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. 29, 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:
Assistant Examiner:
Int. Cl.
CPC ...
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); G06N 3/08 (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 generating multi-modal data archetypes. In one aspect, a method comprises obtaining a plurality of training examples, wherein each training example corresponds to a respective patient and includes multi-modal data, having a plurality of feature dimensions, that characterizes the patient; jointly training an encoder neural network and a decoder neural network on the plurality of training examples; and generating a plurality of multi-modal data archetypes that each correspond to a respective dimension of a latent space, comprising, for each multi-modal data archetype: processing a predefined embedding that represents the corresponding dimension of the latent space using the decoder neural network to generate multi-modal data, having the plurality of feature dimensions, that defines the multi-modal data archetype.


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