VR Error Detection Training System for Knowledge Transfer
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Solution Overview
Problem
Providing effective hands-on training is challenging, especially as the workforce ages and experienced workers retire, leading to discontinuities in knowledge and skill transfer to less experienced workers.
Innovation Solution
A system and method for training machine learning algorithms in a virtual reality environment to detect and correct errors, which includes generating a virtual reality scene model, capturing user actions, training machine learning models, and deploying them in virtual or augmented reality environments to immerse users and provide feedback on task performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional hands-on training methods are used, then knowledge transfer can occur, but training effectiveness is difficult to ensure and operational inefficiencies result
Solution Approach 1:
The patent creates virtual copies of real-world work environments, equipment, and procedures in a virtual reality setting. These digital twins allow trainees to practice tasks repeatedly without affecting actual operations, ensuring consistent training quality while maintaining operational efficiency through asynchronous training.
Solution Approach 2:
The system implements automated feedback mechanisms using machine learning models that analyze trainee actions, provide real-time corrections, and track performance improvements. This ensures reliable knowledge transfer through consistent, objective feedback rather than subjective instructor evaluation.
2Loss of information
If experienced workers retire, then organizational knowledge is lost, but training new workers becomes more challenging
Solution Approach 1:
The system captures and stores expert knowledge, procedures, and decision-making patterns in the virtual environment. This digital knowledge repository preserves organizational memory independently of individual workers, preventing knowledge loss when experienced employees retire.
Solution Approach 2:
The system pre-records expert demonstrations and establishes correct procedural models before training begins. Trainees learn from pre-prepared expert performances rather than relying on live expert availability, making training easier to implement and scale.
3Measurement precision
If machine learning models are trained in virtual reality, then error detection capability improves, but system complexity increases
Solution Approach 1:
The patent transfers the complex training and model development process to a virtual environment, keeping the actual deployment system simpler. Virtual reality handles the computational complexity of model training while the augmented reality deployment maintains ease of use through intuitive interfaces.
Data Source
AI summary
Embodiments of the present disclosure are related to training one or more of machine learning algorithms in a virtual reality environment for error detection and correction and/or for employing one or more trained machine learning models in an augmented reality environment to detect and/or correct user errors associated the performance of one or more tasks.