Neural Network Trigger Detection for Immersive Media Safety
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing immersive media technologies, such as VR and AR, often fail to account for user sensitivities, leading to unpleasant experiences for viewers with phobias or sensitivities, as they may inadvertently expose users to triggers like heights or violent content that can cause severe reactions.
Innovation Solution
The use of neural networks to detect potential triggers in media content and modify the presentation to prevent undesirable reactions, such as adding overlays to obscure sensitive areas, pausing content, or providing notifications, allowing users to skip or stop the experience, while maintaining immersion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If immersive media content is presented without modification to provide realistic experiences, then user immersion and engagement are improved, but users with phobias or sensitivities may be exposed to triggers causing unpleasant or severe reactions
Solution Approach 1:
The system performs preliminary analysis of immersive media content using neural networks to identify potential triggers (heights, enclosed spaces, violence, etc.) before the user experiences the content. Modifications such as adding warning overlays, pausing content, or providing notifications are prepared in advance based on the detected triggers, allowing users with sensitivities to make informed decisions while maintaining immersion for other users.
2Object-affected harmful factors
If content is modified to prevent triggers for sensitive users, then user safety and comfort are improved, but the realism and immersion of the experience deteriorates
Solution Approach 1:
The system applies modifications locally and selectively rather than globally. Neural networks identify specific trigger elements within the immersive content (such as particular scenes, objects, or situations) and apply modifications only to those localized areas. For example, warning overlays are placed only near trigger points, or content is paused only when specific sensitive elements appear, allowing the rest of the immersive experience to maintain its realism and engagement.
Solution Approach 2:
The system dynamically adjusts the level and type of modification based on real-time detection of triggers and user preferences. The neural networks continuously analyze the immersive content stream and adaptively apply modifications such as adding overlays, pausing, or providing notifications only when and where triggers are detected. This dynamic approach allows the system to maintain full immersion during safe content while providing protection during sensitive moments.
3Object-affected harmful factors
If neural networks are used to detect and modify content in real-time, then user safety is improved, but computational resources and processing time increase
Solution Approach 1:
The content analysis and modification process is divided into separate segments or stages. Neural networks are deployed to detect triggers in specific segments of the immersive content (such as individual frames, scenes, or time intervals) rather than analyzing the entire content stream continuously. This segmentation allows the system to process content in manageable chunks, reducing peak computational demands while maintaining effective trigger detection across the full experience.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to determine reactions to content of one or more users. In at least one embodiment, one or more neural networks can predict one or more reactions from one or more content viewers based, at least in part, on one or more images contained in the content.