Local AI Emotion Detection for Live Streaming Feedback
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Solution Overview
Problem
Current network-based streaming video platforms lack the capability to provide real-time feedback to presenters during large-scale live events, as they do not capture audio or video from viewers due to data processing limitations, resulting in an inability to simulate a physical audience experience.
Innovation Solution
Implementing low-latency AI/ML technology on client devices to analyze viewer video and audio, transmitting engagement data to servers, which then mix selected audio and video feedback into the live stream, allowing presenters to receive real-time audience reactions and enhancing the viewer experience by simulating ambient noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If audio and video of all viewers are captured to provide feedback to presenters, then the completeness of audience feedback is improved, but the data processing load and system complexity increase significantly
Solution Approach 1:
The patent segments the audience feedback collection process by dividing viewers into different groups based on their feedback participation. Instead of processing all viewer audio/video, the system selectively captures feedback from a subset of viewers who are identified as willing and able to provide feedback. This segmentation reduces the overall data processing load while still capturing representative audience reactions.
Solution Approach 2:
The patent extracts only the essential feedback elements needed for presenter awareness rather than capturing complete audio/video streams from all viewers. The system extracts key reaction indicators (such as applause, laughter, or other audience responses) from viewer devices and transmits only these extracted feedback signals to the presenter, significantly reducing data transmission and processing requirements.
2Speed
If audio and video of viewers are captured and transmitted to servers, then the real-time feedback capability is improved, but the network bandwidth and processing power requirements increase
Solution Approach 1:
The patent uses simplified representations or proxies of viewer feedback rather than transmitting full audio/video streams. Instead of sending complete media streams from each viewer device, the system creates compressed feedback signals or symbolic representations of audience reactions (such as standardized reaction codes or aggregated feedback metrics) that convey the same information with minimal data volume.
Solution Approach 2:
The patent implements partial feedback collection by capturing feedback from only a portion of the audience rather than all viewers. The system selectively activates feedback collection on viewer devices based on criteria such as viewer engagement level, device capabilities, or random selection, thereby achieving sufficient real-time feedback representation while transmitting significantly less data than universal capture would require.
3Object-affected harmful factors
If viewer privacy is protected by not capturing audio and video, then user privacy security is improved, but the ability to provide audience feedback to presenters deteriorates
Solution Approach 1:
The patent introduces an intermediary feedback mechanism that does not require direct capture or transmission of personal viewer audio/video data. The system uses intermediate feedback signals or proxy indicators that represent audience reactions without exposing actual viewer content. This intermediary layer allows feedback transmission while maintaining privacy boundaries, as the intermediary data does not contain personally identifiable or sensitive viewer information.
Solution Approach 2:
The patent implements viewer-controlled feedback mechanisms where individual viewers can independently choose to provide feedback from their own devices without requiring centralized capture or processing of their personal audio/video streams. Each viewer's feedback participation is self-managed through their device, allowing them to control what feedback is shared while maintaining privacy. The system aggregates these self-provided feedback signals without needing to access or store personal viewer media content.
Data Source
AI summary
The disclosed embodiments are directed toward local emotion detection during live streaming events. A client device receives a video stream from a remote server and captures media content while displaying the video stream. The client device uses a local machine learning/artificial intelligence event detection model to detect events in the media content. The client device then transmits detected events to the remote server involved in the live stream. The client device may additionally stream the locally captured media content to the remote server. In response, the remote server provides an interaction dashboard and, in some embodiments, mixes the local media content with the live stream.