US 11,742,076 B2
Machine learning systems for generating multi-modal data archetypes
Tathagata Banerjee, Waltham, MA (US); and Matthew Edward Kollada, Deerfield, IL (US)
Assigned to Neumora Therapeutics, Inc., San Francisco, CA (US)
Filed by NEUMORA THERAPEUTICS, INC., San Francisco, CA (US)
Filed on Oct. 5, 2022, as Appl. No. 17/960,755.
Claims priority of provisional application 63/413,150, filed on Oct. 4, 2022.
Claims priority of provisional application 63/400,250, filed on Aug. 23, 2022.
Claims priority of provisional application 63/337,753, filed on May 3, 2022.
Claims priority of provisional application 63/328,189, filed on Apr. 6, 2022.
Claims priority of provisional application 63/294,751, filed on Dec. 29, 2021.
Claims priority of provisional application 63/292,115, filed on Dec. 21, 2021.
Claims priority of provisional application 63/252,523, filed on Oct. 5, 2021.
Claims priority of provisional application 63/252,539, filed on Oct. 5, 2021.
Claims priority of provisional application 63/252,562, filed on Oct. 5, 2021.
Claims priority of provisional application 63/252,500, filed on Oct. 5, 2021.
Prior Publication US 2023/0107415 A1, Apr. 6, 2023
Int. Cl. 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)
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)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
generating a plurality of multi-modal data archetypes using an encoder neural network that has been jointly trained along with a decoder neural network, wherein:
the encoder neural network is configured to process input multi-modal data characterizing an input patient to generate an embedding of the input multi-modal data in a multi-dimensional latent space;
the decoder neural network is configured to process the embedding of the input multi-modal data to generate a reconstruction of the input multi-modal data; and
generating the plurality of multi-modal data archetypes comprises:
processing, for each patient in a population of patients, multi-modal data characterizing the patient using the encoder neural network to generate an embedding of the multi-modal data in the latent space,
wherein the embeddings of multi-modal data characterizing the patients in the population of patients collectively define a set of embeddings in the latent space;
processing the set of embeddings in the latent space to generate a set of parameters defining a convex hull of the set of embeddings in the latent space;
processing: (i) the set of parameters defining the convex hull of the set of embeddings in the latent space, and (ii) the set of embeddings, to identify a proper subset of the embeddings in the set of embeddings as being archetype embeddings; and
identifying the respective multi-modal data represented by each archetype embedding as a respective multi-modal data archetype.