Smart Folder Classification for Automatic Content Routing

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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 digital 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 detecting content items in a smart folder, determining appropriate destination folders, and moving them accordingly, with interactive user interface suggestions for user confirmation.

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 system detects document metadata, content, and contextual information 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 documents as they are uploaded or stored, the system prepares the folder structure in advance, so users find documents already organized when they need them, without having spent time on navigation and sorting.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If existing systems require multiple navigation steps to organize documents, then users can locate destination folders, but the process consumes excessive computing resources particularly on mobile devices with limited screen space

Engineering Contradiction:

Improveadaptability to different devicesVSAvoidcomputing resource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system extracts the complex navigation and decision-making process from the user interface and transfers it to automated machine learning models running on the server. This removes the burden of multi-step navigation from mobile devices with limited screen space and computing resources, while maintaining full organizational capability through intelligent automation.

Inventive Principle:

Principle #2Taking out (Extraction)

3Productivity

If existing document hosting systems lack automated organization tools, then users have full control over document placement, but digital documents accumulate as unorganized content requiring manual intervention

Engineering Contradiction:

Improvedocument organization efficiencyVSAvoidsystem complexity for organization

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system replaces the mechanical interaction of manual drag-and-drop or menu-based organization with an intelligent automated classification system. Machine learning models analyze document content, metadata, and contextual information to automatically determine optimal folder placements, substituting user manual operations with intelligent automation that scales efficiently.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Reliability

If users manually organize documents through graphical user interfaces, then documents can be sorted into folders, but the process is tedious and requires numerous user inputs for each document

Engineering Contradiction:

Improveaccuracy of document organizationVSAvoidease of document reorganization

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system implements feedback mechanisms where machine learning models continuously learn from user corrections and adjustments to automated classifications. When users manually adjust document placements, the system uses this feedback to refine its classification algorithms, improving accuracy over time while maintaining the ease of automated operation for routine document organization tasks.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12517866B2Classifying and organizing digital content items automatically utilizing content item classification models

Publication Date: 2026.01.06 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.