Vector-Based Regulatory Control Mapping System
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Solution Overview
Problem
Current methods for updating controls in Regulatory and Corporate Compliance Management (RCCM) solutions are inefficient and time-consuming, requiring thorough reading and extensive searches through text editors to understand new regulatory requirements and map them to existing controls, leading to non-relevant results and high resource wastage.
Innovation Solution
A processing platform that converts new and existing authoritative sources and controls into vector representations, computes similarities, and generates candidate controls for mapping, providing prioritized recommendations to users for efficient compliance management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword searches are used through text editors to find relevant controls, then the search can be performed using available tools, but numerous non-relevant results are retrieved and users must review many controls before finding relevant ones
Solution Approach 1:
The patent replaces manual keyword searching through text editors with an automated computational system that uses natural language processing and machine learning algorithms. The system automatically analyzes new regulatory requirements, compares them against existing controls using vector similarity computations, and ranks relevant controls - substituting the mechanical manual search process with an automated intelligent system that eliminates the need for users to manually review numerous non-relevant controls
Solution Approach 2:
The patent introduces an intermediary automated recommendation system between the keyword search and the user. This intermediary system processes the search results through NLP and similarity computations to filter and rank controls, presenting only the most relevant matches to users. This intermediary layer transforms the raw keyword search results into prioritized recommendations, significantly reducing the time users spend reviewing non-relevant controls
2Reliability
If thorough reading and extensive searches are performed to understand new regulatory requirements, then accurate mapping can be achieved, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces the manual mechanical process of thorough reading and extensive searching with an automated NLP-based analysis system. The system uses natural language processing to comprehend new regulatory requirements, extracts key entities and relationships, and performs automated similarity computations against existing controls. This substitution maintains mapping accuracy through intelligent analysis while dramatically increasing compliance update speed by eliminating manual review processes
Solution Approach 2:
The patent transforms the regulatory requirements and controls into vector representations, changing the parameter space from text-based manual comparison to numerical vector-based similarity computation. This parameter transformation enables automated high-speed processing while maintaining accuracy through mathematical similarity metrics, allowing the system to evaluate numerous controls simultaneously rather than through sequential manual reading
3Measurement precision
If manual review of many controls is performed to determine relevant mappings, then accurate compliance assessment is achieved, but computer resources are wasted
Solution Approach 1:
The patent applies partial action by using automated NLP and similarity computations to pre-filter and rank controls before presenting them to users for final review. The system performs comprehensive automated analysis on a subset of most-relevant controls identified through vector similarity, rather than requiring manual review of all controls or performing exhaustive computations on every possible match. This partial automated action achieves high measurement precision while minimizing computer resource waste by focusing computational effort only on the most promising candidates
4Device complexity
If conventional text editor searches are used, then no specialized processing is needed, but non-relevant results lack search result prioritization
Solution Approach 1:
The patent replaces simple text editor searches with an automated ranking system that uses NLP and machine learning to prioritize results. The system automatically computes similarity scores, ranks controls by relevance, and presents prioritized recommendations to users. This substitution adds intelligent automation that provides result prioritization while maintaining ease of operation through automated processing and clear presentation of ranked results
Data Source
AI summary
A method in one embodiment comprises receiving at least one new authoritative source, accessing a plurality of existing controls and a plurality of existing authoritative sources, converting a data structure of the at least one new authoritative source and data structures of the plurality of existing controls and existing authoritative sources into a plurality of vector representations, using the plurality of vector representations to compute similarities between the at least one new authoritative source and at least a subset of the plurality of existing controls and existing authoritative sources, generating a plurality of candidate controls for mapping to the at least one new authoritative source, and transmitting to a user a recommendation identifying a proposed mapping of one or more of the plurality of candidate controls to the at least one new authoritative source.