View source for 18152553. Schema-Guided Response Generation simplified abstract (GOOGLE LLC)

=Schema-Guided Response Generation=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:Abhinav Kumar Rastogi of Sunnyvale CA (US)|Abhinav Kumar Rastogi of Sunnyvale CA (US)]][[Category:Abhinav Kumar Rastogi of Sunnyvale CA (US)]]

[[:Category:Raghav Gupta of Mountain View CA (US)|Raghav Gupta of Mountain View CA (US)]][[Category:Raghav Gupta of Mountain View CA (US)]]

[[:Category:Xiaoxue Zang of Santa Clara CA (US)|Xiaoxue Zang of Santa Clara CA (US)]][[Category:Xiaoxue Zang of Santa Clara CA (US)]]

[[:Category:Srinivas Kumar Sunkara of Mountain View CA (US)|Srinivas Kumar Sunkara of Mountain View CA (US)]][[Category:Srinivas Kumar Sunkara of Mountain View CA (US)]]

[[:Category:Pranav Khaitan of Mountain View CA (US)|Pranav Khaitan of Mountain View CA (US)]][[Category:Pranav Khaitan of Mountain View CA (US)]]

==Schema-Guided Response Generation - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18152553 titled 'Schema-Guided Response Generation

==Simplified Explanation==

The present disclosure is about systems and methods for task-oriented response generation in artificial intelligence systems or other computing systems with natural language processing capabilities. These systems can interpret user input and process natural language descriptions of various services that can be accessed by the system. 

* The technology focuses on task-oriented response generation in AI systems.
* It involves processing natural language descriptions of services.
* The system identifies relevant values for executing a service based on a comparison of embedded representations of the user input and service descriptions.
* A machine learned model is used for this comparison.

==Potential Applications==
This technology can have various applications in different fields, including:

* Virtual assistants and chatbots that can understand and respond to user queries about different services.
* Customer service systems that can provide accurate and relevant information to users.
* E-commerce platforms that can assist users in finding and purchasing products or services.
* Information retrieval systems that can process natural language queries and provide appropriate responses.

==Problems Solved==
The technology addresses several challenges in natural language processing and task-oriented response generation, such as:

* Interpreting user input accurately and understanding the intent behind it.
* Identifying relevant values and information from natural language descriptions of services.
* Generating appropriate responses that fulfill the user's request or query.
* Improving the overall user experience by providing accurate and helpful information.

==Benefits==
The use of this technology offers several benefits, including:

* Enhanced user experience by providing accurate and relevant responses to user queries.
* Improved efficiency in processing user input and generating appropriate responses.
* Increased automation in tasks that require understanding and interpreting natural language.
* Potential for reducing human involvement in customer service and information retrieval processes.


==Original Abstract Submitted==

Generally, the present disclosure is directed to systems and methods for performing task-oriented response generation that can provide advantages for artificial intelligence systems or other computing systems that include natural language processing for interpreting user input. Example implementations can process natural language descriptions of various services that can be accessed by the system. In response to a natural language input, systems can identify relevant values for executing one of the service(s), based in part on comparing embedded representations of the natural language input and the natural language description using a machine learned model.



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