Data Categorizing System Using Evaluation Component Extraction
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Solution Overview
Problem
Existing data categorization methods, such as Japanese Patent No. 5801611, are limited to character data and struggle to efficiently categorize diverse data types like document, image, and audio data without precise content comparison.
Innovation Solution
A data categorizing system that includes a data acquiring unit, an evaluation component extracting unit, a score value calculating unit, and a categorization determining unit to determine the type of data by calculating score values based on extracted evaluation components for all known types, allowing for categorization beyond character data into document, image, and audio data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If precise content comparison is used to categorize data, then categorization accuracy is improved, but processing complexity and time increase significantly
Solution Approach 1:
The patent extracts key evaluation components from data components to create simplified representations for categorization. Instead of comparing entire data contents, the system extracts specific features (evaluation components) that are sufficient for determining data types, thereby reducing processing complexity while maintaining categorization accuracy
Solution Approach 2:
The patent segments data into data components and further segments those into evaluation components. This hierarchical segmentation allows the system to process only relevant portions of data for categorization purposes, avoiding the need to analyze complete data contents and reducing overall processing complexity
2Adaptability or versatility
If data categorization is extended to multiple data types (document, image, audio), then system versatility is improved, but processing complexity increases
Solution Approach 1:
The patent creates a universal categorization system that handles multiple data types (character data, document data, image data, audio data) through a common framework. The same evaluation component extraction and score calculation processes are applied across all data types, allowing the system to achieve versatility without proportionally increasing processing complexity for each new data type
Data Source
AI summary
It is required to provide a method for classifying a set of data to be examined without a detailed analysis of the content of the set of data to be examined. A data classification system which solves the problem, the data classification system comprising: a data acquiring unit configured to acquire the plurality of data components of the data to be examined; an evaluation component extracting unit configured to extract a plurality of predetermined evaluation components from among the plurality of data components; a score value calculating unit configured to calculate score values for all of the plurality of known types based on the extracted plurality of evaluation components; and a classification determining unit configured to determine that the data to be examined belongs to a type with the highest value among the score values calculated by the score value calculating unit for all of the plurality of known types.