Multi-Model Chatbot Response Handling for Complex Queries
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Solution Overview
Problem
Chatbots often struggle to handle complex questions or maintain user satisfaction due to insufficient or unrelated responses, leading users to prefer live agents.
Innovation Solution
Implementing multiple AI models, including a primary generative AI model and secondary tone and context models, to generate and modify chatbot responses based on conversation history and user interaction attributes, ensuring consistent tone and topic adherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single AI model is used to generate chatbot responses, then the system is simple and fast, but the responses may be insufficient or unrelated for complex questions
Solution Approach 1:
The patent divides the AI response generation system into multiple specialized models: a primary generative AI model for creating responses and a secondary tone and context model for refining them. This segmentation allows each model to focus on specific aspects of response quality, improving overall reliability without requiring a single overly complex model to handle all requirements.
Solution Approach 2:
The patent introduces an intermediary evaluation mechanism that assesses the relevance and quality of generated responses before they are sent to the user. This intermediary layer acts as a filter and quality gate, ensuring only sufficient and related responses reach the user, thereby improving response relevance without changing the fundamental model architecture.
2Reliability
If chatbot responses are generated quickly, then user convenience is improved, but conversation drift and topic abandonment occur
Solution Approach 1:
The patent applies preliminary action by having the secondary tone and context model prepare and refine responses in advance based on the conversation history and detected user tone. This preliminary processing ensures topic adherence is built into the response generation process itself, preventing conversation drift before it occurs without requiring additional post-processing time.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors conversation context and user responses to adjust subsequent generated responses. The tone and context model receives feedback about conversation flow and topic relevance, allowing it to make real-time adjustments that maintain topic adherence while keeping response generation efficient.
3Adaptability or versatility
If chatbots handle complex questions, then user satisfaction improves, but response quality deteriorates due to insufficient understanding
Solution Approach 1:
The patent adds another dimension to response generation by introducing a separate tone and context model that operates in parallel with the primary generative model. This additional dimension focuses specifically on contextual accuracy, tone appropriateness, and relevance to complex questions, allowing the system to handle complex queries with high precision through coordinated effort between the two models.
Data Source
AI summary
An example operation may include one or more of executing an interaction event with an account device and a chatbot within a chat element based on chatbot responses determined by an artificial intelligence (AI) model, wherein the interaction event comprises an exchange of content between the account device and the chatbot through the chat element, determining that a related response to the content has not been output by the chatbot within the chat element, in response, executing a second AI model on the content received from the account device within the chat element to generate a new chatbot response to the content, and outputting the new chatbot response within the chat element.