Dialog Flow Induction via Unsupervised Clustering and Visualization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing customer interactions in conversational systems are manual and limited, failing to effectively identify patterns and insights due to their reliance on human intervention and manual recording of conversations, which restricts the number of interactions that can be analyzed and may miss key patterns.
Innovation Solution
Unsupervised machine learning techniques are used to automatically induce the structure of dialogues, coupled with interactive graphical visualization, allowing analysts to explore and refine dialog analysis by presenting a summary view of dialog collections as interactive flow graphs, enabling the identification of common patterns across multiple dialogues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recording and perusal of chat logs are used to analyze customer interactions, then analysts can examine conversation details, but the number of interactions that can be analyzed is limited and key patterns may be missed
Solution Approach 1:
The patent segments the large dialog collection into individual dialog rounds (agent contribution and customer contribution pairs). Each dialog round is independently processed through embedding and clustering, allowing the system to handle large volumes of interactions while maintaining detailed analysis capability. This segmentation enables scalable processing without losing granular insights.
Solution Approach 2:
The patent replaces the manual mechanical process of recording and perusing chat logs with automated machine learning algorithms. The unsupervised learning system automatically processes dialog collections, creating embeddings and clustering dialog rounds without human intervention, thereby dramatically increasing the number of interactions that can be analyzed while maintaining or improving pattern detection accuracy.
2Productivity
If unsupervised machine learning algorithms are used to automatically induce dialog structure, then the number of interactions analyzed increases, but the complexity of the system increases
Solution Approach 1:
The patent introduces dialog embeddings as an intermediary representation that bridges raw dialog text and clustered dialog states. The embedding process transforms variable-length, unstructured dialog rounds into fixed-dimensional vectors, simplifying subsequent clustering operations. This intermediary step manages system complexity by creating a standardized intermediate representation.
Solution Approach 2:
The patent employs self-supervised learning tasks (response selection and masked language modeling) that allow the system to learn dialog embeddings without external supervision or manual labeling. The system serves itself by using the dialog data to automatically train the embedding model, reducing the need for complex supervised training pipelines and external annotation resources.
3Ease of operation
If interactive graphical visualization is used to present dialog flow graphs, then analysts can explore and refine analysis, but the processing and presentation time increases
Solution Approach 1:
The patent performs preliminary clustering of dialog rounds into dialog states before generating the visual flow graph. By pre-processing the dialog collection and organizing it into clustered states, the system reduces the computational burden during visualization generation. This preliminary action allows interactive exploration without excessive processing delays during user interaction.
Solution Approach 2:
The patent creates a simplified graphical representation (copy) of the dialog flow that captures essential patterns without displaying all raw dialog details. The flow graph is a condensed copy of the underlying dialog structure, allowing analysts to explore patterns efficiently while the full detailed data remains available on demand for deeper investigation when needed.
Data Source
AI summary
Structure of conversations between users and agents and/or systems is discovered and interactively displayed to analysts, thereby better supporting development of automated conversation handling systems for different domains. A corpus of prior dialogs of users with agents (without preexisting semantic labels indicating purposes for different parts of the dialogs) is taken as input, and embeddings are generated for textual units (e.g., rounds) of the dialogs. The embeddings are used to cluster the textual units, and the clusters and their relationships are visualized within a user interface that analysts may use to explore and fine-tune the structure of the conversations.