Unsupervised Intent Classification via Vector Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing intent classification systems require significant processing resources and labeled data for intent classification, making them inefficient and costly, especially when handling repetitive customer inquiries in cloud-based chat systems.
Innovation Solution
An unsupervised intent classification system trained on unlabeled historical conversation data using a word embedding function that generates context-response pairs and intent vectors, allowing for accurate intent classification without the need for extensive labeling or retraining when new intents are added.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional intent classification systems are used to classify customer inquiries, then classification accuracy can be achieved, but processing resources and costs increase significantly
Solution Approach 1:
The patent segments the large vector space into multiple clusters, each representing a group of similar intents. Instead of comparing the input vector to every vector in the entire space, the system first identifies the relevant cluster and then performs detailed comparison only within that cluster. This segmentation dramatically reduces the number of comparisons needed while maintaining classification accuracy.
Solution Approach 2:
The patent extracts and utilizes metadata associated with each vector (such as intent labels, confidence scores, or other descriptive information) to quickly filter and identify the most relevant cluster before performing full vector comparisons. This extraction of key information allows the system to bypass unnecessary computations.
2Adaptability or versatility
If traditional intent classification systems are used, then comprehensive intent coverage is achieved, but system complexity and labeling requirements increase
Solution Approach 1:
The patent performs preliminary clustering of the vector space before the actual classification task. By pre-organizing vectors into clusters based on their semantic similarity, the system creates a structured framework that simplifies subsequent classification operations. This preliminary action reduces the complexity of handling comprehensive intent coverage.
Solution Approach 2:
The patent introduces clusters as intermediary structures between the input vector and the final intent classification. These clusters act as intermediate categories that group related intents, making the overall classification process more manageable and less complex while still achieving comprehensive coverage.
3Reliability
If agents manually respond to repetitive customer inquiries, then customer service quality is maintained, but time and cost efficiency decrease
Solution Approach 1:
The patent enables the chatbot system to automatically classify and handle customer inquiries without requiring agent intervention for routine tasks. By implementing self-service classification, the system reduces the time agents spend on repetitive inquiries while maintaining service quality through accurate automated routing and response generation.
Data Source
AI summary
A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.