Content Collection Seeding with Germane Topic and Visual Signal Grouping

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Solution Overview

Problem

Existing digital content systems inaccurately group content items based on access patterns, leading to inefficient user interactions and wastage of computing resources due to excessive user interactions required to relocate misplaced content items.

Innovation Solution

A collection seeding system that utilizes content-based features such as textual signals and visual signals to select a seed content item, determine germane topics, and cluster additional content items, generating more accurate and efficient suggested content collections.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If content items are grouped based on access patterns, then the system can provide automated content organization, but the grouping accuracy deteriorates leading to unrelated content items being grouped together

Engineering Contradiction:

Improveautomated content organizationVSAvoidgrouping accuracy

Core Design Contradiction:

Extent of automationVSMeasurement precision

Solution Approach 1:

The system transitions from using only access pattern parameters to incorporating content-based parameters (textual signals, visual signals, object classifications, topic models) to fundamentally change the grouping criteria and improve accuracy

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system introduces content analysis intermediaries (topic models, object classifiers, visual signal processors) that mediate between raw access patterns and final content groupings to enhance grouping precision

Inventive Principle:

Principle #24Intermediary (Mediator)

2Extent of automation

If content items are inaccurately grouped, then automated organization is maintained, but user interactions increase due to excessive relocations needed

Engineering Contradiction:

Improvecontent grouping automationVSAvoiduser interaction time

Core Design Contradiction:

Extent of automationVSLoss of time

Solution Approach 1:

The system enables content items to self-organize into accurate groups through content-based analysis, reducing the need for user intervention and relocation operations

Inventive Principle:

Principle #25Self-service

3Measurement precision

If manual grouping operations are required to fix inaccurate groupings, then grouping accuracy can be improved, but computing resources are consumed processing excessive user interactions

Engineering Contradiction:

Improvegrouping accuracyVSAvoidcomputing resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary content analysis (textual signal processing, visual signal processing, topic modeling) to pre-establish accurate groupings before users interact, preventing the need for subsequent relocation operations that consume computing resources

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12524471B2Seeding and generating suggested content collections

Publication Date: 2026.01.13 DROPBOX INC

AI summary

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and suggesting content collections for user accounts of a content management system using combinations of content-based features such as textual signals and visual signals. In some embodiments, the disclosed systems select a seed content item from among a plurality of content items associated with a user account within a content management system. From the seed content item, the disclosed systems can determine one or more germane topics and can cluster additional content items in relation to the germane topic(s). In addition, the disclosed systems can select one or more content items from a content cluster to provide as a suggested content collection.