AI Dialog System Reducing Turn Count via Context Association
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Solution Overview
Problem
Conventional dialog systems provide responses only to user inquiries, leading to unnecessary dialog turns and increased user effort, as they do not offer additional related responses based on user preferences and conversation history.
Innovation Solution
An electronic apparatus and control method that receive user utterances, identify tasks, determine the degree of association between tasks, and set response patterns based on user preference and conversation history, enabling the provision of additional related responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a conventional dialog system provides only direct responses to user inquiries, then the system complexity remains low, but the user has to perform multiple inquiries and the dialog time becomes long
Solution Approach 1:
The system performs preliminary analysis of user preferences and conversation history before providing responses. It pre-processes user data to identify patterns and preferences, then uses this pre-analyzed information to proactively provide additional relevant responses without waiting for explicit user inquiries, thereby reducing dialog time while managing complexity through structured preprocessing
Solution Approach 2:
The system dynamically adjusts its response behavior based on real-time analysis of conversation context and user preferences. It flexibly determines whether to provide only direct responses or additional related responses based on the specific dialog situation, user preferences, and conversation history, optimizing the balance between response completeness and system complexity adaptively
2Ease of operation
If a dialog system provides additional related responses based on user preferences and conversation history, then user experience improves, but the system complexity increases
Solution Approach 1:
The system segments the response generation process into distinct modules: user preference analysis, conversation history processing, task identification, and response pattern selection. Each module handles a specific aspect of the complex task independently, making the overall system more manageable and maintainable while still providing comprehensive additional responses that improve user experience
Solution Approach 2:
The system introduces an intermediary response pattern determination mechanism that mediates between the basic response generation and the additional information provision. This intermediary layer analyzes user preferences and conversation history to selectively enhance responses, improving user experience while preventing unnecessary complexity by only adding information when beneficial
3Loss of information
If a dialog system provides comprehensive additional information, then the quantity of information provided increases, but the response length and information overload may increase
Solution Approach 1:
The system applies partial action by selectively providing additional information based on user preferences and conversation context rather than always providing comprehensive information. It determines the appropriate level of additional response based on analyzed user needs, providing just enough supplementary information to be helpful without overwhelming the user with excessive content
Data Source
AI summary
An electronic apparatus and a control method thereof are provided. A method of controlling an electronic apparatus according to an embodiment of the disclosure includes: receiving input of a first utterance, identifying a first task for the first utterance based on the first utterance, providing a response to the first task based on a predetermined response pattern, receiving input of a second utterance, identifying a second task for the second utterance based on the second utterance, determining the degree of association between the first task and the second task, and setting a response pattern for the first task based on the second task based on the determined degree of association satisfying a predetermined condition. The control method of an electronic apparatus may use an artificial intelligence model trained according to at least one of machine learning, a neural network, or a deep learning algorithm.