Document Clause Validation via Machine Learning
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Solution Overview
Problem
Manual analysis of complex and varied documents, such as legal contracts, is time-consuming, error-prone, and does not leverage collective expertise, as existing solutions struggle to handle arbitrary structures and domain-specific contexts effectively.
Innovation Solution
A system that uses a document engine to identify clauses, a validation engine to evaluate and categorize them using machine learning models, and semantic evaluators, assigning confidence scores, while monitoring inconsistencies and updating models based on feedback for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to break down documents into clauses and sections, then understanding and interpretation can be achieved, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces the mechanical manual analysis process with an automated computer-based system that uses natural language processing, machine learning models, and semantic analysis algorithms to break down documents into clauses and sections, thereby eliminating the time-consuming manual effort while maintaining or improving accuracy
Solution Approach 2:
The system enables documents to be analyzed automatically without human intervention by using pre-trained machine learning models and semantic evaluators that can independently identify clauses, sections, and their hierarchical structures, making the analysis process self-service and highly efficient
2Measurement precision
If manual analysis is performed by skilled individuals, then accurate understanding can be achieved, but the process does not leverage collective expertise
Solution Approach 1:
The patent creates a universal system that can analyze various types of documents across different domains by using configurable domain-specific resources and adaptable machine learning models, allowing the same system to serve multiple functions and leverage collective expertise from diverse fields without requiring separate manual analysis for each case
Solution Approach 2:
The system introduces an intermediary layer of semantic evaluators, machine learning models, and knowledge bases that mediate between the raw document text and the analysis results, enabling the system to capture and utilize collective expertise from training data and domain-specific resources while maintaining consistent and accurate clause identification
3Ease of operation
If existing solutions are used to handle document structures, then basic analysis can be performed, but arbitrary structures and domain-specific contexts are not handled well
Solution Approach 1:
The patent implements a dynamic system where the machine learning models and semantic evaluators can adapt to different document structures and domain-specific contexts by using configurable parameters, domain-specific resources, and trainable architectures that adjust to the characteristics of the input documents, thereby maintaining ease of operation while achieving high adaptability to arbitrary structures
Data Source
AI summary
Embodiments are directed to managing documents where clauses in a document may be identified. Evaluations of the clauses may be provided based on evaluators and machine learning (ML) models that assign each of the clauses to a category and a confidence score. Actions associated with the clauses may be monitored including updates to content of the clauses. Inconsistent evaluations associated with the clauses be identified. The ML models may be retrained based on the content of the clauses associated with the inconsistent evaluations.