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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.

Patent No.:

US 12014833 B1

PDFFull Text
Date of Patent:
Jun. 18, 2024

Filed:

Aug. 06, 2019

System and method for selecting artificially fertilized embryos

Applicants:

Cornell University, Ithaca, NY (US);

Yale University, New Haven, CT (US);

Inventors:

Nikica Zaninovic, New York, NY (US);

Olivier Elemento, New York, NY (US);

Iman Hajirasouliha, New York, NY (US);

Pegah Khosravi, New York, NY (US);

Jonas Malmsten, Gibsonton, FL (US);

Zev Rosenwaks, New York, NY (US);

Qiansheng Zhan, New York, NY (US);

Ehsan Kazemi, New Haven, CT (US);

Assignees:

Cornell University, Ithaca, NY (US);

Yale University, New Haven, CT (US);

Attorney:

McDermott Will & Emery LLP

Primary Examiner:

Congvan Tran

Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06N 3/08 (2023.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01); G16H 10/60 (2018.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01); G16H 70/60 (2018.01);
U.S. Cl.
CPC ...
G16H 50/30 (2018.01); G06N 3/08 (2013.01); G06T 7/0012 (2013.01); G06V 10/454 (2022.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01); G06V 20/698 (2022.01); G16H 10/60 (2018.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16H 70/60 (2018.01); G06T 2207/20036 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30044 (2013.01);
Abstract

A method for classifying human blastocysts includes obtaining images of a set of artificially fertilized (AF) embryos incubating in an incubator. A morphological quality of the AF embryos is determined based on a classification of the images by a convolutional neural network trained using images of pre-classified embryos. Each of the AF embryos is graded based on the morphological quality. A probability that a given graded AF embryo will result in a successful pregnancy after the given AF embryo is implanted in a gestating female is computed for each of the AF embryos from the set based on a grade of the given AF embryo and clinical parameters associated with the gestating female. One or more graded AF embryos to be recommended to be implanted in the gestating female from the set are selected based on the probability of successful pregnancy. An identity of each of the selected graded AF embryos and a number of the selected graded AF embryos to be potentially implanted in the gestating female is then output based on an outcome desired by the gestating female following implantation.

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