Digital Content Clustering From Contextual Web Activity
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Solution Overview
Problem
Existing digital content systems inaccurately group content items based on simplistic factors like file type and access time, leading to inefficient user interactions and excessive resource consumption due to misplaced content items and rudimentary clustering that fails to consider contextual factors.
Innovation Solution
The content scene system generates content clusters based on topic-specific activity patterns using machine learning models, allowing for accurate grouping and efficient restoration of application sessions through a single user interaction, and provides interfaces for creating and storing desktop scene layouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If content items are grouped based on simplistic factors like file type and access time, then the system implementation is simple, but the clustering accuracy deteriorates
Solution Approach 1:
The patent changes the parameters used for clustering from simple file type and access time to multiple contextual factors including content metadata, user interaction patterns, and semantic relationships. This transforms the clustering approach from simplistic to sophisticated, improving accuracy while managing complexity through systematic parameter selection.
Solution Approach 2:
The patent introduces topic models and semantic analysis as intermediary layers between raw content items and clustering results. These intermediaries process and transform content characteristics into meaningful clusters, resolving the contradiction by adding analytical depth without directly increasing operational complexity.
2Productivity
If content items are inaccurately grouped, then the system operation is fast, but the number of user interactions required to relocate misplaced items increases
Solution Approach 1:
The patent performs preliminary accurate clustering using contextual factors and topic models before users need to access or organize content. By pre-grouping items accurately based on multiple dimensions including user behavior patterns, the system reduces subsequent user interactions needed to relocate items, saving time while maintaining operational efficiency.
3Device complexity
If content items are accessed individually through separate interactions, then the system implementation is simple, but the number of user interactions increases
Solution Approach 1:
The patent merges multiple content items into coherent clusters based on contextual relationships and user patterns. Instead of requiring separate interactions for each item, users can manipulate entire clusters as unified groups, dramatically reducing interaction counts while the underlying complex clustering logic remains transparent to users.
Solution Approach 2:
The patent creates cluster representations that serve multiple functions simultaneously: organizing content by topic, enabling batch operations, providing contextual navigation, and supporting various access patterns. This multi-functionality reduces the need for separate interface mechanisms while improving operational efficiency.
4Device complexity
If excessive user interactions are required for content management, then the system implementation is simple, but processing resources are consumed
Solution Approach 1:
The patent performs comprehensive clustering analysis in advance using contextual factors and user patterns, storing the results for efficient retrieval. This preliminary processing consolidates resource-intensive operations into batch executions rather than per-interaction processing, reducing overall resource consumption despite the complexity of the initial analysis.
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating content clusters from topic data and focus data, generating content collections from content clusters, storing and restoring desktop scene layouts, and storing and arranging video call scenes. In some embodiments, the disclosed systems generate content clusters based on topic data and focus data associated with content items within a content management system and/or accessed via the internet. The disclosed systems can also generate content collections for a user account of the content management system from the content clusters. In some embodiments, the content scene system can further store and restore desktop scene layouts for arranging application windows presenting content items. Further, the disclosed systems can store and arrange particular desktop scene layouts for video call scenes.