Game Controller Haptics Generation From Audio Using ML
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Solution Overview
Problem
Existing technologies lack comprehensive data and methods for automating haptic generation in computer games, making it difficult to generalize haptic signals across different games with varying design philosophies, and there is a lack of research on audio-haptics correlations.
Innovation Solution
An apparatus using a machine learning model to input audio segments from computer games, classify them, and actuate haptic generators based on the classified haptic information, incorporating controller operations and genre-specific training to generate appropriate haptic feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual haptic generation is used for each game segment, then haptic quality and customization can be maintained, but developer workload increases significantly
Solution Approach 1:
The system enables automated haptic generation where the computer itself generates haptic signals based on audio analysis and machine learning models, eliminating the need for manual haptic programming by developers while maintaining consistent quality across different game segments
Solution Approach 2:
The patent replaces manual haptic design processes with an automated machine learning-based system that analyzes audio segments and generates appropriate haptic signals automatically, substituting human creative work with an intelligent automated system
2Productivity
If automated haptic generation is implemented, then developer workload is reduced, but generalization across different games with varying design philosophies becomes difficult
Solution Approach 1:
The system dynamically adapts to different game genres and design philosophies by training machine learning models on genre-specific data and adjusting haptic generation parameters based on the particular game's design requirements, allowing the same automated system to serve multiple game types effectively
Solution Approach 2:
The patent modifies haptic generation parameters such as frequency, amplitude, and duration based on audio characteristics and game genre-specific training, enabling the system to produce appropriate haptic feedback for different game types while maintaining automated efficiency
3Measurement precision
If comprehensive haptic data is collected for training, then model accuracy improves, but data capture difficulty and time increase
Solution Approach 1:
The system performs preliminary data collection and model training during game development phases, building comprehensive training datasets in advance before deployment, which reduces the time needed for data capture during actual game play while maintaining high model accuracy
Data Source
AI summary
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.