Automated Application Tracking and Validation System
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Solution Overview
Problem
Conventional data validation processes for direct-to-consumer software applications are highly manual, costly, and time-consuming, involving multiple distinct processes that rely heavily on human participation.
Innovation Solution
An automated system for user application tracking and validation that integrates a scalable GUI tracking framework with simulated device testing, using a machine learning model for anomaly prediction and automated validation, reducing manual effort and enabling concurrent testing of multiple application versions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual validation processes are used for data points, then validation can be performed with human judgment and flexibility, but the process becomes costly and time-consuming
Solution Approach 1:
The patent replaces manual mechanical validation processes with an automated computer-based system that uses machine learning models and algorithms to validate data points, eliminating the need for human operators to manually inspect and validate each data point while maintaining or improving validation quality
Solution Approach 2:
The system enables self-service validation where the automated platform independently performs data validation, anomaly detection, and quality assessment without requiring human intervention, allowing the system to serve itself in the validation process
2Reliability
If multiple distinct manual validation processes are used, then comprehensive validation can be achieved, but the complexity and cost increase
Solution Approach 1:
The patent merges multiple separate validation processes into a single integrated automated platform that combines data collection, validation, anomaly detection, and quality assessment into one unified system, reducing process complexity while maintaining comprehensive validation coverage
Solution Approach 2:
The automated validation platform performs multiple functions including data validation, anomaly detection, quality assessment, and reporting within a single system, eliminating the need for separate manual processes for each validation task
3Reliability
If human participation is heavily relied upon in validation, then nuanced judgment can be applied, but the process becomes expensive
Solution Approach 1:
The patent substitutes human manual validation with automated machine learning-based validation systems that can process large volumes of data at low cost while maintaining high accuracy through sophisticated algorithms and models trained on historical data
Data Source
AI summary
A system includes a computing platform including a hardware processor and a system memory storing a software code. The hardware processor is configured to execute the software code to track interactions with a user application during use of the user application, generate, based on tracking the interactions, interaction data identifying multiple interaction events during the use, and perform a validity assessment of the interaction data. The hardware processor is further configured to execute the software code to identify, based on the validity assessment, one or more anomalies in the interaction data, and output, based on identifying the one or more anomalies in the interaction data, one or more of the interaction events corresponding respectively to the one or more anomalies.