Structured Natural Language Representations for Dialog Systems
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Solution Overview
Problem
Automated dialog systems face logical gaps between natural language representation, data storage representation, and programming language representation, leading to poor information structure reconstruction burdens on underlying applications.
Innovation Solution
A computing device identifies a prompt for an automated dialog application and uses structured natural language representations to populate data fields with natural language input, enabling effective response handling through scalar, list, or hierarchical structured representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If natural language representation is flattened into key-value pair associations, then the burden of reconstructing information structure is reduced for the system, but the underlying applications bear the burden of reconstructing information structure
Solution Approach 1:
The patent introduces structured natural language representations as an intermediary layer between the flattened key-value pairs and the applications. This intermediary preserves the structural information that would otherwise be lost in flattening, allowing applications to access structured data without the system needing to maintain complex structures. The structured representations act as a mediator that balances the needs of both system simplicity and application functionality.
2Ease of manufacture
If representations are designed in isolation from one another, then each representation can be optimized independently, but logical gaps exist between them
Solution Approach 1:
The patent merges multiple isolated representations (natural language, data storage, and programming language representations) into a unified structured natural language representation framework. This unified framework ensures logical consistency across all representations while maintaining the ability to optimize each aspect independently. The structured representations serve as a common foundation that connects all three representation types.
3Productivity
If flatting approach is used for natural language representation, then the representation is simpler to process, but the information structure is poor and reconstruction burden shifts to applications
Solution Approach 1:
The patent segments the natural language representation into structured components that maintain hierarchical and relational information while remaining processable. Instead of completely flattening the representation, it divides the information into structured fields and relationships that can be efficiently processed while preserving the original information structure. This segmentation allows both efficient processing and structure preservation.
Data Source
AI summary
In accordance with aspects of the disclosure, a computing device may identify a prompt associated with an automated dialog application. An application expectation of the automated dialog application may be identified. The application expectation may comprise a structured natural language representation for a natural language response to the prompt. The computing device may receive natural language input responsive to the prompt, populate one or more data fields of the structured natural language representation with at least a portion of the natural language input, and may respond to the application expectation using the one or more data fields of the structured natural language representation.