Regulatory Requirement Description Generator
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Solution Overview
Problem
Enterprises face difficulty in compliance with regulatory content due to the increasing complexity and volume of regulatory documents, which lack clear and concise requirement descriptions, making it hard to identify applicable regulations and conditions for their operations.
Innovation Solution
A computer-implemented method and system that generates regulatory content requirement descriptions by identifying parent and child requirements within hierarchical structures, using a conjunction classifier and neural networks to classify and combine text, resulting in clear, concise requirement descriptions and summarizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If regulatory documents are made more comprehensive to cover all compliance requirements, then the completeness of regulatory coverage is improved, but the complexity and volume of documents increases making them harder to navigate
Solution Approach 1:
The patent segments regulatory documents into hierarchical requirement units with parent-child relationships. Each requirement is broken down into smaller, manageable components that can be independently analyzed and classified, reducing the perceived complexity while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between the comprehensive regulatory text and the user. The conjunction classifier and requirement description generator serve as intermediaries that process the complex regulatory content and present it in a simplified, structured format.
2Measurement precision
If regulatory documents are made more detailed to specify all conditions, then the precision of compliance requirements is improved, but the difficulty of identifying applicable regulations increases
Solution Approach 1:
The patent performs preliminary classification and organization of regulatory requirements before they need to be applied. By pre-processing the regulatory content into structured requirement pairs with hierarchical relationships, the system prepares the information in advance, making it easier to identify applicable regulations when needed.
Solution Approach 2:
The patent replaces manual analysis of detailed regulatory text with an automated machine learning system. The conjunction classifier and neural networks automatically process the detailed requirements, substituting human cognitive effort with computational algorithms that can efficiently handle precision and complexity.
3Measurement precision
If enterprises manually review all regulatory documents to ensure compliance, then the accuracy of compliance identification is improved, but the time and resources required increase significantly
Solution Approach 1:
The patent enables the regulatory content to essentially serve itself by automatically generating requirement descriptions and classifications without human intervention. The system processes, structures, and organizes the regulatory information autonomously, freeing enterprises from manual review while maintaining accuracy.
Solution Approach 2:
The patent changes the parameters of regulatory content from unstructured text to structured data with specific attributes including hierarchical level, parent-child relationships, and classification labels. This parameter transformation enables automated processing while preserving the precision needed for accurate compliance identification.
Data Source
AI summary
A computer-implemented method for generating regulatory content requirement descriptions is disclosed and involves receiving requirement data including a plurality of requirements including hierarchical information extracted from regulatory content. The method involves identifying parent requirements based on the existence of child requirements on a lower hierarchical level and generating requirement pairs including the parent requirement and at least one child requirement. The method also involves feeding each of the pairs through a conjunction classifier which has been trained to generate a classification output indicative of the pair being not a conjunction (NC), a single requirement conjunction (CSR), or a multiple requirement conjunction (CMR). The method involves generating a set of requirement descriptions based on the classification output generated for each parent requirement.