Application Analytics Reporting via Usage Model Comparison
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Solution Overview
Problem
Current application analytics systems lack efficient methods to collect and analyze user interaction data without compromising user privacy, and they often report all metrics generated by applications, leading to unnecessary data transfer and potential privacy concerns.
Innovation Solution
A system and method for generating and reporting analytics data that includes metrics on application states and transitions, comparing these to a usage model to identify differences exceeding a threshold, and only reporting unusual usage patterns, ensuring user privacy by anonymizing data and providing control over personal information collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all metrics generated by applications are reported, then complete analytics data is collected, but data transfer volume increases and user privacy is compromised
Solution Approach 1:
The patent extracts only the essential and anomalous information from complete metrics data. By comparing actual application metrics against expected usage models, the system identifies and extracts only those metrics that deviate from normal patterns, thereby reporting minimal necessary data while maintaining analytics effectiveness and reducing data transfer volume.
2Loss of information
If all metrics generated by applications are reported, then complete analytics data is collected, but user privacy is compromised
Solution Approach 1:
The system extracts only anomalous metrics that deviate from expected usage patterns rather than reporting all collected metrics. This selective extraction approach maintains the ability to detect privacy-violating behaviors while minimizing the exposure of unnecessary personal information, thereby protecting user privacy while preserving analytics completeness for security purposes.
3Loss of energy
If usage models are created to filter metrics, then data transfer is reduced, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing usage models that define expected application behavior patterns before actual metrics collection begins. These models are created in advance and stored locally on devices, enabling immediate comparison and filtering of metrics without requiring complex real-time analysis infrastructure, thus reducing data transfer while managing complexity through pre-computation.
Data Source
AI summary
Systems and methods for application analytics reporting include comparing metrics regarding the use of the application to a usage model for the application. The usage model indicates an expected set of states of the application and transitions between the states during execution of the application. A determined difference between the metrics and the expected states and transitions indicated by the usage model that exceeds a predetermined threshold is provided as analytics data.