Context-Aware XR Policy Refinement Using Interaction Feedback
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Solution Overview
Problem
Extended reality systems lack the ability to intuitively refine context-aware policies, leading to misalignment between provided content and user environments or activities, reducing performance and applicability.
Innovation Solution
A mixed-initiative editing system that continuously monitors user interactions to analyze policy performance, providing refinement recommendations using artificial intelligence, allowing users to update and improve context-aware artificial intelligence policies through data-driven computational approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If context-aware policies are used in extended reality systems, then the system can provide automated responses and services, but the policies may become misaligned with user environments or activities over time, reducing performance
Solution Approach 1:
The system continuously monitors user interactions with extended reality content and uses this feedback to automatically refine context-aware policies. The monitoring component tracks user behaviors and environmental contexts, while the refinement component adjusts policy parameters based on observed patterns, creating a closed-loop system that maintains alignment between automated responses and actual user needs over time.
2Measurement precision
If context-aware policies are continuously refined using user interaction data, then policy accuracy improves, but the system complexity increases
Solution Approach 1:
The system performs self-refinement of context-aware policies by automatically monitoring user interactions and adjusting policy parameters without requiring external intervention. The refinement component uses machine learning algorithms to autonomously analyze interaction data and optimize policy decisions, enabling the system to improve its own accuracy while managing complexity through automated self-adjustment mechanisms.
3Ease of operation
If hand gestures are used for interacting with extended reality applications, then users can control components without controllers, but users must keep arms extended to enter the active area of sensors, causing fatigue
Solution Approach 1:
The system introduces voice commands and eye-tracking as intermediary interaction methods between the user and extended reality components. Instead of requiring direct hand gestures, users can issue verbal commands or use gaze direction to select and manipulate virtual objects, with the system's recognition algorithms translating these indirect inputs into appropriate actions, thereby eliminating the need to extend arms into sensor zones.
4Area of stationary object
If the field of view of extended reality headsets is limited, then the device form factor remains manageable, but hand gestures require arms to be extended to enter the active sensor area
Solution Approach 1:
The system implements multiple interaction modalities including voice commands, eye-tracking, and simplified gestures that work within the limited field of view. The virtual assistant can recognize and process various input types without requiring users to position their hands within the restricted sensor active area, making the interaction system universal across different user positions and reducing the need to extend arms.
Data Source
AI summary
The present disclosure relates to refining context aware policies in extended reality systems. In one aspect, an extended reality system is provided that performs operations including: accessing data collected from user interactions while using a context aware policy in an extended reality environment, determining a support set and confidence score for the context aware policy based on the data, generating replacement policies for the context aware policy, determining a support set and confidence score for each of the replacement policies based on the data, identifying a replacement policy from the replacement policies as a replacement for the context aware policy based on the support sets and confidence scores, and updating one or more conditions or an action defined by the context aware policy with a modified version of the one or more conditions or the action defined by the replacement policy to generate an updated context aware policy.