Regulatory Rule Alignment Using NLP for Policy-to-Code Updates
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Solution Overview
Problem
Existing systems struggle to efficiently correlate regulatory data with executable rules, leading to issues such as missing policy sections in code, legislative errors, and inefficient updates due to the rapid pace of regulatory changes, without effectively linking policy text to implementation.
Innovation Solution
The system employs natural language processing and machine learning to identify and correlate regulatory data with executable rules by aligning policy sections to code, supporting error detection, maintaining rule hierarchies, and updating rules in response to policy changes, using a regulatory data correlation system that includes components for data mining, alignment, mapping, and filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to correlate regulatory data with executable rules, then accuracy of policy-text-to-code alignment can be maintained, but productivity and efficiency deteriorate due to the rapid pace of regulatory changes
Solution Approach 1:
The patent introduces natural language processing (NLP) technology as an intermediary between regulatory text and executable code. The NLP system automatically analyzes policy documents, extracts key entities and relationships, and maps them to corresponding code elements, serving as a mediator that bridges the gap between human-readable regulations and machine-executable rules without requiring manual intervention for each correlation.
Solution Approach 2:
The patent replaces the manual mechanical process of correlating regulatory data with code with an automated computational system. Machine learning models and NLP algorithms substitute human analysts in the task of matching policy sections to executable rules, enabling rapid processing of regulatory changes while maintaining consistent alignment accuracy through algorithmic precision.
2Productivity
If automated systems are used to process regulatory data, then productivity improves, but reliability deteriorates due to missing policy sections in code and legislative errors
Solution Approach 1:
The patent implements feedback mechanisms where the automated system continuously monitors the alignment between regulatory text and executable code. The system detects discrepancies such as missing policy sections or inconsistent mappings and generates feedback signals that trigger corrective actions, either by alerting human reviewers or by automatically adjusting the correlations to maintain compliance accuracy.
Solution Approach 2:
The patent applies preliminary validation and verification steps before finalizing the correlation between regulatory data and code. The system pre-processes policy documents to identify key elements, pre-maps them to potential code sections, and performs preliminary consistency checks to prevent errors before they propagate into the final executable rules, thereby maintaining reliability while processing data automatically.
3Adaptability or versatility
If comprehensive regulatory data is collected and processed, then adaptability to policy changes improves, but device complexity increases due to the need for data mining, alignment, mapping, and filtering components
Solution Approach 1:
The patent divides the complex regulatory data processing system into distinct modular components: a data mining module that extracts relevant information from policy documents, an alignment module that matches extracted elements with code sections, a mapping module that creates correlations, and a filtering module that validates results. Each module handles a specific aspect of the processing pipeline, making the overall system more manageable and adaptable to different regulatory scenarios.
Solution Approach 2:
The patent designs the automated correlation system with universal components that can handle multiple types of regulatory data and various policy domains. The NLP models and machine learning algorithms are configured to process different formats and structures of regulatory documents, enabling the system to adapt to diverse policy changes without requiring complete redesign, thus managing complexity while maintaining versatility.
Data Source
AI summary
Various embodiments are provided for correlating regulatory data in a computing environment by a processor. A rule may be associated with one or more textual paragraphs extracted from a policy document that describes at least a portion of the rule.