Multi-Person Chatbot Timing Logic for Proactive Response Generation
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Solution Overview
Problem
Conventional chatbots lack the ability to actively provide appropriate response suggestions in multi-person conversation environments, resulting in poor chat initiative and response efficiency.
Innovation Solution
A system and method that utilizes an artificial intelligence device, a portable device, and a server-end host to sense, convert, and embed timing and classification labels into speech signals, determine conversation logic, and generate proactive response messages using a large language model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional chatbot uses one-to-one conversation mode, then system complexity is low, but conversational initiative and response efficiency are poor
Solution Approach 1:
The system segments the chatbot functionality into multiple specialized modules: speech processing module, context analysis module, response generation module, and timing logic module. Each module handles specific aspects of conversation processing, enabling parallel operation and improving response efficiency without proportionally increasing overall system complexity.
Solution Approach 2:
The system performs preliminary actions by continuously pre-processing speech signals, pre-analyzing context messages, and pre-generating potential response options before user input is fully processed. This allows the chatbot to be ready with response suggestions, significantly improving response efficiency.
2Ease of operation
If conventional chatbot waits for user questions, then system operation is simple, but chat initiative is poor
Solution Approach 1:
The system implements continuous feedback loops where the chatbot monitors conversation context, analyzes timing patterns, and generates response suggestions proactively. The feedback mechanism evaluates conversation flow and triggers appropriate responses without waiting for explicit user questions, enhancing chat initiative while maintaining operational simplicity through automated decision rules.
Solution Approach 2:
The chatbot performs self-service by automatically analyzing conversation context, determining appropriate timing for responses, and generating response suggestions without requiring manual intervention or complex user prompts. This self-driven operation improves chat initiative while keeping the system easy to operate.
3Speed
If chatbot processes speech signals in real-time, then response speed improves, but processing complexity increases
Solution Approach 1:
Speech signal processing is divided into separate stages: speech-to-text conversion, feature extraction, context analysis, and response generation. Each stage is handled by dedicated modules that can process information in parallel, achieving real-time response speed while managing processing complexity through functional decomposition.
Solution Approach 2:
The system introduces intermediate processing layers including context message buffers and timing logic intermediaries that mediate between raw speech signals and final responses. These intermediaries pre-process and organize data, reducing the computational burden on the response generation module and enabling faster real-time processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves conversational initiative and response efficiency by actively generating and filtering response messages based on conversation timing and topic evolution.
Implementation Method 1
The speech processor is electrically connected to the sensor, the speaker, and the storage device, and configured to convert the sensed speech signals into the feature vectors based on Mel-frequency cepstral coefficients
Data Source
AI summary
A system of generative chatbot in a real multi-person response situation and a method thereof are disclosed. In the system, speech signals are sensed and converted into feature vectors and text messages, and a timing label and a classification label are embedded into the text messages, the text messages are stored as a context message, so that a server-end host can determine a timing logic of a multi-person conversation, and the context message and the timing logic are transmitted to an artificial intelligence device which determines a current conversation stage, a topic evolution, predicts a conversation development, and actively generates and stores a response message to the server-end host; the server-end host can filter out the response messages and transmit the filtered response message to a portable device for output. Therefore, the technical effect of improving conversational initiative and response efficiency can be achieved.