Cloud File Organization via Feature-Based Clustering
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Solution Overview
Problem
Existing file organization systems on cloud platforms are limited in their ability to automatically sort diverse types of files without explicit metadata, failing to effectively cluster and organize files based on various features such as content, keywords, creation time, and user sharing information.
Innovation Solution
A system and method that utilize a processor to identify and cluster files on a cloud file system based on assessed similarity scores across multiple features like file content, keywords, creation time, type, sharing users, folder depth, and origin, allowing for automatic organization and recommendation of folder destinations for new files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed templates and explicit metadata are used for file organization, then organization accuracy is improved, but the system can only handle certain types of content and requires explicit metadata tags
Solution Approach 1:
The system changes from using fixed metadata parameters to using learned feature representations. The machine learning model automatically extracts and compares multiple features (file content, keywords, creation time, modification time, file type, sharing users, folder depth, origination) to determine similarity, allowing accurate organization of diverse file types without requiring explicit metadata tags for each file type.
2Ease of operation
If manual file organization is performed, then files can be organized according to user preferences, but user effort and time consumption increase
Solution Approach 1:
The system enables self-service file organization by automatically analyzing file features and clustering files into groups based on similarity scores. The machine learning model performs the organization task autonomously without requiring user intervention, while still allowing users to review and adjust the automated organization results if needed.
3Adaptability or versatility
If automated clustering based on multiple features is implemented, then file organization versatility is improved, but system complexity increases
Solution Approach 1:
The system implements a universal file organization approach by using a machine learning model that can handle multiple file types through a single clustering framework. The model processes various features (content, keywords, timestamps, sharing information, folder structure) in a unified manner, making the system adaptable to diverse file types without requiring separate organization mechanisms for each file type.
Data Source
AI summary
A method for organizing a plurality of files to be stored on a cloud file system includes receiving user data provided by a user and pertaining to an organization of a plurality of files of the user into folders on the cloud file system, identifying a first file of the plurality of files, determining that the first file is a first email attachment to a first email received by the user, automatically storing the first email attachment in a first folder, identifying a second file of the plurality of files, determining that the second file is a second email attachment to a second email received by the user, determining that the folders associated with the user account do not include a folder for the second email attachment, and causing a new folder associated to be created on the cloud file system as a destination folder for the second email attachment.