Haptic Effect Generation Using Complementary Multimedia Data
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Solution Overview
Problem
Existing automated haptification algorithms for multimedia content are inaccurate and resource-intensive, often over-inclusive or under-inclusive, failing to provide desirable haptic effects during multimedia playback.
Innovation Solution
A method that receives multimedia data along with complementary information, such as metadata or closed captions, to determine and output haptic effects based on this data, using algorithms that analyze audio and video patterns and keywords to generate tailored haptic responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated haptification algorithms are used to generate haptic effects from multimedia content, then productivity is improved by eliminating manual design, but manufacturing precision deteriorates due to inaccuracy in selecting appropriate haptic effects
Solution Approach 1:
The patent introduces complementary information (metadata, closed captions, transcripts) as an intermediary element that bridges the gap between automated processing and accurate haptic effect selection. This intermediary provides contextual clues that guide the algorithm in making more precise decisions about which haptic effects to apply, thereby improving manufacturing precision while maintaining automated productivity
Solution Approach 2:
The system performs preliminary analysis of complementary information before generating haptic effects. By extracting keywords and contextual data from metadata, closed captions, and transcripts in advance, the algorithm prepares a structured understanding of the content that enables more accurate haptic effect selection during playback, resolving the contradiction between automation and precision
2Measurement precision
If automated haptification algorithms analyze all audio and video content, then measurement precision improves in identifying haptic events, but use of energy worsens due to heavy processing requirements
Solution Approach 1:
The patent extracts only the essential information needed for haptic effect determination from the multimedia content by analyzing complementary information such as metadata, closed captions, and transcripts. This extraction approach isolates the key contextual elements that indicate haptic events without requiring full analysis of all audio and video data, thereby maintaining measurement precision while reducing energy consumption
Solution Approach 2:
Instead of analyzing all multimedia content equally, the system applies partial action by focusing computational resources on analyzing complementary information that is most indicative of haptic events. This selective analysis approach achieves sufficient measurement precision for haptic effect selection while significantly reducing the overall processing energy required
3Productivity
If automated algorithms haptify all detected events, then productivity improves by comprehensive coverage, but object-generated harmful factors worsen due to over-inclusivity creating noisy haptic tracks
Solution Approach 1:
The system uses complementary information as feedback to guide the haptification process. By continuously referencing metadata, closed captions, and transcripts during event detection, the algorithm receives contextual feedback that helps distinguish between events that should be haptified and those that should be excluded, thereby maintaining productivity while reducing haptic track noise through more discerning selection
Solution Approach 2:
The patent applies partial action by selectively haptifying only those events that are confirmed through analysis of complementary information. Rather than haptifying all detected events, the system uses the additional contextual data to filter and refine the selection, achieving productive output while avoiding the harmful effect of over-inclusivity and noisy haptic tracks
4Manufacturing precision
If manual haptic design is used for each multimedia content item, then manufacturing precision improves in tailoring haptic effects, but productivity deteriorates due to requiring haptic designers for each item
Solution Approach 1:
The patent enables the multimedia content to essentially design its own haptic track by analyzing its own complementary information such as metadata, closed captions, and transcripts. This self-service approach allows the system to automatically generate customized haptic effects tailored to each content item without requiring manual intervention from haptic designers, thereby achieving both high manufacturing precision and productivity
Solution Approach 2:
The system changes the parameter of haptic effect selection from fixed manual rules to dynamic algorithmic determination based on content-specific complementary information. By adjusting haptic parameters according to the analyzed metadata, captions, and transcripts, the system achieves manual-level customization accuracy while maintaining automated productivity
Data Source
AI summary
The present disclosure is generally directed to systems and methods for providing haptic effects based on information complementary to multimedia content. For example, one disclosed method includes the steps of receiving multimedia data comprising multimedia content and complementary data, wherein the complementary data describes the multimedia content, determining a haptic effect based at least in part on the complementary data, and outputting the haptic effect while playing the multimedia content.