Dialogue Control Device Emotion Estimation Topic Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies for human-machine dialogue lack the ability to effectively estimate and respond to a user's emotions, leading to inadequate communication, as they primarily rely on text conversion and basic emotion recognition without considering contextual changes in conversation.
Innovation Solution
A dialogue control device equipped with an emotion estimator and a dialogue controller that uses a combination of facial expression, prosody, and text analysis to select and adapt topics based on the user's emotions, ensuring a more engaging and context-aware interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If basic text conversion and emotion recognition are used, then the device complexity is reduced, but the dialogue quality and user engagement deteriorate
Solution Approach 1:
The patent combines multiple emotion recognition methods (text analysis, prosody analysis, and facial expression analysis) into a single integrated emotion estimation system. This merging of multiple detection mechanisms allows the system to achieve high reliability in emotion recognition and dialogue quality without requiring overly complex individual components, as the methods work complementarily rather than redundantly.
Solution Approach 2:
The emotion estimation device performs multiple functions simultaneously: it recognizes emotions from text, analyzes prosody patterns, processes facial expression data, and adapts dialogue topics based on detected emotional states. This multi-functionality allows a single system to handle diverse input modalities and provide comprehensive emotion-aware dialogue control without requiring separate specialized systems for each function.
2Measurement precision
If multiple emotion recognition methods are combined, then the emotion estimation accuracy is improved, but the device complexity increases
Solution Approach 1:
The emotion recognition system is segmented into distinct functional modules: a text analysis unit that processes utterance contents, a prosody analysis unit that analyzes speech characteristics, and a facial expression analysis unit that processes visual data. Each module independently processes its specific input type and contributes to the overall emotion estimation, allowing the system to achieve high accuracy through multiple specialized components rather than one monolithic complex system.
Solution Approach 2:
The patent introduces an emotion estimation device as an intermediary component that receives inputs from multiple sources (text, prosody, facial expressions) and synthesizes them into a unified emotion assessment. This intermediary integrates information from different modalities and translates diverse input types into a common emotion representation that the dialogue control system can utilize, simplifying the overall architecture while maintaining high estimation accuracy.
3Adaptability or versatility
If the dialogue topic is dynamically changed based on emotion, then the user engagement is improved, but the loss of information about original topic increases
Solution Approach 1:
The system implements continuous feedback loops where the emotion estimation device constantly monitors the user's emotional state and provides real-time information to the dialogue control device. Based on this feedback, the dialogue control device dynamically adjusts topic selection from the topic map, switching between topics to maintain user engagement while preserving important information through the structured organization of the topic map that allows reversible transitions.
Solution Approach 2:
The dialogue control system dynamically adapts topic selection based on real-time emotion detection results. The topic map structure allows the system to flexibly navigate between different topics depending on the user's emotional state, enabling the dialogue to flow naturally while maintaining coherence. This dynamic adaptation preserves information by allowing the system to return to previous topics if needed, rather than following a rigid linear progression.
Data Source
AI summary
An emotion estimator of a dialogue control device estimates a dialogue partner's emotion based on the dialogue partner's facial expression or voice prosody. A dialogue controller controls the dialogue with the dialogue partner using a topic selected based on whether the emotion estimated by the emotion estimator is good or bad. Specifically, the dialogue controller controls the dialogue with the dialogue partner based on a topic map created by a topic mapper and including a topic that the dialogue partner likes.