Automated Compliance Entity Extraction via NLP and ML
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Solution Overview
Problem
Current systems lack an efficient method for automatically extracting compliance named entities of type organization from vast amounts of digital data, requiring human expertise and time to identify and extract relevant regulatory information, which is crucial for ensuring compliance with ever-changing regulations.
Innovation Solution
A processor-based method using natural language processing (NLP) and machine learning (ML) to automatically extract compliance named entities by identifying obligation-like content, filtering relevant information, and associating it with features such as domains, types, and locations, enabling the automatic recognition and extraction of compliance requirements from text data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction methods are used to identify compliance named entities, then accuracy can be maintained through human expertise, but time consumption and labor resources increase significantly
Solution Approach 1:
The patent replaces manual mechanical extraction processes with an automated computational system that uses natural language processing and machine learning algorithms to identify and extract compliance named entities, thereby eliminating the need for human reviewers while maintaining extraction accuracy
Solution Approach 2:
The system performs self-service by automatically processing text data, identifying compliance obligations, and extracting named entities without requiring human intervention, enabling the system to serve its own information extraction needs efficiently
2Productivity
If traditional data processing methods are used, then system complexity remains manageable, but the ability to process vast amounts of digital data efficiently is insufficient
Solution Approach 1:
The patent segments the complex task of compliance entity extraction into distinct processing stages: text data ingestion, compliance obligation identification, named entity recognition, and result generation. This modular segmentation enables efficient processing of large data volumes while managing system complexity through structured, independent components
Solution Approach 2:
The system employs universal machine learning models and natural language processing algorithms that can handle multiple types of compliance data and extraction tasks simultaneously, increasing productivity across different data sources and compliance domains without proportionally increasing system complexity
3Adaptability or versatility
If static compliance checking methods are used, then implementation is straightforward, but adaptability to changing regulations is poor
Solution Approach 1:
The patent implements a dynamic system where machine learning models are trained on compliance regulations and can be retrained or updated as regulations change. The system adapts its extraction patterns and understanding based on new regulatory requirements, enabling continuous adaptation without complete system redesign
Solution Approach 2:
The system changes its operational parameters, such as extraction patterns, entity types, and processing rules, based on the specific regulation being analyzed. This allows the same system to adapt to different regulatory frameworks by adjusting parameters rather than requiring fundamentally different processing logic
Data Source
AI summary
Embodiments for automatic extraction of data of a compliance named entity of type organization by a processor. One or more segments of 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 conform to an obligation, a law, policy, regulation, or a combination thereof.