CMR Learned Interaction Model for User Experience Optimization
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Solution Overview
Problem
Existing computer-mediated reality (CMR) environments, such as virtual and augmented reality, face challenges in providing optimal user interactions due to user limitations, difficulties, or lack of experience, which can lead to suboptimal experiences and resource overburdening.
Innovation Solution
A method and system that determine probable physical actions of users interacting with CMR environments using a CMR-physical action model, generating learned interactions based on statistical likelihoods to enhance user experiences and mitigate resource usage, by providing augmented physical actions or physical action substitutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the CMR system provides comprehensive interactions without learned interactions, then user experience may be complete but resource consumption increases and user limitations cause suboptimal experiences
Solution Approach 1:
The system performs preliminary actions by determining probable physical actions and generating learned interactions before the user actually performs the physical action. The CMRPA model proactively creates interactions based on predicted user behavior, allowing the system to prepare optimized interaction paths in advance, thereby reducing computational burden during actual interaction and improving experience quality.
2Ease of operation
If the system provides learned interactions for all users, then user experience improves but system complexity and processing requirements increase
Solution Approach 1:
The system applies local quality by providing learned interactions selectively rather than uniformly to all users. The CMRPA model determines probable physical actions and generates learned interactions only for specific users based on their individual characteristics, limitations, and context. This localized approach optimizes ease of operation for users who need it while avoiding unnecessary processing complexity for others.
3Speed
If the CMR system processes all user physical actions in real-time, then interaction responsiveness is maintained but computational resources are overburdened
Solution Approach 1:
The system performs preliminary computation by determining probable physical actions and generating learned interactions before they are needed. The CMRPA model proactively creates interaction pathways based on predicted user behavior, allowing the system to prepare optimized solutions in advance. This shifts computational load from real-time processing to pre-computation, maintaining responsiveness while reducing power consumption during actual interaction.
Data Source
AI summary
Providing learned interactions in a virtual reality or augmented reality (collectively, a computer-mediated reality) can include determining a probable physical action of a user interacting with a computer-mediated reality (CMR) environment. A learned interaction corresponding to the probable physical action can be generated based on a CMR-physical action (CMRPA) model that correlates physical actions with results of the physical actions in a CMR scenario of the CMR environment. In response to determining, based on at least one identified characteristic of the user, a statistical likelihood of benefiting the user by providing the learned interaction, a learned interaction corresponding to the probable physical action can be provided to the user.