Machine Learning Haptic Interface for Dynamic Content Effects
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Solution Overview
Problem
Existing haptic technologies in devices like remote controllers and headphones are limited by pre-programmed haptic effects, which restrict the range of experiences for users, especially in content without explicit haptic instructions, and rely on simple correlations between audio frequencies and haptic responses.
Innovation Solution
A haptic control interface using machine learning algorithms to detect features in content, such as audio or video signals, and dynamically determine and induce corresponding haptic effects in haptic-enabled devices, allowing for more nuanced and context-aware haptic experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-programmed haptic effects are used in video games, then the implementation is simple and reliable, but the haptic effects are limited and not available for content without pre-programmed instructions
Solution Approach 1:
The patent introduces an intermediary system (haptic control interface with machine learning algorithm) that sits between the content source and haptic enabled device. This intermediary analyzes content features and dynamically generates haptic commands, enabling haptic effects for any content without requiring pre-programming in the device itself, thus resolving the contradiction between versatility and complexity
Solution Approach 2:
The system enables haptic enabled devices to automatically generate appropriate haptic effects by themselves through machine learning algorithms that analyze content features in real-time. The device serves itself by autonomously determining haptic commands based on detected content features, eliminating the need for external pre-programming while maintaining simplicity
2Adaptability or versatility
If simple correlation between audio frequencies and haptic effects is used, then the system is simple to implement, but the haptic effects lack nuance and context-awareness
Solution Approach 1:
The patent transitions from static frequency-to-haptic mappings to dynamic, context-aware haptic generation. The machine learning algorithm continuously analyzes content features and dynamically adjusts haptic effects based on the specific context, enabling nuanced and adaptive haptic responses that go beyond simple correlations
Solution Approach 2:
The system changes the parameters used for haptic control from simple audio frequency correlations to complex content feature detections. By analyzing multiple content parameters (visual elements, audio characteristics, contextual information) and transforming them into nuanced haptic commands, the system achieves sophisticated haptic effects without being constrained by simple mapping rules
Data Source
AI summary
Haptic effects have long been provided to enhance content, such as by providing vibrations, rumbles, etc. in a remote controller or other device being used by a user while watching or listening to the content. To date, haptic effects have either been provided by programming controls for the haptic effects within the content itself, or by providing an interface to audio that simply maps certain haptic effects with certain audio frequencies. The present disclosure provides a haptic control interface that intelligently induces haptic effects for content, in particular by using machine learning to detect specific features in content and then induce certain haptic effects for those features.