公开(公告)号：US20210201115A1

公开(公告)日：2021-07-01

申请号：US16776574

申请日：2020-01-30

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Philip Edwin Watson ,  Georgios Evangelopoulos

IPC: G06N3/04 ,  G06T7/00 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a reservoir computing neural network. In one aspect there is provided a reservoir computing neural network comprising: (i) a brain emulation sub-network, and (ii) a prediction sub-network. The brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input. The prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output. The values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusting during training of the reservoir computing neural network.

公开(公告)号：US20210004210A1

公开(公告)日：2021-01-07

申请号：US16458713

申请日：2019-07-01

Applicant: X Development LLC

Inventor： Georgios Evangelopoulos ,  Olivia Hatalsky ,  Bin Ni ,  Qianyu Zhang

IPC: G06F8/33 ,  G06F8/40 ,  G06N3/08

Abstract: Techniques are described herein for using artificial intelligence to “learn,” statistically, a target programming style that is imposed in and/or evidenced by a code base. Once the target programming style is learned, it can be used for various purposes. In various implementations, one or more generative adversarial networks (“GANs”), each including a generator machine learning model and a discriminator machine learning model, may be trained to facilitate learning and application of target programming style(s). In some implementations, the discriminator(s) and/or generator(s) may operate on graphical input, and may take the form of graph neural networks (“GNNs”), graph attention neural networks (“GANNs”), graph convolutional networks (“GCNs”), etc., although this is not required.

公开(公告)号：US20200371778A1

公开(公告)日：2020-11-26

申请号：US16418767

申请日：2019-05-21

Applicant: X Development LLC

Inventor： Bin Ni ,  Benoit Schillings ,  Georgios Evangelopoulos ,  Olivia Hatalsky ,  Qianyu Zhang ,  Grigory Bronevetsky

IPC: G06F8/71 ,  G06N3/08

Abstract: Implementations are described herein for automatically identifying, recommending, and/or effecting changes to a legacy source code base by leveraging knowledge gained from prior updates made to other similar legacy code bases. In some implementations, data associated with a first version source code snippet may be applied as input across a machine learning model to generate a new source code embedding in a latent space. Reference embedding(s) may be identified in the latent space based on their distance(s) from the new source code embedding in the latent space. The reference embedding(s) may be associated with individual changes made during the prior code base update(s). Based on the identified one or more reference embeddings, change(s) to be made to the first version source code snippet to create a second version source code snippet may be identified, recommended, and/or effected.

公开(公告)号：US20220121427A1

公开(公告)日：2022-04-21

申请号：US17563881

申请日：2021-12-28

Applicant: X Development LLC

Inventor： Georgios Evangelopoulos ,  Olivia Hatalsky ,  Bin Ni ,  Qianyu Zhang

IPC: G06F8/33 ,  G06N3/08 ,  G06F8/40

Abstract: Techniques are described herein for using artificial intelligence to “learn,” statistically, a target programming style that is imposed in and/or evidenced by a code base. Once the target programming style is learned, it can be used for various purposes. In various implementations, one or more generative adversarial networks (“GANs”), each including a generator machine learning model and a discriminator machine learning model, may be trained to facilitate learning and application of target programming style(s). In some implementations, the discriminator(s) and/or generator(s) may operate on graphical input, and may take the form of graph neural networks (“GNNs”), graph attention neural networks (“GANNs”), graph convolutional networks (“GCNs”), etc., although this is not required.

公开(公告)号：US20220004167A1

公开(公告)日：2022-01-06

申请号：US17367281

申请日：2021-07-02

Applicant: X Development LLC

Inventor： Kathryn Jane Zealand ,  Joseph Hollis Sargent ,  Georgios Evangelopoulos ,  Elliott J. Rouse

IPC: G05B19/4155 ,  G06N20/00 ,  G06F16/23

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating, using, or both, exosuit historical data. In some implementations, (i) sensor data generated by sensors of an exosuit worn by a user and (ii) control data indicating actions performed by or control signals generated by the exosuit based on the sensor data while worn by the user are received. The sensor data and the control data are added to a database that includes historical data describing use of the exosuit over time by the user. A control scheme of the exosuit is customized for the user by updating the one or more machine learning models or settings that govern the application of the one or more machine learning models. Forces provided by one or more actuators of the exosuit are controlled using the updated one or more machine learning models or the updated settings.

公开(公告)号：US20210201111A1

公开(公告)日：2021-07-01

申请号：US16776579

申请日：2020-01-30

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Georgios Evangelopoulos ,  Philip Edwin Watson

IPC: G06N3/04 ,  G06T7/00 ,  G06F16/901

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an artificial neural network architecture corresponding to a sub-graph of a synaptic connectivity graph. In one aspect, there is provided a method comprising: obtaining data defining a graph representing synaptic connectivity between neurons in a brain of a biological organism; determining, for each node in the graph, a respective set of one or more node features characterizing a structure of the graph relative to the node; identifying a sub-graph of the graph, comprising selecting a proper subset of the nodes in the graph for inclusion in the sub-graph based on the node features of the nodes in the graph; and determining an artificial neural network architecture corresponding to the sub-graph of the graph.

公开(公告)号：US20200205712A1

公开(公告)日：2020-07-02

申请号：US16284607

申请日：2019-02-25

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Gabriella Levine ,  Georgios Evangelopoulos

IPC: A61B5/16 ,  A61B5/04 ,  A61B5/0484 ,  A61B5/00 ,  A61B5/0482 ,  A61B5/048

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for presenting a human participant with information known to stimulate a person's neural reward system. Receiving an EEG signal from a sensor coupled to the human participant in response to presenting the participant with the information, the EEG signal being associated with the participant's neural reward system. Contemporaneously with receiving the EEG signal, receiving contextual information related to the information presented to the human participant. Processing the EEG signal and the contextual information in real time using a machine learning model trained to associate depression in the person with EEG signals associated with the person's neural reward system and the presented information. Diagnosing whether the participant is experiencing depression based on an output of the machine learning model.

公开(公告)号：US20200205711A1

公开(公告)日：2020-07-02

申请号：US16284556

申请日：2019-02-25

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Georgios Evangelopoulos ,  Pramod Gupta

IPC: A61B5/16 ,  A61B5/0484 ,  A61B5/0482 ,  A61B5/00 ,  G06K9/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for causing a stimulus presentation system to present first content to a patient. Obtaining, from a brainwave sensor, electroencephalography (EEG) signals of the patient while the first content is being presented to the patient. Identifying, from within the EEG signals of the patient, first brainwave signals associated with a first brain system of the patient, the first brainwave signals representing a response by the patient to the first content. Determining, based on providing the first brainwave signals as input features to a machine learning model, a likelihood that the patient will experience a type of depression within a period of time. Providing, for display on a user computing device, data indicating the likelihood that the patient will experience the type of depression within the period of time.

公开(公告)号：US11853034B2

公开(公告)日：2023-12-26

申请号：US17110537

申请日：2020-12-03

Applicant: X Development LLC

Inventor： Kathryn Jane Zealand ,  Elliott J. Rouse ,  Georgios Evangelopoulos

IPC: G05B19/4155 ,  B25J9/00 ,  G06N20/00 ,  H04Q9/00

CPC classification number: G05B19/4155 ,  B25J9/0006 ,  G06N20/00 ,  H04Q9/00 ,  G05B2219/40305

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an exosuit activity transition control structure. In some implementations, sensor data for a powered exosuit is received. The sensor data is classified depending on whether the sensor data is indicative of a transition between different types of activities of a wearer of the powered exosuit. The classification is provided to a control system for the powered exosuit. The powered exosuit is controlled based on the classification.

公开(公告)号：US20230229891A1

公开(公告)日：2023-07-20

申请号：US18173133

申请日：2023-02-23

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Philip Edwin Watson ,  Georgios Evangelopoulos

IPC: G06N3/08 ,  G06V30/18 ,  G06T7/00 ,  G06V10/82

CPC classification number: G06N3/045 ,  G06V30/18057 ,  G06T7/0012 ,  G06N3/08 ,  G06V10/82 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06T2207/30016 ,  G06T2207/10061

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a reservoir computing neural network. In one aspect there is provided a reservoir computing neural network comprising: (i) a brain emulation sub-network, and (ii) a prediction sub-network. The brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input. The prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output. The values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusting during training of the reservoir computing neural network.