Optical Mouse Sensor Skating Mode Correlation Matrix
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Optical mouse sensors experience spurious motion during gaming applications due to the limitations of standard 3×3 correlation matrices, which fail to accurately track high-speed mouse movements beyond their range.
Innovation Solution
A method and system that dynamically generate a larger correlation matrix, such as a 15×27 matrix, by offsetting multiple standard 3×3 matrices to cover a larger area, and identify the best region with a correlation peak, which is confirmed over consecutive frames to accurately determine mouse motion in skating mode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard 3×3 correlation matrix is used, then the device complexity and power consumption are kept low, but the measurement precision deteriorates during high-speed skating motion
Solution Approach 1:
The patent implements a dynamic correlation matrix system that adapts its size based on detected skating motion. During normal operation, a compact 3×3 matrix is used for efficiency. When skating motion is detected through analysis of displacement magnitude and direction consistency across multiple frames, the system dynamically expands to a larger correlation matrix (e.g., 5×5 or 7×7) to accurately capture the extended displacement range, then contracts back when skating ends.
Solution Approach 2:
The patent changes the parameter of correlation matrix size dynamically based on operating conditions. The matrix dimension is adjusted from a small 3×3 during normal use to a larger size during skating mode, allowing the system to optimize between precision and complexity by matching the matrix scale to the actual motion characteristics detected in real-time.
2Measurement precision
If a larger correlation matrix is used continuously, then the measurement precision improves for high-speed motion, but the use of energy increases
Solution Approach 1:
The patent employs periodic evaluation of motion characteristics to determine when to switch between small and large correlation matrices. By analyzing displacement patterns, direction consistency, and frame-to-frame changes at regular intervals, the system activates the larger matrix only during skating periods rather than continuously, significantly reducing average power consumption while maintaining precision when needed.
Solution Approach 2:
The system dynamically adjusts computational resources by switching correlation matrix size based on real-time motion detection. This dynamic adaptation ensures that the energy-intensive large matrix operations are performed only during skating motion events rather than continuously, optimizing the trade-off between measurement precision and power consumption.
3Measurement precision
If a larger correlation matrix is used, then the measurement precision improves for high-speed motion, but the productivity decreases due to increased computation
Solution Approach 1:
The patent uses periodic detection of skating motion characteristics to trigger large matrix computation only when necessary. By evaluating displacement magnitude, direction consistency, and frame differences at regular intervals and activating the large correlation matrix only during identified skating periods, the system minimizes computational overhead while maintaining accuracy during high-speed motion events.
Solution Approach 2:
The patent segments the operational timeline into normal operation phases and skating motion phases based on detected motion characteristics. During normal phases, a small 3×3 matrix is used for rapid processing. When skating is detected through analysis of displacement patterns and direction consistency across multiple frames, the system transitions to a larger correlation matrix for that specific segment, then returns to the small matrix when skating ends, optimizing processing speed across different operational segments.
Data Source
AI summary
A method for minimizing spurious motion of a mouse includes: determining whether the mouse enters a specific mode; and when the mouse is determined to enter the specific mode, generating a large correlation matrix by generating a standard size correlation matrix multiple times. The multiple standard size correlation matrices are offset with respect to each other so that an edge of each standard size correlation matrix touches at least an edge of another standard size correlation matrix to form the large correlation matrix. The specific mode is a skating mode wherein the mouse will move at high speed over a large area.