Abstract: A machine learning (ML) model is used to automatically generate haptics signals to actuate a haptics generator in a computer game controller. The haptics signal is generated based on audio from the game input to the ML model. Current controller operation and other parameters also may be input to the M model to modify the haptics signal. Category importance and frequency may be applied to the loss function of the ML model to further refine haptics generation. Post-filtering may be used to reduce false positives. Game genre may be used to reduce the number of candidate haptics signals for generation.

Abstract: A machine learning (ML) model is used to automatically generate haptics signals to actuate a haptics generator in a computer game controller. The haptics signal is generated based on audio from the game input to the ML model. Current controller operation and other parameters also may be input to the M model to modify the haptics signal. Category importance and frequency may be applied to the loss function of the ML model to further refine haptics generation. Post-filtering may be used to reduce false positives. Game genre may be used to reduce the number of candidate haptics signals for generation.