Touch Region Labeling via Scanning Masks
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Solution Overview
Problem
Current touch sensor technologies require significant time and memory resources to label touch regions, especially in multi-touch scenarios, due to the complexity of processing and storing touch data.
Innovation Solution
A method and device that group adjacent raw data into clusters by assigning label data during scanning, using a preset scanning mask to determine label assignments, which reduces the time needed for labeling and memory usage by eliminating the need for secondary labeling and intermediate data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional touch region labeling methods are used, then accurate touch region identification is achieved, but the time required for sensing multi-touch increases
Solution Approach 1:
The patent applies preliminary action by pre-defining scanning masks with predetermined patterns before touch detection. These masks are prepared in advance and applied during the scanning process, allowing the system to quickly identify touch regions without performing complex calculations during the actual sensing operation. This pre-preparation significantly reduces the time required for multi-touch sensing while maintaining accurate touch region identification.
Solution Approach 2:
The patent segments the touch detection process by dividing the touch sensor array into multiple scanning zones defined by the scanning mask. Each mask defines specific scanning patterns that divide the detection process into manageable segments, allowing parallel or sequential processing of different regions. This segmentation enables faster processing of multi-touch scenarios by handling different touch regions independently according to the predefined mask patterns.
2Reliability
If traditional touch region labeling methods are used, then complete touch data is stored, but the memory size required increases
Solution Approach 1:
The patent extracts only the essential touch region information using predefined scanning masks, removing unnecessary intermediate data and redundant calculations. By applying the mask-based scanning approach, the system extracts only the critical touch coordinates and region identifiers needed for accurate touch detection, eliminating the need to store complete raw touch data for all sensor elements. This extraction principle significantly reduces memory requirements while preserving complete and accurate touch information.
Solution Approach 2:
The patent uses scanning masks as templates or copies that can be repeatedly applied to different touch detection scenarios. Instead of storing and processing unique complex data structures for each touch event, the system uses simplified mask patterns that can be copied and applied multiple times. This copying approach reduces memory usage by replacing large amounts of detailed touch data with compact mask definitions that generate the necessary touch region information through systematic application.
3Measurement precision
If complex labeling algorithms are used, then accurate touch region clustering is achieved, but the device complexity increases
Solution Approach 1:
The patent changes the fundamental parameters of the labeling approach by replacing complex iterative algorithms with a mask-based systematic scanning method. The scanning masks define fixed patterns of scan lines and decision thresholds that transform the complex clustering problem into a series of simple binary decisions. This parameter change from dynamic algorithmic parameters to static mask parameters significantly reduces computational complexity while maintaining accurate touch region clustering through the predetermined scanning patterns.
Data Source
AI summary
The embodiments herein relate to a method and device for labeling a touch region, in which adjacent raw data are labeled in groups for reducing a labeling time period as well as a memory size for storing the labeled data.