Contextual Semantic Comparison of Policy Conditions
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Solution Overview
Problem
Current technologies lack an efficient method for contextual comparison of semantics in different policies, making it difficult to identify and explain semantic differences and similarities based on features of entities.
Innovation Solution
A system comprising a processor that executes computer-executable components to contextually compare semantics of conditions in policy data from different policies using a model, providing a contextual explanation of differences based on entity features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional methods are used for comparing policy conditions, then the comparison process is simple, but the ability to contextually explain semantic differences is insufficient
Solution Approach 1:
The patent introduces an intermediary component (comparison component with contextualization capability) that mediates between the policy data and the user. This intermediary extracts semantic information, compares it across policies, and provides contextual explanations, thereby resolving the contradiction between maintaining simplicity and enhancing explanatory capability.
Solution Approach 2:
The patent replaces traditional mechanical/textual comparison methods with an automated computational system that uses processing components to analyze policy data. This substitution enables contextual explanation capabilities while managing system complexity through structured computational approaches.
2Productivity
If manual comparison of policy semantics is performed, then detailed analysis is possible, but the execution time and workload are excessive
Solution Approach 1:
The system performs self-service by automatically extracting, comparing, and analyzing policy semantics without requiring manual intervention. The comparison component autonomously processes policy data, identifies semantic differences, and generates contextual explanations, thereby dramatically improving productivity while reducing execution time compared to manual methods.
Solution Approach 2:
The patent replaces manual semantic analysis with an automated computational system. The processing components substitute human analysts, enabling rapid comparison of policy conditions and generation of contextual explanations, thus resolving the contradiction between detailed analysis capability and execution time.
3Measurement precision
If comprehensive contextual analysis is implemented, then semantic differences are accurately identified, but the processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the policy data into discrete conditions and analyzing each condition's semantics separately. The comparison component processes individual policy conditions, extracts semantic information, and compares them systematically. This segmentation enables accurate semantic comparison while managing processing complexity through structured, modular analysis.
Solution Approach 2:
The system applies local quality by focusing the contextual analysis on specific features of entities relevant to each policy condition. Rather than analyzing all aspects uniformly, the comparison component identifies and analyzes only the locally relevant semantic features, thereby achieving high measurement precision while controlling processing complexity.
Data Source
AI summary
Systems, computer-implemented methods, and computer program products to facilitate contextual comparison of semantics in conditions of different policies are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a comparison component that contextually compares semantics of conditions in policy data of different policies based on a feature of at least one entity. The computer executable components further comprise a contextualization component that employs a model to provide a contextual explanation of how a first condition in first policy data of a first policy is semantically different from a second condition in second policy data of a second policy based on the feature of the at least one entity.