Chatbot Intent Handling with Generative AI Fallback
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Solution Overview
Problem
Interactive platforms face challenges in providing accurate and engaging user interactions, as existing chatbots lack the ability to effectively process multimodal inputs and personalize conversations, leading to suboptimal information delivery and user engagement.
Innovation Solution
A chatbot system that integrates natural language understanding, dialogue management, and response generation using generative AI models, capable of processing text, audio, images, and video inputs, and personalizing conversations through user profiles and chat history, while continuously improving via feedback and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If chatbots use basic text processing, then system complexity is low, but they cannot effectively handle multimodal inputs (text, audio, images, video)
Solution Approach 1:
The system divides the complex multimodal processing task into separate specialized modules: a text processing module, an audio processing module, an image processing module, and a video processing module. Each module handles its specific modality independently, then the results are integrated by a dialogue management component. This segmentation allows the system to achieve multimodal versatility while keeping each individual component relatively simple and maintainable.
Solution Approach 2:
The patent implements a universal chatbot architecture where a single integrated system can process multiple types of inputs (text, audio, images, video) through a common framework. The generative AI model serves as a universal response generation mechanism that can handle queries from any modality, making the system multi-functional without requiring separate specialized systems for each input type.
2Measurement precision
If chatbots provide generic responses, then information delivery is simple, but accuracy and personalization are insufficient
Solution Approach 1:
The system performs preliminary actions by creating and maintaining user profiles that store personalization data before actual conversations occur. It also pre-processes chat history to extract relevant information and prepares it for use in generating personalized responses. This preliminary preparation enables accurate personalized information delivery without adding complexity during the actual conversation flow.
Solution Approach 2:
The system incorporates feedback mechanisms where user responses and interactions are continuously monitored and fed back into the personalization system. This feedback loop allows the chatbot to learn from actual usage patterns and refine its personalized responses over time, improving accuracy while the feedback mechanism itself remains integrated into the existing architecture rather than adding separate complex systems.
3Reliability
If chatbots lack continuous learning capability, then system simplicity is maintained, but they cannot improve over time through feedback
Solution Approach 1:
The system implements continuous learning through feedback by capturing user interactions, responses, and engagement metrics, then feeding this data back into the machine learning models. This feedback mechanism enables the system to continuously improve its understanding and response generation capabilities over time, enhancing reliability while the feedback loop is seamlessly integrated into the operational workflow rather than requiring separate complex training systems.
Solution Approach 2:
The chatbot system performs self-service learning by automatically processing its own interaction data and updating its models without requiring external intervention. The system self-updates its knowledge base and improves its generative capabilities through its own operational data, enabling continuous improvement while avoiding the complexity of external training systems or manual reconfiguration.
Data Source
AI summary
A chatbot system for an interactive platform. The chatbot system receives a prompt from a user and determines an intent of the user using the prompt. If the chatbot system can't determine an intent, the chatbot communicates the prompt as a personality prompt to a generative AI model. If the chatbot system can determine an intent from the prompt, the chatbot system generates an API call to an additional service using the intent. The chatbot system uses values returned by the additional service to generate a hint prompt that is communicated to the generative AI model. The chatbot system receives a response from the generative AI model to the personality prompt or hint prompt and communicates the response to the user.