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:
Jul. 29, 2025

Filed:

Jan. 29, 2021
Applicant:

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

Inventors:

Issam Hadj Laradji, Montreal, CA;

David Vazquez Bermudez, Montreal, CA;

Pau Rodriguez Lopez, Montreal, CA;

Rafael Pardinas, Montreal, CA;

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/70 (2017.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06T 7/70 (2017.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A method for training a machine learning localization model to localize objects belonging to a given class within an image, the method comprising: receiving images each comprising objects of the given class; and for each image: receiving a heat map generated using the machine learning localization model; identifying proposals each corresponding to a potential object, each proposal having associated thereto an initial probability that the proposal corresponds to the potential object; for each proposal, correcting the initial probability using the heat map; selecting given ones of the proposals having a greatest corrected probability, thereby identifying object candidates; and calculating a loss for the machine learning localization model based on a location of the object candidates within the training image and the heat map; and providing the calculated loss to the machine learning localization model.


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