Dynamic Eye-Gaze Selection Zone for UI Interaction
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Solution Overview
Problem
Current eye-gaze technology is limited by eye-jitter, leading to poor pointing accuracy and user fatigue due to the requirement for precise gaze within the designated boundaries of UI elements, failing to recognize selections made near or outside these boundaries.
Innovation Solution
An intelligent UI element selection system that dynamically adjusts selection regions or boundaries based on predictive analysis and machine-learning algorithms, allowing recognition of selections even when gaze locations are outside the traditional boundaries, by assigning point values to various areas around UI elements and using a dynamic scoring algorithm to determine action initiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional boundary-based selection is used, then selection precision is improved, but user fatigue increases and productivity decreases
Solution Approach 1:
The patent applies dynamics by transitioning from static, fixed boundaries to dynamic, adaptive selection regions. The system dynamically adjusts the selection boundary radius based on multiple factors including gaze stability, user history, and contextual information. This allows the boundary to expand when the user's gaze is stable and contract when uncertainty is detected, thereby maintaining high selection precision while reducing user fatigue and improving typing speed.
Solution Approach 2:
The patent implements parameter changes by modifying the selection boundary parameters (radius, threshold values) based on real-time analysis of gaze characteristics and user behavior patterns. The system changes parameters such as the boundary radius from a fixed value to a dynamic value that adapts to the current interaction state, enabling the system to balance between precision and productivity according to the specific usage context.
2Device complexity
If fixed boundaries are used for selection, then system simplicity is maintained, but adaptability to user behavior deteriorates
Solution Approach 1:
The system transitions from static fixed boundaries to dynamic adaptive boundaries that automatically adjust based on user behavior patterns. The dynamic boundary radius is calculated based on real-time gaze stability analysis and historical user data, enabling the system to adapt to individual user habits without requiring manual reconfiguration, thus maintaining simplicity while improving adaptability.
Solution Approach 2:
The system performs self-service by automatically learning and adapting to user behavior patterns without requiring external intervention or manual programming of adaptive rules. The machine learning models continuously process user interaction data and automatically adjust boundary parameters, enabling the system to become increasingly adapted to each user's specific behaviors over time while keeping the system architecture relatively simple.
3Measurement precision
If strict boundary enforcement is applied, then selection accuracy is improved, but user experience deteriorates
Solution Approach 1:
The patent applies dynamics by making the selection boundary adaptive rather than rigid. The boundary dynamically adjusts its radius based on gaze stability and user history, allowing it to expand when the user's gaze is stable (improving ease of operation) and contract when precision is needed (maintaining selection accuracy). This dynamic approach eliminates the harsh binary boundary enforcement while preserving accuracy.
Solution Approach 2:
The system implements parameter changes by modifying the boundary threshold parameters based on real-time analysis of gaze characteristics and user behavior. The boundary radius and activation thresholds are continuously adjusted to balance between selection accuracy and ease of operation, creating a more user-friendly experience that maintains high accuracy through intelligent parameter adaptation rather than rigid enforcement.
Data Source
AI summary
Systems and methods disclosed herein are related to an intelligent UI element selection system using eye-gaze technology. In some example aspects, a UI element selection zone may be determined. The selection zone may be defined as an area surrounding a boundary of the UI element. Gaze input may be received and the gaze input may be compared with the selection zone to determine an intent of the user. The gaze input may comprise one or more gaze locations. Each gaze location may be assigned a value according to its proximity to the UI element and/or its relation to the UI element's selection zone. Each UI element may be assigned a threshold. If the aggregated value of gaze input is equal to or greater than the threshold for the UI element, then the UI element may be selected.