LLM Compliance Review for Data Structures and Documentation
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Solution Overview
Problem
The process of reviewing data structures and data management documentation for compliance with various standards, requirements, and policies is time-consuming and requires multiple human experts from different disciplines.
Innovation Solution
A computing system utilizing large language models (LLMs) to automatically evaluate data structures and data management documentation by transforming inputs into a suitable format, identifying relevant standards and policies, and generating compliance reports, including suggestions for improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review processes are used to evaluate compliance with standards and policies, then evaluation accuracy can be maintained through human expertise, but the process becomes time-consuming and requires multiple human experts from different disciplines
Solution Approach 1:
The patent introduces an intermediary system comprising a language model and a vector database that acts as a mediator between the data structure/documentation and the compliance standards. The language model transforms input into semantic representations, queries the vector database for relevant standards, and generates compliance evaluations, thereby eliminating the need for direct human expert review while maintaining evaluation accuracy
Solution Approach 2:
The patent replaces the mechanical system of human expert review with an automated computational system. The manual process of experts reading, analyzing, and evaluating compliance is substituted with an automated language model that processes documentation, retrieves applicable standards from a vector database, and generates compliance determinations, significantly reducing review time while maintaining accuracy
2Reliability
If multiple human experts from different disciplines are involved in compliance review, then comprehensive evaluation coverage is achieved, but the device complexity and coordination requirements increase
Solution Approach 1:
The patent implements a universal language model that performs multiple functions: understanding data structures and documentation, identifying applicable compliance standards through vector database queries, evaluating compliance across multiple disciplines, and generating comprehensive reports. This single multi-functional system replaces the need for multiple specialized human experts, reducing system complexity while maintaining comprehensive evaluation coverage
Solution Approach 2:
The patent merges the functions of multiple human experts into a single automated system. The language model integrates the knowledge and evaluation capabilities that would otherwise require separate experts in different disciplines, consolidating their roles into one unified system that processes compliance evaluations across all necessary domains simultaneously
3Reliability
If traditional compliance review methods are used, then established evaluation frameworks can be applied, but the process lacks adaptability to evolving standards and requirements
Solution Approach 1:
The patent implements a dynamic system where the vector database can be continuously updated with new compliance standards and requirements. The language model dynamically adapts to evolving standards by querying the updated vector database, ensuring the system remains current with changing regulations while maintaining the stability of the evaluation framework through consistent processing methodology
Solution Approach 2:
The patent performs preliminary actions by pre-processing and storing compliance standards in a vector database before they are needed for evaluation. This allows the system to have relevant standards readily available and properly indexed for quick retrieval, enabling rapid adaptation to new standards without requiring complex reconfiguration of the evaluation framework
4Productivity
If automated systems are implemented to reduce review time, then productivity increases, but the system may lack the nuanced understanding that human experts provide
Solution Approach 1:
The patent substitutes human mechanical review with an automated language model that uses natural language processing to understand and evaluate compliance. The system maintains precision by leveraging the language model's ability to comprehend nuanced requirements and generate reasoned compliance determinations, while achieving high productivity through automated processing of multiple documents simultaneously
Solution Approach 2:
The patent implements feedback mechanisms where the language model generates compliance evaluations that can be reviewed and refined. The system provides detailed compliance determinations with supporting rationale, allowing for verification and correction if needed, thereby maintaining high accuracy while achieving automated processing speeds
Data Source
AI summary
Various embodiments include methods and systems for automatically evaluating data structures and/or data management documentation for compliance with relevant standards, requirements, and policies. Various embodiments provide a framework for automating compliance reviews of data structures and/or data management against the latest version of standards, requirements, and policies. Methods may include transforming input requests for analysis and data structures and/or data management documents into formats suitable for processing by a large language model (LLM), prompting the LLM to identify concepts and/or elements that are likely subject to a standard, requirement, or policy, submitting a query to a vector database to identify standards, requirements, or policies relevant to the requested analysis and documents, prompting the LLM to assess compliance the data structures and/or data management documents with standards, requirements, or policy, and producing a report on compliance of the data structures and/or data management documents with the relevant standards, requirements, or policies.