Dialog Trajectory Analysis for Spoken Systems
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Solution Overview
Problem
Spoken dialog systems lack effective methods for detailed analysis and feedback, leading to inefficient diagnosis and improvement of system performance, resulting in subjective and coarse-grained monitoring data that fails to provide diagnostic value.
Innovation Solution
Implementing a dialog analysis system that uses empirical call-flow representation, specifically a stochastic finite-state machine, to analyze and visualize dialog data, enabling administrators to identify poorly functioning parts and plan improvements, with tools for change detection and compression to highlight significant changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional call monitoring and summary reports are used to monitor dialog systems, then operations can be monitored, but the data is too coarse-grained and subjective to provide diagnostic value
Solution Approach 1:
The patent segments dialog data into discrete trajectories with individual turns, states, and outcomes. Each dialog is broken down into analyzable components (user utterances, system responses, state transitions) that can be independently examined. This segmentation enables fine-grained analysis of specific dialog problems without requiring complex overall system analysis.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes raw dialog data and transforms it into structured trajectory representations. This intermediary layer (the dialog trajectory analysis system) mediates between raw data collection and diagnostic interpretation, providing structured intermediate representations that make diagnostic information accessible without requiring direct complex analysis of raw data.
2Productivity
If detailed dialog analysis is implemented to improve diagnostic capability, then system performance can be optimized, but the complexity of data collection and processing increases
Solution Approach 1:
The patent performs preliminary structuring of dialog data during data collection, organizing interactions into standardized trajectory formats with defined states and transitions before analysis begins. This preliminary organization of data into analyzable structures reduces the complexity of subsequent processing and enables more efficient optimization by having data ready in the appropriate format.
Solution Approach 2:
The patent transforms dialog data from unstructured text into structured parameters including state sequences, transition probabilities, and outcome metrics. By changing the representation parameters of dialog data (from raw text to structured trajectories with measurable properties), the system enables efficient quantitative analysis and optimization while managing processing complexity through standardized parameter sets.
Data Source
AI summary
The invention comprises computer-readable media, methods and systems for performing a dialog analysis and using that analysis to release an updated spoken dialog system. The method embodiment of the invention comprises receiving data associated with dialogs, extracting turn by turn details of the dialog and generating from the extracted details an empirical call-flow representation of the dialog. The call data may be call-logs and user audio. The empirical call-flow representation may be a finite-state machine with nodes that represent call-states and the arcs between nodes represent user responses. Nodes may also represent sub-dialogs. The call-flow representation is presented graphically to a user for easier analysis and understanding. Significant changes in the dialog can be identified as hot-spots for improvement in the next release of the spoken dialog system.