Content Clustering from Topic and Focus Data for 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 rudimentary clustering that fails to reflect user activity patterns.
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 interfaces for restoring application sessions and creating content collections with minimal user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing systems group content items by file type and access time, then the clustering process is simple and fast, but the accuracy of content grouping deteriorates significantly
Solution Approach 1:
The system changes the clustering parameters from simple metadata (file type, access time) to enriched features including NLP-derived topic representations, user behavior patterns, and contextual metadata. This parameter transformation enables accurate semantic clustering while maintaining computational efficiency through optimized feature extraction and embedding techniques.
Solution Approach 2:
The patent replaces mechanical sorting methods (simple file system sorting by name or date) with intelligent information processing systems that use machine learning models, NLP algorithms, and automated topic modeling to achieve accurate content grouping without manual intervention.
2Device complexity
If existing systems use rudimentary clustering methods, then the system complexity is low, but the user interaction efficiency deteriorates due to misplaced content items
Solution Approach 1:
The system performs self-service by automatically analyzing user behavior patterns, inferring content relationships, and organizing content collections without requiring user input. The system monitors user interactions, learns preferences, and autonomously creates accurate groupings, eliminating the need for manual content sorting and reducing user interaction overhead.
Solution Approach 2:
The system performs preliminary actions by pre-processing content items to extract features, generate topic representations, and establish relationships before user interaction is needed. This preparation enables fast and accurate content retrieval and grouping during actual user operations, significantly improving interaction efficiency.
3Measurement precision
If existing systems generate content collections individually, then each collection can be precise, but the overall process time and resource consumption increase significantly
Solution Approach 1:
The patent merges multiple content collection generation tasks into a single unified process that simultaneously creates multiple themed collections from the same input data. By combining collection generation with content analysis, topic modeling, and relationship extraction in one operation, the system achieves both high precision and reduced processing time.
Solution Approach 2:
The system implements a universal content analysis engine that serves multiple functions: topic extraction, relationship detection, collection generation, and recommendation creation. This multi-functional approach eliminates redundant processing steps and enables efficient generation of multiple precise collections from a single analysis pass.
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.