File Save Location Determination via Content Tagging and Correlation
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Solution Overview
Problem
Existing file saving methods on computers lack efficiency in determining the most relevant location for saving new files, leading to user inconvenience and increased time in selecting appropriate folders.
Innovation Solution
An apparatus and method that utilize a processor, memory, and display to acquire metadata, assign content tags, calculate location correlations with a file system database, and present a ranked display of folders, allowing users to efficiently select and save files to the most relevant folder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional file saving methods are used where users manually select folders, then users have full control over file placement, but the process is time-consuming and inefficient
Solution Approach 1:
The system automatically analyzes file metadata and content tags to determine the most appropriate save location without requiring user intervention. The processor calculates location correlations and presents ranked folder suggestions, allowing the system to serve itself in determining save locations rather than relying on manual user selection.
Solution Approach 2:
The system pre-calculates and stores location correlation data in a database before the actual file saving operation. By analyzing file metadata and existing folder structures in advance, the system prepares ranked folder suggestions that are immediately available when a user needs to save a file, eliminating the need for time-consuming manual browsing.
2Ease of operation
If automated folder suggestion systems are implemented, then file saving efficiency improves, but system complexity increases
Solution Approach 1:
The system divides the complex task of determining save locations into separate functional modules: metadata acquisition, content tagging, location correlation calculation, and ranked display presentation. Each module handles a specific aspect of the problem, making the overall system more manageable and maintainable despite the increased functionality.
Solution Approach 2:
The patent introduces a file system database as an intermediary layer that stores pre-calculated location correlation data. This database acts as a mediator between the file analysis components and the display components, simplifying the architecture by centralizing the complex correlation calculations in a searchable database structure.
3Measurement precision
If comprehensive metadata analysis is performed to determine save locations, then accuracy of folder suggestions improves, but processing time and computational resources increase
Solution Approach 1:
The system performs content tagging and metadata analysis selectively based on file characteristics and user preferences. Rather than analyzing every single file attribute equally, the system focuses on the most relevant metadata fields and content features for determining save locations, achieving good accuracy with reduced computational overhead.
Solution Approach 2:
The system pre-calculates and stores location correlation data in a database during file system operations. By performing the computationally intensive analysis in advance and caching the results, the system avoids repeated heavy processing when users need to save files, reducing real-time computational resource consumption while maintaining high accuracy.
Data Source
AI summary
For determining a relevant file save location, a processor acquires metadata for a new file. The processor further assigns content tags for the new file based on file content and the metadata. The processor calculates a location correlation to folders of a file system using a file system database. The processor further presents a ranked display of the folders based on the location correlation on a display. The processor moves the new file to a selected folder.