Dialog Application Service Using NLU Model Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The development and deployment of dialog-driven applications for various domains and devices are complex, requiring expertise in programming dialog flows, automated speech recognition, and natural language processing, and often involve platform-specific details, making it difficult for developers to create applications that can handle multi-step interactions and integrate with different resources efficiently.
Innovation Solution
A network-accessible service that utilizes natural language understanding (NLU) components and programmatic interfaces allows developers to specify dialog steps and intents without writing source code, generating executable applications that can manage multi-step interactions across various devices and platforms, using automated speech recognition and natural language processing algorithms to interpret user inputs and integrate with diverse resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If developers manually program dialog flows, speech recognition, and natural language processing, then application functionality and customization are improved, but development complexity and time consumption increase significantly
Solution Approach 1:
The patent introduces a service as an intermediary between developers and the complex NLU system. This service provides pre-built NLU components and automated generation tools that mediates the complexity, allowing developers to create customized applications without manually programming speech recognition and natural language processing algorithms.
Solution Approach 2:
The system enables self-service through automated generation of application portions based on example inputs. The NLU model automatically generates dialog flows, speech recognition patterns, and natural language processing logic without requiring manual programming, reducing development complexity while maintaining functionality.
2Reliability
If platform-specific details are handled manually, then application performance and optimization are improved, but ease of deployment and portability worsen
Solution Approach 1:
The service provides a universal platform that handles multiple platforms and devices through a common interface. The generated applications can be deployed across different platforms without manual reconfiguration, as the service abstracts platform-specific details while maintaining optimized performance through its standardized NLU components.
3Ease of operation
If comprehensive natural language understanding is implemented, then user interaction quality is improved, but computational resources and processing time increase
Solution Approach 1:
The system implements partial action by providing selectable levels of NLU analysis depth. Developers can choose to implement comprehensive natural language understanding only when necessary, or use simpler matching for routine operations, allowing optimization of computational resources based on specific application needs while maintaining high user interaction quality where required.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A natural language understanding model is trained using respective natural language example inputs corresponding to a plurality of applications. A determination is made as to whether a value of a first parameter of a first application is to be obtained using a natural language interaction. Using the natural language understanding model, at least a portion of the first application is generated.