Subhadip Mitra

This disclosure describes an approach to context processing that models context as interacting fields rather than discrete states. A field-theoretic context system (FTCS) as described herein enables natural context flow, relationship preservation, and dynamic evolution through partial differential equations that govern field dynamics. The FTCS described herein represents an advancement in context processing by replacing discrete states with continuous, evolving fields. This approach more accurately reflects how information behaves in real-world systems. A key aspect is the non-conservative approach of the FTCS by which context can be genuinely created, amplified, diminished, or eliminated as information gains or loses relevance. This overcomes limitations of traditional context systems that rely on discrete states, manual mapping, and rigid hierarchies.

As organizations adopt generative AI, data pipelines often lack the dynamic context needed. This paper introduces ETLC (Extract, Transform, Load, Contextualize), adding semantic, relational, operational, environmental, and behavioral context. ETLC enables Dynamic Context Engines for context-aware RAG, AI co-pilots, and agentic systems. It works with standards like the Model Context Protocol (MCP) for effective context delivery, ensuring business-specific AI outputs.

Collective decisions require debate to surface contrastive points of view and to enable balanced and well-informed decision-making. Large language models (LLMs) can provide an opportunity to formulate deliberative decision making through multiple artificial intelligence (AI) agents. However, existing LLMs struggle with complex group discussions. This disclosure describes an artificial intelligence framework and techniques to support complex debate scenarios and group decision-making. A tiered structure of language models is deployed to dynamically generate and evaluate arguments. The techniques can handle multi-participant debates while considering ethical implications, producing well-reasoned outcomes. The described framework addresses limitations in existing debate systems by offering improved scalability, structured argumentation, and dynamic evaluation criteria. Example applications include policymaking, scientific discourse, corporate strategy, etc., and generally, any situation where thorough analysis and informed decision-making are crucial.

This paper presents an in-depth exploration of Data Monetization for Enterprises that would enable them to effectively transform their data assets into significant economic value. The paper introduces: (a) Data Monetization Framework - that encompasses key strategic technical capabilities, collaborative Data Product development, agile experimentation, product incubation, delivery model, pricing model and privacy/security by design; and (b) Activation of the Data Monetization Strategy - a 12-month long structured approach is discussed to implement, operationalize, scale, and sustain the strategy-through a Data COE. Data Product Development, Pricing Strategy, Delivery Mode and prioritization is also discussed through a Value-effort study. Technology strategy is discussed in detail with MoSCoW scoring. The paper delves into the role of emerging technologies such as generative AI (large language models), big data analytics, artificial intelligence, and machine learning in enhancing data monetization efforts. It also addresses the challenges and opportunities presented by these technologies, including issues related to data privacy, security, and ethical considerations.

A privacy aware open protocol to prove without repudiation an entity’s point-in-time presence, participation and location in physical or virtual space. OLP-protocol.org is a planned implementation node demonstrating the protocol.

In the current connected world - Websites, Mobile Apps, IoT Devices collect a large volume of users’ personally identifiable activity data. These collected data is used for varied purposes of analytics, marketing, personalization of services, etc. Data is assimilated through site cookies, tracking device IDs, embedded JavaScript, Pixels, etc. to name a few. Many of these tracking and usage of collected data happens behind the scenes and is not apparent to an average user. Consequently, many Countries and Regions have formulated legislations (e.g., GDPR, EU) - that allow users to be able to control their personal data, be informed and consent to its processing in a comprehensible and user-friendly manner.This paper proposes a protocol and a platform based on Blockchain Technology that enables the transparent processing of personal data throughout its lifecycle from capture, lineage to redaction. The solution intends to help service multiple stakeholders from individual end-users to Data Controllers and Privacy Officers. It intends to offer a holistic and unambiguous view of how and when the data points are captured, accessed, and processed. The framework also envisages how different access control policies might be created and enforced through a public blockchain including real time alerts for privacy data breach.