Hierarchical Document Compliance Analysis Using Attribute Embeddings
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Solution Overview
Problem
Determining compliance of electronic agreement documents is a time and computational resource-intensive process, often relying on manual review and extensive analysis of historical documents, which is inefficient and prone to missed compliance issues.
Innovation Solution
A document management system performs a hierarchical compliance analysis using attribute embeddings and machine learning models to automatically select a subset of historical documents and identify clauses, reducing computational resources and identifying potential compliance issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review and extensive analysis of historical documents is used to determine compliance, then compliance accuracy is improved, but time consumption and computational resource usage increase
Solution Approach 1:
The patent segments the compliance analysis process into multiple hierarchical levels: document-level filtering using attribute embeddings, clause-level extraction and matching, and issue-level identification. This segmentation allows the system to process only relevant portions of historical documents at each level, maintaining compliance accuracy while significantly reducing time consumption compared to manual review of entire documents.
Solution Approach 2:
The patent introduces attribute embeddings as an intermediary representation that captures essential characteristics of historical documents without requiring direct manual analysis of the full document text. These embeddings serve as a mediator between the working document and the comprehensive historical document corpus, enabling efficient filtering and comparison while preserving compliance detection accuracy.
2Reliability
If all historical documents are analyzed for compliance determination, then compliance completeness is improved, but computational resource usage increases
Solution Approach 1:
The system segments the historical document corpus into manageable units (documents, clauses, provisions) and processes them at different hierarchical levels. By filtering documents using attribute embeddings at the document level before performing detailed clause-level analysis, the system maintains compliance completeness while reducing computational resource usage compared to analyzing all historical documents in full detail.
Solution Approach 2:
The patent applies partial action by performing comprehensive analysis only on the subset of historical documents that are most relevant to the working document, as identified through attribute embedding comparison. Rather than exhaustively analyzing all historical documents, the system focuses computational resources on the most pertinent subset, maintaining compliance completeness while reducing overall computational resource consumption.
3Productivity
If attribute embeddings and hierarchical analysis are used to select a subset of historical documents, then computational efficiency is improved, but risk of missing compliance issues increases
Solution Approach 1:
The patent segments the compliance analysis into hierarchical levels where attribute embeddings perform broad document-level filtering, followed by detailed clause-level extraction and matching on the filtered subset. This multi-level segmentation maintains high computational efficiency while reducing the risk of missing compliance issues by applying increasingly rigorous analysis at each subsequent level.
Solution Approach 2:
The system incorporates feedback mechanisms where compliance issues identified at one hierarchical level inform the analysis at subsequent levels. The clause-level extraction and matching process refines the initial document-level filtering results, and the hierarchical structure allows feedback from detailed analysis to trigger re-examination of potentially relevant documents, thereby maintaining compliance issue detection accuracy while preserving computational efficiency.
Data Source
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AI summary
Techniques are described for a system comprising processing circuitry and computer readable media comprising instructions that, when executed, cause the processing circuitry to determine, for a working document associated with an entity, a working attribute embedding that characterizes attributes for the working document. The instructions may further cause the processing circuitry to select, based on a plurality of compliance attribute embeddings associated with a plurality of historical documents and the working attribute embedding, a plurality of compliance documents from the plurality of historical documents. The instructions may further cause the processing circuitry to determine a compliance report based on a plurality of working document clauses of the working document and a plurality of compliance clauses of the plurality of compliance documents. The instructions may further cause the processing circuitry to output an indication of the compliance report.