Semantic Analysis Engine for Dynamic Text Corpora
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Solution Overview
Problem
Existing semantic analysis methods fail to dynamically track changes in textual corpora over time, lacking the ability to adapt to evolving information and scope of interest, which limits their effectiveness in identifying and assessing potential threats and risks.
Innovation Solution
A method involving the acquisition of multiple text corpora at subsequent points in time, application of a probabilistic concept model to generate concept vector sets, and similarity remapping to minimize concept distance between vectors, enabling dynamic semantic trend analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If prior semantic analysis methods are used, then basic classification can be achieved, but dynamic tracking of changing text corpora over time cannot be performed
Solution Approach 1:
The patent applies dynamics by implementing temporal concept vector mapping that tracks concept evolution across multiple time points. The system dynamically remaps concept vectors from different time periods to identify changing semantic patterns, enabling the analysis to adapt to evolving text corpora while maintaining reliable threat detection through systematic comparison of temporal changes
2Adaptability or versatility
If concept vector sets are generated for multiple text corpora at different time points, then dynamic semantic analysis capability is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and focuses only on the essential temporal evolution patterns by remapping concept vectors to identify significant semantic changes. Rather than processing all possible comparisons between time points, the system extracts key trend information through targeted similarity measurements, reducing computational complexity while maintaining dynamic analysis capability
3Measurement precision
If similarity remapping is applied to minimize concept distance, then semantic trend detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by performing similarity remapping selectively on concept vectors that show significant changes or are most relevant to threat detection. Rather than exhaustively remapping all concept vectors with equal intensity, the system focuses computational effort on the most informative comparisons, achieving adequate precision while reducing overall processing time
Data Source
AI summary
Methods for analyzing text corpora for inferring potential threats and risks are becoming ever more established. While the present achievements are based on an analyst-driven analytical process, the embodiments provide for a semantic analysis of dynamic developments in changing text corpora, involving an acquisition of text corpora, application of a probabilistic concept model, and providing a similarity remapping.