Iterative Clustering for Virtual Agent Intent Labeling
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Solution Overview
Problem
Current natural language understanding systems face challenges in efficiently labeling large datasets for training virtual agents to identify user intent, particularly due to the difficulty in handling unbalanced datasets and outliers, which can lead to mislabeling and increased resource consumption.
Innovation Solution
An iterative clustering process using DBSCAN with adjustable parameters is employed to efficiently label meta data from multiple conversations, reducing mislabeling and resource requirements by narrowing down data points into labeled clusters, and allowing for subsequent refinement of clusters to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional labeling methods are used to label each conversation individually, then labeling accuracy can be maintained, but the time and resources required increase significantly
Solution Approach 1:
The patent combines multiple similar conversations into clusters, where each cluster represents a group of conversations with similar user intents. By labeling the cluster representative once and applying the label to all members, the system maintains labeling accuracy while dramatically reducing the total number of labeling operations required.
Solution Approach 2:
The patent creates cluster representatives as simplified copies or summaries of grouped conversations. These representatives capture the essential characteristics of each cluster, allowing the labeling process to work with fewer, more manageable units while preserving the information needed for accurate intent classification.
2Loss of time
If clustering is used to reduce labeling effort, then time and resources are reduced, but mislabeling may occur due to unbalanced datasets and outliers
Solution Approach 1:
The patent applies multiple rounds of clustering with progressively refined parameters. Instead of relying on a single clustering operation, the system performs sequential clustering passes, each with adjusted parameters, to progressively improve cluster quality and reduce mislabeling while managing computational resources efficiently.
Solution Approach 2:
The patent dynamically adjusts clustering parameters such as distance thresholds and minimum cluster sizes based on the characteristics of the data being processed. By changing parameters adaptively, the system optimizes cluster formation for different datasets and identifies outliers more effectively, reducing mislabeling while maintaining efficiency.
3Measurement precision
If multiple clustering rounds with parameter adjustments are performed, then cluster accuracy improves, but computational complexity increases
Solution Approach 1:
The patent divides the large dataset into smaller manageable subsets and processes them through multiple clustering rounds. By segmenting the computation into phases with different parameter settings, the system achieves high cluster accuracy while keeping each individual computational task manageable and resource-efficient.
Data Source
AI summary
A system and method for training a virtual agent to identify a user's intent from a conversation is disclosed. The system and method use an iterative process of clustering multiple conversations (converted into feature representations) used for training a machine learning model into labeled clusters having similar user intents. Clustering enables labeling a large number of training conversations efficiently. The labeled clusters may be used to train a virtual agent to classify the conversational intent of a conversation. Then, the machine learning model can classify future conversations based on similarity to labeled clusters. By knowing a human user's intent, a virtual agent can deliver what the user desires.