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

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

November 16, 2020 by 2012pharmaceutical

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

Author: Aviva Lev-Ari, PhD, RN

UPDATED on 2/5/2020

Decision RULES:

IF an article is in an e-Book THEN context for NLP is defined to be All articles in its Chapter in the Book
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:

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

(a) WordCloud

(b) Bar diagram

(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

What is REST

REST is acronym for REpresentational State Transfer. It is architectural style for distributed hypermedia systems and was first presented by Roy Fielding in 2000 in his famous dissertation.

Fielding, Roy Thomas. Architectural Styles and the Design of Network-based Software Architectures. Doctoral dissertation, University of California, Irvine, 2000.

Like any other architectural style, REST also does have it’s own 6 guiding constraints which must be satisfied if an interface needs to be referred as RESTful. These principles are listed below.

Guiding Principles of REST

https://restfulapi.net/

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:

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
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
BOTH MAY BE NEEDED TO EXIST ?????
I assume each MODE of implementation has a difference I/O and overhead performance numbers and if Both exists these numbers may be x2 ????

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

Article 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 the article a Conference output or not;
if yes Name of Conference, date, location,
is it part of e-Proceedings?
If yes Title & URL;
Does a Tweet Collection for this Conference exist?
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

WordCloud,
Bar Diagram for Word frequency,
Hyper-graph for concept relations,
Tree Diagram for hierarchical affinity translated into distance proximity among words;
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

Content in Text format, and of the
Digital graphical products generated by NLP
Domain Knowledge Experts interpretations of the Graphical output of NLP
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:

WordCloud,
Bar Diagram for Word frequency,
Hyper-graph for concept relations,
Tree Diagram for hierarchical affinity translated into distance proximity among words;
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.

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]

WordCloud
Bar graph
Hyper-graph or Tree Diagram – ONE to be decided to make to the Standard
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

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:

WordClouds,
Bar charts,
Hyper-graphs and
Expert Interpretation in English and Foreign Languages

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

WordClouds,
Bar charts,
Hyper-graphs and
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:

WordClouds,
Bar charts,
Hyper-graphs and
Expert Interpretation in English and Foreign Languages

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

WordClouds,
Bar charts,
Hyper-graphs and
Expert Interpretation in English and Foreign Languages

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

WordClouds,
Bar charts,
Hyper-graphs and
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

WordCloud (Images files),
Hyper-graphs (graph files),
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

(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

LPBI has a Proof-of-Concept in Medical Text Analysis using NLP, ML-AI – will be completed mid Feb. 2021
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
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

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

XXX Advertising is a company of XXX.com
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]
Upon progress with (j) to (o) represents 2.0 LPBI IP = the results of Text Analysis with NLP, ML-AI
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)

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

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:

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

System Integration job needed for 2.0 LPBI includes the following:

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

AWS
DELL
Other

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

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)

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

and

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

and

LPBI & A2C-AWS regarding Strategy #1: NLP
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

Holding Companies, Investment Bankers and Private Equity
Information Technology Companies – Health Care
Scientific Publishers
Big Pharma
Internet Health Care Media & Digital Health
Online Education
Health Insurance Companies & HMOs
Medical and Pharma Associations
Medical Education
Information Syndicators
Global Biotech & Pharmaceutical Conference Organizer
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

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

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

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

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

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

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

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

Posted in LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management, LPBI Management | Leave a Comment

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Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com

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

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

External Relations:

UPDATED on 1/13/2021

UPDATED on 1/5/2021

UPDATED on 1/4/2021

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

UPDATED on 1/2/2021

We have transitioned from

Content Monetization has TWO distinct parts:

2.0 LPBI had several IT infrastructure needs:

System integration of A, B, C

2.0 LPBI distinct competitive advantages are:

5.0 Article Title

5.1 Is the article in a Book?

5.3 Does article have the Text Analysis features:

5.4 Audio File added to Article

5. Article Titles was translated to

6. Article Interpretation of Hyper-graphs was translated to

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

A.2 A List of 16 e-BOOKS

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

B. 2.0 LPBI – 2021–2025 –

IP Assets under construction –

WILL BE AVAILABLE FOR SALE

B.1 Journal articles

B.2 16 e-Books

B.3 eProceedings & Tweet collections

Data Architecture Questions

For 2.0 LPBI Products

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

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

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