Automated Mobile App Classification via Behavioral Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The manual classification of mobile applications is time-consuming, prone to errors, and cannot effectively categorize applications without pre-defined classifications, making it impractical for large numbers of applications.
Innovation Solution
An automated classification system using a guided classification and machine learning techniques that analyzes app behaviors and features to categorize mobile applications into predefined categories like business, productivity, games, and social apps, without requiring human intervention, and generates reports suggesting alternative approved apps if an app is not approved based on enterprise policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of mobile applications is performed, then classification accuracy can be maintained through human judgment, but the process becomes time-consuming and impractical for large numbers of applications
Solution Approach 1:
The patent replaces the manual mechanical classification process with an automated computer-based system that uses machine learning algorithms and behavioral analysis to classify applications. The system automatically monitors app behaviors, extracts features, and determines classifications without human intervention, thereby eliminating time loss while maintaining accuracy through algorithmic precision.
Solution Approach 2:
The classification system performs self-service by automatically analyzing application behaviors, extracting relevant features, and determining classifications independently. The system uses its own built-in algorithms and machine learning models to categorize applications without requiring external human reviewers, thus resolving the contradiction between accuracy and time efficiency.
2Productivity
If automated classification systems are implemented, then processing speed and scalability improve, but the system requires pre-defined classifications and may struggle with applications outside existing categories
Solution Approach 1:
The patent implements dynamic classification categories that can evolve and adapt over time. The system uses machine learning to identify new behavioral patterns and automatically creates or modifies classification categories to accommodate emerging application types. This dynamic approach allows the system to maintain high productivity while gaining flexibility to handle novel app categories without manual reconfiguration.
Solution Approach 2:
The system performs preliminary action by pre-defining classification categories and behavioral patterns before encountering new applications. This preparation enables rapid automated classification of known app types while the system's learning capabilities allow it to adapt to new categories, balancing throughput with adaptability.
3Measurement precision
If comprehensive behavioral analysis is performed on applications, then classification accuracy improves, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the comprehensive behavioral analysis into distinct modular components: behavior monitoring modules that collect raw data, feature extraction modules that identify relevant characteristics, and classification modules that determine categories. This segmentation maintains high classification accuracy through thorough analysis while managing system complexity through modular architecture, where each component has a specific function and can be independently optimized.
Data Source
AI summary
Automated classification of applications (“apps”) for mobile devices is provided. In some embodiments, automated classification of apps for mobile devices includes receiving an application (“app”); performing an analysis of the app using a classification engine; and determining an app category for the app based on the analysis performed using the classification engine.