公开(公告)号：US20250090033A1

公开(公告)日：2025-03-20

申请号：US18883902

申请日：2024-09-12

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Suhas BETTAPALLI NAGARAJ ,  Yashas Malur Saidutta ,  Rakshith Sharma Srinivasa ,  Jaejin Cho ,  Ching-Hua Lee ,  Chouchang Yang ,  Yilin Shen ,  Hongxia Jin

IPC: A61B5/021 ,  A61B5/00 ,  A61B5/024 ,  A61B5/318

Abstract: A method for performing cuffless blood pressure (BP) measurement, including: obtaining a first physiological signal and a second physiological signal associated with a user; providing the first physiological signal as an input to a first transformer model; providing the second physiological signal as an input to a second transformer model; providing an output of the first transformer model and an output of the second transformer model as inputs to a third transformer model; providing an output of the third transformer model to at least one BP estimation model; and generating an estimated BP value corresponding to the first physiological signal and the second physiological signal based on an output of the at least one BP estimation model

公开(公告)号：US12127726B2

公开(公告)日：2024-10-29

申请号：US17231958

申请日：2021-04-15

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yu Wang ,  Yilin Shen ,  Hongxia Jin

IPC: G06V10/70 ,  A47L9/28 ,  A47L11/40 ,  G06F18/214 ,  G06V20/10

CPC classification number: A47L9/2805 ,  A47L11/40 ,  G06F18/214 ,  G06V10/768 ,  G06V20/10 ,  A47L2201/06

Abstract: A method includes obtaining, using at least one processor of an electronic device, an image-query understanding model. The method also includes obtaining, using the at least one processor, an image and a user query associated with the image, where the image includes a target image area and the user query includes a target phrase. The method further includes retraining, using the at least one processor, the image-query understanding model using a correlation between the target image area and the target phrase to obtain a retrained image-query understanding model.

公开(公告)号：US20240311693A1

公开(公告)日：2024-09-19

申请号：US18592250

申请日：2024-02-29

Applicant: Samsung Electronics Co., Ltd.

Inventor： James S. Smith ,  Yen-Chang Hsu ,  Yilin Shen ,  Hongxia Jin ,  Lingyu Zhang ,  Ting Hua

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A method includes obtaining input data associated with a new concept to be learned by a trained machine learning model. The method also includes identifying initial weights of the trained machine learning model and one or more previous weight deltas associated with the trained machine learning model. The method further includes identifying one or more additional weight deltas based on the input data and guided by the initial weights and the one or more previous weight deltas. In addition, the method includes integrating the one or more additional weight deltas into the trained machine learning model. The one or more additional weight deltas are integrated into the trained machine learning model by identifying updated weights for the trained machine learning model based on the initial weights, the one or more previous weight deltas, and the one or more additional weight deltas.

公开(公告)号：US12081880B2

公开(公告)日：2024-09-03

申请号：US17493268

申请日：2021-10-04

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Jing Zhu ,  Wenbo Li ,  Hongxia Jin

IPC: H04N23/951 ,  G06T5/20 ,  G06T5/50 ,  G06T5/75

CPC classification number: H04N23/951 ,  G06T5/20 ,  G06T5/50 ,  G06T5/75 ,  G06T2207/20016

Abstract: A super resolution is produced using multiple reference images. Reference images are upsampled and blurred as needed for comparison between images of different resolution. Patches in blurred images are searched to find those patches which can be assembled into vectors for improving feature content over multiple resolution levels. The searches are based on similarity maps. The assembled vectors are concatenated with one or more other vectors, up-converted and then passed through convolutional layers to obtain new feature vectors. A final feature vector is passed through a convolutional layer to obtain the super resolution image.

公开(公告)号：US20240256906A1

公开(公告)日：2024-08-01

申请号：US18401074

申请日：2023-12-29

Applicant: Samsung Electronics Co., Ltd.

Inventor： Vikas Yadav ,  Hyuk Joon Kwon ,  Vijay Srinivasan ,  Hongxia Jin

IPC: G06N5/02 ,  G06F40/295

CPC classification number: G06N5/02 ,  G06F40/295

Abstract: A method includes predicting, using the at least one processing device, a question type for each section of a document using a trained question type prediction model, each section including a different portion of the document. The method also includes generating, using the at least one processing device, multiple question-answer pairs using a trained question-answer generation model that receives the predicted question types and the document as input. Each question-answer pair includes (i) a question having a type corresponding to one of the predicted question types and being associated with content in the section corresponding to the type and (ii) an answer to the question. The method further includes outputting, using the at least one processing device, the question-answer pairs for use in training a question answering model.

公开(公告)号：US20230289590A1

公开(公告)日：2023-09-14

申请号：US17940709

申请日：2022-09-08

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Burak UZKENT ,  Vasili Ramanishka ,  Yilin Shen ,  Hongxia Jin

IPC: G06N3/08 ,  G06N3/063 ,  G10L25/54 ,  G10L15/26

CPC classification number: G06N3/08 ,  G06N3/063 ,  G10L25/54 ,  G10L15/26

Abstract: A method of training a model includes configuring a first transformer for visual learning with a first set of weights, configuring a second transformer for textual learning with a second set of weights, adjusting at least the second set of weights based on minimizing a weight difference between the first set of weights and the second set of weights, replacing the first set of weights for the first transformer with the adjusted second set of weights, and updating the first transformer based on the adjusted second set of weights.

公开(公告)号：US11741307B2

公开(公告)日：2023-08-29

申请号：US17075353

申请日：2020-10-20

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yilin Shen ,  Hongxia Jin

IPC: G06F40/30 ,  G10L15/14 ,  G10L15/22 ,  G10L15/18 ,  G06F18/214 ,  G06N7/01 ,  G06F40/216 ,  G06F40/295 ,  G10L15/16 ,  G06F40/279 ,  G10L25/30

CPC classification number: G06F40/30 ,  G06F18/214 ,  G06N7/01 ,  G10L15/14 ,  G10L15/1822 ,  G10L15/22 ,  G06F40/216 ,  G06F40/279 ,  G06F40/295 ,  G10L15/16 ,  G10L25/30

Abstract: A method includes applying, by at least one processor, a natural language understanding (NLU) model to an input utterance in order to obtain initial slot probability distributions. The method also includes performing, by the at least one processor, a confidence calibration by applying a calibration probability distribution to the initial slot probability distributions in order to generate calibrated slot probability distributions. The calibration probability distribution has a higher number of dimensions than the initial slot probability distributions. The method further includes identifying, by the at least one processor, uncertainties associated with words in the input utterance based on the calibrated slot probability distributions. In addition, the method includes identifying, by the at least one processor, a new concept contained in the input utterance that is not recognized by the NLU model based on the identified uncertainties.

公开(公告)号：US11734567B2

公开(公告)日：2023-08-22

申请号：US15895683

申请日：2018-02-13

Applicant: Samsung Electronics Co., Ltd.

Inventor： Shiva Prasad Kasiviswanathan ,  Nina Narodytska ,  Hongxia Jin

IPC: G06N3/082 ,  G06N3/045

CPC classification number: G06N3/082 ,  G06N3/045

Abstract: A method includes deploying a neural network (NN) model on an electronic device. The NN model is generated by training a first NN architecture on a first dataset. A first function defines a first layer of the first NN architecture. The first function is constructed based on approximating a second function applied by a second layer of a second NN architecture. Retraining of the NN model is enabled on the electronic device using a second data set.

公开(公告)号：US11681923B2

公开(公告)日：2023-06-20

申请号：US16728987

申请日：2019-12-27

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Yu Wang ,  Yilin Shen ,  Yue Deng ,  Hongxia Jin

IPC: G06N3/084 ,  G06N20/20 ,  G06F18/241 ,  G06N3/045 ,  G06V10/764 ,  G06V10/778 ,  G06V10/94

CPC classification number: G06N3/084 ,  G06F18/241 ,  G06N3/045 ,  G06N20/20 ,  G06V10/764 ,  G06V10/7784 ,  G06V10/94

Abstract: Intent determination based on one or more multi-model structures can include generating an output from each of a plurality of domain-specific models in response to a received input. The domain-specific models can comprise simultaneously trained machine learning models that are trained using a corresponding local loss metric for each domain-specific model and a global loss metric for the plurality of domain-specific models. The presence or absence of an intent corresponding to one or more domain-specific models can be determined by classifying the output of each domain-specific model.

公开(公告)号：US20230075862A1

公开(公告)日：2023-03-09

申请号：US17899118

申请日：2022-08-30

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Burak UZKENT ,  Vasili Ramanishka ,  Yilin Shen ,  Hongxia Jin

IPC: G06V10/82 ,  G06V10/86 ,  G06V10/75 ,  G06V10/94 ,  G06V10/56 ,  G06F16/53

Abstract: A method of training a neural network model includes generating a positive image based on an original image, generating a positive text corresponding to the positive image based on an original text corresponding to the original image, the positive text referring to an object in the positive image, constructing a positive image-text pair for the object based on the positive image and the positive text, constructing a negative image-text pair for the object based on the original image and a negative text, the negative text not referring to the object, training the neural network model based on the positive image-text pair and the negative image-text pair to output features representing an input image-text pair, and identifying the object in the original image based on the features representing the input image-text pair.