Document Preparation System Using Textual Entailment Analysis
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Solution Overview
Problem
In longer documents, finding and editing parts with changed meanings is time-consuming and increases workload, as existing methods like term replacement are insufficient to maintain consistency and efficiency.
Innovation Solution
A method and system that divide document data into blocks, calculate textual entailment and contradiction between blocks, and highlight differences and changes, using AI and natural language processing to identify and prioritize editing needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If term replacement is used to maintain consistency in documents, then term consistency is improved, but the ability to detect meaning changes and maintain logical consistency deteriorates
Solution Approach 1:
The system changes the detection parameter from simple term matching to textual entailment analysis. By using NLP models to evaluate semantic relationships between sentences, the system can detect meaning changes even when terms remain identical, thus resolving the contradiction between term consistency and logical consistency.
Solution Approach 2:
The patent replaces the mechanical term-replacement approach with an AI-based textual entailment detection system. Instead of relying on simple string matching or term replacement, the system uses NLP models to understand and compare semantic meanings, enabling detection of subtle meaning changes that term replacement cannot capture.
2Measurement precision
If manual review of all document parts is performed to ensure consistency, then detection precision is improved, but time consumption and workload increase
Solution Approach 1:
The system enables self-service by automatically detecting textual entailment relationships and identifying inconsistent parts using NLP models. The document review process no longer requires manual analysis of all content, as the system independently performs semantic comparison and highlights issues, thus maintaining high detection precision while significantly reducing time consumption.
Solution Approach 2:
The patent substitutes manual mechanical review with automated AI-based textual entailment analysis. The NLP model processes and compares document segments automatically, evaluating semantic relationships and identifying inconsistencies without human intervention, thereby achieving both high accuracy and efficiency.
3Reliability
If the entire document is reviewed to find edited parts, then completeness is improved, but productivity decreases
Solution Approach 1:
The system extracts and focuses only on the most relevant information by identifying textual entailment relationships between sentences. Instead of reviewing the entire document uniformly, the NLP model extracts semantically important connections and highlights only the parts where meaning changes occur, thus maintaining completeness of detection while significantly improving productivity.
Solution Approach 2:
The patent applies local quality by providing differentiated review focus based on semantic importance. The system identifies and highlights specific local regions where textual entailment relationships indicate meaning changes, rather than treating the entire document uniformly. This allows users to concentrate on critical areas, maintaining detection completeness while improving editing efficiency.
Data Source
AI summary
To support preparation of a document with consistency. First document data and second document data are received, the first document data is divided into a plurality of first blocks, the second document data is divided into a plurality of second blocks, a plurality of first combinations each including corresponding first and second blocks are determined, and of the plurality of first combinations, one or more second combinations with a difference between the corresponding first and second blocks are shown, any one of the plurality of first blocks included in the second combinations is received as a first designated block, the second block corresponding to the first designated block is received as a second designated block, and of the first combinations, a third combination in which presence or absence of establishment of textual entailment between the first designated block and the first block is different from presence or absence of establishment of textual entailment between the second designated block and the second block is shown.