Chatbot Intent Vector Trigger Control Multi-User Context
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Solution Overview
Problem
Conventional chatbots are often fact-based and fail to capture emotional states and multiple user interactions, leading to irrelevant responses in dynamic environments like vehicles where multiple users are present.
Innovation Solution
A chatbot system that processes user input through an AI model to generate an intent vector, which includes probabilities for various intents, and uses a trigger control model to determine appropriate responses, integrating emotional and car-control oriented intentions, and prioritizing responses based on user engagement and profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional fact-based chatbots are used, then information retrieval from databases is efficient, but responses become irrelevant in dynamic multi-user environments
Solution Approach 1:
The chatbot system dynamically adapts its behavior by continuously analyzing user inputs through AI models to generate intent vectors that represent current user states. The trigger control model dynamically determines whether to respond based on real-time analysis of user intent probabilities, allowing the system to adapt to changing conversational contexts and multi-user interactions rather than following static fact-based protocols
Solution Approach 2:
The system transforms user inputs into intent vectors with probability distributions across multiple intents, changing the parameter representation from simple factual queries to multi-dimensional intent states. This parameter transformation enables the chatbot to interpret user needs in terms of emotional states, information-seeking behaviors, and interaction priorities, thereby improving response relevance in dynamic environments
2Measurement precision
If chatbots focus on fact-based responses, then database information accuracy is maintained, but emotional states and user intentions are missed
Solution Approach 1:
The system segments user intent into multiple distinct categories represented as separate probability dimensions in the intent vector. By dividing the interpretation task into distinct intent types (information-seeking, emotional expression, conversational engagement, etc.), the system can simultaneously maintain factual accuracy for information queries while capturing emotional and contextual nuances through separate intent probability assessments
Solution Approach 2:
The trigger control model serves multiple functions: it determines response necessity, identifies user intent categories, and prioritizes different types of information needs. This multi-functional approach allows the system to handle both factual information retrieval and emotional context recognition through a single unified decision-making mechanism, preventing loss of contextual information while maintaining measurement precision
3Ease of operation
If chatbots respond to all user inputs, then user engagement is maximized, but irrelevant responses increase in multi-user environments
Solution Approach 1:
The trigger control model uses feedback from the intent vector analysis to determine whether a response is appropriate. By continuously monitoring user intent probabilities and comparing them against response thresholds, the system provides feedback-driven response decisions that maximize engagement for relevant user inputs while filtering out situations where responding would be irrelevant, thus maintaining both user engagement and response relevance in multi-user environments
Data Source
AI summary
A method provides information to a user as a function of derived user intent. The method includes receiving input from a user, generating an intent vector by processing the received input though an artificial intelligence model that has been trained with data representative of the user's intention, wherein the intent vector comprises a probability for each intent in a known set of possible intents, executing a trigger control model to determine whether to respond to the user as a function of the input from the user and the intent vector, utilizing the trigger control model, received input, and intent vector input to generate a response via a trained chatbot, and providing the response via an output device.