公开(公告)号：US11847570B2

公开(公告)日：2023-12-19

申请号：US17837096

申请日：2022-06-10

Applicant: ASML NETHERLANDS B.V.

Inventor： Adrianus Cornelis Matheus Koopman ,  Scott Anderson Middlebrooks ,  Antoine Gaston Marie Kiers ,  Mark John Maslow

IPC: G06N3/08 ,  G06N3/084 ,  G06T7/11 ,  G03F7/00 ,  G06T7/00 ,  G06F18/214 ,  G06F18/2431 ,  G06F18/2413 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06N3/084 ,  G03F7/705 ,  G06F18/214 ,  G06F18/2431 ,  G06F18/24133 ,  G06N3/08 ,  G06T7/0004 ,  G06T7/11 ,  G06V10/764 ,  G06V10/82 ,  G06T2207/30148

Abstract: A method for training a deep learning model of a patterning process. The method includes obtaining (i) training data comprising an input image of at least a part of a substrate having a plurality of features and a truth image, (ii) a set of classes, each class corresponding to a feature of the plurality of features of the substrate within the input image, and (iii) a deep learning model configured to receive the training data and the set of classes, generating a predicted image, by modeling and/or simulation of the deep learning model using the input image, assigning a class of the set of classes to a feature within the predicted image based on matching of the feature with a corresponding feature within the truth image, and generating, by modeling and/or simulation, a trained deep learning model by iteratively assigning weights using a loss function.

公开(公告)号：US11797860B2

公开(公告)日：2023-10-24

申请号：US17717696

申请日：2022-04-11

Applicant: Magic Leap, Inc.

Inventor： Tomasz Jan Malisiewicz ,  Andrew Rabinovich ,  Vijay Badrinarayanan ,  Debidatta Dwibedi

IPC: G06T7/70 ,  G06N3/084 ,  G06V10/44 ,  G06V20/64 ,  G06F18/2413 ,  G06N3/044 ,  G06N3/045 ,  G06V30/19 ,  G06V10/82 ,  G06T7/11 ,  G06N3/08

CPC classification number: G06N3/084 ,  G06F18/24133 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06T7/11 ,  G06T7/70 ,  G06V10/454 ,  G06V10/82 ,  G06V20/64 ,  G06V30/19173 ,  G06T2210/12

Abstract: Systems and methods for cuboid detection and keypoint localization in images are disclosed. In one aspect, a deep cuboid detector can be used for simultaneous cuboid detection and keypoint localization in monocular images. The deep cuboid detector can include a plurality of convolutional layers and non-convolutional layers of a trained convolution neural network for determining a convolutional feature map from an input image. A region proposal network of the deep cuboid detector can determine a bounding box surrounding a cuboid in the image using the convolutional feature map. The pooling layer and regressor layers of the deep cuboid detector can implement iterative feature pooling for determining a refined bounding box and a parameterized representation of the cuboid.

公开(公告)号：US11710299B2

公开(公告)日：2023-07-25

申请号：US17335053

申请日：2021-05-31

Applicant: Edge 3 Technologies, Inc.

Inventor： Tarek El Dokor

IPC: G06V10/776 ,  G06V20/10 ,  G06V20/52 ,  G06V40/16 ,  G06V40/20 ,  G06F18/24 ,  G06F18/21 ,  G06F18/2413 ,  G06V10/764 ,  G06N3/02 ,  H04N5/33

CPC classification number: G06V10/776 ,  G06F18/217 ,  G06F18/24 ,  G06F18/24133 ,  G06N3/02 ,  G06V10/764 ,  G06V20/10 ,  G06V20/52 ,  G06V40/16 ,  G06V40/20 ,  H04N5/33

Abstract: A method and apparatus for processing image data is provided. The method includes the steps of employing a main processing network for classifying one or more features of the image data, employing a monitor processing network for determining one or more confusing classifications of the image data, and spawning a specialist processing network to process image data associated with the one or more confusing classifications.

公开(公告)号：US11704790B2

公开(公告)日：2023-07-18

申请号：US16141605

申请日：2018-09-25

Applicant: Eric Leuthardt ,  Carl Hacker ,  Shan Siddiqi ,  Tim Laumann ,  Andy Daniel

Inventor： Eric Leuthardt ,  Carl Hacker ,  Shan Siddiqi ,  Tim Laumann ,  Andy Daniel

IPC: A61B5/00 ,  G06T7/00 ,  A61B34/10 ,  A61B5/055 ,  G16H30/40 ,  G06N3/00 ,  G16H50/20 ,  G06F18/213 ,  G06F18/21 ,  G06F18/2415 ,  G06F18/2413 ,  G06V10/764 ,  G01R33/48 ,  G16H20/40

CPC classification number: G06T7/0012 ,  A61B5/0042 ,  A61B5/055 ,  A61B5/4064 ,  A61B34/10 ,  G06F18/213 ,  G06F18/2178 ,  G06F18/2415 ,  G06F18/24133 ,  G06N3/00 ,  G06V10/764 ,  G16H30/40 ,  G16H50/20 ,  A61B5/4848 ,  A61B5/7267 ,  A61B2034/107 ,  A61B2576/026 ,  G01R33/4806 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06V2201/031 ,  G16H20/40

Abstract: A target location for a therapeutic intervention is determined in a subject with a neurological disorder. The target location is selected within at least one resting state network (RSN) map according to a predetermined criterion for the neurological disorder. The at least one RSN map includes a plurality of functional voxels within a brain of the subject, and each functional voxel of the plurality of functional voxels is associated with a probability of membership in an RSN. Instructions are transmitted to a treatment system that cause operation to be performed on the selected target location.

公开(公告)号：US11675879B2

公开(公告)日：2023-06-13

申请号：US16796112

申请日：2020-02-20

Applicant: K2Ai, LLC

Inventor： Kevin Richard Kerwin ,  Zachary K. Davis ,  Jacob D. O'Boyle

IPC: G06F18/2413 ,  G06F9/54 ,  G06N3/08 ,  G06V20/20 ,  G06F18/214

CPC classification number: G06F18/24133 ,  G06F9/542 ,  G06F18/2148 ,  G06N3/08 ,  G06V20/20

Abstract: A detection and response system includes a central server having at least one continuously trained neural network and at least one remote system connected to the central server. The at least one remote system includes a first sensor configured to provide an analyzable output corresponding to sensed information to an instanced copy of the continuously trained neural network, and a response system configured to generate a response to the instanced copy of the continuously trained neural network providing a positive detection. A training module is stored on the central server and is configured to update one of the continuously trained neural network and the instanced copy of the continuously trained neural network in response to receiving a data set including the positive detection.

公开(公告)号：US11669719B2

公开(公告)日：2023-06-06

申请号：US17174864

申请日：2021-02-12

Applicant: Intel Corporation

Inventor： Jeremie Dreyfuss ,  Amit Bleiweiss ,  Lev Faivishevsky ,  Tomer Bar-On ,  Yaniv Fais ,  Jacob Subag ,  Eran Ben-Avi ,  Neta Zmora ,  Tomer Schwartz

IPC: G06N3/063 ,  G06N3/08 ,  G06N3/04 ,  G06N3/084 ,  G06V20/56 ,  G06V10/44 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045 ,  G06V10/764 ,  G06V10/82 ,  G06F18/2413

CPC classification number: G06N3/063 ,  G06F18/214 ,  G06F18/24133 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to save one or more outputs of a deep learning neural network in a storage system of an autonomous vehicle and upload the one or more outputs to a remote server. Other embodiments are also disclosed and claimed.

公开(公告)号：US11657506B2

公开(公告)日：2023-05-23

申请号：US16293972

申请日：2019-03-06

Applicant: General Electric Company

Inventor： Huan Tan ,  Isabella Heukensfeldt Jansen ,  Gyeong Woo Cheon ,  Li Zhang

IPC: G06F19/00 ,  G06T7/11 ,  G05D1/02 ,  G06T7/73 ,  G05D1/00 ,  G06N3/08 ,  G06T7/194 ,  G06F18/2413 ,  G06N3/045 ,  G06V10/764 ,  G06V10/82 ,  G06V10/44 ,  G06V20/10

CPC classification number: G06T7/11 ,  G05D1/0088 ,  G05D1/0246 ,  G06F18/24133 ,  G06N3/045 ,  G06N3/08 ,  G06T7/194 ,  G06T7/74 ,  G06T7/75 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V20/10 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30244

Abstract: A method of robot autonomous navigation includes capturing an image of the environment, segmenting the captured image to identify one or more foreground objects and one or more background objects, determining a match between one or more of the foreground objects to one or more predefined image files, estimating an object pose for the one or more foreground objects by implementing an iterative estimation loop, determining a robot pose estimate by applying a robot-centric environmental model to the object pose estimate by implementing an iterative refinement loop, associating semantic labels to the matched foreground object, compiling a semantic map containing the semantic labels and segmented object image pose, and providing localization information to the robot based on the semantic map and the robot pose estimate. A system and a non-transitory computer-readable medium are also disclosed.