Content Collection Seeding Using Textual and Visual Topic Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital content systems inaccurately group content items based on access patterns, leading to inefficient user interactions and resource consumption due to misplaced content items and duplicative storage.
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 using machine learning models.
Engineering Contradictions & Design Principles
Engineering 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 misplaced content items
Solution Approach 1:
The patent transitions from using access pattern parameters (time-based metrics) to content-based parameters (textual and visual features). By changing the fundamental parameters used for grouping from temporal access data to intrinsic content characteristics, the system achieves both automation and high accuracy in content organization.
Solution Approach 2:
The patent replaces the mechanical/system-based access pattern analysis with content-based analysis using machine learning models. Instead of relying on systematic tracking of user interactions, the system uses AI-driven analysis of content features to determine grouping, thereby substituting a less accurate mechanical approach with a more intelligent content-aware approach.
2Extent of automation
If content items are inaccurately grouped, then automated organization is maintained, but user interactions increase to relocate misplaced items
Solution Approach 1:
The system performs self-service by automatically analyzing content features and generating accurate groupings without requiring user intervention. The machine learning models autonomously determine appropriate content collections and organize items accordingly, eliminating the need for users to manually relocate misplaced items and reducing interaction requirements.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with suggested content collections are analyzed to refine and improve future grouping accuracy. This feedback loop enables the system to learn from user behavior and continuously enhance its automated organization capabilities, further reducing the need for manual corrections.
3Measurement precision
If manual grouping requests are processed, then content organization accuracy can be improved, but computing resources are consumed
Solution Approach 1:
The system performs preliminary action by proactively analyzing content features and generating suggested groupings before users request them. Machine learning models pre-process content items and prepare organization recommendations in advance, so when users do interact with the system, accurate groupings are already available, eliminating the need for resource-intensive manual processing requests.
Solution Approach 2:
The patent substitutes resource-intensive manual grouping processing with efficient machine learning-based automated analysis. Instead of consuming computing resources to process manual user requests for grouping, the system uses trained models to rapidly analyze content features and generate accurate groupings on-demand, significantly reducing computational overhead.
Data Source
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.