Dynamic Error Resolution in Extended Reality Using Machine Learning
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Solution Overview
Problem
There is a need for a system that automatically resolves user errors in extended reality (XR) environments, as the increasing number of users utilizing XR platforms leads to challenges in navigating new layouts and input techniques, resulting in errors that existing technologies fail to address effectively in real-time.
Innovation Solution
A system comprising a processing device configured to initiate the XR platform, identify errors, determine error classifications using machine learning algorithms, collect digital content files, convert them into XR content files, and display these files to assist users, while also training algorithms based on success scores and metadata encryption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning algorithms are used to automatically resolve user errors in XR environments, then error resolution efficiency and user experience are improved, but computational resource consumption and system complexity increase
Solution Approach 1:
The system segments error resolution into distinct phases: error detection through user input monitoring, classification using the first machine learning algorithm, content retrieval from database, format conversion using the second machine learning algorithm, and delivery through the XR platform. This segmentation allows each component to be optimized independently while managing overall system complexity.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning algorithms on historical error data and pre-storing relevant digital content files in the database associated with different error classifications. This preparation enables faster real-time error resolution without requiring complex processing during actual error events.
2Reliability
If real-time error identification and resolution is implemented, then user experience is enhanced, but processing time and computational resources increase
Solution Approach 1:
The system implements feedback mechanisms where user inputs are continuously monitored and analyzed in real-time. When an error is detected, the system provides immediate classification and resolution through the XR platform, creating a closed-loop feedback system that continuously learns from user interactions and improves error resolution accuracy over time.
Solution Approach 2:
The system changes parameters by transforming digital content files into XR-specific formats using the second machine learning algorithm, and by adapting error classification parameters based on historical data patterns. These parameter transformations enable efficient real-time processing while maintaining high resolution accuracy.
Data Source
AI summary
Systems, computer program products, and methods are described herein for dynamic error resolution in an extended reality environment. The present invention identifies user errors in real time based on user selections in an extended reality (XR) environment. In this regard, the present invention focuses on electronic applications (and the electronic work products/electronic data hosted thereon) within an XR environment and uses machine learning processes to identify real time user errors. The invention may then use a second set of machine learning processes to create, in real-time, digital support content which is visible via an XR platform (accessible using a virtual/augmented/mixed reality device). As such, the system may provide specific improvements over prior systems by automatically providing users with custom error resolution support in real time.