Semantic Clause Detection in Contract Analysis
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Solution Overview
Problem
Conventional approaches for identifying and extracting information from contractual documents are inadequate, particularly in handling frequent changes and semantic variations in clauses, leading to risks of overlooking important contractual terminologies.
Innovation Solution
A system that includes an input processor, discovery engine, and analysis engine to identify standard exact clauses and non-standard clauses by structurally analyzing and normalizing data, using semantic language evaluation to detect clauses with semantic variations, and storing data in a database for improved contract review processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches focus on unstructured text only, then the approach is simple to implement, but the ability to extract relevant and important information correctly deteriorates
Solution Approach 1:
The patent segments the contract document analysis into multiple structured components: clause identification, amendment detection, semantic variation analysis, and clause type classification. This segmentation enables precise extraction of relevant information by breaking down the complex unstructured text into manageable analytical units, directly improving information extraction accuracy while maintaining systematic processing.
Solution Approach 2:
The patent introduces semantic variation analysis as an additional dimension beyond traditional text matching. By analyzing semantic variations in clause wording (e.g., different expressions of the same contractual intent), the system enhances information extraction accuracy without requiring complete restructuring of the analysis framework, thus improving precision with moderate complexity increase.
2Reliability
If conventional approaches are used for finding contracts and amendments, then the system is easy to operate, but the reliability of discovering clauses and types of clauses deteriorates
Solution Approach 1:
The system automatically performs clause identification, amendment detection, and semantic analysis without requiring manual intervention. The automated processing of contractual documents, including the detection of non-standard clauses and semantic variations, enhances reliability while the system maintains user-friendly operation through automated workflows that reduce manual effort.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously refines its clause detection and classification based on identified patterns and semantic variations. This feedback loop improves clause discovery reliability by learning from previous analyses, while the automated nature of the feedback process maintains ease of operation without requiring manual system reconfiguration.
3Measurement precision
If the system analyzes semantic variations in clauses, then the ability to identify non-standard clauses improves, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary structuring and normalization of contractual documents before detailed semantic analysis. By pre-processing the text to identify potential clauses and their structures in advance, the system reduces the time required for subsequent semantic variation analysis while maintaining high precision in identifying non-standard clauses through the pre-established analytical framework.
Solution Approach 2:
The patent applies semantic variation analysis selectively to clauses where it is most needed, rather than uniformly to all text. By focusing computational resources on identifying non-standard clauses and semantic variations in critical sections, the system achieves high clause identification precision while minimizing overall analysis time through targeted rather than exhaustive processing.
Data Source
AI summary
Embodiments relate to a system and a method for identifying, from contractual documents, (i) standard exact clauses matching clause examples and (ii) non-standard clauses semantically related to but not matching the clause examples. A standard feature data set comprising standard exact clauses matching clause examples is obtained. In addition, a mirror feature data set comprising semantically related clauses of the clause examples is obtained using semantic language analysis, where the mirror feature data set encompasses the standard feature data set. Non-standard clauses are obtained by extracting a difference between the mirror feature data set and the standard exact feature data set.