Eye Movement Classifier for Automatic User Intent Activation
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Solution Overview
Problem
Current personal computing devices lack an efficient method to automatically infer user intent from eye movements for activating graphical elements on a display screen, leading to manual intervention for executing actions.
Innovation Solution
A system utilizing an eye tracking sensor and machine learning algorithms to process eye movement patterns, generating classifiers that automatically identify user intent and trigger corresponding actions on the display screen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used to activate graphical elements, then user control is maintained, but interaction efficiency and speed are reduced
Solution Approach 1:
The patent replaces manual mechanical interaction (mouse clicks, keyboard input) with an optical-based eye tracking system. The eye tracking sensor detects eye movements and fixations, converting them into digital signals that trigger activation of graphical elements, thereby eliminating the need for manual physical interaction while maintaining user control.
Solution Approach 2:
The system enables users to interact with the display by simply observing it naturally. The eye tracking system automatically monitors eye movements and interprets them as intent signals, allowing the system to self-activate graphical elements based on where the user is looking, without requiring explicit manual commands.
2Extent of automation
If eye tracking is implemented to infer user intent, then automation of action activation is achieved, but device complexity increases
Solution Approach 1:
The eye tracking sensor and processing system are integrated into existing personal computing devices, allowing the same hardware to serve both traditional computing functions and eye-based interaction. The system can operate in multiple modes: traditional manual interaction, eye movement monitoring, and automatic activation, making the device multi-functional without requiring completely separate systems.
3Speed
If eye movement patterns are analyzed in real-time, then user intent recognition speed is improved, but processing requirements and energy consumption increase
Solution Approach 1:
The system performs preliminary processing of eye movement data by continuously monitoring and pre-analyzing eye tracking signals even when no activation is intended. This allows the system to be prepared and responsive, quickly recognizing intent when it occurs without requiring intensive real-time processing only during activation events, thereby reducing peak energy consumption while maintaining fast response speed.
Data Source
AI summary
A device, method, and system for inferring user intent to perform an action on a computing device includes monitoring the eye movement patterns of a user and determining the action to be performed based on the eye movement patterns. Signals relating to the eye movement of the user while observing a display screen of the computing device are processed to produce at least one eye movement feature. One or more classifiers are generated based on a training set of data of the eye movement feature over a time interval in which the user observes the display screen with an intent to activate the action. Thereafter, an eye-movement-pattern of the user may be analyzed, and features may be extracted, as the user is observing the display screen. The intended action is automatically activated if the user intent is inferred using the one or more of the classifiers.