Adaptive XR Content Filtering for Cognitive Load Management
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Solution Overview
Problem
Existing content filtering technologies are inadequate for Extended Reality (XR) environments, leading to cognitive overload and increased risks of personal injuries and accidents, especially in dynamic and immersive XR settings.
Innovation Solution
The method involves dynamically determining what information to present to the user by using policies and content classification, taking into account the user's current cognitive load, proximity to objects, and direction, to filter and adapt XR content in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classic information filters are used in XR environments, then content filtering is provided, but the filters are inadequate for dynamic XR settings and fail to provide timely information
Solution Approach 1:
The patent implements a dynamic content filtering system that adapts to changing XR environments in real-time. The filter continuously monitors environmental changes, user interactions, and contextual information to dynamically adjust filtering decisions, transitioning from static pre-programmed filters to adaptive filters that respond to live conditions.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions, environmental responses, and contextual data are continuously fed back into the filtering algorithm. This enables the filter to learn from actual usage patterns and environmental conditions, improving its effectiveness in dynamic XR settings through iterative optimization.
2Loss of information
If all XR content is presented to the user, then complete information is provided, but cognitive overload occurs and user safety is compromised
Solution Approach 1:
The patent extracts and separates essential information from redundant or harmful content. By identifying and removing unnecessary elements while preserving critical information, the system reduces cognitive load on users while maintaining safety and relevance. This extraction process prioritizes information based on user needs and environmental context.
Solution Approach 2:
The system dynamically changes parameters such as information density, presentation rate, and content priority based on real-time user state and environmental conditions. When cognitive load indicators are detected, the system adjusts these parameters to reduce information flow while maintaining safety-critical information delivery.
3Object-affected harmful factors
If content filtering is implemented in XR systems, then cognitive overload is reduced, but the system complexity increases
Solution Approach 1:
The filtering system is designed to be self-regulating and autonomous, using built-in sensors and algorithms to automatically adjust filtering decisions without requiring complex external control systems. The system monitors its own performance and environmental conditions, making self-correction and adaptive filtering decisions that reduce cognitive overload while managing internal complexity.
Data Source
AI summary
A method is disclosed for rendering Extended Reality, XR, content to a user. The method is performed in a device and comprises: receiving, in the device, XR content; determining, in a policy entity, the XR content to be rendered based on one or more policies; classifying, in a classification service, the XR content, and rendering the XR content in an XR environment based on the one or more policies and the classification of the XR content. A device, method in a system and a system are also disclosed.