Digital Human Response Adaptation Using Real-Time Sentiment Tags
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Solution Overview
Problem
Existing digital humans lack the ability to predict user questions and adapt responses based on real-time sentiment analysis, leading to reduced engagement and ineffective communication dynamics in remote interactions.
Innovation Solution
Implementing sentiment-based adaptation techniques using audio and video sensor data to determine user sentiment, employing machine learning algorithms and computer vision to generate responsive digital human interactions that adjust tone and content based on user emotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital humans use standard response generation without sentiment analysis, then system complexity is reduced, but user engagement and communication effectiveness deteriorate
Solution Approach 1:
The system segments the response generation process into distinct modules: sentiment analysis module that processes user input to determine emotional state, language model module that generates base responses, and adaptation module that modifies responses based on sentiment. This segmentation allows each module to specialize while maintaining overall system manageability.
Solution Approach 2:
The patent introduces sentiment analysis as an intermediary component between user input and response generation. This intermediary processes the user's emotional state and translates it into adaptation parameters that guide the language model, enabling nuanced response customization without requiring complete system redesign.
2Productivity
If digital humans implement real-time sentiment analysis, then user engagement improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary sentiment analysis on user input before generating the response. By analyzing the sentiment upfront and determining adaptation parameters in advance, the system prepares customization guidelines that streamline the subsequent response generation process, reducing overall processing time.
Solution Approach 2:
The patent implements partial sentiment analysis by focusing on key sentiment indicators rather than comprehensive emotional profiling. This selective approach extracts only the most relevant sentiment dimensions needed for response adaptation, reducing computational overhead while maintaining engagement effectiveness.
3Reliability
If digital humans apply multiple sentiment tags to responses, then communication effectiveness improves, but response generation complexity increases
Solution Approach 1:
The system applies different sentiment tags to different portions of the response rather than uniformly to the entire response. This local quality approach allows specific segments of the response to be customized for particular emotional effects, improving communication effectiveness while maintaining overall structural simplicity.
Solution Approach 2:
The patent uses sentiment tags as adjustable parameters that modify response characteristics. By representing emotional adaptations as discrete parameter changes rather than complex generative processes, the system maintains reliability through systematic parameter control while keeping the generation mechanism relatively simple.
Data Source
AI summary
Techniques are provided for sentiment-based adaptation of digital human responses. One method comprises determining a sentiment of a user by analyzing a vocal sentiment, a text sentiment and/or a facial sentiment of the user; applying the determined sentiment of the user to a language model that determines a sentiment-tagged response to an input of the user based on the determined sentiment, wherein the sentiment-tagged response comprises a predicted sentiment label identifying a sentiment to be employed by a digital human when delivering the sentiment-tagged response to the user; and providing the sentiment-tagged response to the digital human for delivery to the user, wherein the digital human transforms at least a portion of the sentiment-tagged response into a spoken format using the predicted sentiment label and a text-to-speech model. A vocal tone, a facial expression and/or a body positioning of the digital human may be adjusted based on the determined sentiment.