公开(公告)号：US20240203113A1

公开(公告)日：2024-06-20

申请号：US18543506

申请日：2023-12-18

申请人： Samsung Electronics Co., Ltd.

发明人： Yowon JEONG ,  Junsik JUNG ,  Sungeui YOON

IPC分类号： G06V10/82 ,  G06V10/77 ,  G06V20/40

CPC分类号： G06V10/82 ,  G06V10/7715 ,  G06V20/46

摘要： An interpolation model learning method, a non-transitory computer-readable storage medium storing instructions allowing the method to be performed, and a device for learning an interpolation frame generating module are provided. The interpolation model learning method may include extracting, by using a neural network model, a temporal-spatial feature of each of a frame group including an interpolation frame generated by an interpolation model based on a neural network and a frame group including a ground truth (GT) frame corresponding to an interpolation frame and changing a weight and/or a bias of a neural network of an interpolation model to decrease a difference between temporal-spatial features.

公开(公告)号：US20240202850A1

公开(公告)日：2024-06-20

申请号：US18592369

申请日：2024-02-29

申请人： STRONG FORCE TP PORTFOLIO 2022, LLC

发明人： Charles Howard Cella

IPC分类号： G06Q50/18 ,  B60W40/08 ,  G01C21/34 ,  G05B13/02 ,  G06F40/40 ,  G06N3/02 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  G06Q30/02 ,  G06Q50/00 ,  G06Q50/40 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  G06V20/59 ,  G06V20/64 ,  G07C5/00 ,  G07C5/02 ,  G07C5/08

CPC分类号： G06Q50/188 ,  B60W40/08 ,  G01C21/3438 ,  G01C21/3469 ,  G05B13/027 ,  G06F40/40 ,  G06N3/0418 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  G06Q50/40 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  G06V20/597 ,  G06V20/64 ,  G07C5/006 ,  G07C5/008 ,  G07C5/02 ,  G07C5/08 ,  G07C5/0816 ,  G07C5/0866 ,  G07C5/0891 ,  B60W2040/0881 ,  G06N3/02 ,  G06Q30/0281 ,  G06Q50/01

摘要： A vehicle routing system and method involves coordinating a route for a vehicle to a geographic point of interest based at least in part on monitoring an action of a rider of a vehicle to determine if the action is among a plurality of actions classified as a rewardable action, and presenting a reward-based routing to the vehicle within a transportation system upon detection that the action of the rider is a rewardable action.

公开(公告)号：US20240202433A1

公开(公告)日：2024-06-20

申请号：US18593167

申请日：2024-03-01

申请人： Ivalua SAS

发明人： Aurélien COQUARD ,  Christopher BOUREZ

IPC分类号： G06F40/174 ,  G06F16/25 ,  G06F18/2413 ,  G06N3/04 ,  G06N3/08 ,  G06Q10/10 ,  G06Q40/12 ,  G06V10/764 ,  G06V10/82 ,  G06V30/10 ,  G06V30/18 ,  G06V30/412

CPC分类号： G06F40/174 ,  G06F16/258 ,  G06F18/2413 ,  G06N3/04 ,  G06N3/08 ,  G06Q10/10 ,  G06Q40/12 ,  G06V10/764 ,  G06V10/82 ,  G06V30/18057 ,  G06V30/412 ,  G06V30/10

摘要： The present invention concerns a method for transforming an unstructured set of data representing a standardized form to a structured set of data. The method comprises a processing phase comprising the steps of determining a plurality of data blocks in the unstructured set of data using learning parameters determined using a plurality of samples, each data block corresponding to a visual pattern on the standardized form and being categorized to a known class, of processing data in each data block and of forming a structured set of data using the processed data from each data block, according to the class of this block.

公开(公告)号：US20240196780A1

公开(公告)日：2024-06-20

申请号：US18065881

申请日：2022-12-14

申请人： Deere & Company

发明人： Jeremy J. FAUST ,  Kellen O'CONNOR ,  MATTHEW S. WOODALL

IPC分类号： A01B69/04 ,  A01D90/10 ,  G05D1/02 ,  G06T7/73 ,  G06V10/82 ,  G06V20/58

CPC分类号： A01B69/008 ,  A01D90/105 ,  G05D1/0251 ,  G06T7/73 ,  G06V10/82 ,  G06V20/58 ,  G05D2201/0201 ,  G06T2207/20084 ,  G06T2207/30252

摘要： A harvesting machine is configured to gather harvested material into the harvesting machine during a harvesting operation. A conveyance subsystem configured to convey the harvested material from the harvesting machine to a receiving vehicle during the harvesting operation. An image capture system generates an image of the receiving vehicle and an image processor uses a machine learning system to recognize a boundary of the receiving vehicle. A control system determines a position of the receiving vehicle boundary relative to the harvesting machine, and generates a control signal based on the identified receiving vehicle boundary.

公开(公告)号：US12014833B2

公开(公告)日：2024-06-18

申请号：US17266300

申请日：2019-08-06

申请人： Cornell University ,  Yale University

发明人： Nikica Zaninovic ,  Olivier Elemento ,  Iman Hajirasouliha ,  Pegah Khosravi ,  Jonas Malmsten ,  Zev Rosenwaks ,  Qiansheng Zhan ,  Ehsan Kazemi

IPC分类号： G06T7/00 ,  G06N3/08 ,  G06V10/44 ,  G06V10/82 ,  G06V20/69 ,  G16H10/60 ,  G16H30/20 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70 ,  G16H70/60

CPC分类号： G16H50/30 ,  G06N3/08 ,  G06T7/0012 ,  G06V10/454 ,  G06V10/82 ,  G06V20/69 ,  G06V20/698 ,  G16H10/60 ,  G16H30/20 ,  G16H50/20 ,  G16H50/70 ,  G16H70/60 ,  G06T2207/20036 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30044

摘要： A method for classifying human blastocysts includes obtaining images of a set of artificially fertilized (AF) embryos incubating in an incubator. A morphological quality of the AF embryos is determined based on a classification of the images by a convolutional neural network trained using images of pre-classified embryos. Each of the AF embryos is graded based on the morphological quality. A probability that a given graded AF embryo will result in a successful pregnancy after the given AF embryo is implanted in a gestating female is computed for each of the AF embryos from the set based on a grade of the given AF embryo and clinical parameters associated with the gestating female. One or more graded AF embryos to be recommended to be implanted in the gestating female from the set are selected based on the probability of successful pregnancy. An identity of each of the selected graded AF embryos and a number of the selected graded AF embryos to be potentially implanted in the gestating female is then output based on an outcome desired by the gestating female following implantation.

公开(公告)号：US12014552B2

公开(公告)日：2024-06-18

申请号：US17544195

申请日：2021-12-07

申请人： GM GLOBAL TECHNOLOGY OPERATIONS LLC

发明人： Ariel Telpaz ,  Ravid Erez ,  Michael Baltaxe ,  Barak Hershkovitz ,  Nadav Baron ,  Christopher A Stanek

IPC分类号： G06V20/56 ,  B60R16/023 ,  G05D1/00 ,  G06V10/25 ,  G06V10/26 ,  G06V10/44 ,  G06V10/62 ,  G06V10/82 ,  G07C5/00 ,  G07C5/02 ,  H04N23/90

CPC分类号： G06V20/56 ,  B60R16/0231 ,  G05D1/0214 ,  G05D1/0246 ,  G06V10/25 ,  G06V10/267 ,  G06V10/44 ,  G06V10/62 ,  G06V10/82 ,  G07C5/008 ,  G07C5/02 ,  H04N23/90

摘要： Presented are intelligent vehicle systems for off-road driving incident prediction and assistance, methods for making/operating such systems, and vehicles networking with such systems. A method for operating a motor vehicle includes a system controller receiving geolocation data indicating the vehicle is in or entering off-road terrain. Responsive to the vehicle geolocation data, the controller receives, from vehicle-mounted cameras, camera-generated images each containing the vehicle's drive wheel(s) and/or the off-road terrain's surface. The controller receives, from a controller area network bus, vehicle operating characteristics data and vehicle dynamics data for the motor vehicle. The camera data, vehicle operating characteristics data, and vehicle dynamics data is processed via a convolutional neural network backbone to predict occurrence of a driving incident on the off-road terrain within a prediction time horizon. The system controller commands a resident vehicle system to execute a control operation responsive to the predicted occurrence of the driving incident.

公开(公告)号：US12014530B2

公开(公告)日：2024-06-18

申请号：US17286604

申请日：2018-12-21

申请人： HITACHI HIGH-TECH CORPORATION

发明人： Ryou Yumiba ,  Yasutaka Toyoda ,  Hiroyuki Shindo

IPC分类号： G06V10/44 ,  G06F18/20 ,  G06F18/2113 ,  G06F18/213 ,  G06N20/00 ,  G06V10/77 ,  G06V10/82 ,  G06V20/69

CPC分类号： G06V10/44 ,  G06F18/2113 ,  G06F18/213 ,  G06F18/285 ,  G06N20/00 ,  G06V10/7715 ,  G06V10/82 ,  G06V20/695 ,  G06V20/698

摘要： In order to select an optimal learning model for an image when inference is carried out in the extraction of a profile line using machine learning, without requiring a correct value or degree of certainty, a feature extraction learning model group containing a plurality of learning models is used for feature extraction. A recall learning model group containing recall learning models is paired with the feature extraction learning models. A feature amount extraction unit for referencing a feature extraction learning model and extracting a feature amount from input data; a data-to-data recall unit for referencing a recall learning model and outputting a recall result with the feature amount subjected to dimensional compression; and a learning model selection unit for selecting a feature extraction learning model from the feature extraction learning model group under the condition that the difference between the feature amount and the recall result is minimized are provided.

公开(公告)号：US12014482B2

公开(公告)日：2024-06-18

申请号：US17664004

申请日：2022-05-18

申请人： MOTIVE DRILLING TECHNOLOGIES, INC.

发明人： George Michalopulos ,  Richard Kulavik ,  Todd W. Benson

IPC分类号： G06T7/00 ,  E21B7/04 ,  E21B41/00 ,  G06T7/20 ,  G06T7/70 ,  G06T17/00 ,  G06V10/764 ,  G06V10/82 ,  G06V20/52 ,  G06V40/10 ,  G08B7/06 ,  G08B21/02 ,  H04N23/90

CPC分类号： G06T7/0004 ,  E21B41/0021 ,  G06T7/20 ,  G06T7/70 ,  G06T17/00 ,  G06V10/764 ,  G06V10/82 ,  G06V40/10 ,  G06V40/103 ,  G08B7/06 ,  G08B21/02 ,  H04N23/90 ,  E21B7/04 ,  G06T2207/30196 ,  G06V20/52

摘要： Computer vision drilling systems and methods may be used with a drilling rig. The computer vision systems and methods may be used during drilling of a well to monitor the drilling equipment and personnel on the drilling site to provide safer drilling operations. The results from the computer vision drilling system may be used to cause corrective actions to be performed if a safety condition arises. In addition, computer vision systems and methods are provided to automatically monitor the drilling site and drilling operations, such as by tallying pipe in the drill string and by monitoring equipment for anomalous drilling conditions, and automatically taking corrective action as may be needed.

公开(公告)号：US12014275B2

公开(公告)日：2024-06-18

申请号：US17081758

申请日：2020-10-27

申请人： BEIJING SENSETIME TECHNOLOGY DEVELOPMENT CO., LTD.

发明人： Xuebo Liu

IPC分类号： G06V10/40 ,  G06F17/16 ,  G06F40/30 ,  G06N3/04 ,  G06N3/084 ,  G06V10/24 ,  G06V10/82

CPC分类号： G06N3/084 ,  G06F17/16 ,  G06F40/30 ,  G06N3/04 ,  G06V10/24 ,  G06V10/40 ,  G06V10/82

摘要： A method for text recognition, an electronic device and a storage medium are provided. The method includes: performing feature extraction processing on an image to be detected to obtain a plurality of semantic vectors, each of the plurality of semantic vectors corresponds to one of a plurality of characters of a text sequence in the image to be detected; and sequentially performing recognition processing on the plurality of semantic vectors through a convolutional neutral network to obtain a recognition result of the text sequence.

公开(公告)号：US12014271B2

公开(公告)日：2024-06-18

申请号：US18108282

申请日：2023-02-10

申请人： ObjectVideo Labs, LLC

发明人： Sravanthi Bondugula ,  Gang Qian ,  Sung Chun Lee ,  Sima Taheri ,  Allison Beach

IPC分类号： G06N3/08 ,  G06F18/214 ,  G06T7/194 ,  G06V10/20 ,  G06V10/25 ,  G06V10/774 ,  G06V10/82

CPC分类号： G06N3/08 ,  G06F18/2148 ,  G06T7/194 ,  G06V10/25 ,  G06V10/255 ,  G06V10/7747 ,  G06V10/82

摘要： Methods, systems, an apparatus, including computer programs encoded on a storage device, for training an image classifier. A method includes receiving an image that includes a depiction of an object; generating a set of poorly localized bounding boxes; and generating a set of accurately localized bounding boxes. The method includes training, at a first learning rate and using the poorly localized bounding boxes, an object classifier to classify the object; and training, at a second learning rate that is lower than the first learning rate, and using the accurately localized bounding boxes, the object classifier to classify the object. The method includes receiving a second image that includes a depiction of an object; and providing, to the trained object classifier, the second image. The method includes receiving an indication that the object classifier classified the object in the second image; and performing one or more actions.