Desktop Scene Restoration with Topic-Based Content Clustering
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Solution Overview
Problem
Existing digital content systems inaccurately group content items based on rudimentary factors like file type and access time, leading to inefficient user interactions and excessive resource consumption due to misplaced content items and inefficient user interfaces.
Innovation Solution
The content scene system generates content clusters based on topic data and focus data using machine learning models to group items by theme and activity patterns, allowing for single-click restoration of application sessions and efficient generation of content collections, while storing and restoring desktop and video call scene layouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems use rudimentary clustering by file type and access time, then the system complexity is low, but the clustering accuracy is poor and content items are misplaced
Solution Approach 1:
The patent transforms the clustering approach from using simple parameters (file type, access time) to using enriched parameters including topic data extracted via NLP, focus data from user activity patterns, and session-based temporal information. This parameter enrichment enables accurate topic-based clustering while maintaining system manageability through modular implementation.
Solution Approach 2:
The patent introduces topic data and focus data as intermediary elements that mediate between raw content items and clustering results. Topic data serves as a semantic intermediary that bridges content items with their thematic categories, while focus data acts as an behavioral intermediary that captures user engagement patterns, together enabling accurate clustering without excessive system complexity.
2Productivity
If existing systems generate content collections from inaccurate clusters, then the system operation is simple, but excessive user interactions are required to relocate misplaced content items
Solution Approach 1:
The patent performs preliminary clustering of content items into topic-based groups before generating content collections. By pre-organizing content items according to their semantic topics and user focus patterns, the system eliminates the need for subsequent user-driven relocation operations, thereby improving productivity and reducing time loss.
Solution Approach 2:
The patent incorporates focus data derived from user activity patterns as feedback to refine clustering accuracy. By continuously monitoring how users interact with content items and using this feedback to adjust clustering parameters, the system dynamically improves content organization, reducing the need for manual relocation and enhancing user interaction efficiency.
3Device complexity
If existing systems require accessing content items individually through separate device interactions, then the interface simplicity is maintained, but the processing of excessive user interactions consumes computing resources
Solution Approach 1:
The patent merges multiple individual content item access operations into a single batch operation through content collections. Instead of requiring separate device interactions for each content item, the system combines related content items into themed collections that can be accessed and manipulated as unified groups, thereby reducing interface complexity and computing resource consumption.
Solution Approach 2:
The patent creates content collections that serve multiple functions simultaneously: they organize content by topic, enable batch access operations, provide contextual relationships between items, and reduce the number of required user interactions. This multi-functionality approach maintains interface simplicity while significantly reducing computing resource consumption.
4Loss of information
If existing systems group content items without considering contextual factors, then the clustering process is fast, but the generated content collections provide no insight into themes or user activity patterns
Solution Approach 1:
The patent performs preliminary extraction of topic data and focus data from content items and user activity logs before the clustering process. By pre-processing and storing these contextual attributes, the system enables fast clustering operations that incorporate rich thematic and behavioral information without incurring excessive processing time during actual clustering operations.
Solution Approach 2:
The patent replaces traditional mechanical clustering methods with topic-based semantic clustering using NLP techniques. Instead of relying on simple rule-based grouping, the system uses natural language processing to extract meaningful topics and themes from content items, and uses machine learning to analyze user activity patterns, thereby preserving information while maintaining efficient processing.
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.