Stylus Haptic Component Arming via Predictive Weighting
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Solution Overview
Problem
Existing touch-sensitive input devices face challenges in reducing latency and maximizing power efficiency for haptic output, particularly in smaller form factors like styluses, where battery capacity is limited.
Innovation Solution
The method involves using haptic prediction algorithms to selectively arm the haptic feedback component in a stylus based on user interactions, thereby reducing latency and conserving power. This includes transmitting power to the haptic circuit, determining and weighting haptic predictor values from user interactions, and comparing these values to a predictive threshold to decide when to continue or cease power transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the haptic feedback component is continuously armed to reduce latency, then responsiveness is improved, but power consumption increases
Solution Approach 1:
The system performs preliminary actions by arming the haptic feedback component based on predictive analysis of user interactions. The processor analyzes interaction patterns and proactively arms the haptic component before actual haptic output is needed, reducing latency while avoiding continuous arming to conserve power.
Solution Approach 2:
The system dynamically adjusts the arming state of the haptic feedback component based on real-time analysis of user interaction patterns. The processor continuously monitors interactions and adapts the arming decision, transitioning between armed and unarmed states optimally balancing responsiveness and power consumption.
2Measurement precision
If the haptic feedback component is armed based on multiple user interactions, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the processor analyzes user interaction patterns and uses this information to predict future interactions. The weighted combination of multiple interaction signals provides feedback that improves prediction accuracy, with the system learning from past interactions to make better arming decisions.
Solution Approach 2:
The system changes parameters by applying weights to different user interaction types and combining them to generate a predictive result. The processor adjusts the weighting and combination parameters of interaction signals to optimize prediction accuracy while managing computational resources efficiently.
Data Source
AI summary
Examples relate to managing power consumption of a stylus haptic feedback component prior to actuation. In one example, power is transmitted to a haptic circuit and a first haptic predicter value corresponding to a first user interaction with the stylus is determined. A weighted first haptic predicter value is generated by weighting the first haptic predicter value. A second haptic predicter value corresponding to a second user interaction is determined, and a weighted second haptic predicter value is generated by weighting the second haptic predicter value. At least the weighted first and second haptic predicter values are combined to generate a combined weighted predictive result, which is compared to a haptic predictive threshold value. On condition that such comparison yields a haptic predictive result, power continues transmitting to the haptic circuit. On condition that such comparison yields a non-haptic-predictive result, power ceases transmitting to the haptic circuit.