Desktop Application Scene Restoration Through Semantic Content Clustering
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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.
Innovation Solution
The content scene system generates content clusters based on topic data and focus data using machine learning models, allowing for accurate grouping of content items by theme and activity patterns, and provides a user interface for restoring application sessions with a single click.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing systems use simplistic clustering factors like file type and access time, then the system complexity is reduced, but the clustering accuracy deteriorates
Solution Approach 1:
The patent changes the parameters used for clustering from simple metadata (file type, access time) to enriched parameters including semantic content analysis, user behavior patterns, and contextual information. This allows the system to maintain operational simplicity while dramatically improving clustering accuracy by transforming how content items are characterized and grouped.
Solution Approach 2:
The patent introduces an intermediary layer of content enrichment and representation that mediates between raw content items and clustering algorithms. This intermediary layer transforms simple file metadata into enriched representations that capture semantic meaning and user context, enabling accurate clustering without increasing the fundamental simplicity of the clustering mechanism itself.
2Ease of operation
If existing systems use rudimentary clustering methods, then the ease of operation is maintained, but the productivity deteriorates due to excessive user interactions required to relocate content items
Solution Approach 1:
The patent performs preliminary action by automatically enriching content items with semantic and contextual information before clustering occurs. This pre-processing enables the system to automatically organize content accurately, reducing the need for users to manually relocate items and thereby improving productivity while maintaining ease of operation through automated intelligence.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically cluster and organize content items based on enriched representations and user behavior patterns. This eliminates the need for users to manually manage content organization, as the system serves itself by intelligently grouping content, thereby dramatically improving productivity without sacrificing ease of operation.
3Device complexity
If existing systems require accessing content items individually through separate interactions, then the device complexity is reduced, but the loss of time increases due to excessive user interactions
Solution Approach 1:
The patent merges multiple content items and their access operations into unified content collections that can be accessed simultaneously. By combining individually accessed content items into organized collections based on enriched representations, the system enables batch operations that reduce the total number of separate user interactions and time loss, while maintaining reasonable device complexity through intelligent aggregation.
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.