Application Rating System Using Usage Pattern Monitoring
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Solution Overview
Problem
Traditional software application ratings often fail to provide potential purchasers with an accurate assessment of an application's value, as they may be based on limited information and poorly organized user comments, not reflecting the actual usage patterns or true cost of the application.
Innovation Solution
A computer-implemented method that monitors usage patterns, including time spent using the application and in-app purchase activities, to deduce a value-based rating, which indicates the application's worth by considering community data and purchase behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional user-based ratings are used, then the rating system is simple to implement, but the accuracy and objectivity of the rating is insufficient
Solution Approach 1:
The patent introduces an intermediary system that collects and processes usage data from multiple sources (app stores, device usage metrics, purchase behavior) to generate objective ratings. This intermediary layer transforms raw usage data into meaningful rating information, resolving the contradiction between rating accuracy and system complexity by automating the data collection and analysis process rather than relying on manual user reviews.
Solution Approach 2:
The system implements feedback loops where usage data is continuously collected, analyzed, and used to update ratings in real-time. This feedback mechanism ensures that ratings reflect current application performance and user behavior patterns, improving measurement precision while the automated nature of the feedback system prevents excessive complexity accumulation.
2Loss of information
If detailed user comments are included in ratings, then more information is provided, but the information becomes poorly organized and difficult to assimilate
Solution Approach 1:
The patent segments detailed usage information into distinct, organized categories such as usage frequency, time spent, purchase behavior, and performance metrics. Each category is processed and presented separately with clear visual indicators, making the information easily assimilable while preserving the completeness of the underlying data. This segmentation transforms raw comprehensive data into structured, accessible rating components.
Solution Approach 2:
The system transforms one-dimensional text comments into multi-dimensional rating metrics by analyzing usage patterns across multiple parameters (frequency, duration, intensity, purchase behavior). This dimensional transformation allows comprehensive information to be presented in an organized, easily comparable format that maintains information completeness while dramatically improving accessibility.
3Reliability
If traditional ratings based on initial reactions are used, then the rating reflects early user opinions, but it fails to capture the true long-term value of the application
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing usage data from the moment the application is installed, rather than waiting for accumulated user reviews. This preliminary data collection establishes a baseline that reflects true usage patterns from the start, improving rating reliability while eliminating the time delay associated with waiting for sufficient user feedback to accumulate.
Solution Approach 2:
The patent implements continuous data collection and rating updates throughout the application's lifecycle, ensuring that ratings continuously reflect current usage patterns rather than stagnating at initial user reactions. This continuous action maintains rating reliability over time while the automated system manages the time investment efficiently, transforming what would be a lengthy manual process into an ongoing automatic operation.
Data Source
AI summary
The disclosed computer-implemented method for creating application ratings may include (i) determining that a user device has downloaded an application, (ii) monitoring the usage of the application on the user device, (iii) deducing a value of the application based at least in part on the monitored usage, and (iv) creating a rating for the application that indicates the deduced value of the application. Various other methods, systems, and computer-readable media are also disclosed.