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. 29, 2025

Filed:

Aug. 20, 2024
Applicant:

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

Inventors:

Suthirth Vaidya, Bengaluru, IN;

Rakesh Barve, Bengaluru, IN;

Animesh Agarwal, San Mateo, CA;

Samir Awasthi, Boston, MA (US);

Murali Aravamudan, Andover, MA (US);

Maulik Nanavaty, Cambridge, MA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 20/40 (2018.01); A61B 5/00 (2006.01); A61B 5/346 (2021.01); A61B 5/364 (2021.01); A61B 18/00 (2006.01); A61B 18/14 (2006.01); A61B 34/10 (2016.01); G16H 10/60 (2018.01); G16H 50/70 (2018.01);
U.S. Cl.
CPC ...
G16H 20/40 (2018.01); A61B 5/346 (2021.01); A61B 5/364 (2021.01); A61B 5/7246 (2013.01); A61B 5/742 (2013.01); A61B 18/1492 (2013.01); A61B 34/10 (2016.02); G16H 10/60 (2018.01); G16H 50/70 (2018.01); A61B 2018/00577 (2013.01); A61B 2034/104 (2016.02);
Abstract

An apparatus and method for prediction of pulmonary vein reconnection is disclosed. The apparatus includes an electrocardiogram device, at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to generate ablation evaluation training data, train an ablation evaluation machine-learning model using the ablation evaluation training data, receive, from the electrocardiogram device, the electrocardiogram data, and generate, using the ablation evaluation machine-learning model, ablation evaluation data of the patient, wherein generating the ablation evaluation data of the patient includes inputting, into the ablation evaluation machine-learning model, the electrocardiogram data and receiving as output, from the ablation evaluation machine-learning model, the ablation evaluation data of the patient.


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