Probabilistic Model for Touch Input Evaluation
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Solution Overview
Problem
Current point-in-rectangle hit testing mechanisms are inadequate for touch screen user input devices and users without fine motor skills, as they require high precision and can be frustrating due to the need for precise selection of on-screen controls.
Innovation Solution
A multi-factor probabilistic model evaluates user input by considering factors such as proximity, motion consistency, shape, and size, allowing for more flexible interaction methods and reducing the precision required for selecting user interface controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point-in-rectangle hit testing is used to determine user input intent, then precision in control selection is improved, but ease of operation deteriorates due to the need for fine motor skills
Solution Approach 1:
The patent transforms the binary hit-testing decision into a probabilistic evaluation by changing the parameter from precise coordinate matching to probability scoring based on multiple factors including distance, gesture type, and contextual information. This allows the system to accept inputs with lower precision while maintaining accurate control identification.
Solution Approach 2:
The patent introduces an intermediary probabilistic model that acts as a mediator between the raw user input coordinates and the final control selection. Instead of directly comparing coordinates to control boundaries, the system uses probability scores derived from multiple factors to determine the intended control, bridging the gap between imprecise input and accurate selection.
2Measurement precision
If point-in-rectangle hit testing is used, then accuracy in determining intended control is improved, but adaptability to different input devices deteriorates
Solution Approach 1:
The patent creates a universal probabilistic model that can handle multiple types of user input devices (touch screens, mice, styluses, gestures) through a single unified framework. The model evaluates multiple factors including distance, gesture characteristics, and contextual information, making it adaptable to various input methods while maintaining accurate control identification.
Solution Approach 2:
The system changes from fixed coordinate-based parameters to dynamic probability parameters that adapt to different input devices. By evaluating multiple factors with different weights based on the input device type and gesture characteristics, the system achieves both accuracy and versatility across diverse input methods.
3Adaptability or versatility
If multiple factors are evaluated in the probabilistic model, then adaptability to different user interactions is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex evaluation process into distinct, independent factors (distance to control, gesture type, input device characteristics, contextual information) that can be evaluated separately and then combined through probability multiplication. This modular approach manages complexity by breaking down the overall evaluation into manageable components.
Solution Approach 2:
The system implements a tiered evaluation approach where not all factors need to be fully processed for every input. The probabilistic model can operate with varying degrees of factor evaluation based on confidence thresholds, allowing the system to achieve sufficient accuracy without always performing the complete multi-factor analysis, thus reducing effective complexity.
Data Source
AI summary
A multi-factor probabilistic model evaluates user input to determine if the user input was intended for an on-screen user interface control. When user input is received, a probability is computed that the user input was intended for each on-screen user interface control. The user input is then associated with the user interface control that has the highest computed probability. The probability that user input was intended for each user interface control may be computed utilizing a multitude of factors including the probability that the user input is near each user interface control, the probability that the motion of the user input is consistent with the user interface control, the probability that the shape of the user input is consistent with the user interface control, and that the size of the user input is consistent with the user interface control.