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:
Aug. 05, 2025

Filed:

Apr. 19, 2024
Applicant:

Anumana, Inc., Cambridge, MA (US);

Inventors:

Sairam Bade, Suryapet, IN;

Yash Mishra, Bangalore, IN;

Shiva Verma, Bangalore, IN;

Uddeshya Upadhyay, Bengaluru, IN;

Ashim Prasad, Bangalore, IN;

Rakesh Barve, Bengaluru, IN;

Samir Awasthi, Boston, MA (US);

Shashi Kant, Bengaluru, IN;

Assignee:

Anumana, Inc., Cambridge, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/389 (2021.01); A61B 5/257 (2021.01); A61B 5/308 (2021.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
A61B 5/308 (2021.01); G06N 20/00 (2019.01);
Abstract

A system for transforming electrocardiogram images for use in one or more machine learning models, the system including at least a processor configured to receive paired data including a plurality of ECG images correlated to a plurality of standardized images, wherein each ECG image of the plurality of ECG images is correlated with each standardized image of the plurality of standardized images, train an ECG transformation model as a function of the paired data, wherein training the transformation model includes adjusting one or more parameter values of the ECG transformation model as a function of a comparison between at least one predicted standardized image and at least one standardized image of the plurality of standardized images, receive non-conforming data, generate standardized data as a function of the ECG transformation model and the non-conforming data and train an ECG machine learning model as a function of the standardized data.


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