The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 29, 2023

Filed:

Mar. 24, 2021
Applicant:

Servicenow, Inc., Santa Clara, CA (US);

Inventors:

Edwin Sapugay, Foster City, CA (US);

Anil Kumar Madamala, Sunnyvale, CA (US);

Maxim Naboka, Santa Clara, CA (US);

Srinivas SatyaSai Sunkara, Santa Clara, CA (US);

Lewis Savio Landry Santos, Santa Clara, CA (US);

Murali B. Subbarao, Saratoga, CA (US);

Assignee:

ServiceNow, Inc., Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06N 20/00 (2019.01); G10L 15/19 (2013.01); G10L 15/22 (2006.01); G06N 5/022 (2023.01); G06F 40/205 (2020.01); G06F 40/211 (2020.01); G10L 15/18 (2013.01); G10L 15/16 (2006.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/205 (2020.01); G06F 40/211 (2020.01); G06N 5/022 (2013.01); G06N 20/00 (2019.01); G10L 15/19 (2013.01); G10L 15/22 (2013.01); G10L 15/16 (2013.01); G10L 15/1807 (2013.01); G10L 15/1822 (2013.01); G10L 2015/223 (2013.01); G10L 2015/225 (2013.01);
Abstract

An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a model, wherein the model includes at least one original meaning representation. The system includes a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions including: performing rule-based generalization of the model to generate at least one generalized meaning representation of the model from the at least one original meaning representation of the model; performing rule-based refinement of the model to prune or modify the at least one generalized meaning representation of the model, or the at least one original meaning representation of the model, or a combination thereof; and after performing the rule-based generalization and the rule-based refinement of the model, using the model to extract intents/entities from a received user utterance.


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