ML-Based Feature Extraction for Dialogue System Failure Analysis
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Solution Overview
Problem
Dialogue systems face challenges in detecting and analyzing features related to conversation failures, which require significant manual effort and are not efficiently addressed by existing methods.
Innovation Solution
A system that receives conversation logs, trains predictive machine learning models to extract feature values with importance scores, and generates an interactive user interface to display significant features, enabling root cause analysis and integration with other analytics methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis methods are used to detect features related to conversation failures, then analysis can be performed, but significant manual effort is required and efficiency is low
Solution Approach 1:
The patent replaces manual mechanical analysis with automated machine learning models. Predictive ML models are trained on conversation logs to automatically detect and analyze features related to conversation failures, eliminating the need for manual text analysis while significantly improving productivity and reducing time loss.
Solution Approach 2:
The system enables self-service analysis by automatically training ML models on conversation data and generating feature importance rankings without requiring manual intervention. The models autonomously identify patterns and features in escalated conversations, allowing the system to serve its own analysis needs without external manual effort.
2Measurement precision
If all feature values are analyzed in conversation logs, then comprehensive analysis is achieved, but the complexity and computational resources required increase significantly
Solution Approach 1:
The patent extracts only the most important feature values from conversation logs using ML model importance scores. Instead of analyzing all features, the system identifies and extracts top-N significant features based on their contribution to predicting conversation failure, reducing complexity while maintaining comprehensive analysis of critical factors.
Solution Approach 2:
The system applies different levels of analysis depth to different features based on their importance. High-importance features receive detailed analysis while less critical features are analyzed at a lower depth or excluded entirely, optimizing the balance between comprehensiveness and system complexity.
3Adaptability or versatility
If traditional analytics methods are used for conversation analysis, then basic analysis can be performed, but root cause assessment and integration with other analytics methods is limited
Solution Approach 1:
The patent creates a universal analytics platform that integrates multiple analysis functions. The ML model framework can be applied to different conversation datasets and integrated with various other analytics methods (sentiment analysis, topic modeling, etc.), providing versatile root cause assessment capabilities that work across different dialogue system contexts.
Solution Approach 2:
The system implements feedback loops where ML model predictions and feature importance rankings are continuously refined based on analysis results. The importance scores and model predictions provide feedback that improves the reliability of root cause assessment over time, allowing the system to learn from previous analyses and enhance its diagnostic accuracy.
Data Source
AI summary
An example system includes a processor that can receive conversation logs of a dialogue system to be analyzed. The processor can train a predictive machine learning model using a training set of the conversation logs on a selected feature to obtain feature values with associated importance values. The processor can select a number of feature values using a significance score calculated based on the associated importance values. The processor can generate an interactive user interface including the selected number of feature values.