Automated Conversation Flow Generation Using Process Mining
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Solution Overview
Problem
The development of conversation flows for virtual agents is hindered by the time-consuming and subjective nature of manual analysis, and the challenge of efficiently extracting insights from vast text transcripts generated from human-agent interactions.
Innovation Solution
An automated method and system that models conversation flows using process mining and topic modeling to derive designs from text transcripts, clustering topics, and generating conversation flows as directed cyclic graphs to uncover patterns and intents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and design methods are used for conversation flows, then subjective expertise can be applied, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational methods. Process mining algorithms automatically extract conversation patterns from logs, topic modeling algorithms identify subject areas, and clustering algorithms organize intents, substituting human manual work with automated computational systems that achieve both accuracy and efficiency
Solution Approach 2:
The system enables self-service by automatically generating conversation flow designs without requiring manual intervention. The automated pipeline processes conversation logs, extracts patterns, and produces ready-to-use conversation flow designs, allowing the system to serve itself rather than requiring continuous human input
2Loss of information
If manual analysis of text transcripts is performed, then detailed insights can be extracted, but the substantial volume of data cannot be processed efficiently
Solution Approach 1:
The patent replaces manual text analysis with automated natural language processing. Process mining algorithms efficiently scan through large volumes of conversation logs to extract patterns, while topic modeling and clustering algorithms automatically identify and organize intents, enabling high-speed processing that maintains comprehensive insight extraction
Solution Approach 2:
The patent segments the analysis process into distinct automated stages: process mining for pattern extraction, topic modeling for subject identification, and clustering for intent organization. This segmentation allows each algorithm to specialize in specific tasks, improving overall processing efficiency while maintaining comprehensive data analysis
3Productivity
If automated methods are used to generate conversation flows, then processing efficiency improves, but the complexity of the system increases
Solution Approach 1:
The patent merges multiple algorithms into an integrated automated pipeline. Process mining, topic modeling, and clustering algorithms are combined and orchestrated together, allowing them to work synergistically. This merging reduces the operational complexity of managing separate systems while maintaining high processing efficiency through automated workflow integration
Data Source
AI summary
A method and system for generating conversation flows is provided herein. The method and system comprise storing conversations between at least an agent and at least a user in logs. Further, the method and system comprise extracting topics from the logs. Also, the method and system comprise creating clusters from the extracted topics. The method and system further comprise generating the conversation flows from the clusters.