Passive Stylus Detection on Touch Screen Edges
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Solution Overview
Problem
Current touch screens, particularly capacitive ones, face challenges in distinguishing between a user's hand and a passive stylus, especially along the edges, leading to false detections due to minimal sensing data, which affects multi-touch capabilities and user interface accuracy.
Innovation Solution
An object recognition process that selects and analyzes signals from a sense array to determine whether a touch object is a passive stylus or not, using specific signal strength ranges and positional criteria to differentiate between a passive stylus and a user's hand, including calculations of signal sums and ratios to accurately identify the type of touch object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If capacitive touch screens are used to support multi-touch, then multi-touch capability is improved, but object recognition accuracy deteriorates due to difficulty in distinguishing between user's hand and passive stylus
Solution Approach 1:
The patent changes multiple parameters of the sensing signals simultaneously: signal strength thresholds, ratios between adjacent sense element signals, and positional relationships. By analyzing these parameters in combination rather than relying on a single parameter, the system can accurately distinguish between passive stylus and user's hand while maintaining multi-touch capability.
Solution Approach 2:
The patent introduces intermediary calculations (signal ratios and threshold comparisons) as mediators between the raw sensing data and object identification. These intermediary steps process the minimal sensing data from edge sense elements to extract meaningful discrimination features that enable accurate object recognition.
2Area of stationary object
If sensing data from edge sense elements is used, then coverage area is improved, but detection reliability deteriorates due to minimal sensing data
Solution Approach 1:
The patent merges multiple parameters from the same sense element (signal strength, ratio to adjacent elements, positional information) to create a more reliable detection basis. By combining these parameters through threshold comparisons and ratio calculations, the system achieves reliable detection despite the minimal data available from edge sense elements.
3Productivity
If signal strength thresholds are used for object classification, then object recognition speed is improved, but recognition accuracy deteriorates due to false detections
Solution Approach 1:
The patent transitions from one-dimensional signal strength thresholding to multi-dimensional analysis by incorporating ratios between adjacent sense elements and positional relationships. This dimensional expansion allows the system to maintain fast recognition through threshold-based decisions while improving accuracy by considering multiple dimensions of the sensing data simultaneously.
Data Source
AI summary
Various embodiments provide an object recognition process that is configured to detect a passive stylus and reject non-passive stylus objects on a touch screen, including an edge portion of the touch screen. In one embodiment, the object recognition process includes receiving sense signals from sense elements of a sense array in response to a touch object being on the sense array, selecting three sense signals from three respective sense elements, calculating a first sum of the strengths of the three selected signals, calculating a second sum of the strengths of two of the selected signals which are greater than the strength of one of the selected signals; and determining a type of the object (e.g., a passive stylus or a user hand's grip shadow) based on the first sum and the second sum.