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:
Apr. 01, 2025

Filed:

Jul. 30, 2024
Applicant:

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

Inventors:

Rakesh Barve, Bengaluru, IN;

Suthirth Vaidya, Bengaluru, IN;

Animesh Agarwal, San Mateo, CA (US);

Abhijith Chunduru, Bengaluru, IN;

Rohit Jain, Danville, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 50/50 (2018.01); A61B 6/00 (2006.01); A61B 18/00 (2006.01); G06T 7/33 (2017.01); G06T 11/20 (2006.01); G16H 50/70 (2018.01); A61M 25/01 (2006.01);
U.S. Cl.
CPC ...
G16H 50/50 (2018.01); A61B 6/5229 (2013.01); A61B 6/5247 (2013.01); A61B 18/00 (2013.01); G06T 7/33 (2017.01); G06T 11/206 (2013.01); G16H 50/70 (2018.01); A61B 2018/00577 (2013.01); A61B 2018/00839 (2013.01); A61M 2025/0166 (2013.01); G06T 2200/24 (2013.01); G06T 2207/20081 (2013.01); G06T 2210/41 (2013.01);
Abstract

Apparatus for generating electro-anatomical mapping and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive input data, generate, using at least a machine learning model, an electro-anatomical mapping as a function of the input data, and display the electro-anatomical mapping using a user interface, wherein receiving the input data includes receiving, from an imaging device, at least a medical image and receiving, from a signal capturing device, at least an electrogram, wherein the at least a machine learning model is trained using electro-anatomical mapping training data including exemplary medical images and exemplary electrograms as input correlated to exemplary electro-anatomical mappings as output.


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