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. 14, 2025

Filed:

Jul. 18, 2024
Applicant:

Cognitivecare Inc., Milpitas, CA (US);

Inventors:

Venkata Narasimham Peri, Plano, TX (US);

Naresh Nelaturi, Guntur, IN;

Jagannadha Ganti, Hyderabad, IN;

Suresh Venkata Satya Attili, Hyderabad, IN;

Manoj Ramesh Teltumbade, Pittsburgh, PA (US);

Sheena Gill, Centreville, VA (US);

Errol R. Norwitz, Auburndale, MA (US);

Chetan Chavan, Pune, IN;

Prashant Garg, Hyderabad, IN;

Pavan Verkicharla, Hyderabad, IN;

Assignee:

COGNITIVECARE INC., Milpitas, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
A61B 3/14 (2005.12); G06T 7/00 (2016.12); G16H 20/00 (2017.12); G16H 30/40 (2017.12); G16H 50/20 (2017.12); G16H 50/30 (2017.12);
U.S. Cl.
CPC ...
A61B 3/14 (2012.12); G06T 7/0012 (2012.12); G16H 20/00 (2017.12); G16H 30/40 (2017.12); G16H 50/20 (2017.12); G16H 50/30 (2017.12); G06T 2207/20081 (2012.12); G06T 2207/20084 (2012.12); G06T 2207/30041 (2012.12);
Abstract

According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.


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