公开(公告)号：US20230171303A1

公开(公告)日：2023-06-01

申请号：US17540015

申请日：2021-12-01

Applicant: X Development LLC

Inventor： Yueqi Li ,  Alexander Ngai

IPC: H04L67/10 ,  G06N3/12 ,  G06Q50/02 ,  G06F9/50 ,  H04L67/12

CPC classification number: H04L67/10 ,  G06N3/126 ,  G06Q50/02 ,  G06F9/5027 ,  H04L67/12

Abstract: Implementations are disclosed for dynamically allocating aspects of platform-independent machine-learning based agricultural state machines among edge and cloud computing resources. In various implementations, a GUI may include a working canvas on which graphical elements corresponding to platform-independent logical routines are manipulable to define a platform-independent agricultural state machine. Some of the platform-independent logical routines may include logical operations that process agricultural data using phenotyping machine learning model(s). Edge computing resource(s) available to a user for which the agricultural state machine is to be implemented may be identified. Constraint(s) imposed by the user on implementation of the agricultural state machine may be ascertained. Based on the edge computing resource(s) and constraint(s), logical operations of some platform-independent logical routines may be dynamically allocated to the edge computing resource(s), and logical operations of other platform-independent logical routines may be dynamically allocated to a cloud computing resource.

公开(公告)号：US20230057168A1

公开(公告)日：2023-02-23

申请号：US17408866

申请日：2021-08-23

Applicant: X Development LLC

Inventor： Yueqi Li

IPC: G06F8/34 ,  G06N20/00 ,  G06F9/451

Abstract: Implementations are disclosed for facilitating visual programming of machine learning state machines. In various implementations, one or more graphical user interfaces (GUIs) may be rendered on one or more displays. Each GUI may include a working canvas on which a plurality of graphical elements corresponding to at least some of a plurality of available logical routines are manipulable to define a machine learning state machine. One or more of the available logical routines may include logical operations that process data using machine learning model(s). Two or more at least partially redundant logical routines that include overlapping logical operations may be identified, and overlapping logical operations of the two or more at least partially redundant logical routines may be merged into a consolidated logical routine. At least some of the logical operations that were previously downstream from the overlapping logical operations may be logically coupled with the consolidated logical routine.

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：US20220196433A1

公开(公告)日：2022-06-23

申请号：US17131098

申请日：2020-12-22

Applicant: X Development LLC

Inventor： Alan Eneev ,  Jie Yang ,  Yueqi Li ,  Yujing Qian ,  Nanzhu Wang ,  Sicong Wang ,  Sergey Yaroshenko

IPC: G01C21/00 ,  G06K9/00 ,  G05D1/02 ,  G01S19/42 ,  G05D1/00

Abstract: Implementations are directed to assigning corresponding semantic identifiers to a plurality of rows of an agricultural field, generating a local mapping of the agricultural field that includes the plurality of rows of the agricultural field, and subsequently utilizing the local mapping in performance of one or more agricultural operations. In some implementations, the local mapping can be generated based on overhead vision data that captures at least a portion of the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the portion of the agricultural field captured in the overhead vision data. In other implementations, the local mapping can be generated based on driving data generated during an episode of locomotion of a vehicle through the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the vehicle traversing through the agricultural field.

公开(公告)号：US11341656B1

公开(公告)日：2022-05-24

申请号：US17095845

申请日：2020-11-12

Applicant: X Development LLC

Inventor： Yueqi Li

IPC: G06V20/10 ,  G06T7/246 ,  G05D1/00 ,  G06T7/292 ,  B25J9/16 ,  H04N5/232 ,  G06V10/44

Abstract: Implementations are described herein are directed to reconciling disparate orientations of multiple vision sensors deployed on a mobile robot (or other mobile vehicle) by altering orientations of the vision sensors or digital images they generate. In various implementations, this reconciliation may be performed with little or no ground truth knowledge of movement of the robot. Techniques described herein also avoid the use of visual indicia of known dimensions and/or other conventional tools for determining vision sensor orientations. Instead, techniques described herein allow vision sensor orientations to be determined and/or reconciled using less resources, and are more scalable than conventional techniques.

公开(公告)号：US11295331B2

公开(公告)日：2022-04-05

申请号：US16918668

申请日：2020-07-01

Applicant: X Development LLC

Inventor： Nanzhu Wang ,  Chunfeng Wen ,  Yueqi Li

IPC: G06Q10/00 ,  G06Q30/02 ,  G06Q10/04 ,  G06N20/00 ,  G06F16/29 ,  A01B79/00 ,  G06Q10/06

Abstract: Implementations are described herein for using machine learning to determine whether candidate crop fields are suitable for management by particular agricultural entities. In various implementations, a machine learning model may be applied to input data to generate output data. The input data may include a first plurality of data points corresponding to field-level agricultural management practices of an agricultural entity. The output data may be indicative of one or more predicted outcomes of the agricultural entity implementing the field-level agricultural management practices on one or more candidate crop fields not currently managed by the agricultural entity. Based on one or more of the predicted outcomes, one or more computing devices may be caused to provide a user associated with the agricultural entity with information about one or more of the candidate crop fields, and/or one or more parameter inputs of a graphical user interface may be prepopulated.

公开(公告)号：US20200286282A1

公开(公告)日：2020-09-10

申请号：US16297102

申请日：2019-03-08

Applicant: X DEVELOPMENT LLC

Inventor： Elliott Grant ,  Yueqi Li

IPC: G06T17/10 ,  G06T7/20 ,  G06K9/00 ,  G06N3/08 ,  G06N20/00

Abstract: Implementations are described herein for three-dimensional (“3D”) modeling of objects that target specific features of interest of the objects, and ignore other features of less interest. In various implementations, a plurality of two-dimensional (“2D”) images may be received from a 2D vision sensor. The plurality of 2D images may capture an object having multiple classes of features. Data corresponding to a first set of the multiple classes of features may be filtered from the plurality of 2D images to generate a plurality of filtered 2D images in which a second set of features of the multiple classes of features is captured. 2D-3D processing, such as structure from motion (“SFM”) processing, may be performed on the 2D filtered images to generate a 3D representation of the object that includes the second set of one or more features.