公开(公告)号：US12087035B2

公开(公告)日：2024-09-10

申请号：US17640183

申请日：2020-09-03

Applicant: NEC Corporation

Inventor： Taiki Miyagawa ,  Akinori Ebihara

IPC: G06V10/764 ,  G06V10/72 ,  G06V30/19

CPC classification number: G06V10/764 ,  G06V10/72 ,  G06V30/19173

Abstract: An information processing system (10) includes: an acquisition unit (50) configured to sequentially acquire a plurality of elements included in sequential data; a first calculation unit (110) configured to calculate, for each of the plurality of elements, a first indicator indicating which one of a plurality of classes the element belongs to; a weight calculation unit (130) configured to calculate, for each of the plurality of elements, a weight according to a confidence related to calculation of the first indicator; a second calculation unit (120) configured to calculate, based on the first indicators each weighted with the weight, a second indicator indicating which one of the plurality of classes the sequential data belongs to; and a classification unit (60) configured to classify the sequential data as any one of the plurality of classes, based on the second indicator. According to such an information processing system, sequential data can be appropriately classified.

公开(公告)号：US12086544B2

公开(公告)日：2024-09-10

申请号：US17645721

申请日：2021-12-22

Applicant: RAKUTEN MOBILE, INC.

Inventor： Petrit Nahi ,  Madhukiran Medithe

IPC: G06F40/284 ,  G06F40/40 ,  G06V30/19

CPC classification number: G06F40/284 ,  G06F40/40 ,  G06V30/19173

Abstract: Polarity classifications of writing samples are obtained by sentiment analysis operations including embedding each word of a writing sample into a word vector based on surrounding words, extracting one or more features of the writing sample, applying a feature learning function to the one or more features, estimating a polarity of the writing sample based on output from the word learning function and output from the feature learning function, and training the word learning function and the feature learning function based on a loss function relating the estimated polarity to the word vector to produce a model for writing sample polarity classification.

公开(公告)号：US20240296689A1

公开(公告)日：2024-09-05

申请号：US18636719

申请日：2024-04-16

Applicant: KoiReader Technologies, Inc.

Inventor： Ashutosh PRASAD ,  Vivek PRASAD

IPC: G06V30/19 ,  G06V30/14 ,  G06V30/146 ,  G06V30/18 ,  G06V30/26 ,  G06V30/413

CPC classification number: G06V30/19173 ,  G06V30/1448 ,  G06V30/1463 ,  G06V30/1801 ,  G06V30/26 ,  G06V30/413

Abstract: A system is discussed herein that is configured for extracting data from documents. In particular, the system may be utilized for automating and computerized checking of transit and shipping related documents. For example, the documents may include various data, such delivery dates, prices, inventory identification, personnel identification, container identification, customs documents, transport documents, a combination thereof, and the like.

公开(公告)号：US12079728B2

公开(公告)日：2024-09-03

申请号：US16980380

申请日：2019-03-07

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Chihiro Watanabe ,  Kaoru Hiramatsu ,  Kunio Kashino

IPC: G06F18/21 ,  G06F18/23 ,  G06N3/084 ,  G06V30/182 ,  G06V30/19 ,  G06V30/10

CPC classification number: G06N3/084 ,  G06F18/217 ,  G06F18/23 ,  G06V30/1823 ,  G06V30/19173 ,  G06V30/10

Abstract: The present invention enables the structure of a neural network to be quantitatively analyzed. An analyzing unit calculates, for each of combinations of a dimension of input data and a cluster, a sum of squared errors between an output of each unit belonging to the cluster when a value of the dimension of the input data is replaced with an average value of the dimension of the input data included in learning data and an output of each unit belonging to the cluster for the input data before replacement as a relationship between the combinations, and calculates, for each of combinations of the cluster and a dimension of output data, a squared error between the value of the dimension of the output data when an output value of each unit belonging to the cluster is replaced with an average output value of each unit of the cluster when the input data included in the learning data was input and the value of the dimension of the output data before replacement as a relationship between the combinations.

公开(公告)号：US12075190B2

公开(公告)日：2024-08-27

申请号：US18221702

申请日：2023-07-13

Applicant: Snap Inc.

Inventor： Lidiia Bogdanovych ,  William Brendel ,  Samuel Edward Hare ,  Fedir Poliakov ,  Guohui Wang ,  Xuehan Xiong ,  Jianchao Yang ,  Linjie Yang

IPC: G06T7/11 ,  G06F18/214 ,  G06F18/24 ,  G06N3/04 ,  G06N3/08 ,  G06T7/194 ,  G06V10/82 ,  G06V30/19 ,  G06V30/242 ,  H04N7/14 ,  H04N5/445 ,  H04N5/76

CPC classification number: H04N7/147 ,  G06F18/214 ,  G06F18/24765 ,  G06N3/04 ,  G06N3/08 ,  G06T7/11 ,  G06T7/194 ,  G06V10/82 ,  G06V30/19173 ,  G06V30/242 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/20024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20221 ,  G06T2207/30201 ,  H04N5/44504 ,  H04N5/76 ,  H04N7/141

Abstract: A machine learning system can generate an image mask (e.g., a pixel mask) comprising pixel assignments for pixels. The pixels can be assigned to classes, including, for example, face, clothes, body skin, or hair. The machine learning system can be implemented using a convolutional neural network that is configured to execute efficiently on computing devices having limited resources, such as mobile phones. The pixel mask can be used to more accurately display video effects interacting with a user or subject depicted in the image.

公开(公告)号：US20240281894A1

公开(公告)日：2024-08-22

申请号：US18649545

申请日：2024-04-29

Applicant: State Farm Mutual Automobile Insurance Company

Inventor： Jeffrey S. Myers ,  Kenneth J. Sanchez ,  Michael L. Bernico

IPC: G06Q40/08 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00 ,  G06Q30/0207 ,  G06V10/82 ,  G06V30/19 ,  G06V40/16 ,  H04N7/18

CPC classification number: G06Q40/08 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00 ,  G06V10/82 ,  G06V30/19173 ,  G06V40/169 ,  H04N7/185 ,  G06Q30/0207

Abstract: A method of training and using a machine learning model that controls for consideration of undesired factors which might otherwise be considered by the trained model during its subsequent analysis of new data. For example, the model may be a neural network trained on a set of training images to evaluate an insurance applicant based upon an image or audio data of the insurance applicant as part of an underwriting process to determine an appropriate life or health insurance premium. The model is trained to probabilistically correlate an aspect of the applicant's appearance with a personal and/or health-related characteristic. Any undesired factors, such as age, sex, ethnicity, and/or race, are identified for exclusion. The trained model receives the image (e.g., a “selfie”) of the insurance applicant, analyzes the image without considering the identified undesired factors, and suggests the appropriate insurance premium based only on the remaining desired factors.

公开(公告)号：US12062165B2

公开(公告)日：2024-08-13

申请号：US17675879

申请日：2022-02-18

Applicant: KLA Corporation

Inventor： Bradley Ries ,  Tommaso Torelli ,  Muthukrishnan Sankar ,  Vineethanand Hariharan

IPC: G06T7/00 ,  G01N21/88 ,  G06V10/82 ,  G06V30/19

CPC classification number: G06T7/001 ,  G01N21/8851 ,  G06V10/82 ,  G06V30/19173 ,  G01N2021/8887 ,  G06T2207/10061 ,  G06T2207/20081 ,  G06T2207/30148

Abstract: A system is disclosed, in accordance with one or more embodiment of the present disclosure. The system may include a controller including one or more processors configured to execute a set of program instructions. The set of program instructions may be configured to cause the processors to: receive images of a sample from a characterization sub-system; identify target clips from patch clips; prepare processed clips based on the target clips; generate encoded images by transforming the processed clips; sort the encoded images into a set of clusters; display sorted images from the set of clusters; receive labels for the displayed sorted images; determine whether the received labels are sufficient to train a deep learning classifier; and upon determining the received labels are sufficient to train the deep learning classifier, train the deep learning classifier via the displayed sorted images and the received labels.

公开(公告)号：US20240266054A1

公开(公告)日：2024-08-08

申请号：US18595563

申请日：2024-03-05

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Kuan TIAN ,  Cheng JIANG

IPC: G16H50/20 ,  G06F18/22 ,  G06N20/00 ,  G06T7/00 ,  G06T7/11 ,  G06T7/62 ,  G06V10/10 ,  G06V10/25 ,  G06V10/26 ,  G06V10/82 ,  G06V30/19 ,  G06V30/262 ,  G16H30/20 ,  G16H30/40 ,  G16H50/70

CPC classification number: G16H50/20 ,  G06F18/22 ,  G06N20/00 ,  G06T7/0014 ,  G06T7/11 ,  G06T7/62 ,  G06V10/17 ,  G06V10/25 ,  G06V10/26 ,  G06V10/82 ,  G06V30/19147 ,  G06V30/19153 ,  G06V30/19173 ,  G06V30/274 ,  G16H30/20 ,  G16H30/40 ,  G16H50/70 ,  G06T2207/20021 ,  G06T2207/30096 ,  G06V2201/03

Abstract: A medical image processing method includes: obtaining a biological tissue image including a biological tissue, recognizing, in the biological tissue image, a first region of a lesion object in the biological tissue; recognizing a lesion attribute matching the lesion object; dividing an image region of the biological tissue in the biological tissue image into a plurality of quadrant regions; obtaining target quadrant position information of a quadrant region in which the first region is located; and generating medical service data according to the target quadrant position information and the lesion attribute.

公开(公告)号：US12056941B2

公开(公告)日：2024-08-06

申请号：US18100998

申请日：2023-01-24

Applicant: Insurance Services Office, Inc.

Inventor： Khoi Nguyen ,  Maneesh Kumar Singh

IPC: G06V20/62 ,  G06F18/20 ,  G06F18/2413 ,  G06V30/10 ,  G06V30/18 ,  G06V30/19 ,  G06V30/413 ,  G06V30/414

CPC classification number: G06V20/62 ,  G06F18/24147 ,  G06F18/295 ,  G06V30/18057 ,  G06V30/19173 ,  G06V30/413 ,  G06V30/414 ,  G06V30/10

Abstract: Computer vision systems and methods for text classification are provided. The system detects a plurality of text regions in an image and generates a bounding box for each detected text region. The system utilizes a neural network to recognize text present within each bounding box and classifies the recognized text, based on at least one extracted feature of each bounding box and the recognized text present within each bounding box, according to a plurality of predefined tags. The system can associate a key with a value and return a key-value pair for each predefined tag.

公开(公告)号：US12046064B2

公开(公告)日：2024-07-23

申请号：US16999221

申请日：2020-08-21

Applicant: Optum Technology, Inc.

Inventor： Kartik Chaudhary ,  Raghav Bali ,  V Kishore Ayyadevara ,  Yerraguntla Yeshwanth Reddy

IPC: G06V30/413 ,  G06F16/93 ,  G06F18/214 ,  G06N3/04 ,  G06N3/08 ,  G06V10/82 ,  G06V30/19 ,  G06V30/10

CPC classification number: G06V30/413 ,  G06F16/93 ,  G06F18/214 ,  G06N3/04 ,  G06N3/08 ,  G06V10/82 ,  G06V30/19173 ,  G06V30/10

Abstract: There is a need for more effective and efficient predictive document conversion. This need can be addressed by, for example, solutions for performing document conversion using a trained convolutional neural document conversion machine learning. In one example, the trained convolutional neural document conversion machine learning model is associated with a preprocessing block having a plurality of preprocessing subblocks, one or more main processing blocks each having a plurality of main processing subblocks, and a plurality of postprocessing subblocks each having one or more postprocessing subblocks, and the trained convolutional neural document conversion machine learning model is further associated with a preprocessing subblock repetition count hyper-parameter that defines a preprocessing subblock count of the plurality of preprocessing subblocks.