Fitness Assistant Chatbot Intent Classification
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Solution Overview
Problem
Current AI conversational chat programs lack the ability to provide effective fitness assistance to users by failing to accurately interpret user intentions and extract relevant fitness information from messages, leading to inadequate suggestions.
Innovation Solution
A method is introduced where a chatbot receives messages in a conversation session, updates the user's fitness intention, extracts relevant fitness information, applies rules to generate fitness suggestions, and provides these suggestions to the user, utilizing a system that includes a core processing module, intention classification, knowledge graph, and recommendation modules to offer personalized fitness advice.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a chatbot is designed to simulate general conversation, then user engagement is improved, but the ability to provide accurate fitness assistance deteriorates
Solution Approach 1:
The chatbot system segments the conversation processing into distinct modules: a general conversation handler for user engagement and a fitness assistance module with intention classification for accurate fitness advice. This segmentation allows the system to maintain both general conversational ability and specialized fitness interpretation accuracy simultaneously.
2Measurement precision
If the chatbot extracts detailed fitness information from messages, then fitness suggestion accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary intention classification layer that translates user messages into structured fitness intentions and information. This intermediary module simplifies the overall system architecture by providing a standardized interface between message input and fitness suggestion generation, reducing complexity while maintaining extraction accuracy.
3Adaptability or versatility
If the chatbot updates fitness intention dynamically during conversation, then fitness assistance relevance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary classification of fitness intentions and information extraction as messages are received during the conversation. By updating fitness intention dynamically in real-time rather than waiting for the end of the conversation, the system maintains relevance while minimizing processing delays through continuous background updates.
Data Source
AI summary
A method for fitness assistance to a user in a conversation session, comprising: receiving, in the conversation session, at least one message, wherein the conversation session is between the user and an electronic conversational agent (1210); updating, based on the conversation session and one or more factors associated with the at least one message, a fitness intention associated with the user (1220); extracting, based on one or more factors associated with the at least one message, fitness information associated with the user (1230); obtaining at least one response, through applying a set of rules on the fitness information and the fitness intention (1240); generating, according to the at least one response, a response message including a fitness suggestion to the user (1250); and providing the response message including the fitness suggestion to the user (1260).