NLP System for Automated Contract Entity Extraction
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Solution Overview
Problem
Current methods for analyzing contracts and other structured documents are inefficient, as they require manual extraction of data, making it difficult to identify clauses and terms for actionable analytics.
Innovation Solution
A system and method for natural language processing of structured documents using a server with a computer processor that parses documents, generates ontologies, extracts entities, identifies relationships, and creates vector representations, enabling automated data extraction and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction of data from contracts is used, then data extraction accuracy can be maintained, but processing time and labor costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical data extraction with an automated NLP system comprising entity recognition engines, relation extraction engines, and statistical parsers that process contract documents automatically, eliminating human labor while maintaining extraction accuracy through machine learning models
Solution Approach 2:
The system enables self-service data extraction where the contract document itself provides the structured data through automated processing pipelines that extract entities, relationships, and clauses without requiring human analysts to manually review each document
2Productivity
If automated NLP processing is implemented, then processing speed and productivity improve, but system complexity increases
Solution Approach 1:
The NLP system is divided into distinct modular components including entity recognition engines, relation extraction engines, statistical parsers, and ontology generators, allowing each module to be developed, maintained, and optimized independently while working together to achieve high processing speed
Solution Approach 2:
The patent introduces intermediary components such as ontologies and structured schemas that mediate between raw contract text and extracted data, simplifying the overall processing pipeline by providing standardized intermediate representations that reduce system complexity
3Loss of information
If comprehensive data extraction is performed on all contract components, then data completeness improves, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant entities, relationships, and clauses from contract documents using targeted extraction rules and machine learning models that identify and extract critical information while ignoring redundant content, achieving data completeness for key elements without processing every detail
Solution Approach 2:
Different extraction strategies and levels of detail are applied to different sections of the contract based on their importance, with critical clauses receiving more comprehensive analysis while less important sections receive streamlined processing, optimizing the balance between completeness and processing time
Data Source
AI summary
Systems and methods for natural language processing of structured documents. In another embodiment, in an information processing apparatus comprising at least one computer processor, a method for processing a structured document may include: (1) receiving a document; (2) parsing the document into a plurality of components using a statistical parser; (3) extracting a plurality of entities from each component; (4) identifying a potential relationship between two of the plurality of entities; (5) generating a numeric representation for the potential relationship; (6) confirming the potential relationship with a logical regression model; and (7) generating and storing a unified structured file for the document.