XR Validation Pipeline Using Distributed Edge Servers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The validation process for software, particularly for extended reality (XR) applications, is resource-intensive and requires specialized equipment due to the need for testing under various network conditions and device-specific specifications, making it challenging to efficiently validate XR applications across different user devices from multiple original equipment manufacturers (OEMs).
Innovation Solution
Implementing a distributed computing resource-based testing and evaluation pipeline that uses edge servers to execute validation tests concurrently, with test data and parameters managed by a monitoring server, allowing for parallel testing across multiple XR capable devices with different specifications, thereby reducing resource requirements and traffic while meeting low latency targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If validation testing is performed on multiple user devices with different network conditions and device-specific specifications, then validation coverage and reliability are improved, but resource requirements and device complexity increase
Solution Approach 1:
The patent segments the validation testing infrastructure into distributed computing instances that can independently execute validation tests on different user devices. Each computing instance handles specific validation tasks, allowing parallel testing across multiple devices without requiring a monolithic complex testing system. This segmentation enables comprehensive validation coverage while distributing the complexity across multiple simpler, independent components.
Solution Approach 2:
The patent implements a universal validation pipeline that can execute the same validation tests across diverse user devices with different specifications, network conditions, and operating systems. The computing instances are designed to be device-agnostic, adapting to various device types (mobile phones, tablets, wearables, XR devices) while maintaining consistent validation criteria. This multi-functionality allows a single testing infrastructure to validate across the entire device ecosystem without requiring device-specific testing systems.
2Reliability
If comprehensive validation tests are executed across diverse devices, then product quality and compliance are improved, but testing time and productivity efficiency deteriorate
Solution Approach 1:
The patent implements continuous validation testing where computing instances continuously execute validation tests on user devices in real-world conditions. Rather than batch processing, the system maintains continuous testing operations across multiple devices simultaneously, ensuring that validation is an ongoing process rather than a periodic event. This continuity improves both product quality through constant verification and efficiency by utilizing available devices continuously.
Solution Approach 2:
The patent performs preliminary setup of validation tests and computing instances before actual validation execution. Test configurations, validation criteria, and computing instance allocations are prepared in advance, allowing tests to begin immediately when devices become available. This preliminary action reduces the overall testing time by eliminating setup delays during the actual validation process.
3Measurement precision
If manual validation processes are used to ensure thorough testing, then measurement precision and validation accuracy are improved, but automation extent and operational efficiency worsen
Solution Approach 1:
The patent implements automated feedback loops where computing instances continuously monitor validation test results and provide feedback to the validation pipeline. The system automatically compares test outcomes against predefined criteria, triggers re-testing when failures occur, and updates validation status in real-time. This automated feedback mechanism maintains high validation accuracy while eliminating manual intervention, achieving both precision and automation.
Solution Approach 2:
The validation system is designed to be self-service, with computing instances automatically executing tests, collecting results, and generating validation reports without human intervention. The system self-manages the entire validation workflow including test configuration, execution, result analysis, and reporting. This self-service capability achieves full automation while maintaining validation accuracy through consistent, repeatable automated processes.
Data Source
AI summary
Techniques are described herein for implementing a testing and evaluation pipeline. The techniques include receiving testing specifications for validating an XR application executing on XR capable devices and mapping individual testing specifications to a corresponding XR capable device including the XR application. Upon mapping the individual testing specifications, testing configurations for an evaluation pipeline is determined. The evaluation pipeline may include one or more computing instances that execute one or more validation tests for the XR application executing on the corresponding XR capable device according to the individual testing specifications and the testing configurations. The one or more computing instances may operate in parallel to perform the one or more validation tests concurrently. Based at least on test results generated from the one or more computing instances and one or more evaluation criteria, the XR application executing on the corresponding XR capable device may be validated.