Hybrid-Triggered Haptic Modulation for Low-Latency A/V Effects
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Solution Overview
Problem
Existing VR systems lack individualized, home-use devices that can deliver target-specific haptics with next-generation realism and virtually zero latency, and do not employ learning-based approaches to complement user input for real-time haptic responses, nor integrate directly with audio/video signals for actuation or haptic effects.
Innovation Solution
A modular haptic system with a fan assembly and temperature control, integrated with sensors and machine learning, that delivers variable air flow and temperature based on user input and virtual environment data, and directly processes audio/video signals for unscripted haptic effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a modular haptic system with sensors and machine learning is integrated to deliver variable air flow and temperature, then haptic realism and target specificity are improved, but device complexity increases
Solution Approach 1:
The system is divided into modular components including fan assembly, temperature control elements, sensor arrays, and processing units. Each module performs a specific function and can be independently controlled, allowing complex haptic effects to be achieved through coordinated operation of simpler subsystems.
Solution Approach 2:
Machine learning models are pre-trained to recognize patterns in audio/video signals and predict appropriate haptic responses. This preliminary processing enables real-time haptic delivery without requiring complex runtime decision-making, reducing operational complexity while maintaining high precision.
2Productivity
If learning-based approaches are employed to process user input and virtual environment data, then compute efficiency is improved, but processing time increases
Solution Approach 1:
The machine learning models are pre-trained offline to learn the mapping between sensory inputs and optimal haptic responses. This preliminary training phase allows the system to make rapid predictions during actual operation without performing complex learning computations in real-time, thus improving compute efficiency during use while avoiding excessive processing delays.
Solution Approach 2:
The system continuously monitors haptic delivery outcomes and uses this feedback to refine its predictions and adjust parameters. This closed-loop control enables the system to adapt to varying conditions while maintaining efficient real-time operation through learned patterns rather than exhaustive computation.
3Adaptability or versatility
If the system directly processes audio/video signals for unscripted haptic effects, then adaptability is improved, but processing load increases
Solution Approach 1:
The machine learning model serves as an intermediary that processes audio/video signals and translates them into haptic control parameters. This intermediary layer filters and interprets the complex sensory data, converting it into actionable commands for the haptic actuators, thereby managing processing load while maintaining high adaptability to different content types.
Solution Approach 2:
The system creates simplified representations or features from the original audio/video signals that capture the essential characteristics needed for haptic translation. By working with these extracted features rather than raw signals, the system reduces processing load while maintaining the ability to generate appropriate haptic effects across diverse content.
4Measurement precision
If variable air flow and temperature are delivered to specific user portions, then haptic targeting precision is improved, but energy consumption increases
Solution Approach 1:
The system applies haptic effects to specific localized regions rather than uniformly across the entire user interface. By concentrating energy delivery only where and when needed based on sensory input and user position, the system achieves high targeting precision while minimizing overall energy consumption through selective actuation.
Solution Approach 2:
The system delivers haptic effects at varying intensities matched to the significance and intensity of the corresponding sensory events. For subtle virtual elements, minimal haptic feedback is provided, while significant events receive stronger responses. This partial action approach maintains precision for important cues while avoiding excessive energy expenditure on minor details.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise, real-time haptic delivery with reduced latency and increased compute efficiency, allowing users to experience immersive environments without content support hurdles and providing customizable effects through low-level scripting.
Implementation Method 1
A modular haptic system with a fan assembly and temperature control, integrated with sensors and machine learning, that delivers variable air flow and temperature
Implementation Method 2
A modular haptic system with a fan assembly and temperature control, integrated with sensors and machine learning, that delivers variable air flow and temperature
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
A system for end-device modulation by a hybrid trigger comprising at least one end-device (E-D) in communication with at least a first device (D1) outputting audio/video (a/v) programming; a processor; a memory element coupled to the processor; a program executable by the processor to: position a virtual representation of the E-D on a digital canvas displayed on a D1-coupled display representing a user's physical and virtual space; and capture from a corresponding region of the virtual space for modulating an effect on a corresponding portion of the canvas-positioned E-D based on a combination of at least two different triggers recognizing at least one of an a/v element or a/v event from the a/v program.