公开(公告)号：US11508092B2

公开(公告)日：2022-11-22

申请号：US16715285

申请日：2019-12-16

Applicant: X Development LLC

Inventor： Kathleen Watson ,  Jie Yang ,  Yueqi Li

IPC: G06T7/00

Abstract: Implementations are described herein for edge-based real time crop yield predictions made using sampled subsets of robotically-acquired vision data. In various implementations, one or more robots may be deployed amongst a plurality of plants in an area such as a field. Using one or more vision sensors of the one or more robots, a superset of high resolution images may be acquired that depict the plurality of plants. A subset of multiple high resolution images may then be sampled from the superset of high resolution images. Data indicative of the subset of high resolution images may be applied as input across a machine learning model, with or without additional data, to generate output indicative of a real time crop yield prediction.

公开(公告)号：US20220368813A1

公开(公告)日：2022-11-17

申请号：US17869576

申请日：2022-07-20

Applicant: X Development LLC

Inventor： Andrew Rossignol ,  Harrison Pham

IPC: H04N5/067 ,  H04N5/232 ,  H04N5/225

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that provides an enhanced synchronization framework. One of the methods includes a primary and a second device that receive configuration information which identifies one or more actions to be performed by the secondary device when it receives specified pulses of a sequence of pulses from the primary device. The primary device transmits a sequence of pulses. The primary and the secondary device receive a particular pulse from the sequence of pulses. The secondary device determines whether the particular pulse satisfies one or more predetermined criteria and generates an instruction based on the determination.

公开(公告)号：US11500005B2

公开(公告)日：2022-11-15

申请号：US17243675

申请日：2021-04-29

Applicant: X Development LLC

Inventor： Leo Francis Casey ,  Siyuan Xin ,  Peter Evans ,  Shuyu Wang ,  Raymond Daly ,  C. O. Lee Boyce, Jr. ,  Joel Fraser Atwater

IPC: G01R29/18

Abstract: Methods, systems, and apparatus, including computer programs stored on a computer-readable storage medium, for obtaining a reference phase signal that is synchronized with an alternating current (AC) phase of a multi-phase electrical power distribution system. The apparatus obtains output signals from sensors, each output signal representative of an electromagnetic emission detected by a respective sensor. The apparatus identifies, based on comparing respective phases of the output signals to the reference phase signal, a particular AC phase of the multi-phase electrical power distribution system associated with a source of the emissions. The apparatus provides an indication of the particular AC phase to a user.

公开(公告)号：US20220358348A1

公开(公告)日：2022-11-10

申请号：US17307667

申请日：2021-05-04

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Jeffrey Bush ,  Doris Tang

IPC: G06N3/063 ,  G06N3/08 ,  G06K9/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a representation of an image captured by an onboard camera of a drone and providing the representation of the image to a drone image processing neural network having a brain emulation sub-network with an architecture that is specified by synaptic connectivity between neurons in a brain of a biological organism, including instantiating a respective artificial neuron corresponding to each biological neuron of multiple biological neurons, and instantiating a respective connection between each pair of artificial neurons that correspond to a pair of biological neurons that are connected by a synaptic connection, and processing the representation of the image using the drone image processing neural network having the brain emulation sub-network to generate a network output that defines a prediction characterizing the image captured by the onboard camera of the drone.

公开(公告)号：US11496722B1

公开(公告)日：2022-11-08

申请号：US17132097

申请日：2020-12-23

Applicant: X Development LLC

Inventor： Guy Satat

IPC: H04N13/246 ,  G06T7/70 ,  H04N13/271 ,  G06V20/10 ,  G06T7/80 ,  G06N3/08 ,  G06K9/62

Abstract: A method includes receiving, from a multiscopic image capture system, a plurality of images depicting a scene. The method includes determining, by application of a neural network based on the plurality of images, a disparity map of the scene. The neural network includes a plurality of layers, and the layers include a rectification layer. The method include determining a matching error of the disparity map based on differences between corresponding pixels of two or more images associated with the disparity map. The method includes back-propagating the matching error to the rectification layer of the neural network. Back-propagating the matching error includes updating one or more weights applied to the rectification layer.

公开(公告)号：US11487522B1

公开(公告)日：2022-11-01

申请号：US17318687

申请日：2021-05-12

Applicant: X Development LLC

Inventor： Rishabh Singh ,  Nisarg Vyas ,  Jayendra Parmar ,  Dhara Kotecha ,  Artem Goncharuk ,  David Andre

IPC: G06F8/51 ,  G06N3/04 ,  G06F8/53 ,  G06F8/41 ,  G06N3/08 ,  G06F8/52

Abstract: Training and/or utilization of a neural decompiler that can be used to generate, from a lower-level compiled representation, a target source code snippet in a target programming language. In some implementations, the lower-level compiled representation is generated by compiling a base source code snippet that is in a base programming language, thereby enabling translation of the base programming language (e.g., C++) to a target programming language (e.g., Python). In some of those implementations, output(s) from the neural decompiler indicate canonical representation(s) of variables. Technique(s) can be used to match those canonical representation(s) to variable(s) of the base source code snippet. In some implementations, multiple candidate target source code snippets are generated using the neural decompiler, and a subset (e.g., one) is selected based on evaluation(s).

公开(公告)号：US11481210B2

公开(公告)日：2022-10-25

申请号：US17136968

申请日：2020-12-29

Applicant: X Development LLC

Inventor： Rishabh Singh ,  David Andre ,  Bin Ni ,  Owen Lewis

IPC: G06F8/71 ,  G06F40/20 ,  G06N20/00

Abstract: Implementations are described herein for using machine learning to perform various tasks related to migrating source code based on relatively few (“few shots”) demonstrations. In various implementations, an autoregressive language model may be conditioned based on demonstration tuple(s). In some implementations, a demonstration tuple may include a pre-migration version of a first source code snippet and a post-migration version of the first source code snippet. In other implementations, demonstration tuples may include other data, such as intermediate forms (e.g., natural language descriptions or pseudocode), input-output pairs demonstrating intended behavior, etc. The autoregressive language model may be trained on corpora of source code and natural language documentation on the subject of computer programming. A pre-migration version of a source code file may be processed based on the conditioned autoregressive language model, and a post-migration version may be generated based on output generated based on the conditioned autoregressive model.

公开(公告)号：US20220335669A1

公开(公告)日：2022-10-20

申请号：US17718840

申请日：2022-04-12

Applicant: X Development LLC

Inventor： Phillip Ellsworth Stahlfeld ,  Ananya Gupta

IPC: G06T11/20 ,  G06V20/10 ,  G06V20/17 ,  G06V10/82

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for filling gaps in electric grid models are enclosed. A method includes obtaining vector data representing first portions of paths of electric grid wires over a geographic region; converting the vector data to first raster image data that depicts an overhead view of the electric grid wires including a first set of line segments representing the first portions of the paths; processing the first raster image data using a gap filling model; obtaining, as output from the gap filling model, second raster image data including a second set of line segments corresponding to gaps included in the input raster image data and representing second portions of paths of the electric grid wires; and converting the second raster image data to vector data representing the first portions and the second portions of paths of the electric grid wires.

公开(公告)号：US20220330506A1

公开(公告)日：2022-10-20

申请号：US17232851

申请日：2021-04-16

Applicant: X Development LLC

Inventor： Dominico P. Julian ,  Charles Nordstrom ,  Jeff Zerger ,  Joel Fraser Atwater ,  Benjamin Lundquist Saenz

IPC: A01G33/00 ,  G05B13/02 ,  G05D1/02

Abstract: Methods, systems, and computer-readable media that implement an autonomous or semi-autonomous growth platform used to control live cargo exposures to environmental parameters by changing depth in an offshore environment. For example, the growth platform can be lowered at night so that farmed seaweed can perform luxury uptake of nutrients and raised during the daytime so that the farmed seaweed can capture sunlight.

公开(公告)号：US11475291B2

公开(公告)日：2022-10-18

申请号：US15855329

申请日：2017-12-27

Applicant: X Development LLC

Inventor： Nareshkumar Rajkumar ,  Patrick Leger ,  Nicolas Hudson ,  Krishna Shankar ,  Rainer Hessmer

IPC: G06F13/00 ,  G06N3/08 ,  B25J9/00 ,  B25J9/16 ,  H04L67/12 ,  H04L67/568

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing learned information among robots. In some implementations, a robot obtains sensor data indicating characteristics of an object. The robot determines a classification for the object and generates an embedding for the object using a machine learning model stored by the robot. The robot stores the generated embedding and data indicating the classification for the object. The robot sends the generated embedding and the data indicating the classification to a server system. The robot receives, from the server system, an embedding generated by a second robot and a corresponding classification. The robot stores the received embedding and the corresponding classification in the local cache of the robot. The robot may then use the information in the cache to identify objects.