Emotion Detection System for Real-Time Coaching
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Solution Overview
Problem
Current technologies lack effective solutions for machine-supported emotion analysis in human-human interactions, particularly in service industries like sales and education, and fail to provide real-time feedback on emotional dynamics during online communications, leading to cultural misunderstandings and ineffective communication.
Innovation Solution
A system that includes a media feature extractor, a perceiver module for emotion detection and prediction, and a coaching module for personalized feedback, capable of analyzing audio, video, and text data to provide real-time emotional analysis and coaching, and storing data for retrospective analysis, which can handle multiple speakers, cultural contexts, and various emotional states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-supported emotion analysis is implemented, then communication effectiveness is improved, but device complexity increases
Solution Approach 1:
The system segments emotion analysis into multiple independent modules: media feature extractor for processing audio/video/text, perceiver module for detecting emotional responses, and coaching module for providing feedback. Each module handles specific tasks independently, improving reliability while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary emotion analysis system between human communicators that objectively measures emotional dynamics. This intermediary processes media features and provides coaching feedback, enhancing communication effectiveness without requiring direct modification of human interaction patterns.
2Reliability
If real-time emotion detection and feedback is provided, then communication effectiveness is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by providing real-time coaching feedback during communication events rather than waiting for retrospective analysis. The coaching module delivers immediate guidance on emotional dynamics, allowing users to adjust their communication approach during the interaction itself, thereby improving effectiveness without significant time loss.
Solution Approach 2:
The system maintains continuous emotion detection and feedback provision throughout the communication event. The perceiver module continuously analyzes media features and the coaching module continuously provides feedback, ensuring uninterrupted emotion analysis that improves communication effectiveness without requiring periodic interruptions for analysis.
3Measurement precision
If comprehensive media feature extraction from multiple modalities is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The media feature extractor is segmented into specialized sub-components that process different media modalities independently: audio feature extraction, video feature extraction, and text feature extraction. Each sub-component is optimized for its specific modality, improving measurement precision while managing complexity through division of labor.
Solution Approach 2:
The system implements a universal media feature extractor that handles multiple media modalities (audio, video, text) through a common architecture. This multi-functional extractor uses consistent processing principles across different modalities, improving emotion detection accuracy by综合分析 multiple sources while avoiding the complexity of entirely separate processing systems for each modality.
Data Source
AI summary
A system that performs emotion detection, prediction, and coaching system is disclosed. The system includes a media feature extractor, which extracts features from different media modalities, a perceiver module which detects or predicts emotional response for a given audience, a coaching module that generates context-based, personalized coaching in the form of commentary, retrospective analysis, tips kudos, events, and scores. The intermediate results, like media feature, perceiver output, and context, are stored in an emotion association database, which can be used as reference data by the system.