Eye-Tracking UI Selection Using Adaptive Targeting Regions
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Solution Overview
Problem
Existing eye tracking systems for selecting user interface elements are prone to inadvertent or false positive selections, especially when multiple UI elements are proximate to each other, due to noisy and inaccurate gaze tracking.
Innovation Solution
Implementing multiple targeting criteria and adjusting selection regions based on eye tracking data, including gaze position, duration, and movement, to accurately select a UI element, and reducing the size or deactivating overlapping selection regions to minimize false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If eye tracking is used to select UI elements, then selection speed and ease of operation are improved, but measurement precision and reliability deteriorate due to noisy gaze tracking
Solution Approach 1:
The patent divides the selection process into multiple independent criteria: a first criterion based on gaze position within a selection region, and a second criterion based on gaze duration or movement patterns. This segmentation allows the system to filter out noisy single-point gaze data by requiring multiple independent conditions to be satisfied simultaneously, thereby improving measurement precision while maintaining ease of operation.
Solution Approach 2:
The patent implements preliminary action by establishing a selection region around the target UI element before actual selection occurs. The system preliminarily identifies potential target areas and then applies multiple criteria within this pre-defined region to confirm final selection. This preliminary positioning reduces the impact of gaze tracking noise by constraining the selection decision to a controlled spatial and temporal framework.
2Quantity of substance
If multiple UI elements are displayed proximate to each other, then information density and display efficiency are improved, but selection reliability deteriorates due to increased risk of false positive selections
Solution Approach 1:
The patent applies segmentation by creating distinct selection regions for each UI element and implementing multiple independent targeting criteria. When multiple UI elements are displayed closely, each element's selection requires satisfaction of both the first criterion (gaze position within region) and the second criterion (gaze duration or movement pattern), which segment the selection decision process and reduce false positives between adjacent elements.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors gaze data against multiple criteria and provides intermediate feedback through selection region highlighting or state changes. This feedback loop allows the system to distinguish between intentional selection (where gaze remains stable within a region) and accidental proximity (where gaze moves between regions), thereby maintaining reliability even with high information density.
3Device complexity
If single criterion eye tracking selection is used, then device complexity is reduced, but selection reliability and false positive reduction deteriorate
Solution Approach 1:
The patent segments the selection system into two independent criterion modules: a first criterion module that evaluates gaze position within selection regions, and a second criterion module that evaluates gaze duration or movement patterns. This segmentation allows the system to maintain modular, manageable complexity while achieving improved reliability through the combination of multiple independent evaluation stages.
Solution Approach 2:
The patent implements multi-functionality by designing the eye tracking system to perform multiple functions: initial target acquisition through the first criterion, and final selection confirmation through the second criterion. This universal approach allows the same eye tracking hardware to serve both rapid selection initiation and reliable selection confirmation, reducing overall system complexity while improving reliability.
Data Source
AI summary
A method is performed at an electronic device with one or more processors, a non-transitory memory, and a display. The method includes displaying, on the display, a first user interface (UI) element that is associated with a first selection region and a second selection region. The method includes, while displaying the first UI element, determining, based on eye tracking data, that a targeting criterion is satisfied with respect to the first selection region or the second selection region. The eye tracking data is associated with one or more eyes of a user of the electronic device. The method includes, while displaying the first UI element, selecting the first UI element based at least in part on determining that the targeting criterion is satisfied.