digital store is the FORUM to buy goods by digital download of content
• $30 for One digital article or Audio article
• REFERRAL to Amazon Website to buy a book or the book in AUDIO format or a book in Japanese and Spanish – Russia is not served by Amazon – we can sell directly to consumers
• $100 download of an e-Proceedings for a Conference or the Tweet collection

For 2.0 LPBI Products

Bundles of Insights for Targeted Industries – B–to-B

• Tier #1:  Insights for drug discovery embedded in consulting engagements
• Tier #2:  Insights for drug repurposing embedded in consulting engagements
• Tier #3:  Insights for Health Care Insurers embedded in consulting engagements

Bundles of insights for theScientific Community – B–to-C

UPDATED on 6/18/2021

From: Erich Greenebaum <erich@prosperci.com>

Date: Friday, June 18, 2021 at 10:16 AM

To: “Stephen Williams, PhD” <sjwilliamspa@comcast.net>

Cc: “Aviva Lev-Ari, PhD, RN” <aviva.lev-ari@comcast.net>

Subject: Re: Exploration of Collaboration on Medical Text Analysis using Machine Learning (ML) and Natural Language Processing (NLP)

In most enterprise computing projects, it is very typical to have “development” vs” production” environments. In this context, it seems that you are in the “development” mode, and so it makes good sense to do that work in a separate environment in my view.

I will be learning more about BurstIQ next month, but I did want to share a little detail about the open source semantic graph database project called “Fluree.”

https://flur.ee/2020/02/25/wake-forest-school-of-medicine-partners-with-fluree-to-improve-research-analysis/

Critically, when it comes to hydrating a knowledge graph using NLP, Fluree supports SPARQL queries directly, and so I believe you would be able to interact against it directly from Wolfram. As graph databases are finding currency in NLP/ML applications, this struck me as potentially powerful tool in your work.

An interesting property of Fluree is that its state is persisted on a blockchain style database, which facilitates what they refer to as “time travel” across the history of the graph. This comes along with providing cryptographically provable provenance of the data. Finally, they build a “smart contract” approach into their data model to handle access control and other rule based logic within the graph, which opens up a lot of possibilities of exposing datasets publicly while still protecting proprietary data at a very fine grained level, i.e. one might want to provide search facilities while not actually exposing the content without some licensing agreement.

Again, I want to avoid speculating too far before I have a better sense of the BurstIQ architecture, but I mention Fluree mostly because you might find the technology interesting in your NLP work in general. If it proves of interest to you,  I’d be happy to chat about it more.

Hope this finds you well!

/eg

On Jun 14, 2021, at 8:21 PM, Stephen Williams <sjwilliamspa@comcast.net> wrote:

Dear Dr. Greenebaum,

I was referred on this email as I am working, along with Aviva, on NLP strategies with a few fellows and interns.  We are currently using the environment on Wolfram to host data as well as algorithms to conduct text cleanup and analysis.  This platform has the ability to integrate Python scripts.  As such I feel it might be more useful to have students use their UTA platform to test Python scripts for NLP use eventually on LPBI’s Wolfram account and space.  I look forward to getting your opinion on the matter and hopefully early next week we could get together on a Zoom meeting to discuss this further.

Sincerely

Stephen J Williams, PhD

LPBI Group, CSO

Assistant Professor

Temple University, CST Biology

UPDATED on 6/7/2021

Review Graphics DB:

https://flur.ee

UPDATED on 5/8/2021

Discussion on the Number of Relations EXPECTED to be revealed by NLP by Linguamatics

I used the ratio of 673 found relations in 33 articles to say about 20 relations in one article

• Thus, in 600 articles x 20 = 12,000 Relations – In the 4 volumes: 2 Cancer, @ Genomics – Together ~600 articles
• Thus, 6,000 articles as Posts in the ENTIRE Journal Corpus (Plus ~400 Pages) x 20 = 120,000 Relations

Blockchain Infrastructure will be designed for On demand Analytics of LPBI Stored Content:

The Data Science functionality of the Blockchain IT Infrastructure will enable to perform NLP, TEXT MINING and Analytics on article collections.

Content Consumer Specifies preference/selection of the topic CONTEXT from the following three Collection Types

• B2C – Independent Scientists select topic context
• B2B – inside an organization, Knowledge workers select topic context

Suggested are the following Article Collection Types for CONTEXT of Semantic Analysis:

Article Collection Type 1: All Articles in a Chapter in a Book

• In Book x  [x = 1,2,3,…,18]
• An Article Collection is defined as = All Articles in a Chapter in a Book for Book x  [x=1,2,3,…,18]

Article Collection Type 2: The Research Category attribution assignment made by authors/curators at Publishing time

• Type 2 is defined as = any subset of articles in a given RESEARCH CATEGORY (RC) 
• Dynamic Journal Ontology [RC = 1,2,3…, 733]
• For Article Collection Type 2, it is suggested to rank all articles in a given RC by Number of Views, selection top 12, from top to 12th by Views

Article Collection Type 3:  Keywords in the Article Title 

• Search for all articles by a keyword or keywords in the Article Title 
• Select by either Number of Views, or by
• Most recent published

HYPOTHESES :

#1:

Highest Number of Relationships EXPECTED to be found, in ranked order

1. Article Collection Type 1

2. Article Collection Type 2 

3. Article Collection Type 3

#2:

Strength of relationship suggested by Dr. John McCarthy. 

A STRENGTH Measure for semantic relationship needs to be developed, it is like an analogy for Affinity or Similarity

THEN 

Highest STRENGTH of relationships EXPECTED to be found, in ranked order

1. Article Collection Type 2

2. Article Collection Type 1 

3. Article Collection Type 3

UPDATED on 4/30/2021

Spanish Edition

of LPBI Group’s BioMed e-Series

18 Books in Medicine 

https://pharmaceuticalintelligence.com/biomed-e-books/

All books are available for Sale and Page Downloads on Amazon.com

https://lnkd.in/ekWGNqA

The Context:

Montero’s partner, known as Leaders in Pharmaceutical Business Intelligence (LPBI) Group, HQS in Boston, MA, USA is planning the launch of its Digital Store in a Healthcare Digital Marketplace designed and operated by BurstIQ. The Digital Store is using a Blockchain Transactions Network as its IT platform for B2C and B2B transactions for their digital content.

The available digital content in Life Sciences, Pharmaceutical, Healthcare, Medicine, Medical Devices, Medical equipment, Biotech and Bioscience includes the 1.0 LPBI IP Portfolio of an e-Scientific Publisher – 3.3 Giga bytes of English text and Biological images. The portfolio contains four IP asset classes:

• 6,000 scientific Journal articles – curations of peer reviewed scientific findings – with clinical interpretation written by experts.
• 18 Books in Medicine and Pharmaceutics
• 100 e-Proceedings of the most impoetant Medical and Biotech Global Conferences that we covered in real time using PRESS passes and Tweet Collections from 36 events
• 5,100 Biological images used in the articles mentioned above

The Blockchain design of the IT platform for Content Transactions will include, in addition to the 1.0 LPBI IP Portfolio (2012-2020) described above (the four IP asset classes), the 2.0 LPBI IP Portfolio of visualization artifacts currently under development. 

2.0 LPBI IP Portfolio (2021-2025) consists of expert Interpretation of the visualization products resulting from Medical Text Analysis and Text Mining of all its Digital Published Products. The Text Analysis and Text Mining is performed by advanced algorithms from Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP).

• Montero is currently translating from English into Spanish the cover pages and the electronic Table of Contents of 18 Books in Medicine and Pharmaceutics
• This project originator is Dr. Aviva Lev-Ari, PhD, RN, who is the Founder of 1.0 LPBI and 2.0 LPBI and Editor-in-Chief of the Journal [com] and of the BioMed e-Series [https://lnkd.in/ekWGNqA]

In 2021 LPBI Group began the transition from:

A nine years young profile of being

• A very dynamic and cutting age electronic Scientific Publisher,

Known as 1.0 LPBI during 2012 – 2020

toward designing its new image while becoming a new Company with a new profile,

Known as LPBI, in 2021-2025

• A Medical Text Analysis company using (NLP-ML-AI) – Software as a Service (SaaS) and
• Content Monetization (on a Blockchain Transactions Network) – Blockchain as a Service (BaaS).

The Blockchain platform design includes the following five features:

1. Recommendation Engine residing on a blockchain
2. Permissions,
3. Immutable LEDGER,
4. Smart contracts and
5. Cyber-security for protecting the IP

 

Economies of scale will be achieved by:

• Development of one content promotion system
• Unified IT cloud-based infrastructure
• Maintenance of B2C IT transaction system in a Digital Store at a Healthcare Marketplace [monthly fee paid for the use of the network and
• Installations of B2B Point-of-Research (PORs) at institution – pay per use vs subscription base – type of contracts not specified yet.

 

The Competitive Landscape

 

2.0 LPBI is a Very Unique Organization

https://pharmaceuticalintelligence.com/2021/03/02/2-0-lpbi-is-a-very-unique-organization/

 

The uniqueness and the competitive space is addressed at length in 1.0 LPBI Prospectus, a 300 page document

• It Is sent as an attachment separately
• List of competitors using Blockchain are telemedicine companies not scientific e-publishers

https://pharmaceuticalintelligence.com/blockchain-transactions-network/

• NO other e-Scientific Publisher is Using NLP on a Blockchain platform.
• LPBI has the FIRST MOVER ADVANTAGE over all other e-Scientific Publishers
• LPBI had the FIRST MOVER ADVANTAGE in curation of scientific findings in 2012.
• Our NLP Partner, Linguamatics said: No client ever asked us about Blockchain
• Our Blockchain IT Partner, BurstIQ said: No client ever asked us about NLP
• LPBI is now working with both on an entirely solution.
• All the Text Analysis with NLP currently done converns Literature in the Public Domain: PubMed, MedLIne, Ontologies and Formularies
• Peer reviewed articles in PubMed, MedLine publish content only on EXPERIMENTS and on Clinical Trials
• LPBI content is CURATIONS by Experts, secondary research on the clinical interpretation of primary research using ONLY peer reviewed published articles as sources.

1.0 LPBI Products versus 2.0 LPBI Products

1. 1.0 LPBI – Blockchain LEDGER for Content Monetization of IP Asset Classes I, II, III, V

LPBI 1.0, 2012-2020 is the creator and the custodian of the Portfolio of ten IP Assets Classes. For content monetization, we identified four of the ten assets that are related to the curation methodology and process:

Class I: Journal articles,

Class II: 18 Books,

Class III: 100 e-Proceedings & Tweet Collections,

Class V: +5,100 Biological Images

The Use Case for data entity design and meta data architecture is a Journal article. It has the following Profile:

• Article ID – IP Asset Class I
• Author
• URL
• Date of Publication
• Research Categories assigned by Author(s)
• Is this Article a Curation or a Scientific Report
• Is this Article included in a Book? If yes, in which Books – IP Asset Class II
• Is this Article a component of an e-Proceedings? If Yes, What is the Conference Title, Date, Location – IP Asset Class III
• List of Biological Images included in this article – IP Asset Class V

 

2. 2.0 LPBI Digital Products:

ALL the content of 3.3 Giga Bytes is to be subjected to Text Analysis with AI/ML/NLP. The Products of this Machine Learning analysis of text are in the format of visualization artifacts (Graph Files). They represent the SEMANTIC relationships between concepts and keywords ACROSS (A) all articles in One Chapter of the book or (B) in several main Categories of Research.

• This aggregation of content, i.e., 20 articles making up a Chapter in a book or
• 20 articles were all written by different authors/curators, yet all have been assigned the same research categories. This means that semantically these two collections of articles represent a common theme or similar location on the Tree of knowledge, represented by spatial proximity to a similarity graph (Hyper-graph) or on an hierarchical clustering graph (Tree Diagram).

The Benefits of Text Analysis performed by Machine Learning Algorithms

• All articles are in one Chapter in the book
• Some Articles in several main Categories of Research are assigned to the article by the Author/Curator
• Some research categories have +1,000 articles assigned, i.e., Cancer Biology & Therapies

These attributes: Assignment of an article a Chapter in a book or a research category represent the thematic context of the article.

The context reveals INSIGHTS needed for understanding relationships among articles vs each of the 6,000 articles to stand alone as a singular point in knowledge space. Thus these two affiliation criteria serve as classifiers.

LPBI’s Journal has an ontology of 670 categories of research. In theory one could run NLP on all the articles in each of these 670 categories and reach a semantic map for the entire universe of the Journal Corpus.

Current offering from LPBI are four corpuses, Text Analysis with NLP done by Machine Learning software is the ENGINE for identification of conceptual relationship in context.

The Suite of Products – A Portfolio of Intellectual Properties (IP):

Four Corpuses in details:

1. 6,000 scientific Journal articles – curations of peer reviewed scientific findings – with clinical interpretation written by experts.
2. 18 Books in Medicine and Pharmaceutics
3. 100 e-Proceedings of the most important Medical and Biotech Global Conferences covered in real time owithPRESS passes and Tweet Collections from 36 events
4. 5,100 Biological images used in the articles above

The Journal consists of

• Posts (6,037 on 4/23/2021),
• Pages (393 on 4/23/2021)

Posts consist of four Article Types:

• Type A: Authored article by an Expert, Author, Writer (EAW) or more then one – all are PhD, MD, MD/PhD, PharmD level
• Type B: Curated article by an EAW or more then one – all are PhD, MD, MD/PhD, PharmD level
• Type C: Scientific Reporting by an EAW, by a PostDoc level or by a Masters Level
• Type D: e-Proceedings of Conferences and Tweet Collections. Namely, all e-Proceedings are Posts not Pages

Pages consist of three Page Types

• Public Published Page
• Password Protected Page
• Public Published Page that is a Book

Example of Recently Published Posts, Live links

• Apr 19th, 10:22 AM Identification of novel genes in humans that fight COVID-19 infection
• Apr 14th, 4:42 PM The top 10 medtech M&A deals of 2020
• Apr 14th, 12:56 PM Mechanism of Thrombosis with AstraZeneca and J & J Vaccines: Expert Opinion by Kate Chander Chiang & Ajay Gupta, MD
• Apr 13th, 3:37 PM Fighting chaos with care, community trust, engagement must be cornerstones of pandemic response
• Apr 12th, 3:34 PM Linking Thrombotic Thrombocytopenia to ChAdOx1 nCov-19 Vaccination, AstraZeneca

18 Books in Medicine and Pharmaceutics

The BioMed e-Series, 18 volumes consist of five multi volume series.

BioMed e-Series

• Series A: e-Books on Cardiovascular Diseases – Six volumes
• Series B: Frontiers in Genomics Research – Two volumes
• Series C: e-Books on Cancer & Oncology – Two volumes
• Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases and Genomic Endocrinology – Four volumes
• Series E: Patient-Centered Medicine – Four volumes

Of Note,

• The majority of the articles in these books are CURATIONS
• Curation of Scientific Findings is a unique methodology for creation of Posts which are Journal articles of com.
• The Curation process is explained in Chapter 1 in Series A, Volume 2

Cardiovascular Diseases, Volume Two: Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation. On Amazon.com since 11/30/2015

http://www.amazon.com/dp/B018Q5MCN8

 

• These 18 Books consist of application of the Curation Methodology for the creation of electronic Table of Contents (eTOCs) for each of the 18 books
• This Methodology allowed our Expert Editors to produce systematic classification of all eTOCs by culling articles from the journals’ research categories to create a one of a kind eTOCs for each volume
• Each eTOCs represents a Non Fungible Token (NFT)
• An Update to existing Journal articles represents a Non Fungible Token (NFT)
• Dr. Aviva Lev-Ari was involved in the creation of 14 of the eTOCS of the BioMed e-Series books
• Except for the following four volumes: Series B, Volume 1 (Dr. Williams & 3 Editors); Series D, Volume 1 and Series E, Volume 2 & 3 (Single Author/Editor, Dr. LHB]

In 2021, 2.0 LPBI is planning to launch a Blockchain Transactions Network Ecosystem to sell Journal Articles, e-Books, e–Proceedings & Tweet Collections and Biological Images

Regarding Selling books and the Blockchain IT Platform:

The current plan is to promote the books and refer the interested Content Consumer/End-User to purchase the Books on Amazon.com which grants 35% of books Sales to Authors. Amazon.com does not allow selling the book on any other platform, per contract signed by authors under KDP.

However, the Transfer of Ownership of the LPBI IP Portfolio can include a condition for removal of the Books from the Amazon.com platform, Kindle Store and the permission to republish the book under a New Publisher Title, keeping all contents and authors as currently listed on the Amazon platform. Under that condition, a book priced $135 may remain at the same price or the price may change; in either case 100% of the Price upon a book sale will be recorded and credited to the new Publisher.

 

This scenario may be favorable to a Scientific publisher with a Global distribution of Books infrastructure in place.

The Process of Content Purchase and Monetization – How will it work on the Blockchain Transactions Network?

• The content will be downloaded into a Digital Cart subsequent to Content Customer conducting a query to interrogate the Knowledge repositories of our four corpuses stored on a Blockchain IT infrastructure, which represents the back-end of a Digital Store and executes the data and transaction processing functionality on the Healthcare Digital Marketplace network
• The Recommendation Engines (one for Text) and (one for Biological Images) present the Content Customer with selection choices and a Price Tag associated with all selection options
• Content Customer performs selections on a FORM after reviewing all recommendations – The Front-end of the transaction GUI.
• Form submission generates an Invoice
• Invoice is Paid
• Permission is authorized by the blockchain system
• All contents selected is downloaded in Content Customer’s cart and become available for use instantaneously
• On the back-end, the transaction is recorded on the LEDGER and funds are transferred from the Content Consumer to LPBI Account Receivable

Content Customer/End-user interact with a computer screen or a mobile device for submission of queries to DBs in the Digital Store:

 

Options for selection include:

• Knowledge repositories [1.0 LPBI IP Asset Classes I, II, III, V]
• Content Customer/End-user will submit a query and will Specify

Current, Choices for the search:

• Original articles,
• Books,
• e-Proceedings and Tweet Collections and
• Biological Images or
• All of the above

 

A.  The current choices for the search are NOW in Read Only mode since the content in the WordPress.com Cloud is not connected to a Transactions Network.

We design the Blockchain and the digital store to enable transactions for our current and future digital content.

LPBI’s new Content will continue to be added to the WordPress.com Cloud and migrated to the Blockchain system

• This process has not yet been specified since the indexing and the current content migration of 3.3 Giga Bytes has not yet started. The Blockchain is under DESIGN. BETA testing, Launch will follow

B.  Work-in-Progress – Future Digital Products for Content Customer/End-user to specify during interaction with the System

 

• Spanish Translated eTOCs of 18 Books [Montero current involvement]

 

C.  Work-in-Progress – Content Customer/End-user will specify during interaction with the Text Analysis by AI/ML/NLP

 

• Specifying Visualization artifacts to be generated by AI/ML/NLP as a result of Text Analysis and Text Mining
• Specifying the Foreign Language for the Interpretation of Visualization: Spanish, Japanese, Russian [Montero potential future involvement]

 

The Objective: Content Monetization & Global Dissemination of Life Sciences Scientific Innovations

The transformative work done by LPBI Group allows cutting-edge biomedical research innovation to be widely disseminated and accessible to the global research and non-research communities

The Blockchain Transactions Network enables Selling Content on the INTERNET to B2C and to B2B

• LPBI’s method of curation represents a mode of scientific communication including synthesis, analysis, and interpretation done by experts in +6,000 Journal Posts and ~400 Pages
• Experts, authors, and writers add their knowledge and expertise in re-thinking and conceptualizing subjects selected in their domain of expertise, to form new curations and update existing ones.
• The books are transformative in their capacity to accelerate diffusion of scientific innovations. They represent the frontier of life sciences research.
• The curation is done by experts with a perspective within each field, allowing for the creation of scientific content that combines conceptual evolution within the scientific breakthroughs analyzed together with their anticipated future implications.

The Content is offered to the Content Consumer: B2B and B2C

 

LPBI content is in Life Sciences, Pharmaceutical, Healthcare, Medicine, Medical Devices. Thus, it would attract institutions active in several verticals

 

1.0 LPBI IP Portfolio of an e-Scientific Publisher

Present 3.3 Giga bytes of English text and Biological Images

Intellectual property of LPBI is of four types:

A corpus of curated articles,

• 6,000 scientific Journal articles – curations of peer reviewed scientific findings – the clinical interpretation written by experts.

A corpus of e-books

• 18 Books in Medicine and Pharmaceutics

A corpus of e-proceedings

• 100 e-Proceedings of the most important Medical and Biotech Global Conferences covered in real time using PRESS passes and Tweet Collections from 36 events

A Gallery of Biological Images

• 5,100 Biological images used in the articles above

Future 2.0 LPBI Suite of Visualization Artifacts created by Text Analysis with AI/ML/NLP

Will be available on the Blockchain platform and will be produced on the fly per distinct queries submitted by the Content Consumer to the Content Databases:

Visualization artifacts produced by AI/ML/NLP include the following files: As scaling up takes place, these artifacts will become available for download and monetized at a

1. per Article basis in the +6,000 corpus
2. Collections of articles in Books’ chapters
3. Main research categories assigned to articles by authors/curators
4. On demand, collections specified by end-users produced on the fly on the Blockchain platform enriched with Data Science & Analytics features [some are currently done in the NLP environment; more can be done on the Blockchain when all the four corpuses become live for transactions and for Analytics]

 

List of New digital products to be produced by LPBI Team working on Medical Text Analysis using NLP strategy:

 

Graph Files

• WordClouds representing Article abstracts
• Bar Diagrams representing Word Frequencies
• Hyper-graphs representing Semantic relationships
• Tree Diagrams representing hierarchical clustering of conceptual similarities

Text Files

Interpretations of the visualization artifacts generated by AI/ML/NLP are included in the plan 

Multi-Lingual Translation of the Text Files produced by Domain Knowledge Experts.

1. Spanish
2. Japanese
3. Russian

The Volume of Content Consumed to date:

• Books published on Amazon.com – To date: +135,000 pages have been downloaded from the 18 Volumes.
• Journal articles (Posts and Pages): To date: ~2MM Views
• We used data on Actual Article Views since date of publication (2012-2020) for projection of Article Views (2021-2025)
• Assumption: One view is a download of a $30 article
• Projection of Revenues: 2021-2025 based on actual ~2MM views, 2012-2020

PharmaceuticalIntelligence.com Journal – Projecting the Annual Rate of Article Views

https://pharmaceuticalintelligence.com/vision/pharmaceuticalintelligence-com-journal-projecting-the-annual-rate-of-article-views/

See explanations in 1.0 LPBI Prospectus

Content Availability by Access Mode

 

Read Only

• Present – All content Is hosted on

https://pharmaceuticalintelligence.com/

• 2021 – New Website is under construction
• New URL for 2.0 LPBI, Medical Text Analysis with AI/ML/NLP and Blockchain for Content Monetization – Work-in-Progress
• See two alternative Site Maps for new website design – Work-in-Progress

https://pharmaceuticalintelligence.com/2020/12/02/two-site-map-proposals-for-lpbis-new-web-site/

Transactions enabled Website

for Books on Amazon.com – Kindle Store, Bookshelf: Life Sciences & Medicine – 18 Books in Medicine & Pharmaceutics

https://lnkd.in/ekWGNqA

http://www.amazon.com/dp/B00DINFFYC

http://www.amazon.com/dp/B018Q5MCN8

http://www.amazon.com/dp/B018PNHJ84

http://www.amazon.com/dp/B018DHBUO6

http://www.amazon.com/dp/B013RVYR2K

http://www.amazon.com/dp/B012BB0ZF0

http://www.amazon.com/dp/B019UM909A

http://www.amazon.com/dp/B019VH97LU

http://www.amazon.com/dp/B071VQ6YYK

https://www.amazon.com/dp/B075CXHY1B

https://www.amazon.com/dp/B076HGB6MZ

https://www.amazon.com/dp/B078313281

https://www.amazon.com/dp/B078QVDV2W

https://www.amazon.com/dp/B07MGSFDWR

https://www.amazon.com/dp/B07MKHDBHF

https://www.amazon.com/dp/B08385KF87

http://www.amazon.com/dp/B08VTFWVKM

Aviva Lev-Ari, the Editor-in-Chief that had uploaded all these books to Amazon.com, is the only person that can remove them from Amazon.com and transfer ownership of these 18 books to another Publisher.

LPBI Digital Store in Healthcare Marketplace – Ecosystem for content downloads and content monetization – Transactions enabled interface

• Design of Blockchain IT Transactions Network – Work-in-Progress

 

Marketing Communication Needs: 1 – 7

 

1. Spanish Edition – Content promotion of 18 Medical books in Spanish speaking Countries
2. LPBI has needs in Marketing Communication, Media & PR for the venture’s potential M&A by a 3^rd party: i.e., Scientific Publisher, Healthcare NGO, Ministry of Education in Country x,y,z, Research Institute, i.e., National Institute of Health in Country x,y,z
3. 0 LPBI is producing new digital media: Priority #1: Audio Podcasts. Future plans under new ownership: Audio Articles, Audio Books,
4. 0 LPBI is producing new Visualization artifacts as outcomes of Text Analysis with AI/ML/NLP
5. 0 LPBI is Planning Advertisement for Amazon Books using Amazon Advertising in different countries for different book volumes, i.e., Genomics Volume 2 in the UK, Cancer Volume 1 & 2 in Latin America – This is a case of promotion of Books – expertise in auctions used in experimental design of advertisement running Ads is needed.
6. NEW documentation on IT Architecture for Content Monetization of Journal articles on the Blockchain IT infrastructure – Work-in-Progress
7. NEW documentation for content promotion and Monetization of other IP Asset Classes: Biological Images, e-Proceedings – Work-in-Progress

The Targets: END-USERS are the Life Sciences Content Consumers: including physicians, biotech knowledge worker, big pharma R&D and Medical Affairs Departments, Investment community in Healthcare

MedCity SPOTLIGHT Video – Healthcare Trends and Venture Capital Outlook

https://www.youtube.com/watch?v=YEfNWan0l5Q

 

For the Transfer of Ownership – Global Scope

Business Model for Blockchain Platform:

 

Product Price List Itemized for 1.0 LPBI & 2.0 LPBI

 

B2B & B2C will access 1.0 LPBI & 2.0 LPBI Products

 

Price List below represents B2C. Market installations in B2B will have a different Pricing structure based on Point-of-Research (POR)

 

• 1.0 LPBI – Digital Products
• 2.0 LPBI – Visualization (Graphical) Products & Multi-Lingual Interpretations

Product Price List Itemized for 1.0 LPBI Digital Published Products

 

• Article Download                   $30
• Book Purchase                   Amazon.com

(1) Price List of Books

(Price range $75 to $135 per book)

https://lnkd.in/ekWGNqA

• Book Page Download – price set by Amazon.com

(2) Page per View LPBI Digital Products

DOWNLOADS of 1.0 LPBI Other Digital Products

• eProceedings/Tweet Collections $100
• One Biological Image                    $30
• Spanish eTOCs – One volume      $15
• Spanish eTOCs 18 Volume          $125

 

Product Price List Itemized for 2.0 LPBI Visualization Artifacts produced by AI/ML/NLP & Interpretation Text Products

 

A PowerPoint Presentation based on a Proof-of-Concept of 33 articles in Cancer, including examples for each Visualization Artifact is available

Currently, these products are not YET available for sale – to download digital content following payment requires a BLOCKCHAIN platform with the features mentioned above – it is under design – Work-in-Progress

• WordClouds representing Article abstracts             $20
• Bar Diagrams representing Word Frequencies        $20
• Hyper-graphs representing Semantic relationships $20
• Tree Diagrams representing hierarchical clustering of conceptual similarities  $20

• Expert Interpretation of Visualization Artifacts

English                                                    $20

Spanish                                                  $30

Japanese                                                $30

Russian                                                   $30

The Transition from e-Publishing to Text Analysis by ML and Content Monetization

Phase I: Transformation and Transition

Phase I requires for the following projects:

1. Global content promotion using Amazon Advertising that provides Analytics on $ spent and sales gained
2. Marketing Communication projects
3. Blockchain infrastructure design and implementation
4. Data indexing and data migration to blockchain platform: +6,000articles and ~400 pages
5. Scaling up the NLP phase to 3.3 Giga bytes of data
6. Translations to Foreign languages: Spanish, Japanese, Russian
7. Decisions on Audio articles and Audio Books and estimating the cost involved
8. Management of the Digital Store Shelves beyond the Network management provided with the monthly fee by the host of the Digital Marketplace
9. Subletting shelves in the Digital Store to cover the monthly fees of network usage would require Recruitment of Content Creators to host and transact their content in LPBI’s Digital Store.
10. Enabling a content marketplace for 3^rd party content creators to contribute and monetize their own content (was discussed as a future phase after the foundational marketplace is created using LPBI content).

Phase II: Pursuit of Conceptualization for the pipelines leading to the transition to 2.0 LPBI.

Phase II is paving the way to

• A new organization
• Need for new ownership
• Need for new management

Phase III: Preparation for M&A and Exit.

See Elevator Pitches by all team members:

Versions of LPBI Group’s Elevator Pitch: 2.0 LPBI Group’s Team – In Our Own Words

https://pharmaceuticalintelligence.com/2020/10/20/versions-of-lpbi-groups-elevator-pitch-2-0-lpbi-groups-team-in-our-own-words/

In light of Phase I, II, III – LPBI’s Founder is fully engaged and is running in parallel three strategic courses:

1. The transition plan and new technologies emergence: NLP and Blockchain
2. The recruitment of External Business Relation, External Scientific Business relations, NLP team members, New Domain Knowledge Experts
3. The prospecting process in the event of Technology Transfer of Ownership: M&A talent

List of IP Assets for Technology Transfer of Ownership – DIGITAL PUBLISHED PRODUCTS:

• IP Asset Class I: The Journal +6,000 Scientific articles https://lnkd.in/erfbayJ
• IP Asset Class II: 18 Volumes in BioMed e-Series https://lnkd.in/ekWGNqA
• IP Asset Class III: +100 eProceedings of BioTech & Medical Conference and Tweet Collections

https://pharmaceuticalintelligence.com/press-coverage/part-three-conference-eproceedings-deliverables-social-media-analytics/

• IP Asset Class V: A Gallery of 5,100 Biological Images

https://pharmaceuticalintelligence.com/

See below considerations for Venture Valuation addressing IP Asset Classes: IV, VI, VII, VIII, IX, X, which are NOT related to the curation methodology

 

1.0 LPBI – Inventory of Digital Products – a VAST portfolio of IP developed by 1.0 LPBI since inception

2012-2020

• +6,000 articles and 5,100 biological images,
• 18 books in Medicine
• 100 e-Proceedings & Tweet Collections
• +3.3 Giga Bytes of IP
• Translation of 18 books in Medicine: Title page and electronic Table of Contents to Spanish for 22 Counties speaking Spanish

 

2.0 LPBI – Technology and Marketing Strategies

2021-2025

• Working with BurstIQ, a leader in Blockchain, on architecture of a platform for LPBI’s Content Monetization

1. A Digital Store on BurstIQ HealthCare Digital Marketplace
2. Features of the Blockchain IT infrastructure defined
3. Transactions Network: Recommendation Engine, Permissions, Smart Contracts, Immutable LEDGER, CyberSecurity, Content Promotions
4. We co-design the architecture to include NLP features to compute on Demand visualization artifacts

• Working with Linguamatics/IQVIA on NLPscaling up from a Proof-of-concept to +6,000 articles, all books all e-Proceedings and Tweet collections and Biological images

1. Will get a quote for Licensing Linguamatics NLP Platformto LPBI 
2. Or Licensing Linguamatics NLP Platform to BurstIQ

• Working with Montero LS, Madrid, Spain on a Marketing Campaign for the SPANISH Edition resulting from translation of 18 books in Medicine: Title page and electronic Table of Contents to SPANISH for 22 Counties speaking Spanish

BioMed e-Series: 18 Volumes – electronic Table of Contents (eTOCs) of each Volume

https://pharmaceuticalintelligence.com/2017/12/12/biomed-e-series-16-volumes-electronic-table-of-contents-of-each-volume/

1. Accepted a quote for the translation job [Translation of 18 books in Medicine: Title page and electronic Table of Contents to SPANISH for 22 Counties speaking Spanish]
2. Will review a quote for the joint Marketing Campaign for Latin America with a focus on Mexico, Spain, Argentina
3. Will review a quote for Marketing Communications projects

UPDATED on 2/5/2020

Decision RULES:

1. IF an article is in an e-Book THEN context for NLP is defined to be All articles in its Chapter in the Book
2. IF an article is NOT in an e-Book THEN context for NLP is defined to be Articles in Main Research Category Top 12 by Views

Pending estimation of:

1. Investment needed for Text Analysis with NLP 
2. Investment needed for Content Monetization on Blockchain IT Infrastructure by vendor
3. Investment needed for Text to Audio conversion
4. Investment needed for Translation to Foreign languages
5. Cost of translation of (e), below to several Foreign Languages
6. Pricing EACH OUTPUT of NLP process: 

(a) WordCloud 

(b) Bar diagram 

(c) Hyper-graph

(d) Tree Diagram

(e) Expert Interpretation of (a) to (d)

UPDATED on 2/1/2021

At present, I see the following:

LPBI 1.0 – Blockchain LEDGER for Monetization of Class I, II, III, V

• Custodian of the LPBI 1.0, 2012-2020 Portfolio of IP ten Assets Classes
• For content monetization, we identified four of the ten assets: 

Class I: Journal articles, 

Class II: 18 Books, 

Class III: 100 e-Proceedings & Tweet Collections, 

Class V: +5,100 Biological Images

• Content monetization requires a Blockchain Transaction Networks: Immutable ledger, permissions, smart contracts, recommendation engine

LPBI 2.0 – Blockchain LEDGER for Monetization of Graphics generated by ML and Experts interpretation in several Foreign languages

• NLP, Machine Learning-AI applied for Text Analysis of Class I, II, III, V
• Content monetization requires a Blockchain Transaction Networks

Economies of scale will be achieved by:

• Development of one Content Promotion System
• Unified IT Cloud-based infrastructure
• Maintenance of B2C IT transaction system in a Digital Store at a Healthcare Marketplace [monthly fee paid for the use of the network and hosting content]
• Installations of B2B at institution – pay per use vs subscription base

UPDATED on 1/28/2021

REST API Tutorial

UPDATED on 1/27/2021 – Additional Observation

From: Amber 

Date: Thursday, January 28, 2021 at 11:21 AM

To: “Aviva Lev-Ari, PhD, RN” <AvivaLev-Ari@alum.berkeley.edu>

Subject: Re: Data Architecture for Blockchain Deployment of Digital Assets: LPBI IP Asset Classes I,II,III,V | Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Thank you, Aviva. This is consistent with my understanding as well. A couple of notes:

1. We can build the analytics that you described directly on the BurstIQ Platform; you do not need NLP to render these visuals (although you can certainly use NLP if you want to). The visuals can be presented in the marketplace either as a static image, or as a dynamic visual that changes based on how the user filters the data.

2. With respect to your note re: using one block for NLP: one block equals one piece of data, like a word cloud image or an author’s name. To incorporate NLP, we would integrate with the NLP services via a REST integration, so that the platform can both present data to the NLP service and ingest processed data from the NLP.  Then the output files from the NLP service would be stored in one or more blocks on the platform.

I hope that additional info helps.

Cheers,

Amber

We are still working to produce the 

• INPUT two TEXT files for LINGUAMATICS to run their NLP
• We will run on SAME Text our access to Wolfram’s NLP
• On BurstIQ end: 
• FOR OUR PROJECT – may be it is worth exploring having ONE block in the blockchain to be the processor of NLP – this is OUR IDEA for our own needs

We will get back to you as soon as we clarify which one runs supreme Linguamatics vs Wolfram 

We are to meet with CS CMU experts to clarify our specs about that interface that will be best:

• Static Graphic files vs 
• Graphic production on the fly by ONE NLP block on your Blockchain [That will need to be tested???? 

Observations:

1. Advantage of static files – Graphics produced by NLP exist for Content Promotion and are available to the Recommendation Engine to display as a result of a query
2. Advantage of compute on the fly – done on subset of article collection ON DEMAND not in existence in the statics files generated on 2 article sets: All articles in one Chapter and Same number of articles form the Main category of research
3. BOTH MAY BE NEEDED TO EXIST ?????
4. I assume each MODE of implementation has a difference I/O and overhead performance numbers and if Both exists these numbers may be x2 ????

PS

• The first Quote was for Existing IP – 1.0 LPBI
• The amended Quote [PENDING] – will be addition to consider the NLP Graphical output been ingress or created on fly or both (reasons, above, why both are needed). Graphical output from NLP are Content Products to be available on the Transaction infrastructure for download and monetizing of the IP involved

We are now designing the requirement for the Data Architecture for the blockchain Transaction Network for Content Monetization.

https://pharmaceuticalintelligence.com/2020/11/16/data-architecture-for-blockchain-deployment-of-digital-assets-lpbi-ip-asset-classes-iiiiii/

• The unit case is an “Article” – a Longitudinal Profile of Classifiers

1. Article has date of publication, 
2. Author(s) Name, 
3. Title, 
4. Length, 
5. URL 
6. is it in a Book? 
7. Series, Volume, Chapter; 
8. Views end of each year since published; 
9. is the article a Conference output or not; 
10. if yes Name of Conference, date, location, 
11. is it part of e-Proceedings? 
12. If yes Title & URL; 
13. Does a Tweet Collection for this Conference exist? 
14. If yes Title & URL

• Each of the is a columns added in an Excel file FOR the same article in one row A to Z
• Same is repeated for Row 2 – A to Z for article #2 
• End of Rows is +6,000
• End of Columns is Last Classifier, 1 to n
• The Views per article times length of article # Words = Score for Authors contribution times all article by same Author = Total score for potential compensation AFTER Exit.

Currently, for performing NLP:

• The content – is an MS Word file of the article 
• It is INGRESS to a platform that has Natural Language Processing [NLP] Algorithms on it
• Semantic Text Analysis is Performed
• NLP system generate Graphical OUTPUT 

1. WordCloud, 
2. Bar Diagram for Word frequency, 
3. Hyper-graph for concept relations, 
4. Tree Diagram for hierarchical affinity translated into distance proximity among words; 
5. Domain Knowledge Expert writes Interpretation of the Graphs

FUTURE

• These Graphical OUTPUTS EGRESS the NLP platform
• These Graphical OUTPUTS will INGRESS the Blockchain Transaction infrastructure
• That interface NEED to be design on several layers. For our ability to declare our SPECS on that we will meet with experts from CS @CMU 
• LPBI does not have enough expertise onboard at that level of data engineering, data workflow & system design to be able to submit specs.

UPDATED on 1/27/2021 – This update deals with Integration of NLP Graphical output on a Blockcahin transaction network IT infrastructure

Our content is in Life Sciences, Pharmaceutical, Healthcare, Medicine, Medical Devices

1.0 LPBI IP Portfolio of an e-Scientific Publisher – 3.3 Giga bites of English text and Biological graphics

• 6,000 scientific Journal articles – curations of peer reviewed scientific findings – the clinical interpretation written by experts.
• 18 Books in Medicine and Pharmaceutics
• 100 e-Proceedings of Medical and Biotech top Global Conferences we covered in realtime on PRESS passes and Tweet Collections from 36 events
• 5100 Biological images used in the articles above

2.0 LPBI IP Portfolio of a Medical Text Analysis w/ Machine Learning-AI (SaaS) and Content Monetization Blockchain company: BaaS.

We plan to apply Natural Language Processing, ML-AI on that content for Semantic Medical Text Analysis on 1.0 LPBI IP portfolio, listed above and generate graphical representation of the semantic relations:

• WordClouds
• Hyper-graphs
• Tree Diagrams
• Domain Knowledge Interpretation of Graphical output of NLP, ML-AI

Our Proof-of-Concept is on–going 

• Interested party in NLP on our content in Genomics & Cancer is a Healthcare Insurer in UT.
• We are interested in NLP on ALL our content: Cardiovascular, Genomics, Cancer, Immunology, Metabolomics, Infectious Diseases, Genomic Endocrinology and Precision Medicine – our 18 books in medicine, average book size 2400 pages ~ 1800 articles in the entire BioMed e-Series and the 4200 articles in the Journal not in Books
• We are interested in content monetization of the

1. Content in Text format, and of the
2. Digital graphical products generated by NLP 
3. Domain Knowledge Experts interpretations of the Graphical output of NLP
4. These Interpretations of the digital graphical products generated by NLP are and will be a fundamental resource for consultancy of drug discovery, drug repurposing , drug substitution. Team of 10.

External Relations:

NLP 

• LINGUAMATICS / IQVIA will run on their NLP system our test sample TEXT files and we are using internally Wolfram for Biological Sciences 
• We will compare the two graphical outputs: theirs and ours 

Blockchain

We work with a leader in Blockchain IT vendor in Colorado on the design of a cloud-based Transaction Network IT infrastructure for content monetization taking place on an IT system with Blockchain features: Permissions, Smart Contracts, Immutable Ledger, Recommendation Engine

Two types of markets will be served: 

• B2C – a digital store in a Healthcare Digital Marketplace for 1.0 LPBI IP Portfolio and 2.0 LPBI IP Portfolio
• B2B – Special installations at Big Pharma R&D and at Healthcare Insurers

2.0 LPBI IP Portfolio and strategy represent the first implementation ever done of

NLP on a Blockchain backbone

[we were told so by the leader in NLP and by the leader in Blockchain]

We explore to discuss our plans with with additional experts from CS at CMU 

• Experts on NLP 
• Experts on Blockchain Transaction Network
• We need to decide on between two designs considered for the interface between NLP & Blockchain
• The interface is related to two methods of input graphic data processing: (a) ingress NLP outputs to the blockchain system from a DB vs creation of NLP graphic products on the fly
• We need to discuss the System design and the data architecture with CMU experts in both fields: NLP & Blockchain
• We will need expert assistance in defining each of the Blockchain features: Permissions, Smart Contracts, Immutable Ledger, Recommendation Engine Rule Base

Business Side

• We are seeking new ownership
• We are seeking new management
• Scaling up from the proof-of-concept to commercialization and content monetization represents a scale of operation that is beyond us. 
• We have a VAST IP Portfolio and a Team of Experts N=10
• We are the creators of the IP portfolio of 1.0 LPBI – 3.3 Giga bites
• We are the creators of the Vision for 2.0 LPBI IP 

Strategy #1: NLP for Text analysis of 1.0 LPBI content and 

Strategy #1: Content monetization on Blockchain IT Transaction network: Original Content and NLP digital graphical products

• All the content is in the Cloud hosted by Wordpress.com
• PharmaceuticalIntelligence.com is the Domain Name – it is listed on my own name. Formula for post-Exit compensation of Experts, Authors, Writers of the 6,000 articles is in place.

UPDATED on 1/26/2021 – This Update is on “The unit case is an “Article” – a Longitudinal Profile of Classifiers”

The unit case is an “Article” – a Longitudinal Profile of Classifiers

• It has date of publication, Author(s) Name, Title, Length, URL is it in a Book? Series, Volume, Chapter; Views end of each year since published; is it a Conference or not; if yes Name of Conference, date, location, is it part of e-Proceedings; is there a Tweet Collection for that Conference?
• The content – an MS Word file of the article is INGRESS by a platform that has Natural Language Processing [NLP] Algorithms on
• Semantic Text Analysis is Performed
• Graphical OUT is created and EGRESS:

1. WordCloud,
2. Bar Diagram for Word frequency,
3. Hyper-graph for concept relations,
4. Tree Diagram for hierarchical affinity translated into distance proximity among words;
5. Domain Knowledge Expert writes Interpretation of the Graphs

• Each of the is a column added in an Excel file FOR the same article on one row in (i to n) columns
• Same is repeated for Row 2 – (i to n) columns for article #2 
• End of Rows is +6,000
• End of Columns is Last Classifier, n
• The Views per article times article length = Score for Authors contribution times all article by same Author = key score for potential compensation AFTER Exit.
• ORIGINAL Excel file on Article Views has the VIEWS data organized as a Classifier in a LONGITUDINAL Article profile

UPDATED on 1/18/2021 – adding data fields or DBs for Content monetization

The hyper-graphs and the Tree Word are including all words – that does not affect the revealed SIGNIFICANT words.

1. We include all of the NEW runs in the POWERPOINT Presentation

We need to present YOUR PowerPoint on 

• 1/20 Zoom with NLP Vendor
• 1/22 Zoom with Blockchain Vendor

All the iterations are needed for as to test the concepts of the 16 articles – ALSO on

A. One article and all the OTHER articles in ONE CHAPTER in ONE Book, I.e., Genomics Volume 1, Chapter 1

B. One article and other articles included in the MAIN Research Category this article was assigned to by the Author

We will need Hyper-graphs and Tree Diagrams for A and for B, above – THEN

• we will decide on 2.0 LPBI standard: Hyper-graphs or Tree Diagrams as the INPUT for Domain Knowledge Expert’s Interpretation.

C. Announcing Proof-of-Concept for Genomics and Cancer is COMPLETE and CLOSED.

D. Enumeration of all artifacts in one “STANDARD 2.0 LPBI Medical Text Analysis OPERATION” [by Code Author: Madison Davis]

1. WordCloud
2. Bar graph
3. Hyper-graph or Tree Diagram – ONE to be decided to make to the Standard
4. Text – Interpretation by Domain Knowledge Expert for 1,2,3, above

E. Announcement of Scaling up Project by BioMed e-series: A, B,C, D, E

• using the “STANDARD 2.0 LPBI Medical Text Analysis OPERATION” [Standard was developed by the Proof-of-Concept.

UPDATED on 1/18/2021 – adding features to Content monetization

We are 2.0 LPBI

1. Medical Text Analysis

2. Content monetization

IF

3rd party requests services we did in 1.0 LPBI

THEN

We offer the service for a fee and the monetization will be held by the Blockchain transaction system

Thus, we need to guide our IT Vendor designer of our Blockchain features platform to DESIGN the LEDGER to include few additional categories such as:

1. Consulting Services – Fee for Service

Types of Service:

1.1 Implementation of Medical Text Analysis for Pharma

1.2 Implementation of Medical Text Analysis for Healthcare Insurers

2. Response by 2.0 LPBI to Requests to promote content by 3rd party: 

2.1 Co-marketing of a Conference organized by 3rd Parties – promotion on LPBI Channels

2.2 LPBI to Publish 3rd Party contents, i.e., Articles by guest authors: Payment based on # of views every 90 days at $30 per view

3. Consulting on Media development

3.1 Conference organization

3.2 Book content development

3.3 Real time Press coverage

UPDATED on 1/13/2021

• We will have from our IT Vendor a BLUEPRINTS for the content monetization system design with all the components laid out in a workflow for a production process to incorporate two sources of data:

1.0 LPBI four IP Asset classes: I, II, III, V will be available for monetization 

The Design include all monetization Features to incorporate the 2.0 LPBI NEWLY TO BE CREATED PRODUCTS by NLP integrated at the article level with the 1.0 LPBI IP.

We will generate four Text Analysis products, like the FOUR outcomes of NLP included in the Proof-of-Concept: 

NLP Products: Will be available for monetization as 2.0 LPBI IP: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

PHASE I: All Articles in ALL Books at the Chapter Level – THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

For:

Series A:  6 volumes, 

Series B:  2 volumes

Series C:  2 volumes

Series D:  4 volumes – 1, 2&3 in one Book, 4

Series E:  4 volumes

Total 17 Books for 18 Volumes

PHASE II: All Articles Not in Books and Not as e-Proceedings – – THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

PHASE III: 60 e-Proceedings + 36 Tweet Collections – – THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

PHASE IV: 5,100 Biological Images -– THEY WILL HAVE: 

1. WordClouds, 
2. Bar charts, 
3. Hyper-graphs and 
4. Expert Interpretation in English and Foreign Languages

UPDATED on 1/5/2021

• WE ARE ARE DOING THE PROOF-OF-CONCEPT in house with INTERNS on a one year Internship on a volunteer basis.
• My intent was to TEAM UP with AWS and one of their PARTNERS to REDO the POC on the VERSION that XXX has in the NLP Software and with that Partner jointly to Present to the INSURER and secure a contract for that PARTNER that will scale up from
• (a) 16 articles on Genomics to Volume 1 and Volume 2 Genomics Books and
• (b) 16 articles on Cancer to Volume 1 and Volume 2 Cancer Books.
• Hoping in the following phase of the relations with the INSURER –
• they will be interested in all medical indications covered in our 16 Books (#17 due 1/11/2021) – Namely, they have Patients with Heart problems – LPBI has 6 Volumes in Cardiovascular, books on Immunology, Infectious disease, Metabolic, Endocrine and 4 volumes on Precision Medicine.
  • We mean to use the POC as a Lead toward having the INSURER involved in performing Medical Text Analysis on our 17 books
  • Since they will be the first to get access to the outcomes of such a massive NLP, ML-AI on 17 books
  • They will get access to Hyper-graphs and Domain Expert Interpretations for their INTEREST in Drug substitution and Cost containment and access to our TEAM for ad hoc genomics challenges.
• The full scale implementation of the POC on all the content in the books requires a PARTNER with expertise and a platform for NLP 
• It was my intent to find that PARTNER at XXX and its system of Partnerships
• Our alternative is to Team up with another player in the NLP arena that is not AWS – in the case that XXX can’t team us up with their NLP capabilities
• WE have approached XXX because our architecture REQUIRES INTEGRATIONS OF THE RESULTS on Medical Text Analysis

1. WordCloud (Images files),
2. Hyper-graphs (graph files),
3. Interpretation of Hyper-graphs (Text file in English and in several Foreign Languages)

• WITH A CONTENT MONETIZATION SYSTEM that is to be designed for our journal articles, Books, e-Proceedings, Tweet Collections, Biological images

• Such an Integration will allowing for a

Customer to be able to request to review

(a) articles on Topic x

(b) receive from the system 12 top articles

(c) select one or more

(d) pay for them

(e) download the articles they paid for

Expand (a) to (e) to Books, e-Proceedings, Tweet Collections, Biological images

(a) to (e) represents 1.0 LPBI IP

• Such an Integration will allowing for a

Customer to be able to request to review 

(f) WordClouds = Article ABSTRACTS

(g) Hyper-graphs

(h) Domain Expert Interpretations

(I) Interpretations in Few Foreign Languages

Customer will receive from the RECOMMENDATION engine 12 WordClouds of related top articles

Customer will receive from the RECOMMENDATION engine 12 Hyper-graphs of related top articles or one or more research categories

(j) Customer will select one or more

(k) pay for them

(l) download the WordClouds they paid for

(m) Download the HyperGraph they paid for

(n)  Download the Domain Expert Interpretations for the hyper-graph(s)

(o)  Select for the Interpretations to be in one of Few Foreign Languages the system offer

 (j) to (o) represents 2.0 LPBI IP

THE NEEDS OF LPBI IS for ONE INTEGRATED SYSTEM THAT CONTAINS:

(a) to (e) represents 1.0 LPBI IP

AND

(j) to (o) represents 2.0 LPBI IP

AND

CONTENT MONETIZATION SYSTEM with features such as:

PERMISSIONS, LEDGER, RECOMMENDATION ENGINE

It may be the case that YYY has competence in monetization system design BUT DOES NOT currently have what LPBI needs in the Text Analysis with NLP, ML-AI

• As a result XXX needs to pair us up with one additional XXX-Partner in the space of Text Analysis with NLP, ML-AI – to understand our requirements and to enable scaling up from POC to all the 17 Volumes in Medicine
• YYY’s Monetization design needs to be INTEGRATED with the the system design for Text Analysis with NLP, ML-AI done by a second AWS partner
• THEN
• Hosting on XXX needs to be discussed
• LPBI’s IP Asset Classes: I,II,III,V –  journal articles, Books, e-Proceedings, Tweet Collections, Biological images – FIT very well AWS Marketplace
• Please introduce us to the XXX contact for discussion on LPBI and XXX Market place
• See, Priority #3, Below and due to Priority #1 & #2
• It seems to be the case that the DEVELOPMENT efforts are expansive for a venture like LPBI, therefore I requested to receive a POINTER to the XXX Venture Acquisition department/team/one person

• Aviva: We need a Partner to Use our Content and use NLP, ML-AI to execute the SEMANTIC Medical Text Analysis to convert TEST to WordClouds and to Hyper-Graphs
• if YYY can declare expertise in the Medical Text Analysis with NLP, ML-AI
• If not, XXX may introduce us to another XXX Partner that can handle for LPBI Priority #1, below

 

• Aviva: We need a Partner to design CONTENT MONETIZATION for existing content AND for the RESULTS of the Medical Text Analysis

EXPLANATIONS:

All of the above MUST bring all parties to an understanding of the NEEDS that LPBI has:

PRIORITY #1:

Medical Text Analysis using NLP, ML-AI

1. LPBI has a Proof-of-Concept in Medical Text Analysis using NLP, ML-AI – will be completed mid Feb. 2021
2. LPBI has a Client – a Healthcare Insurer interested in Genomics and Cancer and potentially, because they are also a HMO, in all other medical indications covered in LPBI BioMed e-Series – 17 BOOKS
3. To present to this client (and to other Healthcare Insurers) – LPBI needs one  IT Partner in Medical Text Analysis using NLP, ML-AI able to GET a contract from the INSURER for using the POC to SCALE UP to 2 books in Genomics and 2 books in Cancer – desirable – to be followed up by the remaining (17 – 4) = 13 Books

PRIORITY #2 and PRIORITY #3: need to be running in parallel

PRIORITY #2

DESIGN and ENABLEMENT of Content Monetization for

(a) EXISTING digital products and

(b) the results of PRIORITY #1, above: Medical Text Analysis using NLP, ML-AI

1. LPBI needs a Content Monetization System (CMS) that we believe YYY has the competences to design
2. Continuing of progress on this design need to take place
3. LPBI needs a Proposal and costs of monetization system design for presentation to IB and other funding sources
4. LPBI is anticipating 3rd parties that will invest in IT infrastructure development.
5. LPBI created a e-Scientific Publishing venture second to none – based on ~2MM Views has projected revenues to $ZZZ MM
6. The Content Monetization Cloud-based IT System DESIGN needs to satisfy the following:
7. THE NEEDS OF LPBI are of ONE INTEGRATED SYSTEM THAT CONTAINS:

[(a) to (e) represents 1.0 LPBI IP] – existing products 
AND 
[(j) to (o) represents 2.0 LPBI IP] – to be developed by NLP, ML-AI of the existing products
AND 
ENABLES CONTENT MONETIZATION of the two sources with features such as:
PERMISSIONS, LEDGER, RECOMMENDATION ENGINE

PRIORITY #3

DESIGN of CONTENT PROMOTION campaigns

1. XXX Advertising is a company of XXX.com
2. We need to be teamed up with a Partner or an inside Group to XXX for the DESIGN of CONTENT PROMOTION campaigns for (a) to (e) represents 1.0 LPBI IP [digital products: journal articles, e-Proceedings, Tweet Collections, Biological images]
3. Upon progress with (j) to (o) represents 2.0 LPBI IP = the results of Text Analysis with NLP, ML-AI 
4. We need to be teamed up with a Partner or an inside Group to XXX for the DESIGN of CONTENT PROMOTION campaign for WordClouds, Hyper-graphs and Domain Expert Interpretation of the Hyper-graphs in foreign languages

UPDATED on 1/4/2021

SPECIFICATION for the Road Map toward an Architecture for Monetization of Content at LPBI

1 – Data entry done by 2.0 LPBI Team of Interns 

2 – Data entry done by IT Vendor

3 – Architecture will be for monetization of 1.0 LPBI IP Asset Classes I,II,III,V

and for

4 – Architecture will also include the infrastructure for the data generated by Medical Text Analysis with NLP, ML, AI done on 1.0 LPBI IP Asset Classes I,II,III,V – called Results of Text Analysis

5. Results of Medical Text Analysis with NLP, ML, AI will include the following Databases (DB):

PHASE I: 

IP Asset Class II – e–Books

• WordClouds for all articles in 17 BioMed e-Series BOOKS – [Image file – DB]
• Number of words of which each WordCloud was built on [Text file – DB]
• Hyper-grapah for articles in each Chapter in the book [Graph file – DB]
• DomainExpert interpretation of the Hyper-graphs  [English Text file – DB]

1. TITLES of each article in the eTOCs of a Book across all books will be TRANSLATED into Spanish, Japanese, Russian [Text file – DBs, one per language]

2. One page of Domain Expert interpretation of the Hyper-graphs will be TRANSLATED into Spanish, Japanese, Russian  [Text file – DBs, one per language]

PHASE II:

Scale up PHASE I – from IP Asset Class II [all articles in 17 Books]  TO all the articles in the Journal = IP Asset Class I

PHASE III:

Scale up from PHASE I: from All Books (IP Asset Class II) and PHASE II: all the articles in the Journal (IP Asset Class I)

TO 

• IP Asset Class III (e-Proceedings/Tweet Collections), 

PHASE IV:

• IP Asset Class V (Biological Images)

UPDATED on 1/2/2021

Announcing Strategic Transition from 1.0 LPBI to 2.0 LPBI on 1/1/2021: New Management, Marketing Communication and New Scientific/Technical Opportunities

Author: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/01/01/announcing-strategic-transition-from-1-0-lpbi-to-2-0-lpbi-on-1-1-2021-new-management-and-new-technical-opportunities/

We have transitioned from

• 1.0 LPBI was an electronic Scientific Publisher, 2012 – 2020

to

• 2.0 LPBI a Medical Text Analysis (NLP-ML-AI) – SaaS and Content Monetization (Blockchain) – BaaS.

• A new company profile, 2021 – 2025

Content Monetization has TWO distinct parts:

2.1   Belongs to 1.0 LPBI: exist in WordPress.com cloud EXISTING digital IP asset classes: Articles, Books, e-Proceedings/Tweet Collections, biological images

2.2   Belongs to 2.0 LPBI: will be created by Text Analysis with NLP. ALL NEW TO BE CREATED digital IP asset classes by 2.0 LPBI as a result of Strategy #1: Text Analysis using NLP, ML, AI:

2.2.1 WordClouds – a DB of all images created by NLP one per article. This will be IP Asset Class 11, will belong to 2.0 LPBI (1 to 10, exist and belong to 1.0 LPBI)

2.2.2 Hyper-graphs – a DB of all graphs,  the hyper-graphs created by NLP. This will be IP Asset Class 12, will belong to 2.0 LPBI.

Examples:

• One hyper-graph for articles in a Book Chapter x 20 Chapter per one book x 17 books
• One hyper-graph for articles in Categories on the Journal ontology
• N=730 categories

2.2.3 English Text interpretation of each Hyper-graph – a DB of text Interpretations linked to DB of graphs and DB of Images. This will be IP asset Class 13, belongs to 2.0 LPBI

These Text interpretations of hyper-graphs will be translated to foreign languages. Example, Spanish, Japanese

ONE DB of Text interpretations per one language

2.0 LPBI had several IT infrastructure needs:

A.  Infrastructure for Text Analysis with NLP of all IP assets in 2.1

B.  Monetization infrastructure for IP Assets of 2.1, above

C.  Monetization infrastructure for IP Assets of 2.2, above

 

System integration of A, B, C

My understanding is that you wish to address B.

Leaving A and C for later.

My view is:

• B and C are one project because a USE CASE called A Journal Article Profile needs to have all the data fields I covered, in the e-mail, below 2.1 plus 2.2, as above. The architecture for B and C are inseparable – Meta data needs to be comprehensive
• A – Infrastructure for Text Analysis needs to be developed in parallel to the Content Monetization B and C.

If All what 2.0 LPBI will do will be

• monetization of Content generated, 2012-2020 – it’s valuation will be x

Versus

2.0 LPBI

(a) A Medical Text Analysis Company – SaaS and a

(b) Content Monetization Company – Blockchain as a Service (BaaS)

2.0 LPBI distinct competitive advantages are:

1. we created content we own it vs applying NLP on PubMed.
2. we create Value-Add by NLP with Expert INTERPRETATION in multi languages
3. We monetize digital content
4. We monetize WordClouds “image files and Hyper-graphs “graph files”

System Integration job needed for 2.0 LPBI includes the following:

1. Our IP on WordPress needs to be migrated into a Cloud Computing environment of an INTEGRATOR i.e.,

• AWS
• DELL
• Other

2. That integrator needs to have the two technologies we need:

Strategy #1: Text Analysis by ML

• Medical Text Analysis SW: NLP, ML, AI

This is Strategy #1 for 2.0 LPBI, namely

Conversion of 3.2 Giga bites of English Text into Hyper-graphs of Semantic content relationships for applications such as:

– drug discovery (needed by Big Pharma)

– drug repurposing (needed by Big Pharma)

– drug substitution & cost containment (needed by Healthcare Insurers)

Strategy #2: Content Monetization by Blockchain IT infrastructure features:

• Permission granting to download content on a cyber-secure IT platform
• immutable LEDGER – recording payments
• Recommendation Engine: choose one or more article from this list of 12
• Blockchain SW: Transaction network for Ledger, Immutability, Recommendation engine and Permission to download

This is Strategy #2 for 2.0 LPBI, namely

Content monetization requires IT infrastructure

We understand that 2.0 LPBI need to

• partner

or

• be acquired by a 3^rd party

(a) to invest in the IT needed for content monetization of

1.0 LPBI IP asset classes: I, II, III, IV

2.0 LPBI IP novel asset classes such as

IP Asset Class 11: WordClouds

(image file DB)

IP Asset Class 12: Hyper-graphs

(graph file DB)

IP Asset Class 13: Domain Expert interpretation of Hyper-graphs

(text file DB, one DB for a Language, expert interpretation translated in several languages)

2. 2.0 LPBI Strategy #1: Medical Text Analysis (NLP, ML, AI) (SaaS)

and

2. 2.0 LPBI Strategy #2:  Monetization of Text Analysis Results as Products (Blockchain as a Service (BaaS))

and

1. LPBI & A2C-AWS regarding Strategy #1: NLP
2. LPBI & A2C-AWS regarding Strategy #2: Monetization

I believe that the definition for the Profile of an Article I am providing below will clarify matters more and your feedback will be helpful.

1.0 LPBI had created 6,000 articles in need for monetization

2.0 LPBI is launching Six new initiatives the relations of four of the six are tied with the definition of an Article PROFILE, as below.

• The monetization INFRASTRUCTURE needs to accommodate TWO types of Digital Products:

(a) The existing Journal articles

(b) The RESULTS generated from Journal articles being subjected to TEXT ANALYSIS with NLP, ML, AI

Therefore we need to address:

C.  LPBI & A2C-AWS regarding Strategy #1: NLP on 1.0 LPBI Text

D.  LPBI & A2C-AWS regarding Strategy #2: Monetization

Let’s start with C.

LPBI & A2C-AWS regarding Strategy #1: NLP on 1.0 LPBI Text

It seems that AWS has technologies in place for A2C to use for performing Medical Text Analysis using AWS NLP, ML, AI on 1.0 LPBI’s 6,000 articles

– Thus, we need to explore HOW we can use AWS NLP, ML, AI technologies and produce for 2.0 LPBI the following Text Analysis features:

[they are derived from our Proof-of-Concept is on–going]

5.3 Does the article have the Text Analysis features which are obtained by performing text analysis with NLP:

5.3.1.  a WordCloud – needs to be stored in graph file of WordClouds

5.3.2.  # words used

5.3.3.  Hyper-graphs – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4.  Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – performed by 2.0 LPBI Experts generating Text files

5.3.4.2 Domain Expert interpretation for 5.3.3.2 – performed by 2.0 LPBI Experts generating Text files

Let’s continue with D.

LPBI & A2C-AWS regarding Strategy #2: Monetization

A2C will design a Cloud-based IT Infrastructure that will enable monetization of two types of products:

Type One: 1.0 LPBI Asset Classed I, II, III, V

• Below is the Profile Definition for the Unit Case: A Journal Article (1.0 LPBI Asset Classed I) – See below
• Same Profile Definitions needs to be done for 1.0 LPBI Asset Classed II (books), III (e-Proceedings/Tweet Collections), V (Gallery of 5,100 Images) – PENDING

Type Two: POST Medical Text Analysis using NLP, ML, AI – the following NEW PRODUCTS are created and NEED TO BE MONETIZED

Text Analysis features to be produced by NLP, ML, AI:

5.3.1. a WordCloud – needs to be stored in graph file of WordClouds

5.3.2. # words used

5.3.3. Hyper-graphs  – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4. Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – performed by 2.0 LPBI Experts generating Text files

5.3.4.2 Domain Expert interpretation for 5.3.3.2 – performed by 2.0 LPBI Experts generating Text files

BASED on the definition provided, below, suggested steps by 2.0 LPBi are the following:

• A2C-AWS and 2.0 LPBI will generate a PROPOSAL for AWS to fund that effort for future placement in AWS Marketplace
• A2C-AWS and 2.0 LPBI will develop Plans and Cost Structures of the infrastructure needed for CONTENT monetization – to be presented to Investment Banker in NYC
• A2C-AWS and 2.0 LPBI will take the LPBI Proof-of-Concept on Medical Text Analysis with NLP in Genomic and Cancer and will create jointly TWO skeleton IT Structures

#1 Skeleton IT Structure: 

Reproduce the Proof-of-Concept using AWS – NLP–ML-AI technology and Scale up to One Chapter in Genomics Volume 1 and One Chapter in Cancer Volume 2

That will be JOINTLY presented at a Healthcare Insurer [LPBI’s Contact] by LPBI AND A2C-AWS – with the scope of getting a Contract that A2C-AWS will define, execute and manage the Statement Of Work (SOW) and submit Costs to the Healthcare Insurer. Prospects of expansion to Cardiovascular and Immunology, beyond Genomics and Cancer are strong.

#2 Skeleton IT Structure: 

Produce a Skeleton for Monetization of 

• 0 LPBI – Journal articles AND 
• 0 LPBI — the Results of #1 Skeleton IT Structure: PRODUCTION OF FEATURES of TEXT ANALYSIS using AWS NLP technologies

That will be presented at 

• an Investment Banker in NYC [LPBI’s Contact], 

and 

• by LPBI 

to other funding sources, and 

• by A2C-AWS to other funding sources, Chiefly, AWS – internally.

             

The Opportunities MAP written on 2/2019 for LPBI M&A or Exit include

Twelve Economic Segments for LPBI Group’s IP – Prospects for Transfer of Ownership

1.     Holding Companies, Investment Bankers and Private Equity
2.     Information Technology Companies – Health Care
3.     Scientific Publishers
4.     Big Pharma
5.     Internet Health Care Media & Digital Health
6.     Online Education
7.     Health Insurance Companies & HMOs
8.     Medical and Pharma Associations
9.     Medical Education
10.     Information Syndicators
11.     Global Biotech & Pharmaceutical Conference Organizer
12.     CRO & CRA 

Information Technology Sector: Cloud-based – 

Amazon Web Services (AWS),  Alphabet – Verily, Apple-Health,  IBM Watson

Information Technology Sector: Cloud & Server-based – 

Microsoft-Health, Dell Boomi, Oracle-Health, SAP, Intel-Health

 

Please review this LINK:

https://pharmaceuticalintelligence.com/2019-vista/opportunities-map-in-the-acquisition-arena/

For the DESIGN of IT Infrastructure for Monetization, the following is an essential 

DEFINITION of a USE CASE for “PROFILE of an Article”: 

1.0 LPBI BEGINS 

Monetization of 6,000 Digital Products – USE CASE: A Journal Article

5.0 Article Title

5.0.1 Article URL

5.0.2 Author 1: Name

5.0.2.1 Author 2: Name

5.0.2.2 Author 3: Name

5.0.2.3 Author 4: Name

5.0.3  Date of Publication

5.0.4  # Words

5.0.5  # Views since Published to DATE

5.1 Is the article in a Book?

5.1.1 Article is not in a Book only in the Journal

5.2 Article is in a Book – In which one(s)?

5.2.1 LPBI Series A

5.2.1.1 Volume 1

5.2.1.2 Volume 2

5.2.1.3 Volume 3

5.2.1.4 Volume 4

5.2.1.5 Volume 5

5.2.1.6 Volume 6

5.2.2   LPBI Series B

5.2.2.1 Volume 1

5.2.2.2  Volume 2

5.2.3   LPBI Series C

5.2.3.1 Volume 1

5.2.3.2 Volume 2

5.2.4   LPBI Series D

5.2.4.1 Volume 1

5.2.4.2 Volume 2

5.2.4.3 Volume 3

5.2.4.4 Volume 4 [Dr. Williams and Dr. Irina are adding editorials, NOW]

5.2.5   LPBI Series E

5.2.5.1 Volume 1

5.2.5.2 Volume 2

5.2.5.3 Volume 3

5.2.5.4 Volume 4

1.0 LPBI ENDS

2.0 LPBI BEGINS

Strategy #1: Medical Text Analysis (NLP, ML, AI) (SaaS)

and 

Strategy #2: Monetization of Text Analysis Results as Products (Blockchain as a Service (BaaS)

5.3 Does article have the Text Analysis features:

5.3.1.a WordCloud – needs to be stored in graph file of WordClouds

5.3.2. # words used

5.3.3. Hyper-graphs  – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4.  Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – Translated into few other languages

5.3.4.2 Domain Expert interpretation for 5.3.3.2 -– Translated into few other languages

5.4 Audio File added to Article

5.4.1 In place – Audio file type [Text to Audio]

5.4.2 SoundCloud file

5.  Article Titles was translated to

5.5.1   Spanish

5.5.2   Japanese

5.5.3   Russian

6.   Article Interpretation of Hyper-graphs was translated to

5.6.1   Spanish

5.6.2   Japanese

5.6.3   Russian

The content below was not Updated on 1/2/2021

Distinction between A and B, below

• A.  1.0 LPBI – 2012–2020 – IP Assets available for sale

• B.  2.0 LPBI – 2021–2025 – IP Assets under construction – WILL BE AVAILABLE FOR SALE

A.  1.0 LPBI – 2012–2020 – IP Assets available for sale

A.1 A List of Scientific articles N=6,000 

STORED in Excel file run on 6/30/2020 and 12/31/2020

They need to be Indexed by several keys:

A.1.1  Author Name

A.1.2  Article Title

A.1.3  Category of Research, see article example , below

For the Cancer category

• we have the following tree structure
• System had data on how many articles are in each category

•  Cancer – General
•  Cancer and Current Therapeutics
  •  interventional oncology
    •  Breast Cancer – impalpable breast lesions
    •  Prostate Cancer: Monitoring vs Treatment
•  CANCER BIOLOGY & Innovations in Cancer Therapy
  •  Anaerobic Glycolysis
  •  Cachexia
  •  Cancer Genomics
    •  Circulating Tumor Cells (CTC)
      •  Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood
        •  mRNA
      •  MagSifter chip
    •  KRAS Mutation
    •  Li-fraumeni syndrome.
    •  TP53 – Germline mutations
  •  cancer metabolism
  •  Funding Opportunities for Cancer Research
  •  Genomic Expression
  •  Glioblastoma
  •  Hexokinase
  •  Loss of function gene
  •  Metabolic Immuno-Oncology
  •  Metastasis Process
  •  Methylation
  •  Microbiome and Responses to Cancer Therapy
  •  Monoclonal Immunotherapy
  •  mtDNA
  •  Oxidative phosphorylation
  •  Pancreatic cancer
  •  Pyruvate Kinase
  •  The NCI Formulary
  •  tumor microenvironment
  •  Warburg effect
•  Cancer Informatics
•  Cancer Prevention: Research & Programs
•  Cancer Screening
•  Cancer Vaccines: Targeting Cancer Genes for Immunotherapy
  •  Engineering Enhanced Cancer Vaccines

A.1.4  Type of article: by the role of the author: 

• If the Author is Curator THAN this article is a curation
• If the Author is Reporter THEN this article is a Scientific reporting article

A.1.5  Article Abstract will be a WordCloud created by ML – one image per article

Example

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?  <<<<<<<<< Article Title

Author: Larry H. Bernstein, MD, FCAP  <<<<<<<<< Author’s Name

https://pharmaceuticalintelligence.com/2012/10/17/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view/   <<<<<<< URL

• The system provides: “Related” what you named associated, see below – will need to be placed in the article description
• The system provides: “Posted in” – meaning – ALL the categories of research checked off by the author that this article belong to by the SUBJECT MATTER of the article

EXAMPLE for “Related” what you named associated

Related

What can we expect of tumor therapeutic response?

In “Biological Networks, Gene Regulation and Evolution”

WordCloud Visualization of LPBI’s Top Twelve Articles by Views at All Time and their Research Categories in the Ontology of PharmaceuticalIntelligence.com

In “Academic Publishing”

AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

In “Biological Networks, Gene Regulation and Evolution”

Examples for >>>>>>>> Category of Research – live links listing in parenthesis number of articles in one category

Posted in Biological Networks, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Disease Biology, Genome Biology, Imaging-based Cancer Patient Management, International Global Work in Pharmaceutical, Liver & Digestive Diseases Research, Metabolomics, Molecular Genetics & Pharmaceutical, Nutrition, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Population Health Management, Proteomics, Stem Cells for Regenerative Medicine, Technology Transfer: Biotech and Pharmaceutical | Tagged Adenosine triphosphate, ATP, Glycolysis, Hypoxia-inducible factors, Kreb, Lactate dehydrogenase, Mammalian target of rapamycin, Mitochondrion, Warburg Effect | 40 Comments

Below, an excerpt from the 6,000 LIST: Top Posts by VIEWS for all days ending 2020-06-02 (Summarized)

All Time
Title	Views	Author Name	Type of Article
Home page / Archives	676,690	Internet Access	Tabulation
Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?	17,117	Larry H. Bernstein, MD, FACP	Investigator Initiated Research
Recent comprehensive review on the role of ultrasound in breast cancer management	14,242	Dr. D. Nir	Commission by Aviva Lev-Ari, PhD, RN
Do Novel Anticoagulants Affect the PT/INR? The Cases of XARELTO (rivaroxaban) and PRADAXA (dabigatran)	13,839	Dr. Pearlman, MD, PhD, FACC & Aviva Lev-Ari, PhD, RN	Commission by Aviva Lev-Ari, PhD, RN
Paclitaxel vs Abraxane (albumin-bound paclitaxel)	13,709	Tilda Barliya, PhD	Investigator Initiated Research
Apixaban (Eliquis): Mechanism of Action, Drug Comparison and Additional Indications	8,230	Aviva Lev-Ari, PhD, RN	Investigator Initiated Research
Clinical Indications for Use of Inhaled Nitric Oxide (iNO) in the Adult Patient Market: Clinical Outcomes after Use, Therapy Demand and Cost of Care	7,903	Dr. Pearlman, MD, PhD, FACC & Aviva Lev-Ari, PhD, RN	Investigator Initiated Research
Mesothelin: An early detection biomarker for cancer (By Jack Andraka)	6,540	Tilda Barliya, PhD	Investigator Initiated Research
Our TEAM	6,505	Internet Access	Tabulation
Biochemistry of the Coagulation Cascade and Platelet Aggregation: Nitric Oxide: Platelets, Circulatory Disorders, and Coagulation Effects	5,221	Larry H. Bernstein, MD, FACP	Investigator Initiated Research
Interaction of enzymes and hormones	4,901	Larry H. Bernstein, MD, FACP	Commission by Aviva Lev-Ari, PhD, RN
Akt inhibition for cancer treatment, where do we stand today?	4,852	Ziv Raviv, PhD	Investigator Initiated Research
AstraZeneca’s WEE1 protein inhibitor AZD1775 Shows Success Against Tumors with a SETD2 mutation	4,535	Stephen J. Williams, PhD	Investigator Initiated Research
The History and Creators of Total Parenteral Nutrition	4,511	Larry H. Bernstein, MD, FACP	Commission by Aviva Lev-Ari, PhD, RN
Newer Treatments for Depression: Monoamine, Neurotrophic Factor & Pharmacokinetic Hypotheses	4,365	Zohi Sternberg, PhD	Investigator Initiated Research
FDA Guidelines For Developmental and Reproductive Toxicology (DART) Studies for Small Molecules	4,188	Stephen J. Williams, PhD	Investigator Initiated Research
The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets	4,038	Dr. Pearlman, MD, PhD, FACC, Larry H. Bernstein, MD, FACP & Aviva Lev-Ari, PhD, RN	Commission by Aviva Lev-Ari, PhD, RN
Founder	3,895	Aviva Lev-Ari, PhD, RN	Investigator Initiated Research

EndFragment

A.2 A List of 16 e-BOOKS

https://lnkd.in/ekWGNqA

A.2.1   Each book is made of articles included in the N=6,000

A.2.2 Books will list the URL of each book

http://www.amazon.com/dp/B00DINFFYC

http://www.amazon.com/dp/B018Q5MCN8

http://www.amazon.com/dp/B018PNHJ84

http://www.amazon.com/dp/B018DHBUO6

http://www.amazon.com/dp/B013RVYR2K

http://www.amazon.com/dp/B012BB0ZF0

http://www.amazon.com/dp/B019UM909A

http://www.amazon.com/dp/B019VH97LU

http://www.amazon.com/dp/B071VQ6YYK

https://www.amazon.com/dp/B075CXHY1B

https://www.amazon.com/dp/B076HGB6MZ

https://www.amazon.com/dp/B078313281

https://www.amazon.com/dp/B078QVDV2W

https://www.amazon.com/dp/B07MGSFDWR

https://www.amazon.com/dp/B07MKHDBHF

https://www.amazon.com/dp/B08385KF87

A.3 A List of e-Proceedings and Tweet Collections

• Part Two: List of BioTech Conferences 2013 to Present
• Part Three: Conference eProceedings DELIVERABLES & Social Media Analytics

A.3.1 each entry is an article included in N=6,000

B.   2.0 LPBI – 2021–2025 –

IP Assets under construction –

WILL BE AVAILABLE FOR SALE

B.1 Journal articles

• Will be subjected to ML and a NEW product will be created
• Instead of N=6,000 article – we will have N= 6,000 Medical INSIGHTS

B.2 16 e-Books

• Will be subjected to ML and a NEW product will be created
• Instead of 16 Books – we will have 16 COLLECTIONS of Medical INSIGHTS derived from Text Analysis of ONLY the articles included on each Volume
• 16 e-Books will become 16 AUDIO BOOKS
• 16 e-Books will become 16 Books in Japanese, Spanish and Russians

B.3 eProceedings & Tweet collections

• Will be subjected to ML and a NEW product will be created
• Instead of 60 e-Proceedings and 30 Tweet collections we will get 100 Business INSIGHTS Collections in the domain of each conference

We believe that Blockchain will enable STORAGE of each item that will be available for sale

• LPBI will have team members Bundling items per customer needs 
• Promotion can be done OUTSIDE the Blockchain system – STIRRING Customers to the Blockchain transaction system for TRADE and recording of transactions
• That is true for A and for B, below

A.   1.0 LPBI – 2012–2020 – IP Assets available for sale

B.   2.0 LPBI – 2021–2025 – IP Assets under construction – WILL BE AVAILABLE FOR SALE

Data Architecture Questions

1. In what data format is the content stored? In other words, is the content in image pdfs, searchable document pdfs, html, xls, word documents, text files, or some other form?

Example: TEXT

Versions of LPBI Group’s Elevator Pitch: 2.0 LPBI Group’s Team – In Our Own Words

• Aviva Lev-Ari, PhD, RN, Founder, LPBI Group: 1.0 & 2.0

My proposed Elevator Pitch

For the first time in the ten years of our private ownership, the opportunity to acquire the Inventor of Scientific curation has become a reality, Available for Transfer of ownership.

You can own a portfolio of Intellectual Property Assets that commands ~2MM e-Readers and offers ~6,000 of the best interpretive articles in five specialties of Medicine and Life Sciences. Pages of our 16 books have been downloaded ~125,000 times and over 100 of the top biotech and medical conferences were covered in real time and recorded in writing and Tweets. New strategies in AI and Blockchain are now applied on LPBI’s content for INSIGHT searches and pattern recognition by automated Machine Learning algorithms for use in drug discovery and drug repurposing. All of LPBI’s content was created by our Experts, Authors, Writers (EAWs).

• • We UPLOAD MS Word file NOT PDF
  • INVENTORY is stored in Excel
  • Top Posts for all days ending 2020-11-16 (Summarized)
    1. 7 Days |30 Days |Quarter |Year |All time
  • All Time

Title		Views
Home page / Archives		716,030
Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?		17,263
Recent comprehensive review on the role of ultrasound in breast cancer management		15,300
Paclitaxel vs Abraxane (albumin-bound paclitaxel)		14,341
Do Novel Anticoagulants Affect the PT/INR? The Cases of XARELTO (rivaroxaban) and PRADAXA (dabigatran)		14,006
Apixaban (Eliquis): Mechanism of Action, Drug Comparison and Additional Indications		8,770
Clinical Indications for Use of Inhaled Nitric Oxide (iNO) in the Adult Patient Market: Clinical Outcomes after Use, Therapy Demand and Cost of Care		8,398
Our TEAM		6,632
Mesothelin: An early detection biomarker for cancer (By Jack Andraka)		6,580
Interaction of enzymes and hormones		5,536
Biochemistry of the Coagulation Cascade and Platelet Aggregation: Nitric Oxide: Platelets, Circulatory Disorders, and Coagulation Effects		5,304
The History and Creators of Total Parenteral Nutrition		5,056
Akt inhibition for cancer treatment, where do we stand today?		4,899
Pyrroloquinoline quinone (PQQ) – an unproved supplement		4,712
AstraZeneca’s WEE1 protein inhibitor AZD1775 Shows Success Against Tumors with a SETD2 mutation		4,665
Newer Treatments for Depression: Monoamine, Neurotrophic Factor & Pharmacokinetic Hypotheses		4,453
FDA Guidelines For Developmental and Reproductive Toxicology (DART) Studies for Small Molecules		4,416
Evolution of Myoglobin and Hemoglobin		4,335
Isoenzymes in cell metabolic pathways		4,206
The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets		4,126
Founder		4,118

2. Within each content file or dataset, is the content metadata already defined, or would we need to parse the file to pull out the metadata? In other words, in the file for a journal article, do you already have the author, date, abstract, keywords, etc. defined as discrete pieces of data, or is all of this information embedded within the overall file?

YES

They need to be Indexed by several keys:

A.1.1  Author Name

A.1.2  Article Title

A.1.3  Category of Research

3. Do you expect to use a single type of subscription (such as a monthly subscription), or will different types of data have different types 

of subscription options (similar to how journals offer both one-time 24-hour subscriptions to a single article as well as monthly ongoing subscriptions)?

We wish to SELL ARTICLE DOWNLOAD vs Subscriptions

4. Does the marketplace need to include fuzzy search (i.e., the ability to find content based on “similar to” criteria, instead of just exact match searches)? Does it need to present the user with related content, or only the content that was searched for?

Our system attaches to each article RELATED content

5. We assume that the marketplace is not intended to replace your current LPBI company website? We are not scoping the quote to include a full website rebuild; it is assumed that the marketplace is separate (and your users would access the marketplace via the LPBI website).

YES – the digital store will connect to our newly to be designed web site for 2.0 LPBI on WordPress.com

• The digital store is the FORUM to buy goods by digital download of content
• $30 for One digital article or Audio article
• REFERRAL to Amazon Website to buy a book or the book in AUDIO format or a book in Japanese and Spanish – Russia is not served by Amazon – we can sell directly to consumers
• $100 download of an e-Proceedings for a Conference or the Tweet collection

For 2.0 LPBI Products

Bundles of Insights for Targeted Industries – B–to-B

• Tier #1:  Insights for drug discovery embedded in consulting engagements
• Tier #2:  Insights for drug repurposing embedded in consulting engagements
• Tier #3:  Insights for Health Care Insurers embedded in consulting engagements

Bundles of insights for theScientific Community – B–to-C