Active Chatbot System Using Composite Finite State Machine for Emotional Response
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Solution Overview
Problem
Conventional chatbots lack flexibility and realism in their interactions, as they primarily respond to specific questions and fail to perceive user states or emotions, leading to a rigid and non-humanized communication experience.
Innovation Solution
An active chatbot system with a composite finite state machine that includes sensors to detect physiological and behavioral states, generating natural language questions and emotional responses based on client behavior and on-demand conversation settings, using a combination of finite state machines and a trained emotion AI model to filter and output relevant messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conventional chatbot uses a passive chat mode to respond only to specific questions, then the system complexity is low, but the conversational flexibility and realism are poor
Solution Approach 1:
The chatbot system is segmented into multiple independent modules: a state perception module that detects user states, a question generation module that creates questions based on detected states, an emotion analysis module that processes emotional content, and a response generation module that formulates final responses. This modular segmentation allows the system to achieve high conversational flexibility through coordinated module operations while keeping individual module complexity manageable.
Solution Approach 2:
The chatbot system is designed as a multi-functional platform that can perform diverse functions including state perception, question generation, emotion analysis, and response generation within a single integrated architecture. This universality enables the system to adapt to various conversation scenarios and user states, achieving high conversational flexibility without requiring separate specialized systems for each function.
2Adaptability or versatility
If a conventional chatbot actively asks questions based on user browsing records, then the chatbot can actively engage users, but the interactive manner is rigid and not humanized
Solution Approach 1:
The chatbot employs dynamic question generation that adapts to real-time user states and emotions rather than following fixed templates based on browsing records. The system continuously perceives user state changes and dynamically adjusts question content, timing, and emotional tone, making interactions feel more natural and humanized while maintaining active engagement capability.
Solution Approach 2:
The system incorporates feedback loops where user responses to generated questions are analyzed to update the understanding of user state and emotions. This feedback mechanism allows the chatbot to refine its question generation strategy in real-time, improving interactive naturalness by responding appropriately to user reactions while maintaining active engagement through continuous conversation flow.
3Adaptability or versatility
If the chatbot uses sensors to continuously sense user states and generate emotional responses, then the conversational realism is improved, but the device complexity and energy consumption increase
Solution Approach 1:
The sensor-based state perception operates periodically rather than continuously, detecting user states at intervals and triggering question generation only when state changes are detected. This periodic operation maintains conversational realism by responding to actual user state changes while significantly reducing energy consumption compared to continuous sensing and processing.
Solution Approach 2:
The system applies different processing intensities to different aspects of user interaction: intensive emotion analysis is applied only when emotional states are detected, while routine state monitoring uses lighter processing. This local quality approach ensures high conversational realism for emotionally significant interactions while minimizing energy consumption during neutral or routine conversation phases.
Data Source
AI summary
An active chatbot system with composite finite state machine and a method thereof are disclosed. In the active chatbot system, a rough question message having a natural language structure is generated based on a client behavior state and an on-demand conversation setting, and the rough question message is inputted to a question optimization circuit to generate a precise question message, and the precise question message is transmitted to an artificial intelligence platform to obtain a corresponding answer message, the answer message is inputted to a trained emotional AI model to generate an emotional answer message and the emotional answer message is stored in an answer list, so that the emotional answer message matching the on-demand conversation setting can be filtered out as an on-demand conversation message, the on-demand conversation message is transmitted to the client-end host for output. Therefore, the technical effect of improving conversational flexibility and realism of chatbot can be achieved.