File Categorization via Key Characteristic Extraction
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Solution Overview
Problem
Existing document categorization systems are limited by their reliance on pre-defined samples and require costly and ambiguous mathematical comparisons, failing to accurately categorize and identify various types of documents effectively.
Innovation Solution
A hybrid method that uses a recognition sample with key characteristics and instructions to compare files heuristically, forming clusters based on appearance or content, eliminating the need for explicit training and allowing for multiple cluster affiliations without prior knowledge of cluster numbers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mathematical pairwise comparison is used to categorize documents, then categorization accuracy can be improved, but computational cost and complexity increase significantly
Solution Approach 1:
The patent segments the document comparison process into distinct phases: first extracting key characteristics from documents, then comparing these extracted features rather than performing full mathematical pairwise comparisons. This segmentation reduces computational complexity while maintaining categorization accuracy by focusing comparisons on essential document attributes.
Solution Approach 2:
The patent applies preliminary action by pre-extracting and organizing key characteristics from documents before categorization. The system identifies and stores essential document features in advance, so that during categorization, comparisons are made against these pre-processed characteristics rather than performing complex mathematical operations on the full document content.
2Device complexity
If pre-defined samples are used for document categorization, then system simplicity is maintained, but categorization accuracy is limited by existing knowledge
Solution Approach 1:
The patent implements self-service by enabling the system to automatically learn and extract key characteristics from documents without requiring pre-defined samples or manual categorization rules. The system serves itself by autonomously identifying document features and using these to improve categorization accuracy, eliminating the limitation of existing knowledge while maintaining system simplicity.
Solution Approach 2:
The patent applies parameter changes by transitioning from static pre-defined samples to dynamic extracted characteristics. The system changes the fundamental parameter used for categorization from fixed sample data to adaptively extracted document features, thereby improving accuracy without significantly increasing system complexity.
3Productivity
If mathematical comparison methods are used, then document grouping can be achieved, but the process becomes ambiguous and costly
Solution Approach 1:
The patent applies the extraction principle by removing the ambiguous mathematical comparison process and replacing it with direct comparison of extracted key characteristics. The system takes out only the essential document features needed for categorization, eliminating the ambiguity inherent in full mathematical comparisons while maintaining processing efficiency.
Data Source
AI summary
Example file management systems and methods are described. In one implementation, a system identifies multiple files associated with a user where the multiple files are stored on multiple file storage systems. A search request is received from the user for at least one file. The system locates at least one file based on the search request by analyzing file categorization and characterization data associated with the multiple files.