Conference Support System for Automated Problem Classification and Prediction
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Solution Overview
Problem
Existing conference support systems fail to predict future problems and overlooked issues, and lack accuracy in proposals based on biological and contextual information, leading to inefficient problem resolution.
Innovation Solution
A conference support system that includes a user information acquisition unit to gather voice, biological, and image data, and an analysis unit to generate text data, classify problems, and learn relevance using models to automatically assign classification information, predicting future problems and proposing solutions based on user psychological states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only biological information and context are used for proposals, then the system is simple to operate, but the accuracy of problem prediction and proposal is insufficient
Solution Approach 1:
The system segments the analysis process into multiple independent modules: voice information processing, text data generation, classification information assignment, and learned model application. Each module handles a specific aspect of the analysis, allowing the system to achieve high accuracy through comprehensive multi-dimensional analysis while maintaining manageable complexity through modular design.
2Productivity
If manual classification is performed for all user statements, then classification accuracy is high, but the time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary automated classification of user statements using learned models before human review. Classification information is automatically assigned to text data based on voice information and contextual analysis, preparing the data in advance for more efficient human verification and decision-making, thereby reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The system implements feedback mechanisms where classification results are continuously refined based on user responses and conference outcomes. The learned models are updated with feedback from manual classifications and actual problem resolution effectiveness, creating a closed-loop system that improves efficiency over time while maintaining high accuracy through iterative optimization.
Data Source
AI summary
Provided is a conference support system that includes a user information acquisition unit that acquires voice information of each of users, and an analysis unit that generates text data of each user statement on the basis of the voice information, wherein the user information acquisition unit acquires classification information of a problem proposal or problem handling assigned by the users to the text data of the user statement, and acquires classification information of a satisfaction level or a problem resolution level assigned by the users to the text data to which the classification information of the problem proposal or the problem handling is assigned.