Concept Coverage Radar Chart Visualization
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Solution Overview
Problem
Conventional semantic analysis tools struggle to effectively identify and summarize key concepts in large bodies of documents, often relying on term frequency ranking and failing to provide comprehensive visual representations of concept domains and coverage across multiple content sources.
Innovation Solution
A system and method that utilize a meaning taxonomy and syntactic structures to analyze content from various sources, calculating statistical metrics and generating visual representations like radar charts to summarize concept coverage, enabling users to understand concept domains and depth of coverage across different sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional semantic analysis tools use term frequency ranking to identify key concepts, then the processing is simple and fast, but the tools fail to provide comprehensive visual representations of concept domains and coverage
Solution Approach 1:
The patent transforms conventional term frequency analysis into a multi-dimensional visual representation system. Instead of merely ranking terms by frequency, the system creates radar charts with multiple axes representing different concept domains, enabling comprehensive visualization of concept coverage across multiple dimensions simultaneously.
Solution Approach 2:
The patent introduces visual representation components (radar charts, graphical interfaces) as intermediaries between the semantic analysis process and the user. These visual intermediaries translate complex concept coverage data into intuitive graphical displays, making comprehensive concept information accessible without increasing system complexity.
2Reliability
If the system analyzes content from multiple sources to provide comparative analysis, then the comprehensiveness of concept coverage improves, but the complexity of processing and presenting the data increases
Solution Approach 1:
The patent merges content from multiple sources into a unified visual representation framework. The radar chart system combines concept coverage data from different sources across multiple axes, allowing comparative analysis while maintaining ease of interpretation through consistent graphical visualization.
Solution Approach 2:
The patent adds visual dimensionality to multi-source concept analysis. By representing concept coverage from multiple sources as radial axes in radar charts, the system enables intuitive comparison across sources and dimensions simultaneously, maintaining user comprehension ease despite increased analytical comprehensiveness.
3Measurement precision
If conventional tools focus on term frequency ranking, then the processing is straightforward, but they fail to discern concept domains and coverage levels effectively
Solution Approach 1:
The patent segments the concept space into distinct domains represented by separate axes in the radar chart visualization. Each axis corresponds to a specific concept domain, allowing precise identification and measurement of concept coverage within each domain while maintaining overall system manageability.
Solution Approach 2:
The patent transitions from one-dimensional term frequency ranking to multi-dimensional concept domain visualization. The radar chart structure creates multiple measurement dimensions (axes) that enable precise concept domain identification and coverage level assessment without proportionally increasing analysis complexity.
Data Source
AI summary
A method includes storing a meaning taxonomy including meaning loaded entities and associations between the meaning loaded entities and a plurality of syntactic structures; receiving, from a first source, first content including one or more first syntactic structures; identifying, based at least in part on identification criteria, one or more meaning loaded entities that are linked to the one or more first syntactic structures by one or more first associations of the plurality of associations, the identification criteria including at least one of a type of the first content, a document size of the first content, a publication date of the first content, and an importance of the meaning load entities; calculating a first content summary indicating a level of coverage of the meaning loaded entities within the first content; and generating a first visual representation of the first content summary.