Contract Evaluation Apparatus Using Multi-Source Feature Extraction
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Solution Overview
Problem
Existing contract evaluation technologies do not accurately consider external circumstances and user-specific preferences when determining the importance of contracts, as they primarily rely on document content and metadata without accounting for background information or user-specific criteria.
Innovation Solution
An information processing apparatus that acquires contract features using both contract data and external data sources, and calculates an evaluation value based on user-specific calculation criteria, allowing for a more comprehensive assessment of contract importance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contract evaluation uses only document content and metadata, then the evaluation process is simple, but the evaluation accuracy is insufficient because external circumstances and user-specific preferences are not considered
Solution Approach 1:
The evaluation process is segmented into multiple independent modules: a feature acquisition unit that collects contract features from multiple sources, a storage unit that maintains calculation criteria, and a calculation unit that computes evaluation values. This segmentation allows the system to handle complex multi-source data integration while maintaining manageable system architecture and improving evaluation accuracy through comprehensive feature analysis.
Solution Approach 2:
The information processing apparatus is designed with multi-functionality to handle diverse data sources (contract text, metadata, external circumstances) and serve multiple user types with different calculation criteria. The system can evaluate contracts from multiple perspectives simultaneously, making it universally applicable to various evaluation scenarios while maintaining a unified processing framework.
2Measurement precision
If contract evaluation considers multiple data sources and user-specific criteria, then the evaluation becomes more accurate, but the data processing requirements increase
Solution Approach 1:
The system extracts only the essential features from each data source rather than processing complete datasets. The feature acquisition unit identifies and extracts relevant contract features from contract text, metadata, and external circumstances, then stores these extracted features for evaluation. This extraction approach maintains high evaluation accuracy while significantly reducing the volume of data that needs to be processed and stored.
3Adaptability or versatility
If the system stores user-specific calculation criteria, then the evaluation can be tailored to individual users, but the storage requirements increase
Solution Approach 1:
The storage unit implements local quality by maintaining distinct calculation criteria for different users rather than using a single universal criterion. Each user's calculation criteria are stored separately, allowing the system to adapt the evaluation process to specific user needs and preferences. This approach enhances adaptability while organizing storage in a structured, user-specific manner that manages complexity through localized customization.
Data Source
AI summary
An acquiring unit includes a first acquiring unit, a second acquiring unit, and an extracting unit. The first acquiring unit acquires first data from contract data indicating a contract. The second acquiring unit acquires, from an information source other than contract data, second data different from the first data by using the first data acquired by the first acquiring unit. The extracting unit extracts features of the contract from first data acquired by the first acquiring unit and second data acquired by the second acquiring unit. The features include at least second data. A calculating unit calculates an evaluation value of the contract according to a user, by using the features of the contract acquired by the acquiring unit and a calculation criterion read from a calculation criterion DB.