Semantic Vector Analysis for Textual Document Comparison
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Solution Overview
Problem
The exponential growth of unstructured textual data poses a challenge for businesses to efficiently extract valuable content and compare similarities or divergences between documents, requiring an automated system for natural language processing that is economically viable.
Innovation Solution
A system and method for comparative analysis of textual documents using semantic vectors, which involves linguistic analysis, semantic net creation, and vector comparison to quantify and measure semantic closeness or distance between documents, allowing for automated processing and identification of unique semantic content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human reviewers manually compare and analyze textual documents to extract valuable content and identify similarities, then the quality and accuracy of analysis is improved, but the time consumption and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical human review process with an automated computer-based system that performs linguistic analysis, semantic net creation, and vector comparison. The system uses natural language processing algorithms to analyze documents, create semantic representations, and compute similarity metrics automatically, eliminating the need for manual human analysis while maintaining analysis quality.
Solution Approach 2:
The patent introduces semantic vectors as an intermediary representation between raw text and analysis results. By converting documents into quantitative semantic vectors through linguistic analysis and semantic net creation, the system enables automated comparison and similarity measurement, bridging the gap between unstructured text and structured analysis outcomes.
2Reliability
If dedicated departments are established to perform document analysis tasks, then the thoroughness and expertise of analysis is improved, but the economic cost becomes unjustifiable
Solution Approach 1:
The patent enables documents to analyze themselves through automated processing. The system performs linguistic analysis, semantic extraction, and comparison tasks automatically without requiring human reviewers or dedicated departments. The automated system handles the entire analysis pipeline, from raw text input to similarity measurement output, making the organization self-sufficient in document analysis.
Solution Approach 2:
The patent replaces the organizational structure of dedicated analysis departments with an automated computational system. By substituting human expertise and organizational complexity with algorithmic processing, the system maintains analysis thoroughness while eliminating the need for specialized departments and their associated costs.
3Productivity
If automated natural language processing systems are implemented to process large volumes of textual data, then the productivity and efficiency are improved, but the system complexity increases
Solution Approach 1:
The patent segments the document analysis process into distinct modular components: linguistic analysis module, semantic net creation module, and vector comparison module. Each module performs a specific function and can be processed independently, allowing the system to handle large volumes of documents efficiently while managing complexity through functional decomposition.
Solution Approach 2:
The patent transforms unstructured textual data into structured semantic vectors by changing the representation parameters. By converting text into quantitative vectors with specific dimensions and properties, the system enables automated processing and comparison while simplifying the handling of complex linguistic information through parameter standardization.
Data Source
AI summary
A system and method are presented for the comparative analysis of textual documents. In an exemplary embodiment of the present invention the method includes accessing two or more documents, performing a linguistic analysis on each document, outputting a quantified representation of a semantic content of each document, and comparing the quantified representations using a defined metric. In exemplary embodiments of the present invention such a metric can measure relative semantic closeness or distance of two documents. In exemplary embodiments of the present invention the semantic content of a document can be expressed as a semantic vector. The format of a semantic vector is flexible, and in exemplary embodiments of the present invention it and any metric used to operate on it can be adapted and optimized to the type and/or domain of documents being analyzed and the goals of the comparison.