Adaptive XR Shortcuts Using Predictive Interaction-Time Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Extended reality systems face challenges in designing and assigning efficient shortcuts due to diverse environments, user variance, and context-dependent interactions, leading to suboptimal user experience.
Innovation Solution
A predictive model is used to collect personalized usage data, estimate interaction times, and determine optimal shortcut assignments based on user preferences, optimizing the graphical user interface for reduced interaction time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional user interfaces with menus, toolbars, and icons are used in extended reality systems, then comprehensive functionality is provided, but significant time and effort are required to access commands
Solution Approach 1:
The system pre-assigns shortcut keys to frequently used commands and functionalities before the user needs them. The predictive model analyzes usage patterns and proactively configures the shortcut map, so that when users need to access commands, they can do so quickly through pre-established shortcuts rather than navigating through complex menus and toolbars.
Solution Approach 2:
The system automatically optimizes and adjusts shortcut assignments based on observed user behavior and usage patterns. The predictive model continuously learns from user interactions and self-adjusts the shortcut map without requiring manual reconfiguration by the user, making the interface adapt to individual user needs and preferences over time.
2Adaptability or versatility
If static shortcut assignments are used in extended reality systems, then simple implementation is achieved, but user variance and context-dependent interactions lead to suboptimal user experience
Solution Approach 1:
The shortcut assignment system transitions from a static configuration to a dynamic one that automatically adapts to different users and contexts. The predictive model continuously monitors usage patterns and adjusts shortcut assignments in real-time based on the current user, task context, and environmental factors, ensuring optimal performance for varying user needs and situations.
Solution Approach 2:
The system changes the parameters of shortcut assignments based on observed usage data and contextual information. Instead of fixed key-command mappings, the system dynamically modifies which keys are assigned to which commands based on frequency of use, user preferences, and contextual relevance, allowing the interface to adapt to individual user behaviors and situational requirements.
3Loss of time
If personalized shortcut optimization using predictive models is implemented, then interaction time is reduced by 25%, but data collection and processing requirements increase
Solution Approach 1:
The system implements a feedback loop where usage data is continuously collected, analyzed by the predictive model, and used to refine shortcut assignments. The model learns from user interactions and provides feedback to optimize the shortcut map, creating a continuous improvement cycle that reduces interaction time while systematically managing the complexity of data processing through iterative learning.
Data Source
AI summary
Disclosed herein is an extended reality system, and associated techniques, whereby personalized usage data of a user in one or more extended reality environments over a period of time can be collected and provided to a predictive model to determine optimal shortcut assignments for presentation to and subsequent use by the user. Determining the optimal shortcut assignments can involve estimating a plurality of interaction times for the user in the one or more extended reality environments, generating an optimized graphical user interface within the one or more extended reality environments with the optimal shortcut assignments reflected in the optimized graphical user interface, and rendering the optimized graphical user interface in the one or more extended reality environments to the user. The system may collect additional personalized usage data and periodically update the optimal shortcut assignments based on ongoing use of the system by the user.