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:
Dec. 16, 2025

Filed:

Jan. 19, 2022
Applicant:

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

Inventors:

Phani Bhushan Kumar Nivarthi, Fremont, CA (US);

Edwin Sapugay, Foster City, CA (US);

Omer Anil Turkkan, Santa Clara, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 40/211 (2020.01); G06F 40/216 (2020.01); G06F 40/284 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/211 (2020.01); G06F 40/284 (2020.01); G06N 20/00 (2019.01);
Abstract

A natural language understanding (NLU) framework includes an ensemble scoring system designed to receive indicators determined by various systems of the NLU framework when inferencing a user utterance. The ensemble scoring system uses the received indicators, along with a set of ensemble scoring weights, to determine a respective ensemble score for each artifact of the utterance identified during inference. For example, segmentations provided by a lookup source system may be used to boost scores of intent and/or entities identified during a meaning search operation of a NLU system. The NLU framework may also include an ensemble scoring weight optimization subsystem that automatically determines optimized ensemble scoring weight values from labeled training data using an optimization plugin. Accordingly, the NLU framework enables these indicators to be suitably weighted and combined to provide a desired level of performance (e.g., computational resource consumption, precision, recall) of the NLU framework during operation.


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