Clause Detection System for Contractual Document Analysis
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Solution Overview
Problem
Conventional approaches in natural language processing fail to accurately identify and extract non-standard and standard clauses from contractual documents, leading to difficulties in recognizing contractual terminologies and managing amendments, which can result in overlooking unusual contractual terms and increased risk for parties involved.
Innovation Solution
A system comprising an input processor, discovery engine, analysis engine, and semantic language evaluator that processes raw data to structurally analyze and normalize contractual documents, applying predefined and custom policies to identify and extract non-standard and standard clauses through semantic language analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches focus on unstructured text only, then document-level information can be located, but relevant and important information cannot be extracted correctly
Solution Approach 1:
The patent segments contractual documents into structured clauses with hierarchical organization (document level, section level, clause level, sentence level). This segmentation enables precise extraction of relevant information while maintaining system manageability through modular processing of discrete clause units rather than treating the entire document as unstructured text.
Solution Approach 2:
The patent introduces clause templates as intermediary structures that bridge unstructured contract text and structured information extraction. These templates serve as mediators by providing predefined schemas for standard clauses, enabling accurate information extraction without requiring complex custom processing for each clause type.
2Measurement precision
If conventional approaches locate information at document level only, then text boundaries can be identified, but clause types cannot be recognized and grouped
Solution Approach 1:
The patent implements multi-level segmentation that divides documents into sections and clauses, with each clause further divided into sentences. This hierarchical segmentation enables precise identification and grouping of clause types by creating discrete units that can be individually analyzed and categorized, moving beyond document-level processing to clause-level precision.
Solution Approach 2:
The patent changes the parameter of information granularity from document-level to clause-level by introducing structured clause representations. This parameter change enables the system to recognize and group clause types by transforming the processing unit from entire documents to specific clause segments, thereby improving clause identification accuracy.
3Loss of information
If conventional approaches cannot identify standard clauses, then processing is simpler, but distinctive clauses and terminologies cannot be discovered
Solution Approach 1:
The patent introduces clause templates as intermediary structures that enable discovery of standard clauses and their distinctive terminologies. These templates act as reference models that facilitate recognition of standard clause patterns and their characteristic terminology, preventing information loss while maintaining manageable analysis complexity through template-based matching.
Solution Approach 2:
The patent creates copies of standard clause templates that can be matched against actual contract clauses. This copying approach enables discovery of standard clauses and their terminologies by comparing actual text against template copies, thereby preserving contractual terminology information while using simple pattern-matching operations rather than complex analysis.
4Reliability
If conventional approaches cannot identify non-standard clauses, then standard processing can be used, but unusual variations and risks cannot be detected
Solution Approach 1:
The patent extracts non-standard clauses by taking them out from the set of standard clauses through comparison against clause templates. This extraction approach enables detection of unusual variations and contractual risks by identifying clauses that do not match standard templates, thereby improving reliability without requiring the system to be explicitly programmed for each possible risk scenario.
Solution Approach 2:
The patent implements feedback mechanisms that compare actual clauses against standard templates and provide feedback on deviations. This feedback approach enables detection of non-standard clauses and associated risks by continuously comparing clause patterns and highlighting variations from standard forms, thereby improving contractual risk detection through iterative comparison rather than single-pass analysis.
Data Source
AI summary
A non-standard and standard clause detection system imports raw input data or contractual documents, and extracts non-standard and standard clauses that are semantically linked. One embodiment of a disclosed configuration is a system and a method for identifying non-standard and standard clauses in contractual documents. The system and the method comprise of generating a primary policy and a secondary policy, obtaining a first feature data set by applying the primary policy to a semantic language evaluator, and obtaining a second feature data set by applying the secondary policy to the semantic language evaluator. The first feature data set obtained is the aggregation of the standard clauses used in the document. Furthermore, the second feature data set encompasses the first feature data set, thus the difference between the first feature data set and the second feature data set is the aggregation of the non-standard clauses.