Virtual Human Clone Multimodal Conversation Digital Twin
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Solution Overview
Problem
Current human-computer interactions lack the effectiveness of face-to-face conversations due to limited knowledge and inability to seamlessly transition between human and virtual agents, resulting in inadequate handling of multimodal conversations.
Innovation Solution
A system and method that parses multimodal conversations, recognizes verbal and non-verbal behaviors, and trains a virtual human clone to mimic human behavior, enabling seamless transitions between human and virtual agents using digital twin technology, natural language processing, computer vision, and deep quantum learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a virtual agent is used to handle multimodal conversations, then automation and efficiency are improved, but the effectiveness and naturalness of interaction deteriorate due to limited knowledge and inability to interpret human behavior
Solution Approach 1:
The patent creates a digital twin (virtual human clone) that copies the behavioral patterns, verbal and non-verbal characteristics, and knowledge base of a human agent. This copy enables the virtual agent to interact naturally with users while maintaining automation, resolving the contradiction between automation efficiency and interaction effectiveness
Solution Approach 2:
The system performs preliminary training of the digital twin using recorded human agent interactions before deployment. By pre-learning from human behavior patterns and knowledge bases, the virtual agent is prepared to handle conversations effectively without requiring real-time human intervention, thus maintaining both automation and effectiveness
2Reliability
If human agents are involved in conversations to improve interaction quality, then naturalness and knowledge are improved, but system complexity and operational difficulty increase
Solution Approach 1:
Instead of directly involving human agents in every interaction, the system creates a digital copy that encapsulates human knowledge and behavior patterns. This copying approach maintains conversation quality while eliminating the need for complex human-in-the-loop coordination mechanisms
Solution Approach 2:
The digital twin is designed to autonomously handle conversations using the knowledge and behavioral patterns learned during training. This self-service capability eliminates the need for continuous human agent involvement, reducing system complexity while maintaining interaction quality
3Measurement precision
If comprehensive multimodal analysis is performed to improve behavior recognition, then accuracy of understanding is improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs comprehensive multimodal analysis during the training phase rather than during real-time interactions. By pre-processing and analyzing verbal and non-verbal behaviors during training, the digital twin learns accurate behavior patterns without requiring complex real-time processing, thus achieving high recognition accuracy while maintaining operational efficiency
Data Source
AI summary
Methods and systems for a multimodal conversational system are described. A method for interactive multimodal conversation includes parsing multimodal conversation from a physical human for content, recognizing and sensing one or more multimodal content from the parsed content, identifying verbal and non-verbal behavior of the physical human from the one or more multimodal content, generating learned patterns from the identified verbal and non-verbal behavior of the physical human, training a multimodal dialog manager with and using the learned patterns to provide responses to end-user multimodal conversations and queries, and training a virtual human clone of the physical human with interactive verbal and non-verbal behaviors of the physical human, wherein appropriate interactive verbal and non-verbal behaviors are provided by the virtual human clone when providing the responses to the end-user multimodal conversations and queries.