Context-Aware Dialog Processing for Ambiguous User Requests
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Solution Overview
Problem
Conventional dialog systems struggle to accurately process user requests that are not understandable out of speech or environmental context, leading to inconvenience and annoyance for users.
Innovation Solution
A dialog system that incorporates a Dialog System Engine and Interface, utilizing machine learning classifiers and rule engines to identify speech and environmental contexts, allowing for accurate processing of user requests through a context-based approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional dialog systems process each user request independently without context, then the system complexity remains low, but the accuracy of processing requests that depend on speech or environmental context deteriorates
Solution Approach 1:
The system performs preliminary actions by maintaining and updating context information (speech context, environmental context, user profile) before processing each user request. The dialog system engine continuously monitors and stores contextual data from multiple sources, so when a request arrives, the relevant context is already prepared and available for accurate processing, eliminating the need to analyze context from scratch for each request.
Solution Approach 2:
The patent introduces context databases as intermediary components that mediate between the user requests and the dialog system engine. These databases store speech context, environmental context, and user profile information, acting as intermediaries that provide necessary contextual information to the processing engine without requiring direct complex interactions between all system components for each request.
2Reliability
If the dialog system integrates multiple context sources (speech, environmental, user profile), then the accuracy of processing context-dependent requests improves, but the device complexity increases
Solution Approach 1:
The system segments context information into distinct, manageable categories: speech context, environmental context, and user profile context. Each context type is stored in separate database structures with specific schemas tailored to its characteristics. This segmentation allows the system to integrate multiple context sources reliably while maintaining manageable system architecture through modular, independent context modules.
3Measurement precision
If the dialog system processes requests in isolation without contextual information, then the processing speed remains high, but the accuracy of understanding requests like 'What about Los Angeles?' deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-fetching and caching relevant context information from context databases before requests arrive. When processing a request, the dialog system engine quickly retrieves pre-prepared contextual data rather than gathering it in real-time, significantly reducing processing time while maintaining high understanding accuracy for context-dependent requests.
Data Source
AI summary
A method for context-based natural language processing is disclosed herein. The method comprises maintaining a plurality of dialog system rules, receiving a user request from a Dialog System Interface, receiving one or more attributes associated with the user request from the Dialog System Interface or a user device, and identifying a type of context associated with the user request based on the user request and the one or more attributes. A context label is assigned to the user request associated with the type of context. Based on the context label and the user request, a particular dialog system rule is selected from the plurality of dialog system rules. A response to the user request is generated by applying the dialog system rule to at least a part of the user request.