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:
Jan. 21, 2025

Filed:

Aug. 15, 2022
Applicant:

Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US);

Inventors:

Tonislav Ivanov, Brooklyn, NY (US);

Denis Babeshko, New York, NY (US);

Vadim Pinskiy, Wayne, NJ (US);

Matthew C. Putman, Brooklyn, NY (US);

Andrew Sundstrom, Brooklyn, NY (US);

Assignee:

Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/214 (2023.01); G06F 18/40 (2023.01); G06N 3/08 (2023.01); G06N 20/20 (2019.01); G06T 7/00 (2017.01); G06V 10/22 (2022.01); G06V 10/70 (2022.01); G06V 10/774 (2022.01); G06V 10/778 (2022.01); G06V 10/94 (2022.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/2148 (2023.01); G06F 18/41 (2023.01); G06N 3/08 (2013.01); G06N 20/20 (2019.01); G06T 7/001 (2013.01); G06V 10/235 (2022.01); G06V 10/70 (2022.01); G06V 10/7747 (2022.01); G06V 10/7784 (2022.01); G06V 10/7796 (2022.01); G06V 10/945 (2022.01); G06T 2207/30148 (2013.01); G06V 2201/06 (2022.01);
Abstract

A computing system generates a training data set for training the prediction model to detect defects present in a target surface of a target specimen and training the prediction model to detect defects present in the target surface of the target specimen based on the training data set. The computing system generates the training data set by identifying a set of images for training the prediction model, the set of images comprising a first subset of images. A deep learning network generates a second subset of images for subsequent labelling based on the set of images comprising the first subset of images. The deep learning network generates a third subset of images for labelling based on the set of images comprising the first subset of images and the labeled second subset of images. The computing system continues the process until a threshold number of labeled images is generated.


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