Dialog Management System for Intent Prediction
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Solution Overview
Problem
Current natural language processing systems face challenges in predicting user intent and providing relevant information in ongoing dialog sessions, often resulting in undesired responses due to misunderstandings or system errors, which negatively impact user experience.
Innovation Solution
A two-stage processing system that utilizes historic interaction data and context information to predict potential user inputs, ranking candidate NLU hypotheses based on past user interactions and system responses, thereby improving the accuracy of intent prediction and user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional natural language processing systems are used to predict user intent, then the system can process user inputs, but the accuracy of intent prediction is low resulting in undesired responses
Solution Approach 1:
The system performs preliminary encoding of interaction data and training of machine learning models before actual dialog processing. The dialog manager is pre-trained with historical interaction data to learn patterns of user behavior and system responses, enabling more accurate intent prediction during actual usage without requiring complex processing during real-time interactions.
Solution Approach 2:
The patent introduces encoded interaction data as an intermediary representation between raw user inputs and intent prediction. The encoding process transforms complex interaction data into a structured format that machine learning models can efficiently process, serving as a mediator that bridges the gap between unstructured user inputs and structured intent classification.
2Measurement precision
If more processing stages are added to improve prediction accuracy, then intent prediction improves, but system complexity increases
Solution Approach 1:
The system segments the intent prediction process into two distinct stages: an encoding stage that processes interaction data into structured representations, and a prediction stage that uses machine learning models to classify intents. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining high prediction accuracy.
Solution Approach 2:
The machine learning models automatically learn optimal processing patterns from encoded interaction data without requiring manual feature engineering or complex configuration. The system self-optimizes by training on historical data, reducing the need for manual system tuning and simplifying deployment while maintaining high accuracy.
Data Source
AI summary
Techniques for determining an intent of a subsequent user input in a dialog are described. The system processes historic interaction data that is structured based on natural language understanding (NLU) hypotheses, with each NLU hypothesis being associated with one or more past user inputs received by the system, one or more sample inputs, and one or more past system responses. Based on processing of the historic interaction data and dialog data of previous turns of the dialog, the system determines candidate intents for the subsequent turn of the dialog. The system also uses context data to determine the candidate intents.