Multimodal Response Generation in Conversation Services
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing conversation service software applications are monomodal, limiting their ability to provide complex and customized responses to user inquiries, which often require both text and video modalities.
Innovation Solution
A system and method that utilize machine learning algorithms to generate multimodal responses by quickly identifying relevant video clips and text answers from a large universe of video content, leveraging dense passage retriever models and relevancy classification models for advanced relevancy determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conversation service applications use monomodal text-based responses, then the system complexity remains low, but the responsiveness and usefulness to user inquiries are limited
Solution Approach 1:
The system segments video content into individual frames and processes them separately through the relevancy classification model. Each video frame is evaluated independently for its relevance to the user query, allowing the system to handle complex multimodal content through manageable discrete units rather than processing entire videos as single entities.
Solution Approach 2:
The patent introduces an intermediary relevancy classification model that acts as a bridge between the user's text query and the video content. This model translates the user's intent into relevance scores for different video frames, enabling seamless integration between text-based user input and video-based responses without requiring direct complex interaction between the two modalities.
2Measurement precision
If the system processes a large universe of video content to find relevant clips, then the relevancy and usefulness of responses improve, but the time required to generate responses increases
Solution Approach 1:
The system performs preliminary actions by pre-processing video content into extractable frames and pre-training the relevancy classification model on extensive video data before deployment. This preliminary preparation allows the system to quickly evaluate new user queries against pre-processed video content without having to analyze raw videos in real-time, significantly reducing response generation time while maintaining high relevancy accuracy.
Solution Approach 2:
The patent replaces traditional keyword-matching mechanical search methods with a machine learning-based relevancy classification system. Instead of relying on simple text-based video searching, the system uses trained neural network models to automatically determine frame relevancy, achieving both higher precision in identifying relevant content and improved processing speed through efficient model inference.
3Adaptability or versatility
If the system generates customized multimodal responses with video frames, then user satisfaction and response usefulness increase, but the computational resources and processing power required increase
Solution Approach 1:
The system applies local quality by selecting and presenting only specific video frames that are highly relevant to the user's query, rather than providing generic or comprehensive video content. The relevancy classification model identifies and extracts only the portions of video content that locally match the user's intent, reducing the amount of computational resources needed to process and present information while maintaining high customization and user satisfaction.
Data Source
AI summary
Methods and apparatuses for multimodal enhancement of interactions in conversation service applications include a server that establishes a chat-based communication session between a conversation service application and a client computing device. Query input is captured from a user during the session. The server converts an audio segment corresponding to a video into a video transcript using speech recognition, and determines a transcript portion for the video that is responsive to the query input using a dense passage retriever model. The server selects video frames from the video that correspond to the transcript portion and determines, for the selected frames, a predicted relevancy between the selected frame and the query input. The server generates a response to the query input comprising the selected frames based upon the relevancy. The server transmits the response to the client device for display.