Automated Regulatory Term Interpretation via Machine Learning
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Solution Overview
Problem
Regulations often contain indeterminate terms that are open to varying and conflicting interpretations due to evolving norms and unclear definitions, making it difficult for non-experts to understand and comply with them accurately.
Innovation Solution
An automated system and framework that applies four interpretation canons - literal, systematic, historical, and objective-teleological - to provide a non-arbitrary, definitive interpretation of indeterminate terms in regulations, using machine learning and formalized norm graphs to process textual data and generate a consistent interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts interpret regulatory terms, then interpretation accuracy is improved, but accessibility and ease of operation deteriorate because such knowledge is out of reach for many people
Solution Approach 1:
The patent creates an automated interpretation system that copies the expertise of human lawyers and compliance officers into a machine-learning model. The system trains on vast amounts of regulatory text and legal decisions to replicate human expert knowledge, making it accessible to anyone without requiring specialized training or education in regulatory interpretation
Solution Approach 2:
The patent replaces the mechanical system of human expert interpretation with an automated computational system. The machine learning model processes regulatory text and generates interpretations algorithmically, substituting human cognitive processes with automated computational analysis, thereby providing consistent access to expert-level interpretation for all users
2Reliability
If multiple interpretation canons are applied, then interpretation reliability is improved, but device complexity increases due to the need for multiple processing components
Solution Approach 1:
The patent merges multiple interpretation canons (literal, systematic, historical, and objective-teleological) into a single integrated machine learning model. Rather than implementing separate systems for each canon, the model combines all interpretive approaches into one unified framework that processes regulatory text through all canons simultaneously, reducing overall system complexity while maintaining comprehensive interpretation reliability
Solution Approach 2:
The patent creates a universal interpretation system that performs multiple interpretation functions through a single machine learning model. The model is designed to apply all four interpretation canons and handle various types of regulatory terms across different domains, making the system multi-functional and reducing the need for specialized components for each interpretation task
Data Source
AI summary
A system and method including the reception of an input of a set of textual terms including a subject matter parameter value and an indeterminate term parameter value; automatically determining, by a machine learning process, the subject matter parameter value is subsumed within a specified data model including the indeterminate term parameter value; automatically processing the indeterminate term parameter value to execute a combination of literal, systemic, historical perspective, and teleological interpretations thereof to generate an overall assessment that includes a non-arbitrary interpretation of the indeterminate term parameter value; and presenting a representation of the set of textual terms expanded to include the non-arbitrary interpretation of the indeterminate term parameter value.