公开(公告)号：US20230222176A1

公开(公告)日：2023-07-13

申请号：US17574915

申请日：2022-01-13

Applicant: X Development LLC

Inventor： Garrett Raymond Honke ,  Baihan Lin ,  Anupama Thubagere Jagadeesh

IPC: G06K9/62 ,  G01N33/68 ,  G01N33/50

CPC classification number: G06K9/6256 ,  G06K9/623 ,  G01N33/6848 ,  G01N33/5038

Abstract: The invention features a computer-implemented biological data classification method executed by one or more processors and including receiving, by the one or more processors, a first biological data set comprising a first plurality of biological sample data collected from a set of patients; processing, by the one or more processors, the first biological data set using a first variational autoencoder (VAE) to generate a first trained VAE comprising a first latent space vector of the first biological data set comprising a plurality of values corresponding to each latent space dimension of the latent space vector, the latent space vector having lower dimensionality than the biological sample data set; receiving, by the one or more processors, a second biological data set comprising a second plurality of biological sample data collected from a patient, different from the set of patients; and generating, by the one or more processors, a latent space representation of the second biological data set based on a first latent space vector.

公开(公告)号：US20240346362A1

公开(公告)日：2024-10-17

申请号：US18135043

申请日：2023-04-14

Applicant: X Development LLC

Inventor： David Andre ,  Garrett Raymond Honke

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Disclosed implementations relate to preserving individuals' semantic privacy while facilitating automation of tasks across a population of individuals. In various implementations, data indicative of an observed set of interactions between a user and a computing device may be recorded and used to simulate multiple different synthetic sets of interactions between the user and the computing device. Each synthetic set may include a variation of the observed set of interactions at a different level of abstraction. User feedback may be obtained about each of the multiple different sets. Based on the user feedback, one of the multiple different synthetic sets of interactions may be selected and used to train a machine learning model.

公开(公告)号：US20220068476A1

公开(公告)日：2022-03-03

申请号：US17007193

申请日：2020-08-31

Applicant: X Development LLC

Inventor： Katherine Elise Link ,  Vladimir Miskovic ,  Nina Thigpen ,  Mustafa Ispir ,  Garrett Raymond Honke ,  Pramod Gupta

IPC: G16H50/20 ,  G06N3/08 ,  G06N7/00

Abstract: Systems and processes described herein can expand a limited data set of EEG trials into a larger data set by resampling subsets of EEG trial data. Implementations may employ one or more of a variety of different resampling techniques. For example, a subset of the available training data is selected to form a new set of training data. The subset can be selected using replacement (e.g., a sample can be selected more than once, and thus represented multiple times in the new set of training data). Alternatively the subset can be selected without using replacement (e.g., each sample is able to be selected only once, and thus represented a maximum of one time in the new set of training data).

公开(公告)号：US20220015659A1

公开(公告)日：2022-01-20

申请号：US16930122

申请日：2020-07-15

Applicant: X Development LLC

Inventor： Mustafa Ispir ,  Asim Iqbal ,  Pramod Gupta ,  Garrett Raymond Honke ,  Vladimir Miskovic

IPC: A61B5/0484 ,  G06N3/04 ,  G06N3/08 ,  G06K9/00 ,  A61B5/04 ,  G16H50/30 ,  G16H50/20

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating embeddings of EEG measurements. One of the methods includes obtaining a two-dimensional time-frequency electroencephalogram (EEG) representation corresponding to one or more EEG signal measurements of a user; processing the time-frequency EEG representation using a first neural network having a plurality of first network parameters to generate an embedding of the time-frequency EEG representation, wherein the first neural network has been trained using transfer learning; and providing the embedding of the time-frequency EEG representation to a downstream neural network to generate a mental health prediction for the user.

公开(公告)号：US20220005603A1

公开(公告)日：2022-01-06

申请号：US16921224

申请日：2020-07-06

Applicant: X Development LLC

Inventor： Garrett Raymond Honke ,  Pramod Gupta ,  Irina Higgins

IPC: G16H50/20 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an auto-encoder to de-noise task specific electroencephalogram (EEG) signals. One of the methods includes training a variational auto-encoder (VAE) including to learn a plurality of parameter values of the VAE by applying, as first training input to the VAE, training data, the training data comprising electroencephalogram (EEG) data representing brain activities of individual persons when performing different tasks; and after the training, adapting the VAE for a specific task by applying, as second training input to the VAE, adaptation data, the adaptation data comprising task-specific EEG data representing brain activities of individual persons when performing the specific task.