ML Content Classification for Automatic Folder Organization
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Solution Overview
Problem
Existing document hosting systems face inefficiencies and inflexibilities in organizing digital documents, requiring cumbersome navigation and multiple user interactions, leading to unorganized content accumulation and excessive computing resource consumption, particularly on mobile devices.
Innovation Solution
A content item classification system utilizing machine-learning models to automatically classify and organize digital content items within a file structure, providing interactive user interfaces for content item move suggestions and dynamically generating or locating destination folders based on classification confidence values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual organization methods are used in existing document hosting systems, then users can organize digital documents into folders, but the process requires cumbersome navigation and multiple user interactions, consuming excessive computing resources and time
Solution Approach 1:
The system enables self-service by implementing automated classification algorithms that independently organize digital documents into appropriate folders without requiring user navigation or manual categorization. The classification model automatically analyzes document content, determines suitable destination folders, and completes the organization process autonomously, eliminating the need for users to manually navigate through folder hierarchies or make repeated interactions.
Solution Approach 2:
The patent replaces the mechanical interaction system (manual clicking, dragging, and folder navigation) with an intelligent automated system. Instead of requiring users to physically navigate through graphical interfaces and manually move documents, the system uses machine learning models to detect document categories, determine appropriate destinations, and execute the organization process automatically, substituting human mechanical actions with computational intelligence.
2Measurement precision
If automated classification is implemented, then document organization accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary classification model that acts as a bridge between raw document content and folder organization. This intermediate layer analyzes document content, extracts features, determines document categories, and maps them to appropriate destination folders. By inserting this intelligent intermediary, the system achieves high classification accuracy without requiring complex direct manipulation of document structures, as the model handles the complexity internally while presenting simple results to the user.
3Adaptability or versatility
If existing manual organization systems are used, then users have control over document placement, but the systems require excessive user inputs and navigational steps, particularly on mobile devices with limited screen space
Solution Approach 1:
The system performs self-service by automatically detecting document categories and selecting appropriate destination folders without requiring user inputs or navigational steps. The classification model independently completes the entire organization process, from analyzing document content to placing files in correct folders, thereby maintaining adaptability while eliminating the operational burden on users, especially on mobile devices with limited screen real estate.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine-learning models to classify content items and automatically organize the content items within a file structure according to their content item classifications. For instance, a content item classification system generates one or more content item classification models to determine classifications for content items and/or folders. In some instances, the classification system detects when new content items are added to a smart folder, determines destination folders to which the content items belong based on classifying the content items, and automatically moves the content items accordingly. In various instances, the classification system generates and utilizes a classification model to organize content items into dynamically-generated folders. In example implementations, the classification system generates and utilizes a classification model to automatically organize existing content items into existing folders.