Content Clustering for One-Click Desktop Scene Restoration
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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 resource consumption due to misplaced content items, and lack insight into cohesive user activity patterns.
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 a user interface for creating content collections and 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 transforms the clustering approach by changing the parameters used for grouping content items. Instead of using simple parameters like file type and access time, the system employs machine learning models that analyze multiple contextual factors including user interactions, content relationships, and usage patterns. This parameter transformation enables accurate clustering while maintaining system manageability through automated model-based decisions.
2Productivity
If content collections are generated based on inaccurate clustering, then the system operation is fast, but the user interaction efficiency deteriorates
Solution Approach 1:
The system performs preliminary accurate clustering using machine learning models to pre-organize content items into meaningful collections before users need to access them. This preliminary action ensures that when users interact with the system, the content is already grouped accurately according to contextual factors, eliminating the need for users to manually reorganize misplaced items and improving operational efficiency.
3Device complexity
If users must access each content item individually through separate interactions, then the system maintains simple interface structure, but the time consumption increases
Solution Approach 1:
The patent merges multiple content item access operations into a single unified interface interaction. By accurately clustering related content items together based on contextual analysis, the system allows users to access grouped content through one interaction rather than requiring separate operations for each item. This merging approach reduces time consumption while the interface maintains reasonable structural simplicity.
4Device complexity
If excessive user interactions are required to relocate misplaced content items, then the system maintains simple clustering logic, but the processing resource consumption increases
Solution Approach 1:
The system replaces manual mechanical user interactions with automated machine learning-based clustering logic. Instead of requiring users to repeatedly interact with the system to correct misplaced content, the machine learning model automatically analyzes contextual factors and organizes content items accurately from the beginning. This substitution eliminates excessive user interactions and reduces the processing resources wasted on correcting clustering errors.
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.