Specialized Dictionary for Regulatory Compliance NLP
Solution Overview
Problem
Existing automated governance tools struggle to effectively understand and comply with complex bodies of regulations due to their reliance on general-purpose dictionaries that fail to capture domain-specific information and relationships between terms, leading to inaccurate compliance efforts.
Innovation Solution
A specialized dictionary and facility for understanding authority documents, which manages multiple definitions, recognizes ambiguities, and establishes complex hierarchies of terms, supporting named entity recognition, parts of speech tagging, and natural language processing to provide a coordinated understanding of regulatory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general-purpose dictionaries are used for natural language processing in regulatory compliance, then device complexity is reduced and ease of operation is improved, but measurement precision and reliability of compliance understanding deteriorate
Solution Approach 1:
The patent segments the dictionary into domain-specific modules (e.g., financial regulations, healthcare compliance) rather than using a single general-purpose dictionary. Each module contains specialized terminology, relationships, and context relevant to specific regulatory domains, enabling precise NLP processing for compliance tasks while keeping individual modules manageable in size and complexity.
Solution Approach 2:
The patent applies local quality by tailoring dictionary content and processing rules to specific regulatory domains. Instead of uniform processing across all text, the system adapts terminology, relationships, and analysis depth to the local requirements of each regulatory context (e.g., different treatment for HIPAA compliance versus SEC regulations), thereby improving precision without requiring the entire system to be maximally complex.
2Adaptability or versatility
If general-purpose dictionaries are used, then ease of manufacture and implementation is improved, but the ability to capture domain-specific information and relationships deteriorates
Solution Approach 1:
The patent implements a dynamic dictionary system that can be updated and adapted to new regulatory requirements without complete reconstruction. The system allows incremental addition of domain-specific terms, relationships, and rules, enabling continuous improvement of adaptability while spreading the manufacturing effort over time rather than requiring a single complex upfront creation process.
Solution Approach 2:
The patent structures the dictionary with nested hierarchies where general regulatory concepts contain more specific sub-concepts and terms. This nested organization allows the system to handle both broad regulatory frameworks and detailed domain-specific requirements within a unified structure, improving adaptability while organizing content in a manageable, hierarchical manner that simplifies creation and maintenance.
3Reliability
If specialized dictionaries with complex hierarchies and relationships are implemented, then reliability and precision of regulatory understanding is improved, but device complexity and difficulty of operation increase
Solution Approach 1:
The patent introduces an intermediary layer between the complex specialized dictionary and the user interface. This intermediary translates complex regulatory relationships and terminology into simpler, more user-friendly outputs while maintaining the precision of the underlying domain-specific knowledge. Users interact with simplified representations rather than the full complexity of the specialized dictionary structure.
Solution Approach 2:
The patent performs preliminary processing and organization of complex regulatory information before presenting it to users. The system pre-computes relationships, identifies key compliance issues, and structures information in advance, so that users receive pre-processed, organized results rather than having to navigate the full complexity of the specialized dictionary and relationships themselves.
Data Source
AI summary
A dictionary data structure is described. The data structure is made up of first, second, and third tables. The first table is comprised of entries each representing a natural language term, each entry of the first table containing a term ID identifying its term. The second table is comprised of entries each representing a definition, each entry of the second containing a definition ID identifying its definition. The third table is comprised of entries each representing correspondence between a terminate definition defining the term, each entry of the third table containing term ID identifying the defined term and a definition ID identifying the defining definition. The contents of the data structure are usable to identify any definitions corresponding to a term.