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 excessive user interactions, leading to unorganized content accumulation and resource consumption, particularly on mobile devices.

Innovation Solution

A content item classification system utilizing machine-learning models automatically classifies and organizes digital content items within a file structure by generating and utilizing classification models to determine appropriate destination folders, providing interactive user interfaces for confirmation, and adapting to system-generated or user-defined folder structures.

Engineering Contradictions & Design Principles

VSEngineering 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 excessive user interactions and navigation steps, consuming excessive computing resources and time

Engineering Contradiction:

Improveease of organizing documentsVSAvoidtime for navigation and organization

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system enables self-service organization by automatically classifying digital documents into appropriate folders using machine learning models. The classification system analyzes document content, metadata, and user behavior patterns to autonomously determine the most suitable destination folders, eliminating the need for manual user intervention in the organization process.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary classification and organization actions before users need to access or manage their documents. By pre-organizing incoming documents into appropriate folders based on automated classification, the system prepares the document structure in advance, so users find documents already organized when they need them.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If automated classification is implemented, then organization accuracy improves, but system complexity and computing resource requirements increase

Engineering Contradiction:

Improveclassification accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The classification system is segmented into multiple independent machine learning models, each specialized for different types of documents or classification tasks. This modular architecture allows the system to achieve high classification accuracy through specialized models while managing complexity by dividing the overall classification problem into smaller, more manageable segments that can be developed, trained, and maintained independently.

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If automated classification is implemented, then organization accuracy improves, but computing resource consumption increases

Engineering Contradiction:

Improveclassification accuracyVSAvoidcomputing resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies partial classification action by using different levels of classification detail for different documents based on their importance, user preferences, and resource availability. For some documents, full automated classification is applied, while for others, simplified or deferred classification is used, reducing overall computing resource consumption while maintaining adequate accuracy for all documents.

Inventive Principle:

Principle #16Partial or excessive action

4Adaptability or versatility

If manual organization is required for each document, then flexibility in organization is maintained, but productivity and efficiency decrease

Engineering Contradiction:

Improveflexibility in organizationVSAvoidorganization efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system incorporates feedback mechanisms where user corrections and manual organization actions are continuously monitored and used to retrain and improve the automated classification models. This feedback loop allows the system to learn from user behavior and preferences, maintaining flexibility and adaptability in organization while progressively improving automated classification accuracy, thereby increasing overall productivity.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12511255B2Classifying and organizing digital content items automatically utilizing content item classification models

Publication Date: 2025.12.30 DROPBOX INC

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.