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
(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
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 ????
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.
- 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
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:
- 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
(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
- 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)
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
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:
- 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
or
- be acquired by a 3rd 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
- interventional oncology
- 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
- Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood
- KRAS Mutation
- Li-fraumeni syndrome.
- TP53 – Germline mutations
- Circulating Tumor Cells (CTC)
- 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
- 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”
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
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
- 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
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).
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- 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
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