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:
Dec. 02, 2025

Filed:

Sep. 24, 2020
Applicant:

Carnegie Mellon University, Pittsburgh, PA (US);

Inventors:

John Galeotti, Pittsburgh, PA (US);

Tejas Sudharshan Mathai, Seattle, WA (US);

Assignee:

Carnegie Mellon University, Pittsburgh, PA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06T 7/00 (2017.01); G06T 7/20 (2017.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06T 7/0016 (2013.01); G06T 7/20 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01);
Abstract

Provided is a system, method, and computer program product for analyzing spatio-temporal medical images using an artificial neural network. The method includes capturing a series of medical images of a patient, the series of medical images comprising visual movement of at least one entity, tracking time-varying spatial data associated with the at least one entity based on the visual movement, generating spatio-temporal data by correlating the time-varying spatial data with the series of medical images, and analyzing the series of medical images based on an artificial neural network comprising a plurality of layers, one or more layers of the plurality of layers each combining features from at least three different scales, at least one layer of the plurality of layers of the artificial neural network configured to learn spatio-temporal relationships based on the spatio-temporal data.


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