Thesis Map Creation Device for Visualizing Research Correlations
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Solution Overview
Problem
Current methods for examining and clarifying correlations between prior theses in research are time-consuming and lack visual perception-based tools for identifying correlations.
Innovation Solution
A thesis map creation method and device that systematically represent correlations between theses using conceptual levels, allowing for visual recognition of thesis positions and relationships through a base map with progressively lower conceptual levels and provisional levels, enabling the creation of an updated thesis map without reading each thesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If researchers manually examine and read prior theses to clarify correlations, then accurate understanding of thesis contents is achieved, but time consumption and operational burden increase significantly
Solution Approach 1:
The patent replaces the mechanical process of manually reading and analyzing thesis contents with an information processing system that automatically extracts feature terms, collates them against registration terms in a database, and generates visual thesis maps. This substitution eliminates the need for researchers to physically read each thesis while maintaining accurate correlation identification through automated text processing and comparison algorithms.
Solution Approach 2:
The system creates visual copies (thesis maps) that represent the essential correlations and relationships between theses without requiring direct engagement with the original thesis texts. These visual representations capture the semantic relationships and conceptual hierarchies, allowing researchers to understand correlations through mapped positions and connections rather than reading full documents.
2Measurement precision
If researchers manually read prior theses to ascertain correlations, then comprehensive understanding is achieved, but operational complexity and burden increase
Solution Approach 1:
The complex mental operations required for manual thesis correlation analysis are replaced by an automated information processing system. The system performs feature term extraction, database collation, and correlation determination automatically, transforming a complex cognitive task into a simple visual inspection of generated thesis maps.
Solution Approach 2:
The system generates disposable visual representations (thesis maps) that capture correlation information in an easily consumable format. These visual maps serve as temporary but sufficient representations for understanding correlations, eliminating the need for sustained engagement with complex original texts.
3Ease of operation
If no visual perception-based means is used for ascertaining correlations, then automated processing is simple, but ability to visually recognize thesis relationships is lacking
Solution Approach 1:
The system introduces visual thesis maps as an intermediary representation between the raw thesis data and human understanding. These maps serve as a mediator that translates complex textual relationships into visual spatial arrangements, allowing researchers to perceive correlations through visual patterns, positions, and connections without directly analyzing the underlying complex data structures.
Solution Approach 2:
The system transforms one-dimensional textual thesis information into two-dimensional visual representations. By mapping thesis features onto spatial dimensions in visual maps, the system enables researchers to perceive correlations through spatial relationships, distances, and positions, adding a visual dimension to otherwise abstract textual data.
Data Source
AI summary
A thesis map creation method easily obtains a thesis map to ascertain a correlation of theses through visual perception, and a thesis map creation device implements the method. A storage unit and a processing unit are prepared. The storage unit stores a plurality of conceptual levels, and registration terms or the like that belong to the plurality of conceptual levels. The processing unit sequentially executes, by each element thereof, processing of (a) extracting a feature term from an imported thesis and determining to which of the plurality of conceptual levels the feature term belongs, and (b) incorporating a node related to the thesis into a region of a lowest conceptual level among conceptual levels to which the feature term belongs.