Touch Screen Array Context-Sensitive Search Algorithm
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Solution Overview
Problem
The existing methods for determining a location on a touch screen display are inefficient due to high computational and memory access requirements, which grow exponentially with the array length and vector dimension, leading to slow response times.
Innovation Solution
A method that assigns weights to input vectors, structures them in a searchable order, and uses a fast search algorithm like binary search to minimize computations, restricting score computations to a reduced search range based on a noise-splatter coefficient, thereby reducing the number of memory accesses and operations needed to find a match.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search is used to find the closest match by computing scores for all array elements, then measurement precision is improved, but productivity deteriorates due to high computational and memory access requirements
Solution Approach 1:
The patent segments the exhaustive search process into two phases: (1) compute weights for all N vectors once and store them, and (2) for each new sample, compute weights for only K neighboring vectors where K << N. This segmentation reduces per-sample computational complexity from O(N×L) to O(K×L), significantly improving response time while maintaining accuracy through the weight-based scoring mechanism.
Solution Approach 2:
The patent performs preliminary computation of weights for all array vectors during an initialization phase, storing these pre-computed weights for later use. When a new sample arrives, only the weight computation for K neighboring vectors is required, rather than recomputing all N weights. This preliminary action eliminates redundant computations and dramatically speeds up location determination.
2Measurement precision
If the entire array of N vectors is accessed every time a new sample arrives, then measurement precision is improved, but device complexity worsens due to high memory access requirements
Solution Approach 1:
The patent applies local quality by organizing the array vectors in sorted order based on their pre-computed weights and restricting the search to a local window of K neighboring vectors around the position corresponding to the new sample. This local search approach reduces memory access complexity from accessing all N vectors to accessing only K vectors, while the sorted organization ensures that the relevant local region contains the correct match.
3Measurement precision
If computational requirements grow exponentially with array length N and vector dimension L, then measurement precision is improved, but loss of time worsens
Solution Approach 1:
The patent changes the parameter scope by introducing a reduction factor that limits the search from the full array of N vectors to only K neighboring vectors, where K is substantially less than N. This parameter change transforms the computational complexity from exponential O(N×L) to a manageable O(K×L), reducing processing time significantly while the weight-based scoring maintains the accuracy of location determination.
Data Source
AI summary
A system and method for efficient computation in the course of locating a position on the face of a touch-screen-equipped display device by limiting the amount of computations to weighted vectors within a range substantially less than the entire range of data input from the touch screen sensors.