公开(公告)号：US20240394286A1

公开(公告)日：2024-11-28

申请号：US18674444

申请日：2024-05-24

Applicant: X Development LLC

Inventor： Garrett Raymond Honke ,  Jeffrey Bush ,  Klara Kaleb ,  Brian Mark Rosen ,  David Andre

IPC: G06F16/332 ,  G06F16/33 ,  G06F40/284

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing tasks. One of the methods includes obtaining a prompt, obtaining a set of documents, generating an input, providing the input to a plurality of language models, generating a distribution from intermediate answers from the language models; and generating an answer to the prompt by performing a probabilistic inference over the distribution.

公开(公告)号：US20220015657A1

公开(公告)日：2022-01-20

申请号：US16933219

申请日：2020-07-20

Applicant: X Development LLC

Inventor： Mustafa Ispir ,  Pramod Gupta ,  Garrett Raymond Honke

IPC: A61B5/04 ,  A61B5/00 ,  G06N3/04 ,  G06N3/08

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 plurality of electroencephalogram (EEG) signal measurements of a user, wherein each EEG signal measurement corresponds to one of a plurality of prompt types of an EEG task; generating, from the plurality of EEG signal measurements, a plurality of network inputs each corresponding to a different prompt type of the plurality of prompt types of the EEG task; processing the network inputs using a twin neural network to generate respective network outputs each corresponding to a different prompt type of the plurality of prompt types of the EEG task; and providing the network outputs to a downstream neural network to generate a mental health prediction for the user.

公开(公告)号：US20250028995A1

公开(公告)日：2025-01-23

申请号：US18224889

申请日：2023-07-21

Applicant: X Development LLC

Inventor： Rishabh Singh ,  David Andre ,  Garrett Raymond Honke ,  Falak Shah ,  Nisarg Vyas ,  Jayendra Parmar ,  Brian M. Rosen ,  Shaili Trivedi

IPC: G06N20/00

Abstract: Disclosed implementations relate to adding “bottleneck” models to machine learning pipelines that already apply domain models to translate and/or transfer representations of high-level semantic concepts between domains. In various implementations, an initial representation in a first domain of a transition from an initial state of an environment to a goal state of the environment may be processed based on a pre-trained first domain encoder to generate a first embedding that semantically represents the transition. The first embedding may be processed based on one or more bottleneck models to generate a second embedding with fewer dimensions than the first embedding. In various implementations, the second embedding may be processed in various ways to train one or more of the bottleneck model(s) based on various different auxiliary loss functions.

公开(公告)号：US20230196059A1

公开(公告)日：2023-06-22

申请号：US17557618

申请日：2021-12-21

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Lam Thanh Nguyen ,  Baihan Lin ,  Julia Renee Watson ,  Garrett Raymond Honke

IPC: G06N3/00

CPC classification number: G06N3/008

Abstract: In one aspect, there is provided a method performed by one or more data processing apparatus, the method includes: obtaining a network input including a respective data element at each input position in a sequence of input positions, and processing the network input using a neural network to generate a network output that defines a prediction related to the network input, where the neural network includes a sequence of encoder blocks and a decoder block, where each encoder block has a respective set of encoder block parameters, and where the set of encoder block parameters includes multiple brain emulation parameters that, when initialized, represent biological connectivity between multiple biological neuronal elements in a brain of a biological organism.

公开(公告)号：US20210391086A1

公开(公告)日：2021-12-16

申请号：US16897981

申请日：2020-06-10

Applicant: X Development LLC

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

IPC: G16H50/70 ,  G16H10/20 ,  G16H50/20 ,  G16H10/60 ,  G16H70/60 ,  G16H15/00 ,  G06F16/901 ,  A61B5/16

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining psychological data, generating a psychopathological analysis data structure (PADS), applying a latent factor analysis algorithm to the PADS to obtain a psychopathological latent factor space (PLFS), generating a latent factor graph, and outputting the latent factor graph.

公开(公告)号：US20230223144A1

公开(公告)日：2023-07-13

申请号：US17574834

申请日：2022-01-13

Applicant: X Development LLC

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

IPC: G16H50/20 ,  G16B50/00 ,  G16H50/50 ,  G16H10/40

CPC classification number: G16H50/20 ,  G16B50/00 ,  G16H10/40 ,  G16H50/50

Abstract: The invention features a computer-implemented biological data prediction method executed by one or more processors including receiving, by the one or more processors, a biomedical data set comprising biomedical data corresponding to a plurality of detected analytes in a biological sample collected from a set of patients at intermittent time intervals, the biomedical data set having a first plurality of feature dimensions; processing, by the one or more processors, the biomedical data set to generate a low-rank tensor having a second plurality of feature dimensions, wherein the second plurality of feature dimensions can be lower than the first plurality of feature dimensions; generating, by the one or more processors, predicted biomedical data along the second plurality of feature dimensions corresponding to the intermittent time intervals; and creating a reconstructed biomedical data set including the predicted biomedical data and the biomedical data along the first plurality of feature dimensions.

公开(公告)号：US20220101997A1

公开(公告)日：2022-03-31

申请号：US17039303

申请日：2020-09-30

Applicant: X Development LLC

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

IPC: G16H50/20 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing representations of EEG measurements. One of the methods includes obtaining a plurality of EEG signal measurements corresponding to respective EEG trials of a user; generating a time-domain representation from the plurality of EEG signal measurements, where the time-domain representation includes a plurality of rows, and where each row corresponds to a different set of one or more EEG signal measurements; applying the time-domain representation as input to a neural network having a plurality of network parameters, final values of the network parameters having been determined by a transfer learning process where the neural network is initially trained to perform an image processing task and the neural network is subsequently trained to perform EEG analysis; and obtaining, from the neural network, a mental health prediction for the user.

公开(公告)号：US20220054033A1

公开(公告)日：2022-02-24

申请号：US16999636

申请日：2020-08-21

Applicant: X Development LLC

Inventor： Garrett Raymond Honke ,  Pramod Gupta ,  Mustafa Ispir ,  Nina Thigpen

IPC: A61B5/048 ,  A61B5/0478 ,  A61B5/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, from one or more electrodes, electroencephalographic (EEG) signals from a user; generating signal vectors from the EEG signals, each signal vector representing one channel of EEG signals. The actions include providing the signal vectors as input data to a variational autoencoder (VAE), wherein the VAE generates a latent representation of the input data, the latent representation having lower dimensionality than the signal vectors, and reconstructs the latent representation into an event related potential (ERP) of the corresponding EEG signal. The actions include providing, for display to a user, a graphical representation of the ERPs.

公开(公告)号：US20240143929A1

公开(公告)日：2024-05-02

申请号：US17977681

申请日：2022-10-31

Applicant: X Development LLC

Inventor： Garrett Raymond Honke ,  David Andre ,  Alberto Camacho Martinez ,  Irhum Shafkat

IPC: G06F40/30 ,  G06F8/41 ,  G06N20/00

CPC classification number: G06F40/30 ,  G06F8/436 ,  G06N20/00

Abstract: Disclosed implementations relate to using mutual constraint satisfaction to sample from different stochastic processes and identify coherent inferences across domains. In some implementations, a first domain representation of a semantic concept may be used to conditionally sample a first set of candidate second domain representations of the semantic concept from a first stochastic process. Based on second domain representation(s) of the first set, candidate third domain representations of the semantic concept may be conditionally sampled from a second stochastic process. Based on candidate third domain representation(s), a second set of candidate second domain representations of the semantic concept may be conditionally sampled from a third stochastic process. Pairs of candidate second domain representations sampled across the first and second sets may be evaluated. Based on the evaluation, second domain representation(s) of the semantic concept are selected, e.g., as input for a downstream computer process.

公开(公告)号：US20230342589A1

公开(公告)日：2023-10-26

申请号：US17728398

申请日：2022-04-25

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Julia Renee Watson ,  Garrett Raymond Honke ,  Estefany Kelly Buchanan ,  Hailey Anne Trier ,  Grayr Bleyan ,  Blair Armstrong ,  Rebecca Dawn Finzi

IPC: G06N3/04 ,  G06N20/20 ,  G06K9/62

CPC classification number: G06N3/0454 ,  G06N20/20 ,  G06K9/6215 ,  G06K9/6227 ,  G06K9/6262

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for executing ensemble models that include multiple reservoir computing neural networks. One of the methods includes executing an ensemble model comprising a plurality of reservoir computing neural networks, the ensemble model having been trained by operations comprising, at each training stage in a sequence of training stages: obtaining a current ensemble model that comprises a plurality of current reservoir computing neural networks; determining a respective performance measure for each current reservoir computing neural network in the current ensemble model; determining one or more new reservoir computing neural networks to be added to the current ensemble model based on the performance measures for the current reservoir computing neural networks; and adding the new reservoir computing neural networks to the current ensemble model.