Dynamic Domain-Specific Chatbot Introduction via Semantic Routing
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Solution Overview
Problem
Conventional natural language chatbots, such as ChatGPT, fail to provide real-time adapted responses to users and lack domain-specific capabilities, making them inadequate for meeting the diverse needs of users across various domains.
Innovation Solution
A method and system that dynamically introduce a domain-specific chatbot based on the semantics of dialogue content, user preferences, and real-time environmental information, utilizing natural language processing (NLP) and generative artificial intelligence technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general natural language chatbot is used, then it can handle various domains, but it fails to provide domain-specific and real-time adapted responses
Solution Approach 1:
The system segments the chatbot functionality into a general chatbot for broad domain handling and domain-specific chatbots for specialized domains. The general chatbot routes queries to appropriate domain-specific chatbots when needed, allowing each component to specialize without requiring the entire system to be complex for every domain.
Solution Approach 2:
The general chatbot serves as a universal interface that can handle various domains through integration with multiple domain-specific chatbots. This multi-functional architecture allows a single system to provide both general conversation capabilities and specialized domain expertise without requiring separate systems for each domain.
2Ease of operation
If a general natural language chatbot is used, then it can converse with users, but it fails to provide real-time adapted responses to user status
Solution Approach 1:
The system implements feedback mechanisms where the chatbot continuously receives user inputs, analyzes user status information in real-time, and adapts its responses accordingly. The chatbot monitors conversation context and user preferences, using this feedback to dynamically adjust its communication style and content relevance.
Solution Approach 2:
The system performs preliminary analysis of user preferences and status information before generating responses. By pre-processing user data and maintaining context awareness, the chatbot is prepared to provide adapted responses without losing critical user status information during the conversation flow.
3Reliability
If conventional chatbots are used, then they provide standard answers, but these answers are not adapted to current user status in real time
Solution Approach 1:
The chatbot maintains continuous operation with uninterrupted analysis of user inputs and real-time generation of adapted responses. The system processes user status information continuously during the conversation, ensuring that each response is accurately adapted to the current user context without delays or interruptions in the adaptation process.
Data Source
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AI summary
A method and a system for introducing an intelligent dialogue are provided. The system provides a cloud server (100) that uses a processing circuit to perform the method. In the method, an online dialogue procedure is initiated, and a first chatbot is introduced. The first chatbot receives a user-input content via a dialogue interface (115), and semantic features of the user-input content, user data, and real-time environmental information obtained from an external system can be extracted. According to such information, a second chatbot of a specific domain can be introduced into the dialogue interface (115). A natural language model and a generative artificial intelligence technology operated in the second chatbot generate a dialogue content that matches the semantic features, a user preference, and the real-time environmental information. The dialogue content is outputted via the dialogue interface (115).