XR User Interaction Analysis System for Quality of Experience Evaluation
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Solution Overview
Problem
There is a lack of systematic methods to evaluate and improve the efficiency of user performance when using Extended Reality (XR) devices for tasks in virtual environments, such as education and training, due to limitations in communication and discomfort, and existing VR/AR/XR platforms do not provide effective evaluation or improvement of user effectiveness.
Innovation Solution
An apparatus and method that utilize a processor and machine-learning model to generate user interaction feature information from XR device sensors, calculate Quality of Experience (QoE) through multiple experience indices, and evaluate effectiveness by mapping these indices to metrics, providing treatments to improve user performance based on XR interactions, including motion, eye gaze, and sense of touch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If XR devices are used for virtual task performance, then user engagement and immersion are improved, but systematic evaluation of effectiveness is lacking
Solution Approach 1:
The system implements feedback by continuously monitoring user interactions through sensors (eye tracking, motion, touch) and providing real-time QoE evaluation results that feed back into the system to improve task performance assessment and guide future XR experience optimization
Solution Approach 2:
The patent replaces manual evaluation methods with an automated machine learning-based QoE evaluation system that processes sensor data to objectively measure user effectiveness, substituting the need for complex manual assessment mechanisms with an intelligent automated system
2Adaptability or versatility
If multiple interaction modalities are integrated, then user experience is enhanced, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by integrating multiple interaction modalities (eye tracking, motion sensing, touch input) into a unified QoE evaluation framework, allowing a single system to process diverse interaction types through common machine learning models and evaluation metrics
Solution Approach 2:
The patent merges different sensor data streams and interaction modalities into a consolidated QoE evaluation process, combining eye tracking, motion, and touch information through integrated machine learning models to produce unified effectiveness assessments
3Measurement precision
If QoE metrics are quantified through machine learning, then measurement precision is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models and pre-processing sensor data before actual QoE evaluation, preparing computational resources and evaluation frameworks in advance to reduce real-time computational burden during user interaction
Data Source
AI summary
Disclosed herein is an apparatus for analyzing efficiency of virtual task performance of a user interacting with eXtended Reality (XR). The apparatus includes memory in which at least one program is recorded and a processor for executing the program. The program may perform generating user interaction feature information from sensor information of a virtual reality (VR) device, calculating the quality of experience of a user as the values of multiple experience indices based on the feature information by applying a machine-learning model, and evaluating an experience based on a result of mapping the values of the multiple experience indices to generated metrics in order to analyze the effectiveness of the VR experience of the user.