Conversational Video Feedback During Playback for Nuanced Sentiment
Find Innovative SolutionsGenerate Solutions
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, 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, using a trained model to generate prompts based on viewer inputs, video content, and external information, adapting to responses for precise feedback capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional video feedback technologies (comment sections, polls, rating systems) are used, then basic viewer feedback can be collected, but the system cannot effectively handle the volume and complexity of user interactions or capture nuanced viewer sentiment
Solution Approach 1:
The patent introduces an AI conversational agent as an intermediary between the video content and the viewer feedback system. This agent engages viewers in natural conversations during video playback, extracting nuanced sentiment and feedback that traditional systems miss. The intermediary handles the complexity of interpreting user interactions while maintaining a simple user interface.
Solution Approach 2:
The patent replaces traditional mechanical feedback collection mechanisms (buttons, polls, forms) with an AI-based conversational system using natural language processing. This substitution enables the system to handle complex user interactions and extract meaningful sentiment without requiring complicated interface elements or manual analysis processes.
2Loss of time
If real-time feedback capture is implemented during video playback, then timely viewer insights are obtained, but the viewing experience may be disrupted by frequent prompts and interruptions
Solution Approach 1:
The patent implements periodic conversational prompts during video playback rather than continuous interruptions. The AI agent engages viewers at strategically chosen moments based on conversation flow and video content, capturing real-time feedback while maintaining a natural rhythm that doesn't overly disrupt the viewing experience.
Solution Approach 2:
The patent creates a dynamic conversational system that adapts to viewer responses in real-time. The AI agent adjusts the pace, depth, and timing of feedback requests based on the ongoing conversation and viewer engagement level, allowing flexible real-time feedback capture without rigid interruptions that would disrupt viewing.
3Measurement precision
If dynamic conversation-based feedback is implemented using AI models, then precise and nuanced viewer sentiment is captured, but the computational resources and processing time required increase significantly
Solution Approach 1:
The patent performs preliminary processing by pre-training AI models on video content and viewer interaction patterns before actual feedback collection. This preliminary action enables the system to efficiently process real-time conversations with higher precision, as the models are already tuned to the specific content and audience, reducing computational overhead during live feedback capture.
Solution Approach 2:
The patent segments the feedback analysis process into distinct conversational phases and processing stages. The AI agent breaks down complex sentiment analysis into smaller conversational turns and feedback categories, allowing efficient processing of nuanced viewer responses while managing computational resources through structured, modular analysis.
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.