Active Chatbot System with Client-Side Model Customization
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Solution Overview
Problem
Conventional chatbots lack customization and specificity, leading to dull interactions and inefficient computing power usage, as they fail to adapt to individual users and require extensive server-end resources for model training.
Innovation Solution
An active chatbot system with a client-end host and server-end host, utilizing sensors to sense user behavior and generate customized parameters, which are used to create precise question messages for an AI platform, allowing for dynamic training and weighting adjustments to enhance interaction specificity and reduce server load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional AI models are used to answer user questions, then the chatbot can provide standard responses, but the dialogues become dull and lack specificity for different users
Solution Approach 1:
The patent applies local quality by customizing the AI model's response characteristics for different users based on their individual behavior patterns and preferences. Instead of using a uniform model for all users, the system creates user-specific model instances that adapt their dialogue style and content to match each user's unique characteristics, thereby improving both adaptability and interaction quality
Solution Approach 2:
The system implements self-service by enabling users to actively participate in their own model customization through behavior data collection and feedback mechanisms. Users' interaction patterns, preferences, and responses are automatically captured and used to refine their personal AI model, allowing the system to adapt to user needs without requiring manual programming or complex setup
2Ease of operation
If AI models are trained to be emotional and customized, then the answers become more engaging, but the server-end host requires a large amount of computing power
Solution Approach 1:
The patent segments the AI model training process by dividing the customization into smaller, user-specific model instances rather than training one large comprehensive model. Each user receives a personalized model that is trained only on their specific behavior data, significantly reducing the computational resources required compared to training a single emotional and customized model for all users
Solution Approach 2:
The system applies partial action by training AI models only to the extent necessary for individual user customization rather than creating fully emotional and customized models for everyone. The model training is performed partially on user-specific data and behavior patterns, achieving sufficient customization and engagement without the excessive computing power required for complete model retraining
Data Source
AI summary
An active chatbot system with customized setting and updating download 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 customized parameter, and the rough question message is inputted to a logic 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 which is used as first training data, the customization is used as second training data, the first training data and the second training data are inputted to an AI model to perform a training, weighting values of the AI model are translated to correspond to different customized parameter, a translating result is provided for the client-end host to download for customization setting and update. Therefore, the technical effect of improving convenience and specificity in model customization can be achieved.