公开(公告)号：US20240232915A9

公开(公告)日：2024-07-11

申请号：US18496533

申请日：2023-10-27

申请人： Capital One Services, LLC

发明人： Geoffrey DAGLEY ,  Stephen ANDERSON ,  Stephen WYLIE ,  Qiaochu TANG ,  Micah PRICE ,  Jason HOOVER ,  Kristen PRZANO

IPC分类号： G06Q30/0202 ,  G06F18/2137 ,  G06F18/214 ,  G06F18/23 ,  G06F18/2413 ,  G06N20/00 ,  G06Q20/38 ,  G06Q20/40 ,  G06Q30/0201

CPC分类号： G06Q30/0202 ,  G06F18/2137 ,  G06F18/214 ,  G06F18/23 ,  G06F18/24147 ,  G06N20/00 ,  G06Q20/389 ,  G06Q20/4016 ,  G06Q20/405 ,  G06Q30/0201

摘要： A computer-implemented method for using a machine-learning trained predictive engine to predict failures includes receiving electronic prior transaction data corresponding to a plurality of prior successful transactions and a plurality of prior unsuccessful transactions, and training a machine learning predictive engine based on the plurality of prior successful transactions and the plurality of prior unsuccessful transactions. Electronic transaction data may be received, the electronic transaction data being associated with a user, an item, and candidate transaction terms, the electronic transaction data being associated with a candidate transaction. The machine learning predictive engine may determine a likelihood of success of the candidate transaction based on the electronic transaction data, and display the likelihood of success of the candidate transaction.

公开(公告)号：US20240211647A1

公开(公告)日：2024-06-27

申请号：US18085665

申请日：2022-12-21

申请人： Ansys, Inc.

发明人： Anupam Ashish ,  Evren Yortucboylu

IPC分类号： G06F30/15 ,  G05D1/00 ,  G06F18/2137

CPC分类号： G06F30/15 ,  G05D1/0088 ,  G06F18/2137 ,  G06F2123/02

摘要： Systems and methods are provided for simulating operation of an autonomous vehicle control system. Three dimensional multi-sensor data associated with a plurality of real-world drives in a sensor equipped vehicle is accessed. For a particular drive, the three dimensional multi-sensor data is reduced to a time series of two dimensional representations. The time series of two dimensional representations is classified into a sequence of states, where the sequence of states associated with the particular drive and the three dimensional multi-sensor data are stored in a computer-readable medium as a scenario. A query is received that identifies a state criteria, and the scenario is accessed based on the sequence of states matching the state criteria of the query. The three dimensional multi-sensor data of the scenario is provided to an autonomous driving system to simulate behavior of the autonomous driving system when faced with the scenario.

公开(公告)号：US12008797B2

公开(公告)日：2024-06-11

申请号：US17383181

申请日：2021-07-22

申请人： Huawei Technologies Co., Ltd.

发明人： Zhi Tian ,  Tong He ,  Chunhua Shen ,  Youliang Yan ,  Songcen Xu ,  Yiren Zhou ,  Xiaofei Wu ,  Jianzhuang Liu

IPC分类号： G06V10/44 ,  G06F18/2135 ,  G06F18/2137 ,  G06F18/214 ,  G06F18/25 ,  G06N3/08 ,  G06T3/40 ,  G06V10/77 ,  G06V10/82

CPC分类号： G06V10/457 ,  G06F18/2135 ,  G06F18/2137 ,  G06F18/214 ,  G06F18/253 ,  G06N3/08 ,  G06T3/40 ,  G06V10/454 ,  G06V10/7715 ,  G06V10/82

摘要： This application discloses an image segmentation method in the field of artificial intelligence. The method includes: obtaining an input image and a processing requirement; performing multi-layer feature extraction on the input image to obtain a plurality of feature maps; downsampling the plurality of feature maps to obtain a plurality of feature maps with a reference resolution, where the reference resolution is less than a resolution of the input image; fusing the plurality of feature maps with the reference resolution to obtain at least one feature map group; upsampling the feature map group by using a transformation matrix W, to obtain a target feature map group; and performing target processing on the target feature map group based on the processing requirement to obtain a target image.

公开(公告)号：US11989959B2

公开(公告)日：2024-05-21

申请号：US17257959

申请日：2019-07-05

申请人： UNIVERSITÄT ZÜRICH

发明人： Lucas Pelkmans ,  Gabriele Gut

IPC分类号： G06V20/69 ,  G01N1/30 ,  G06F18/2137 ,  G06F18/2323 ,  G06V10/762

CPC分类号： G06V20/695 ,  G01N1/30 ,  G06F18/2137 ,  G06F18/2323 ,  G06V10/7635

摘要： The invention relates to a method for processing large multiplexed image data of a biological sample, the method comprising the steps of, recording a plurality of images of a biological sample, wherein the plurality of images comprises images having a different entity of the biological sample targeted with a predefined stain, determining spatially corresponding image pixels in the plurality of registered images, associating the spatially corresponding image pixels to a pixel profile, wherein each pixel profile comprises the pixel values of the spatially corresponding pixels and wherein the pixel profile is associated with the respective image coordinate of the spatially corresponding pixels, pooling the pixel profiles by means of a clustering method configured to determine pixel profiles with similar values, and thereby generating a plurality of clusters, each comprising pixel profiles with similar pixel values, for each cluster assigning a cluster value to the image coordinate of the pixel profiles comprised by said cluster and thereby generating a cluster image with cluster pixels.

公开(公告)号：US11989201B2

公开(公告)日：2024-05-21

申请号：US17383009

申请日：2021-07-22

申请人： Adobe Inc.

发明人： Akash Rupela ,  Piyush Gupta ,  Nupur Kumari ,  Bishal Deb ,  Balaji Krishnamurthy ,  Ankita Sarkar

IPC分类号： G06F16/22 ,  G06F3/0481 ,  G06F16/248 ,  G06F16/26 ,  G06F16/28 ,  G06F18/213 ,  G06F18/2137

CPC分类号： G06F16/26 ,  G06F3/0481 ,  G06F16/2264 ,  G06F16/248 ,  G06F16/283 ,  G06F18/213 ,  G06F18/2137

摘要： This disclosure relates to methods, non-transitory computer readable media, and systems that generate and render a varied-scale-topological construct for a multidimensional dataset to visually represent portions of the multidimensional dataset at different topological scales. In certain implementations, for example, the disclosed systems generate and combine (i) an initial topological construct for a multidimensional dataset at one scale and (ii) a local topological construct for a subset of the multidimensional dataset at another scale to form a varied-scale-topological construct. To identify a region from an initial topological construct to vary in scale, the disclosed systems can determine the relative densities of subsets of multidimensional data corresponding to regions of the initial topological construct and select one or more such regions to change in scale.

公开(公告)号：US11941502B2

公开(公告)日：2024-03-26

申请号：US16560842

申请日：2019-09-04

申请人： Optum Services (Ireland) Limited

发明人： Lorcan B. MacManus ,  Conor Breen ,  Peter Cogan

IPC分类号： G06N20/20 ,  G06F18/21 ,  G06F18/2137 ,  G06F18/214 ,  G06F18/2433 ,  G06N5/01 ,  G06N7/01 ,  G06N20/00

CPC分类号： G06N20/20 ,  G06F18/21375 ,  G06F18/2148 ,  G06F18/2185 ,  G06F18/2433 ,  G06N5/01 ,  G06N7/01 ,  G06N20/00

摘要： Systems, methods, and apparatuses for detecting and identifying anomalous data in an input data set are provided.

公开(公告)号：US11840998B2

公开(公告)日：2023-12-12

申请号：US17859244

申请日：2022-07-07

申请人： Zhejiang University

发明人： Zheming Tong ,  Jiage Xin ,  Shuiguang Tong

IPC分类号： F03B11/00 ,  G06F18/2137 ,  G06F18/243

CPC分类号： F03B11/008 ,  F05B2260/80 ,  F05B2270/305 ,  F05B2270/333 ,  F05B2270/404 ,  F05B2270/709 ,  F05B2270/80 ,  G06F18/2137 ,  G06F18/24323

摘要： The present invention provides a hydraulic turbine cavitation acoustic signal identification method based on big data machine learning. According to the method, time sequence clustering based on multiple operating conditions under the multi-output condition of the hydraulic turbine set is performed by utilizing an neural network, characteristic quantities of the hydraulic turbine set under a steady condition in a healthy state is screened; a random forest algorithm is introduced to perform feature screening of multiple measuring points under steady-state operation of the hydraulic turbine set, optimal feature measuring points and optimal feature subsets are extracted, finally a health state prediction model is constructed by using gated recurrent units; whether incipient cavitation is present in the equipment is judged. The present invention can effectively identify the occurrence of incipient cavitation in the hydraulic turbine set, reducing unnecessary shutdown of the equipment and prolonging the service life.

公开(公告)号：US11830242B2

公开(公告)日：2023-11-28

申请号：US17450261

申请日：2021-10-07

申请人： BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

发明人： Yue Yu ,  Xiao Tan ,  Hao Sun

IPC分类号： G06K9/00 ,  G06K9/62 ,  G06N3/08 ,  G06V10/94 ,  G06V20/56 ,  G06F18/241 ,  G06F18/2137 ,  G06F18/214 ,  G06F18/21 ,  G06V20/62

CPC分类号： G06V10/95 ,  G06F18/2137 ,  G06F18/2148 ,  G06F18/2178 ,  G06F18/241 ,  G06N3/08 ,  G06V20/56 ,  G06V20/625

摘要： A method for generating a license plate defacement classification model, a license plate defacement classification method an electronic device and a storage medium, and related to the technical field of artificial intelligence, and specifically, to the technical field of computer vision and the technical field of intelligent transportation are provided. The method for generating a license plate defacement classification model includes: acquiring training data, wherein the training data includes a plurality of annotated vehicle images, annotated content includes information indicating that a license plate is defaced or is not defaced, and the annotated content further includes location information of a license plate area; and training a first neural network by using the training data, to obtain the license plate defacement classification model for predicting whether the license plate in a target vehicle image is defaced. A robust license plate defacement classification model can be obtained by using embodiments.

公开(公告)号：US11809514B2

公开(公告)日：2023-11-07

申请号：US17519425

申请日：2021-11-04

申请人： Groq, Inc.

发明人： Jonathan Alexander Ross ,  Thomas Hawkins ,  Gregory Michael Thorson ,  Matt Boyd

IPC分类号： G06F17/15 ,  G06F17/16 ,  G06N20/10 ,  G06N3/08 ,  G06F7/76 ,  G06N7/00 ,  G06F7/544 ,  G06F9/54 ,  G06N3/04 ,  G06F18/2137

CPC分类号： G06F17/153 ,  G06F7/5443 ,  G06F7/76 ,  G06F9/544 ,  G06F17/16 ,  G06F18/2137 ,  G06N3/04 ,  G06N3/08 ,  G06N7/00 ,  G06N20/10

摘要： A method comprises receiving a kernel used to convolve with an input tensor. For a first dimension of the kernel, a square block of values for each single dimensional vector of the kernel that includes all rotations of that single dimensional vector is generated. For each additional dimension of the kernel, group blocks of an immediately preceding dimension into sets of blocks, each set of blocks including blocks of the immediately preceding dimension that are aligned along a vector that is parallel to the axis of the dimension; and generate, for the additional dimension, one or more blocks of values, each block including all rotations of blocks within each of the sets of blocks of the immediately preceding dimension. The block of values corresponding to the last dimension in the additional dimensions of the kernel is output as the expanded kernel.

公开(公告)号：US11803867B2

公开(公告)日：2023-10-31

申请号：US17499912

申请日：2021-10-13

申请人： Capital One Services, LLC

发明人： Geoffrey Dagley ,  Stephen Anderson ,  Stephen Wylie ,  Qiaochu Tang ,  Micah Price ,  Jason Hoover ,  Kristen Przano

IPC分类号： G06Q30/0202 ,  G06Q30/0201 ,  G06N20/00 ,  G06Q20/38 ,  G06Q20/40 ,  G06F18/23 ,  G06F18/214 ,  G06F18/2137 ,  G06F18/2413

CPC分类号： G06Q30/0202 ,  G06F18/214 ,  G06F18/2137 ,  G06F18/23 ,  G06F18/24147 ,  G06N20/00 ,  G06Q20/389 ,  G06Q20/405 ,  G06Q20/4016 ,  G06Q30/0201

摘要： A computer-implemented method for using a machine-learning trained predictive engine to predict failures includes receiving electronic prior transaction data corresponding to a plurality of prior successful transactions and a plurality of prior unsuccessful transactions, and training a machine learning predictive engine based on the plurality of prior successful transactions and the plurality of prior unsuccessful transactions. Electronic transaction data may be received, the electronic transaction data being associated with a user, an item, and candidate transaction terms, the electronic transaction data being associated with a candidate transaction. The machine learning predictive engine may determine a likelihood of success of the candidate transaction based on the electronic transaction data, and display the likelihood of success of the candidate transaction.