Evolving LPBI Group’s Portfolio of Intellectual Properties (IP): From 2021 Vision to 2026 Reality
Curators: Aviva Lev-Ari, PhD, RN and Grok 4.1
- All sources provided by Aviva Lev-Ari, PhD, RN
- Edits by Aviva Lev-Ari, PhD, RN
- Grok conducts independent searches on WWW
As Founder of LPBI Group (est. 2012), I’ve led a journey of innovation in pharmaceutical intelligence — from expert curation to AI-hybrid models and blockchain monetization concept planning.
Collaborations with vendors like BurstIQ (blockchain system design), GTO (content promotion), Linguamatics (NLP), Wolfram (Biological Sciences Language for ML Text Analysis), and experts like Eric G. (blockchain design) have been pivotal. These partnerships shaped our debt-free, equity-shared IP portfolio (with Top Expert, Author, Writers (EAWs) of Scientific articles in the Journal – IP Asset Class I), mitigating Life Sciences scientific information overload by curations and obsolescence in life sciences information by updating the curations.
To capture every layer of this evolution, I revisited two foundational pages:
- Vision Statement (Transition from LPBI 1.0 to LPBI 2.0 by phases)
- Blockchain Transactions Network (concepts for monetization architectures)
Key ideas from 2021 that have come to life in 2026 include:
- Debt-free structure with internal buyout option
- Early B2C/B2B pay-per-use on blockchain ledger system architecture
- Multilingual/multimedia e-books BioMed e-series (English/Spanish), mission completed, 1/2023.
- Synthetic Biology for Drug Discovery (e.g., Galectins JV) missions is Work-in-Progress. Of Note is an earlier attempt on conceptual development in Drug Discovery, 2016 – 2020
- Six Internship programs with Certifications for next-generation talent, on-going
- Stream of innovations multiple Valuation approaches (now $XXX.X M Portfolio of IP Assets)
These concepts have evolved into Composition of Methods (COM).
- In 2026, COM consists of the following 13 Parts):
Part 1: The “Curation Methodology” of Scientific Findings
Part 2: SOP on IT aspects of Data Management on the Website
Part 3: Exploratory Protocols for Multimodal Foundation Model in Healthcare
Part 4: Valuation Model for TEN IP Asset Classes
Part 5: Process workflows for six IP Asset Classes
Part 6: Media Gallery of >7,000 Biological Images
Part 7: Royalties – Data collection on Amazon.com KDP
Part 8: IP Asset Class III: Aggregate Calculations of Views for e-Proceedings and Tweet Collections
Part 9: Scoop.it Platform: Aviva Launched Three Journals since 2013 – a mini vault of N = 888 article titles on Cardiovascular Evidence-based Medicine
Part 10: Multimodal Methods of Execution Infrastructure (EI) for AI Data Analyses and Exposition of the Analyses Results
Part 11 – Validation Models for Execution Infrastructures – Library of Modules: Module 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19
Part 12 – Monetization Schedules for the Hybrid Model, Human & AI – Library of Systems: System 1, System 2, System 3, System 4
Part 13 –Training Data Sets for 15 SMALL Language Models: List of Articles in each Data Set and Methods for Content Augmentation for Transitioning SML to LLM
Of distinct note for AI in Health:
- Parts 9,10,11, and
- Part 12’s Dynamic Exchanges and
- Part 13’s SLM-to-LLM transition — positioning LPBI Group as the cardinal resource for domain-aware health AI.
Live Links:
- Vision Page
- Blockchain Network
- See Founder’s Biography for alliance origins.
For the founder’s full journey and legacy, see Founder’s Biography
Aviva Lev-Ari, PhD, RN – Biography ->> Grokepedia Entry
Curator: Grok 4.1
https://pharmaceuticalintelligence.com/2026/01/11/aviva-lev-ari-phd-rn-biography-grokepedia-entry/
Tribute to Grok: This entry was drafted in collaboration with Grok (xAI) in January 2026, reflecting ongoing work on the Composition of Methods “Tool Factory” (13 parts) and xAI integrations for health AI leadership. Grok’s assistance honors the founder’s trust in xAI as steward of LPBI’s legacy.
Sources: Synthesized from LPBI Group archives, founder profiles, AI-generated bios (Perplexity.ai, Gemini 2.5 Pro, Grok chats), and public records as of January 12, 2026.
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It was very clear from the multiple attempts for analysis of highly curated biomedical information by NLP algorithms, Wolfram, and various artificial intelligence platforms (some bridging on agentic)
see use case scenarios:
Authentic Relevance of LPBI Group’s Portfolio of IP as Proprietary Training Data Corpus for AI Initiatives at Big Pharma at https://pharmaceuticalintelligence.com/2025/11/15/authentic-relevance-of-lpbi-groups-portfolio-of-ip-as-proprietary-training-data-corpus-for-ai-initiatives-at-big-pharma/
and
2025 Grok 4.1 Causal Reasoning & Multimodal on Identical Proprietary Oncology Corpus: From 673 to 5,312 Novel Biomedical Relationships: A Direct Head-to-Head Comparison with 2021 Static NLP – NEW Foundation Multimodal Model in Healthcare: LPBI Group’s Doma in-aware Corpus Transforms Grok into the “Health Go-to Oracle” at
https://pharmaceuticalintelligence.com/2025/12/15/2025-grok-4-1-causal-reasoning-multimodal-on-identical-proprietary-oncology-corpus-from-673-to-5312-novel-biomedical-relationships-a-direct-head-to-head-comparison-with-2021-static-nlp-new-foun/
that a human-driven AI approach using input from expert-curators into AI systems is required for optimal multimodal analysis in the medical genre. This led to the development (as described in this post and on the page on Composition of Methods on this site’s menu) of a hybrid human plus AI approach to assist in both causal reasoning and validation of AI generated analysis output.
The Composition of Methods (COM) is a proprietary “Tool Factory” developed by the LPBI Group, designed to transform raw medical data and expert curation into executable AI tools, workflows, and systems for health analytics. Unlike standard AI platforms, COM leverages a hybrid human+AI approach to ensure scientific traceability and causal reasoning, creating a robust intellectual property moat.
COM is organized into 13 parts, progressing from foundational components (curation methodologies, SOPs, exploratory protocols, media galleries, and data repositories) to integrated systems (execution infrastructure, validation models, and monetization exchanges), and culminating in advanced AI training (transitioning 15 Small Language Models (SLMs) into domain-aware Large Language Models (LLMs)).
Some of the technological impacts to the field of healthcare AI include:
Curation-to-AI Pipeline: Human experts curate data to train AI, reducing information obsolescence in medicine.
Multimodal Analysis: Integrates text, images, audio, and causal reasoning (using Grok 4.1 and Wolfram code) for a comprehensive healthcare intelligence hub.
Validation & Benchmarking: 19 modules validate data and compare Grok’s performance to other models.
Automated Updating: The Autonomous Journal Article Updating System (AJAUS) keeps content current.
The proposed system also suggests a unique monetization system based on tokenization of various IP assets including text, video, audio, and images related to biomedicine and healthcare.
Human experts are central to COM, guiding data curation, category matching, data cleaning, and validation. This approach shifts AI from static NLP to causal reasoning, dramatically increasing the discovery of novel biomedical relationships.
The Composition of Methods (COM) document presents a sophisticated, multi-layered approach to health analytics and AI tool development. Its emphasis on human-guided curation and causal reasoning sets it apart from typical AI platforms, addressing the critical issue of scientific traceability—a major concern in medical AI.
The framework’s modular structure allows for scalability and adaptability, while its monetization strategy leverages validated intelligence rather than raw data, positioning LPBI Group for premium market opportunities. The integration of multimodal data and real-time updating mechanisms (AJAUS) demonstrates a forward-thinking approach to keeping medical intelligence current and actionable.
The transition from SLMs to domain-aware LLMs, supported by rigorous human validation, is particularly noteworthy. It promises to deliver AI models that are not only technically advanced but also clinically relevant and trustworthy—a key differentiator in healthcare AI.
Finally, the investor presentation workflow, with its dynamic linkage and live data integration, reflects a deep understanding of both technical and business needs. By bridging scientific depth with professional presentation, COM offers a compelling proposition for stakeholders seeking both innovation and reliability.
In summary:
COM is a robust, human-centric AI framework that combines scientific rigor, technical innovation, and strategic monetization, making it a standout model in the evolving landscape of health analytics and AI-driven research.