Visual Metadata Clustering for Software Application Categorization
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Solution Overview
Problem
The increasing number and variety of software applications make it challenging for users to find desired applications due to inaccurate or imprecise categorization, necessitating an improved system for recognizing and presenting applications in a coherent manner.
Innovation Solution
A dynamical system and method that identifies visual metadata from screenshots using machine learning to extract visual features and cluster software applications based on common metadata, enabling accurate classification and presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional categorization methods are used for software applications, then the system is simple to operate, but the categorization accuracy is poor making it difficult for users to find desired applications
Solution Approach 1:
The patent replaces traditional manual categorization methods with machine learning-based visual analysis. The system automatically extracts visual features from application screenshots and uses clustering algorithms to categorize applications, substituting human judgment with computational analysis to improve accuracy while maintaining ease of use.
Solution Approach 2:
The system creates visual representations (feature vectors) that copy the essential visual characteristics of application interfaces. By analyzing these copied visual features rather than raw screenshots, the system achieves accurate categorization through computational comparison of visual metadata.
2Measurement precision
If visual feature extraction is performed on all applications, then categorization accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant visual features from application screenshots rather than analyzing all visual data. By selecting key visual metadata elements that are most indicative of application category, the system achieves accurate categorization while reducing processing time and computational overhead.
Solution Approach 2:
The system performs visual feature extraction on a representative subset of applications or uses pre-extracted visual metadata for commonly encountered applications. This partial action approach provides sufficient categorization accuracy for most cases while significantly reducing overall processing time.
Data Source
AI summary
The present disclosure provides a system and method for automatic clustering and recognition of software applications using metadata. The system selects and extracts visual features from software applications which are then classified, analyzed using a cluster analysis, and then used to assign the software application to a cluster group.