Folder Destination Suggestion via Similarity Metrics
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Solution Overview
Problem
Current file system management lacks an efficient method to accurately suggest destination folders for saving files, leading to reduced productivity and increased navigation efforts for users.
Innovation Solution
A computer-implemented method that determines a starting folder, computes similarity metrics for child folders, selects the two most similar folders, compares their similarity metrics to a threshold, and conditionally recommends the starting folder as the saving destination based on the comparison, using language models for files and folders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual folder selection is used for saving files, then users have full control over destination choice, but user productivity decreases due to increased navigation efforts
Solution Approach 1:
The system automatically analyzes the file's content, metadata, and folder characteristics to self-determine the most appropriate destination folder, eliminating the need for manual user navigation and selection while maintaining intelligent decision-making
Solution Approach 2:
The patent replaces the manual mechanical navigation process with an automated computational system that uses language models, similarity metrics, and threshold comparisons to automatically determine folder destinations, substituting human effort with algorithmic processing
2Measurement precision
If simple folder suggestion methods are used, then system complexity is reduced, but suggestion accuracy decreases
Solution Approach 1:
The patent segments the folder suggestion process into distinct computational stages: generating language models for files and folders, computing similarity metrics between them, comparing metrics against thresholds, and making conditional recommendations. This segmentation enables complex accurate suggestions while managing system complexity through modular processing
Solution Approach 2:
The system changes parameters by computing similarity metrics between language models of files and folders, using these metrics as quantitative measures to determine destination accuracy. The introduction of similarity metrics as a parameter enables precise measurement of folder-file compatibility without requiring overly complex system architecture
3Measurement precision
If comprehensive folder analysis is performed for all child folders, then suggestion relevance is improved, but computing resource consumption increases
Solution Approach 1:
The patent applies partial action by computing similarity metrics for all child folders but then filtering results through threshold comparisons to identify only the most relevant matches. This approach performs comprehensive analysis where needed while avoiding unnecessary processing of clearly unsuitable folders, optimizing computing resource usage while maintaining suggestion relevance
Solution Approach 2:
The system applies local quality by focusing computational resources on computing and comparing similarity metrics for folders that are locally relevant to the file being saved. By using threshold-based filtering, the system concentrates computing effort on promising candidates rather than uniformly processing all folders in the file system
Data Source
AI summary
A computer-implemented method according to one embodiment includes determining a starting folder within a file system, computing, for each child folder of the starting folder, a similarity metric indicating a level of similarity to a file, selecting two child folders of the starting folder having greatest similarity metrics, comparing a difference between the greatest similarity metrics of the two child folders to a predetermined threshold, and conditionally selecting the starting folder as a recommended folder to which the file is saved, based on the comparing.