Automated Mobile App Feature Analysis System
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Solution Overview
Problem
The mobile game industry faces challenges in selecting games for publishing due to the tedious and error-prone manual process of extracting features from games, which hinders the ability to understand what features make a game successful and which games to invest in, as conventional methods rely heavily on human intuition and manual data extraction.
Innovation Solution
A method and system for analyzing mobile apps that collect feature data from historical apps, extract and classify features, assign weights, and apply this reference data to test apps to determine their commercial potential, enabling automated and data-driven decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature extraction is used, then human operators can understand game features, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of human operators playing games and answering questions with an automated computer-based system. The system automatically extracts features by analyzing game data, metadata, and user reviews using natural language processing and machine learning algorithms, eliminating the need for manual human intervention while maintaining extraction accuracy.
Solution Approach 2:
The system enables self-service feature extraction where the computer automatically performs all extraction tasks without human operators. The automated system collects game data, processes metadata, analyzes user reviews, and generates feature sets independently, making the process efficient and scalable without relying on human time and effort.
2Reliability
If manual feature extraction is used, then features can be obtained, but human errors are introduced
Solution Approach 1:
The patent replaces human operators with an automated computer-based extraction system that eliminates human errors. The system uses consistent algorithms and automated processes to extract features, ensuring reliable and reproducible results without the variability and mistakes associated with manual human extraction.
Solution Approach 2:
The system incorporates feedback mechanisms where extracted features are validated and refined through automated processes. The system can iteratively improve extraction accuracy by analyzing results and adjusting parameters, ensuring high reliability without introducing human errors that would compromise precision.
3Productivity
If automated feature extraction is implemented, then efficiency is improved, but system complexity increases
Solution Approach 1:
The patent divides the feature extraction system into distinct modular components: data collection modules that gather game data and metadata, natural language processing modules that analyze user reviews, feature identification modules that extract relevant characteristics, and validation modules that ensure quality. This segmentation allows each component to be developed and optimized independently, improving overall efficiency while managing complexity through modular architecture.
Data Source
AI summary
A method for analyzing at least one test app is provided. The method comprises collecting feature data related to a plurality of historical apps, extracting features of the plurality of historical apps from the feature data, classifying the features of the plurality of historical apps into a plurality of feature groups, and assigning weights to the features of the plurality of historical apps to generate reference data. The method further comprises collecting feature data related to the at least one test app, extracting features of the at least one test app from the feature data, classifying the features of the at least one test app into the plurality of feature groups, and assigning weights to the features of the at least one test app based upon the reference data.