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:
Jun. 06, 2023

Filed:

Oct. 05, 2022
Applicant:

Neumora Therapeutics, Inc., San Francisco, CA (US);

Inventors:

Tathagata Banerjee, Waltham, MA (US);

Matthew Edward Kollada, Deerfield, IL (US);

Assignee:

Neumora Therapeutics, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 50/30 (2018.01); G06N 3/08 (2023.01); G16H 20/60 (2018.01); G16H 40/20 (2018.01); G16H 50/70 (2018.01); G16H 50/20 (2018.01); G06N 3/02 (2006.01); G06N 3/045 (2023.01); G16H 30/40 (2018.01); G06V 10/77 (2022.01); G06V 10/82 (2022.01); G06V 10/774 (2022.01); G06T 7/00 (2017.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G16H 40/20 (2018.01); G06N 3/02 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 7/0016 (2013.01); G06V 10/774 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G06N 20/00 (2019.01); G06T 2207/10088 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30104 (2013.01); G06V 2201/03 (2022.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for classifying a patient. In one aspect, a method comprises: receiving multi-modal data characterizing a patient, wherein the multi-modal data comprises a respective feature representation for each of a plurality of modalities; processing the multi-modal data characterizing the patient using an encoder neural network to generate an embedding of the multi-modal data characterizing the patient; determining a respective classification score for each patient category in a set of patient categories based on the embedding of the multi-modal data characterizing the patient; and classifying the patient as being included in a corresponding patient category from the set of patient categories based on the classification scores.


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