Adaptive File Tagging via Common Feature Extraction
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Solution Overview
Problem
Existing file management systems lack an effective method for appropriately classifying data files, as they do not adequately utilize common features across files with the same tags to provide accurate and adaptive tagging solutions.
Innovation Solution
A file management device and method that extracts common features from data files with the same tags, stores these features and tags as provision rules, and updates these rules based on user interactions, allowing for the automatic and adaptive tagging of newly input data files by selecting and proposing tags based on feature matching and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging methods are used, then tagging accuracy can be controlled by user judgment, but productivity and time efficiency deteriorate
Solution Approach 1:
The system automatically extracts features from data files and generates tags without requiring manual user intervention for each tagging decision. The feature extraction unit analyzes file contents and the tag generation unit creates appropriate tags autonomously, allowing the system to serve itself rather than requiring continuous manual operation.
Solution Approach 2:
The system allows users to correct automatically generated tags, and these corrections are fed back into the learning mechanism. The learning unit stores user corrections and uses them to improve future automatic tag generation, creating a feedback loop that enhances accuracy over time while maintaining high productivity.
2Productivity
If automatic tagging without feature extraction is used, then productivity is improved, but tagging precision and adaptability deteriorate
Solution Approach 1:
The system performs preliminary feature extraction from data files before generating tags. The feature extraction unit analyzes file contents, metadata, and other characteristics in advance, preparing structured feature data that the tag generation unit then uses to create accurate tags. This preliminary analysis enables both speed and precision.
Solution Approach 2:
The system extracts multiple different features from data files (such as file name, size, creation date, content keywords, etc.) and transforms these diverse parameters into a unified feature representation that can be systematically processed to generate accurate tags. This parameter transformation enables precise tagging while maintaining automation.
3Ease of operation
If rigid tagging rules are used, then ease of operation is improved, but adaptability to different data types deteriorates
Solution Approach 1:
The tagging system dynamically adapts to different data types and contents through automated feature extraction. Rather than requiring rigid predefined rules for every scenario, the system extracts relevant features from the actual data and generates appropriate tags dynamically, making it flexible while remaining easy to operate through automation.
Solution Approach 2:
The feature extraction unit and tag generation unit work universally across different data types (documents, images, videos, etc.). The same automated pipeline can extract features from various data formats and generate appropriate tags, making the system multi-functional and adaptable without requiring separate rigid rules for each data type.
4Device complexity
If no rule updating mechanism is used, then device complexity is reduced, but reliability of tagging deteriorates over time
Solution Approach 1:
The system incorporates a learning unit that receives feedback from user corrections and stored data files. This feedback mechanism continuously improves the tagging reliability by learning from actual usage patterns and corrections, ensuring consistent and accurate tagging over time without requiring complex manual rule maintenance.
Solution Approach 2:
The system automatically updates its tagging rules and improves its performance autonomously through the learning unit. Rather than requiring manual rule updates and maintenance, the system serves itself by automatically learning from data and user interactions, improving reliability while keeping the operational complexity manageable.
Data Source
AI summary
A file management device comprising a memory; and a processor coupled to the memory and the processor configured to: a common feature extracting unit that extracts a feature common to a plurality of data files to which a same tag is provided from the data files; a tagging rule storage DB that stores a feature extracted by the common feature extracting unit and the tag provided to the data files in association with each other as a tagging rule; and a tag providing unit that provides a tag to a newly input data file based on the tagging rule stored in the tagging rule storage DB.