Touch Location Predictor Using Direction Vectors
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Solution Overview
Problem
Current touchscreen technologies struggle to accurately predict touch locations and recognize gestures without physical contact, limiting the effectiveness of floating touch capabilities and air view techniques.
Innovation Solution
A smart touch location predictor based on direction vectors is implemented, which detects hover inputs and computes 3D points to determine a position vector, allowing for the prediction of touch locations on a touchscreen by analyzing user finger movements and capacitance changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If air view technique is used to sense fingers hovering on touchscreen, then floating touch capabilities are enabled, but gesture recognition accuracy deteriorates and use cases are limited
Solution Approach 1:
The patent introduces a third dimension (z-axis) to touch detection by calculating depth information based on capacitance changes. This transforms 2D touchscreen detection into 3D spatial understanding, enabling the system to track finger trajectory and predict touch location accurately even when the finger is hovering above the screen surface.
Solution Approach 2:
The system performs preliminary action by predicting the touch location before actual contact occurs. By continuously monitoring capacitance changes and calculating direction vectors during the hover phase, the system pre-determines where the finger will touch, allowing for proactive gesture recognition and improved response accuracy.
2Adaptability or versatility
If capacitance threshold is increased for air view detection, then hovering detection is enabled, but touch event detection reliability deteriorates
Solution Approach 1:
The patent implements feedback by continuously monitoring capacitance changes and using this information to update the predicted touch location. The system adjusts its detection parameters based on real-time capacitance data, allowing it to distinguish between intentional hover gestures and accidental capacitance variations, thereby maintaining high reliability in touch event detection.
Solution Approach 2:
The system dynamically adjusts detection parameters based on real-time capacitance changes and finger movement patterns. By making the detection threshold and prediction algorithm adaptive rather than static, the system can reliably distinguish between valid hover gestures and noise, maintaining high accuracy across different usage scenarios.
3Productivity
If touch location prediction is implemented using direction vectors, then search space for next inputs is reduced, but computational complexity increases
Solution Approach 1:
The patent changes the parameters used in touch detection from simple 2D coordinates to 3D spatial vectors that include depth information. By calculating direction vectors based on capacitance change patterns, the system efficiently narrows down the search space for predicted touch locations, improving input processing efficiency through mathematically sound parameter transformations.
Solution Approach 2:
The system replaces complex mechanical touch sensing mechanisms with a computational approach based on capacitance field analysis. By substituting physical touch detection hardware with an algorithmic prediction system that processes capacitance changes, the patent achieves high processing efficiency while managing computational complexity through optimized mathematical models.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enhances touchscreen operations by improving the accuracy of touch event detection and gesture recognition, reducing the search space for next inputs and enhancing performance by predicting probable touch points before actual contact, thus improving user interaction with electronic devices.
Implementation Method 1
A touchscreen of an electronic device (such as a smart phone, a tablet computer) is comprised based upon self and mutual capacitance values among other techniques. The self and mutual capacitance values are periodically read to determine where the touchscreen has been touched by a user.
Data Source
AI summary
A method and electronic device for predicting a touch location. The method includes detecting a hover input over a touchscreen and computing a first point of the hover input that includes first x and y coordinates associated with a surface of the touchscreen and z coordinate first distance. The method further includes detecting a movement of the hover input over the touchscreen and computing a second point associated with the movement of the hover input that includes second x and y coordinates associated with the surface of the touchscreen and a z coordinate second distance. The method includes predicting the touch location that is expected to contact the surface of the touchscreen using a position vector determined based on the first point and the second point. The method may further include dynamically recalibrating the prediction based on newer points observed as and when the hover input hovers over the touchscreen.