Automated Compliance Profile Extraction via NLP
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Solution Overview
Problem
Current methods for ensuring regulatory compliance in organizations are inefficient and require significant human intervention, struggling to keep pace with the vast amount of changing regulatory documents and the need for accurate extraction of compliance profiles.
Innovation Solution
An automated system using natural language processing (NLP) and machine learning to extract compliance profiles from text data, identifying obligations and features such as domains, types, and locations, enabling the computation of compliance profiles for organizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated NLP and machine learning systems are used to extract compliance profiles, then productivity and accuracy of compliance extraction are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent employs natural language processing algorithms and machine learning models as intermediary systems between the raw regulatory text data and the compliance profile extraction task. These intermediaries automatically process and interpret unstructured text, transforming it into structured compliance information without requiring direct human intervention in the extraction process.
Solution Approach 2:
The compliance profile extraction system is designed to be self-service by automatically identifying obligations, extracting relevant features, and generating compliance profiles without manual expertise. The system autonomously processes regulatory documents, performs text analysis, and produces compliance assessments, enabling organizations to independently maintain up-to-date compliance profiles.
2Loss of time
If manual methods are used for compliance profile extraction, then system complexity is reduced, but loss of time and productivity decrease
Solution Approach 1:
The system performs preliminary actions by pre-processing regulatory text data, identifying key obligations, and extracting features before the actual compliance profile generation. This preliminary processing automates the initial stages of compliance assessment, significantly reducing the time required for manual review and extraction while maintaining high accuracy.
3Measurement precision
If comprehensive text data extraction is performed from multiple data sources, then measurement precision and completeness of compliance profiles are improved, but loss of time and processing resources increase
Solution Approach 1:
The system selectively extracts only the necessary text data and features from multiple data sources that are directly relevant to compliance profile generation. Rather than processing all available data, the NLP algorithms identify and extract specific obligations, entities, and contextual information, reducing processing time while maintaining comprehensive and accurate compliance profiles.
Data Source
AI summary
Embodiments for automatic extraction of a compliance profile for an organization by a processor. Text data may be extracted from one or more data sources representing one or more objects describing a compliance named entity of type organization expected to perform an obligation. A compliance profile of type organization may be determined for the compliance named entity of type organization according to the extracted text data.