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. 25, 2023

Filed:

Dec. 16, 2021
Applicant:

Paige.ai, Inc., New York, NY (US);

Inventors:

Danielle Gorton, Beacon, NY (US);

Patricia Raciti, New York, NY (US);

Jillian Sue, New York, NY (US);

Razik Yousfi, Brooklyn, NY (US);

Assignee:

Paige.AI, Inc., New York, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/00 (2017.01); G06V 10/25 (2022.01); G06V 10/12 (2022.01); G06V 10/77 (2022.01); G16H 10/40 (2018.01); G16H 15/00 (2018.01); G16H 50/20 (2018.01); G16H 30/40 (2018.01); G16H 80/00 (2018.01); G06T 11/60 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); G06T 11/60 (2013.01); G06V 10/12 (2022.01); G06V 10/25 (2022.01); G06V 10/7715 (2022.01); G16H 10/40 (2018.01); G16H 15/00 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 80/00 (2018.01); G06T 2207/10004 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30024 (2013.01); G06V 2201/03 (2022.01);
Abstract

A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.


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