Live Stream Topic Prompting for Real-Time AI User Interaction
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Solution Overview
Problem
Live video streaming lacks engaging elements to keep users actively participating in the experience, despite advancements in video and audio quality.
Innovation Solution
A system that uses generative artificial intelligence to analyze live video streams in real-time, generate contextually relevant prompts, and facilitate user interaction through a graphical interface with a generative machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If live video streaming provides high-quality video and audio, then technical quality is improved, but user engagement and active participation deteriorate
Solution Approach 1:
The system implements real-time feedback loops where AI models analyze video content and user interactions, then dynamically adjust and generate new prompts based on this feedback. This creates an adaptive system that responds to user behavior patterns, maintaining engagement through continuous improvement of interaction relevance.
Solution Approach 2:
The AI-generated prompts serve as self-service interaction elements that automatically engage users without requiring manual content creation or moderation. The system autonomously generates contextually relevant prompts based on video content analysis, reducing the need for human intervention while maintaining high engagement levels.
2Ease of operation
If the system generates contextually relevant prompts in real-time, then user engagement is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of real-time prompt generation into distinct functional modules: video content analysis module, AI model inference module, prompt generation module, and interaction tracking module. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable despite its complexity.
Solution Approach 2:
The patent introduces an intermediary AI processing layer that sits between the video stream and user interface. This intermediary layer handles the complex real-time analysis and prompt generation, shielding the user interface from complexity while maintaining high engagement through intelligent interactions.
3Loss of information
If AI models analyze video content in real-time, then prompt relevance is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing video content and pre-generating potential prompts before they are needed for user interaction. AI models analyze video segments in advance and prepare contextually relevant prompts, reducing real-time processing requirements and maintaining high prompt relevance.
Solution Approach 2:
The system employs periodic action by analyzing video content at strategically selected intervals rather than continuously processing every frame. This periodic analysis approach maintains prompt relevance while significantly reducing computational overhead and processing time compared to continuous real-time analysis.
Data Source
AI summary
A computer-implemented method may include identifying a video stream for presentation to a user, extracting, from content of a segment of the video stream, at least one topic, presenting, to the user, the at least one topic as a selectable interface item in a graphical user interface when the segment of the video stream is presented to the user, receiving an input from the user selecting the at least one topic via the selectable interface item, and providing, in response to receiving the input from the user, the at least one topic as a prompt to a generative machine learning model. Various other methods, systems, and computer-readable media are also disclosed.