Conversation-Based Video Feedback for Real-Time Viewer Sentiment
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Solution Overview
Problem
Existing video feedback technologies struggle with handling the volume and complexity of user interactions, motivating users to provide relevant feedback, capturing feedback in real-time while maintaining a seamless viewing experience, and enabling content creators to extract meaningful viewer sentiment using intuitive interfaces.
Innovation Solution
A computer system engages in a dynamic conversation with viewers during video playback, generating prompts based on viewer inputs, video content, and external information using a trained model to capture temporally and spatially precise feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video feedback technologies (comment sections, polls, rating systems) are used, then users can provide feedback, but the system cannot effectively handle the volume and complexity of user interactions or capture feedback in real-time
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the video content and user feedback. The AI assistant dynamically generates contextualized prompts based on video content analysis, transforming the complex task of handling raw user feedback into a structured conversation flow. This mediator processes and structures user interactions, making the system manageable despite high feedback volume
Solution Approach 2:
The patent replaces traditional mechanical feedback collection mechanisms (manual comment sections, poll systems) with an AI-driven automated system. The AI assistant automatically analyzes video content, generates relevant prompts, and structures feedback without requiring manual intervention, thereby handling large volumes of interactions efficiently
2Loss of time
If users are prompted for feedback during video playback, then real-time feedback can be captured, but the viewing experience may be disrupted
Solution Approach 1:
The patent implements dynamic prompt generation that adapts to the current video context and user engagement level. The AI assistant adjusts prompt timing, frequency, and complexity based on real-time analysis of video content and user responses, creating a flexible feedback mechanism that minimizes disruption while maximizing capture effectiveness
Solution Approach 2:
The patent applies different feedback strategies to different segments of the video based on content analysis. High-engagement moments trigger more frequent prompts, while transitional or less critical segments use fewer interruptions. This localized approach ensures feedback capture aligns with content importance, maintaining viewing flow
3Loss of information
If generic feedback prompts are used, then users can respond easily, but the feedback lacks relevance and meaningful sentiment extraction
Solution Approach 1:
The patent performs preliminary analysis of video content before generating feedback prompts. The AI assistant pre-processes video metadata, scene understanding, and contextual information to create tailored prompts that are inherently relevant to the specific video segment. This preliminary contextualization ensures feedback relevance without requiring complex post-processing
Solution Approach 2:
The patent divides the video into meaningful segments and generates specific prompts for each segment based on its content characteristics. This segmentation allows the system to capture targeted feedback about specific scenes, objects, or narrative elements, making sentiment extraction more straightforward and relevant
4Loss of information
If a trained model generates dynamic prompts, then feedback relevance improves, but computational resources and processing time increase
Solution Approach 1:
The patent implements partial prompt generation where the AI model generates prompts selectively based on video segment importance and predicted user engagement. Not every video segment requires AI-generated prompts; the system uses judgment to apply computational resources only where they will most improve feedback quality, reducing overall energy consumption
Data Source
AI summary
A computer system engages in a dynamic conversation with a viewer of a video while the video is being played. The system generates prompts to the viewer based on one or more of the following: previous inputs received from the viewer, content of the video, information extracted from the video (such as objects, characters, and scenes in the video), and external information (such as information about the series that contains the video). The system may use a trained model, such as a large language model (LLM), to generate the prompts. The conversation may be initiated by the system or by the viewer. The system may generate and adapt additional prompts based on the responses that the viewer provides to previous prompts in the conversation.