Chatbot Intent Recognition via Graph Kernel Analysis
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Solution Overview
Problem
Chatbots often struggle to understand user intent, leading to user dissatisfaction due to unrecognized messages, which can result in incomplete interaction and inefficient responses.
Innovation Solution
A system utilizing advanced machine learning classification algorithms and graph analysis to classify user messages, cluster comprehensible and incomprehensible messages, generate sub-graph data structures, and train a classification model to predict intent identifiers for incomprehensible messages, thereby improving chatbot recognition of user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the chatbot uses traditional intent recognition models, then the system is simple to implement, but the chatbot cannot accurately understand user intent leading to unrecognized messages
Solution Approach 1:
The patent introduces graph analysis as an intermediary layer between the user message and the intent recognition model. The graph data structure serves as a mediator that captures contextual relationships and semantic connections, enabling the classification model to achieve higher accuracy in recognizing user intent without directly increasing the complexity of the core NLP processing pipeline.
Solution Approach 2:
The system performs preliminary graph construction and feature extraction before feeding data to the classification model. By pre-processing messages into graph representations with extracted features, the system prepares enriched data structures that improve intent recognition accuracy while keeping the main classification process efficient and manageable.
2Reliability
If the chatbot re-trains the intent recognition model to handle unrecognized messages, then the chatbot understanding improves, but the training process becomes more complex and time-consuming
Solution Approach 1:
The system implements self-service through automatic graph construction and feature extraction that occurs continuously as users interact with the chatbot. The graph data structure automatically captures new patterns and relationships from incoming messages, enabling the system to learn and adapt without requiring manual intervention or lengthy re-training cycles, thus improving reliability while minimizing time loss.
Solution Approach 2:
The patent incorporates feedback mechanisms where unrecognized messages are fed back into the graph analysis system for further processing. The system uses feedback from user interactions to dynamically update graph representations and refine classification models, creating a continuous improvement loop that enhances understanding reliability without requiring complete model re-training.
3Ease of operation
If the chatbot responds with default messages for unrecognized inputs, then the system response is simple, but user satisfaction decreases due to incomplete interaction
Solution Approach 1:
The system performs preliminary graph analysis and intent prediction before generating responses. By pre-processing user messages through graph construction and feature extraction, the system identifies potential user intentions even in ambiguous cases, enabling it to provide more informative and satisfying responses rather than simple default messages, thus improving ease of operation while reducing information loss.
Data Source
AI summary
Methods and apparatuses are described for enhancing chatbot recognition of user intent through graph analysis. A computing device classifies user messages associated with a chat session as either comprehensible user messages that are mapped to a user intent or incomprehensible utterances that are not mapped to a user intent. The computing device arranges the comprehensible messages into first clusters and arranges the incomprehensible messages into second clusters. The computing device generates a first sub-graph for each first cluster and generates a second sub-graph for each second cluster. The computing device calculates a graph kernel matrix using the sub-graphs and trains a classification model using the graph kernel matrix. The computing device executes the trained classification model using the graph kernel matrix as input to generate a predicted intent identifier for the incomprehensible messages.