Contract Metadata Extraction Using ML Segmentation and Rule Validation
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Solution Overview
Problem
Manual tracking of contract obligations and service level agreements in contract documents is laborious and time-consuming, especially for large organizations with numerous documents, making it difficult to extract relevant information effectively.
Innovation Solution
A data processing system and method that extracts performance segments and metadata values from contract documents using machine learning models and a rule-based engine, identifying segment types and extracting relevant metadata based on predefined models, reducing the need to process the entire document and increasing accuracy by validating outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracking of contract obligations and service level agreements is performed, then information extraction can be done with human judgment, but the process becomes laborious and time-consuming
Solution Approach 1:
The patent replaces manual mechanical tracking with an automated computer-based system that uses natural language processing and machine learning models to extract performance segments and metadata from contract documents, eliminating human labor while maintaining extraction accuracy
Solution Approach 2:
The system enables self-service extraction by automatically identifying and extracting relevant contract obligations and service level agreements without human intervention, using trained models to process documents independently
2Loss of information
If the entire contract document is processed to extract relevant information, then comprehensive information can be obtained, but the processing time increases significantly
Solution Approach 1:
The patent divides the contract document into multiple segments and uses natural language processing to identify and extract only the relevant performance segments containing obligations and service level agreements, rather than processing the entire document, thus reducing processing time while maintaining information completeness
Solution Approach 2:
The system extracts only the necessary performance segments and metadata from the contract document using targeted extraction models, obtaining comprehensive relevant information without processing unnecessary portions of the document
3Productivity
If automated extraction systems are implemented, then processing time is reduced, but extraction accuracy may decrease without proper validation
Solution Approach 1:
The patent implements a rule-based validation engine that provides feedback on the extracted performance segments and metadata, validating the automated extraction results against predefined rules and business logic to ensure accuracy while maintaining high processing speed
Solution Approach 2:
The system combines multiple approaches - machine learning models for extraction and rule-based validation for verification - creating a composite extraction system that leverages the strengths of both methods to achieve high speed and high accuracy simultaneously
Data Source
AI summary
A data processing system for extracting metadata values is described. The data processing system includes an input unit and a processor communicably coupled to the input unit. The input unit is configured to receive a contract document. The processor is configured to extract at least one segment from the contract document and identify a type of the at least one segment. The processor is further configured to extract at least one metadata value from the at least one segment based on a model, wherein the model is determined based on the identified type of the at least one segment.