Mixed Reality Clutter Management via Gaze-Adaptive Display
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Solution Overview
Problem
In mixed reality environments, users face visual clutter due to overlapping virtual and real-world elements, leading to overwhelmed users and inefficient use of computing resources, as existing clutter management methods fail to account for individual user perception and reaction times.
Innovation Solution
A system that computes an overall clutter metric by combining virtual UI clutter, real-world clutter, and user reaction time metrics using image analysis techniques and machine learning models, allowing for adaptive management of clutter by adjusting virtual element placement, size, and layout in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple virtual elements are displayed simultaneously in mixed reality environment, then information completeness is improved, but visual clutter increases causing user overwhelm and safety concerns
Solution Approach 1:
The system applies different visual presentation qualities to different regions or elements within the mixed reality display. Virtual elements are selectively adjusted in terms of opacity, size, position, or visual emphasis based on their importance and the user's current focus area, allowing critical information to remain prominent while reducing the visual weight of less important elements.
Solution Approach 2:
The clutter management system dynamically adjusts the display characteristics of virtual elements in real-time based on user behavior patterns, gaze data, and interaction context. Elements that are currently relevant to the user's task are maintained with higher visibility, while irrelevant elements are automatically dimmed, minimized, or temporarily hidden, creating a dynamic information hierarchy.
2Loss of information
If comprehensive UI elements are displayed to ensure all information is available, then user awareness is improved, but computing resources are wasted on displaying non-interesting elements
Solution Approach 1:
The system implements partial action by selectively rendering and processing only the subset of UI elements that are currently relevant to the user's task or context. Rather than continuously processing and displaying all possible information, the system identifies and prioritizes only the necessary elements for current user needs, reducing computational overhead while maintaining adequate user awareness.
Solution Approach 2:
The system employs feedback mechanisms by monitoring user interactions, gaze patterns, and engagement metrics to continuously adjust which UI elements are actively displayed and processed. This feedback loop allows the system to identify and eliminate elements that are not currently of interest to the user, optimizing resource allocation to only the most relevant information displays.
3Ease of operation
If visual clutter management is implemented to reduce overwhelming information, then user experience is improved, but system complexity increases
Solution Approach 1:
The clutter management system operates autonomously by automatically analyzing user behavior patterns, determining information relevance, and adjusting display parameters without requiring manual user intervention. The system self-regulates the presentation of virtual elements based on real-time context analysis, user attention patterns, and interaction data, eliminating the need for complex manual configuration interfaces.
Solution Approach 2:
The system performs preliminary analysis of user preferences, usage patterns, and contextual requirements in advance to pre-configure optimal display settings for different scenarios. By anticipating user needs and pre-establishing clutter management strategies for common situations, the system reduces the computational and interface complexity required during actual operation.
Data Source
AI summary
In particular embodiments, a computing system may receive an image comprising one or more virtual elements associated with a virtual environment and one or more real-world elements associated with a real-world environment. The system may determine a first metric and a second metric indicative of a measure of clutter in the virtual environment and the real-world environment, respectively. The system may determine gaze features associated with a user based on a user activity and predict, using a machine learning model, a reaction time of the user based on the gaze features. The system may determine a third metric indicative of the measure of clutter in the image based on predicted reaction time. The system may compute an overall clutter metric based on the first, second, and third metrics. The system may perform one or more actions to manage the clutter in the image based on the overall clutter metric.