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:
Oct. 06, 2026

Filed:

Mar. 08, 2024
Applicant:

Idexx Laboratories, Inc., Westbrook, ME (US);

Inventors:

Jessica Solomon, Westbrook, ME (US);

Scott Bender, South Portland, ME (US);

Caitlin Robar, Westbrook, ME (US);

Suzanne Magoon, Westbrook, ME (US);

Conrad Sastre, Westbrook, ME (US);

Allison Spake, Westbrook, ME (US);

Assignee:

IDEXX LABORATORIES, INC., Westbrook, ME (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 6/00 (2024.01); A61B 6/46 (2024.01); A61B 6/50 (2024.01); A61B 8/00 (2006.01); A61B 8/08 (2006.01); G06T 7/00 (2017.01); G06T 7/62 (2017.01); G06T 11/60 (2006.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
A61B 6/5217 (2013.01); A61B 6/463 (2013.01); A61B 6/503 (2013.01); A61B 6/5247 (2013.01); A61B 8/0883 (2013.01); A61B 8/463 (2013.01); A61B 8/5223 (2013.01); A61B 8/5261 (2013.01); G06T 7/0012 (2013.01); G06T 7/62 (2017.01); G06T 11/60 (2013.01); G16H 50/20 (2018.01); G06T 2200/24 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30048 (2013.01); G06T 2210/41 (2013.01);
Abstract

An example computer-implemented method for identifying a heart condition in a non-human subject includes receiving a medical image of the non-human subject, determining by a processor executing a first machine-learning logic and based on the medical image a dimensional feature of a heart of the non-human subject, displaying the dimensional feature of the heart on a graphical user interface, receiving medical information associated with the non-human subject on the graphical user interface, determining by the processor executing a second machine-learning logic and based on the medical information and the dimensional feature of the heart a likelihood of a heart disease, and displaying the likelihood of the heart disease on the graphical user interface.


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