公开(公告)号：US20220028545A1

公开(公告)日：2022-01-27

申请号：US17498724

申请日：2021-10-11

申请人： Sharecare AI, Inc.

发明人： Walter Adolf DE BROUWER ,  Srivatsa Akshay SHARMA

IPC分类号： G16H40/67 ,  G16H80/00 ,  G16H50/20 ,  G16H50/30 ,  G16H20/60 ,  G06K9/00 ,  G06N3/08

摘要： System and method for remote medical information exchange are disclosed. The system comprises a patient-end digital device configured to capture video stream of face of the patient. The system comprises a doctor-end digital device configured to show the video stream of the face of the patient and one or more physiological inference in accordance with the video stream. The system comprises a processor configured to receive a facial image from the captured video stream. The processor can provide the facial image to a trained neural network configured to predict a sentiment of the patient based on features in the facial image. The processor is configured to receive the sentiment of the patient from the trained neural network indicating how the patient is feeling. The processor is further configured to provide the predicted sentiment to the doctor-end digital device to aid in diagnosis of the patient.

公开(公告)号：US11145421B2

公开(公告)日：2021-10-12

申请号：US16802485

申请日：2020-02-26

申请人： Sharecare AI, Inc.

发明人： Walter Adolf De Brouwer ,  Srivatsa Akshay Sharma

IPC分类号： G16H80/00 ,  G16H50/30 ,  G16H50/20 ,  G16H40/67

摘要： Method and system for remote medical information exchanging are disclosed. The system for remote medical information exchanging comprises a computer application program and a data storage server. The computer application program is electrically coupled with a data storage server via communication network, comprising an end user end, configured to capture video stream of face of the end user; and a doctor end, configured to show the video stream of the face of end user and one or more physiological inference in accordance with the video stream. The data storage server, configured to store a scalable video server module, one or more artificial intelligence modules, a poly-omics uni-matrix pipeline, and a combined inference module.

公开(公告)号：US11998364B2

公开(公告)日：2024-06-04

申请号：US17339887

申请日：2021-06-04

申请人： Sharecare AI, Inc.

发明人： Walter De Brouwer ,  Apurv Mishra ,  Samia De Brouwer

IPC分类号： G06V40/16 ,  A61B5/00 ,  A61B5/103 ,  A61B5/107 ,  G06F18/2413 ,  G06N3/045 ,  G06N3/082 ,  G06N5/046 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G16H30/40 ,  G16H50/30 ,  A61B5/024 ,  A61B5/16 ,  G06N7/01 ,  G06N20/10

CPC分类号： A61B5/7275 ,  A61B5/0077 ,  A61B5/1032 ,  A61B5/1072 ,  A61B5/441 ,  A61B5/4872 ,  A61B5/7267 ,  G06F18/24133 ,  G06N3/045 ,  G06N3/082 ,  G06N5/046 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V40/169 ,  G06V40/172 ,  G16H30/40 ,  G16H50/30 ,  A61B5/024 ,  A61B5/02405 ,  A61B5/163 ,  A61B5/442 ,  G06N7/01 ,  G06N20/10 ,  G06V40/178

摘要： System and method for determining physiological parameters of a person are disclosed. A physiological parameter may be obtained by analyzing a facial image of a person, and determining, from the facial image, a physiological parameter of the person by processing the facial image with a data processor. A neural network model such as regression deep learning convolutional neural network is used to predict the physiological parameter. An image processor screens out images which can't be recognized as facial images and adjust facial images to frontal facial images for predicting of physiological parameters.

公开(公告)号：US11853891B2

公开(公告)日：2023-12-26

申请号：US16816153

申请日：2020-03-11

申请人： SHARECARE AI, INC.

发明人： Walter Adolf De Brouwer ,  Srivatsa Akshay Sharma ,  Neerajshyam Rangan Kashyap ,  Kartik Thakore ,  Philip Joseph Dow

IPC分类号： G06K9/00 ,  G06N3/084 ,  G16H70/60 ,  G06N3/08 ,  G06V10/764 ,  G06V10/80 ,  G06V10/94 ,  G06V10/96

CPC分类号： G06N3/084 ,  G06N3/08 ,  G06V10/764 ,  G06V10/809 ,  G06V10/945 ,  G06V10/95 ,  G06V10/96 ,  G16H70/60

摘要： Method and system with federated learning model for health care applications are disclosed. The system for federated learning comprises multiple edge devices of end users, one or more federated learner update repository, and one or more cloud. Each edge device comprises a federated learner model, configured to send tensors to federated learner update repository. Cloud comprises a federated learner model, configured to send tensors to federated learner update repository. Federated learner update repository comprises a back-end configuration, configured to send model updates to edge devices and cloud.

公开(公告)号：US11664089B2

公开(公告)日：2023-05-30

申请号：US16575277

申请日：2019-09-18

申请人： SHARECARE AI, INC.

发明人： Geert Trooskens ,  Wim Maria R. Van Criekinge

IPC分类号： G06F16/00 ,  G16B30/00 ,  G16B40/00 ,  G16B10/00 ,  G06F16/22 ,  G16B20/20 ,  G16B45/00 ,  G16B20/40 ,  G16B5/00 ,  G16B40/10 ,  C12Q1/6827 ,  C12Q1/6869 ,  G06F17/18 ,  G16B50/00

CPC分类号： G16B30/00 ,  C12Q1/6827 ,  C12Q1/6869 ,  G06F16/2255 ,  G06F17/18 ,  G16B5/00 ,  G16B10/00 ,  G16B20/20 ,  G16B20/40 ,  G16B40/00 ,  G16B40/10 ,  G16B45/00 ,  G16B50/00

摘要： The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

公开(公告)号：US12039012B2

公开(公告)日：2024-07-16

申请号：US17508985

申请日：2021-10-23

申请人： Sharecare AI, Inc.

发明人： Salvatore Giuliano Vivona ,  Marina Titova ,  Srivatsa Akshay Sharma ,  Gabriel Gabra Zaccak

IPC分类号： G06F18/214 ,  G06F18/213 ,  G06F18/24 ,  G06N5/04

CPC分类号： G06F18/2155 ,  G06F18/213 ,  G06F18/24 ,  G06N5/04

摘要： The technology disclosed relates to a system and method of exporting learned features between federated endpoints whose learning is confined to respective training datasets. The system includes logic to access a first training dataset to train a first federated endpoint and a second training dataset to train a second federated endpoint. The first and second training datasets have first and second sample sets that share one or more shared sample features. The shared sample features are common between the first and second sample sets. The system includes logic to train a first generator on the first federated endpoint. The system includes logic to use the first trained generator for a second inference on a second performance task executed on the second federated endpoint.

公开(公告)号：US11755709B2

公开(公告)日：2023-09-12

申请号：US17676797

申请日：2022-02-21

申请人： SHARECARE AI, INC.

发明人： Axel Sly ,  Srivatsa Akshay Sharma ,  Brett Robert Redinger ,  Devin Daniel Reich ,  Geert Trooskens ,  Meelis Lootus ,  Young Jin Lee ,  Ricardo Lopez Arredondo ,  Frederick Franklin Kautz, IV ,  Satish Srinivasan Bhat ,  Scott Michael Kirk ,  Walter Adolf De Brouwer ,  Kartik Thakore

IPC分类号： G06F21/32 ,  G06F21/45 ,  G06N20/00 ,  H04L9/32 ,  G16H10/60 ,  G06K7/14 ,  G06N5/04 ,  H04L9/08 ,  G06V40/70 ,  G06K19/06 ,  G06F18/214 ,  G06V10/74 ,  G06V10/77 ,  G06V10/80 ,  G06V10/44 ,  G06V40/16 ,  G06V10/774 ,  H04L9/40 ,  G06N3/08 ,  G06N3/04

CPC分类号： G06F21/32 ,  G06F18/214 ,  G06F21/45 ,  G06K7/1417 ,  G06K19/06037 ,  G06N5/04 ,  G06N20/00 ,  G06V10/451 ,  G06V10/761 ,  G06V10/774 ,  G06V10/7715 ,  G06V10/803 ,  G06V40/161 ,  G06V40/168 ,  G06V40/70 ,  G16H10/60 ,  H04L9/085 ,  H04L9/0841 ,  H04L9/0866 ,  H04L9/0894 ,  H04L9/3228 ,  H04L9/3231 ,  H04L9/3236 ,  H04L9/3239 ,  H04L9/3242 ,  H04L9/3247 ,  H04L9/3297 ,  G06N3/04 ,  G06N3/08 ,  H04L63/0861

摘要： The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

公开(公告)号：US11177960B2

公开(公告)日：2021-11-16

申请号：US17235889

申请日：2021-04-20

申请人： Sharecare AI, Inc.

发明人： Axel Sly ,  Srivatsa Akshay Sharma ,  Brett Robert Redinger ,  Devin Daniel Reich ,  Geert Trooskens ,  Meelis Lootus ,  Young Jin Lee ,  Ricardo Lopez Arredondo ,  Frederick Franklin Kautz, IV ,  Satish Srinivasan Bhat ,  Scott Michael Kirk ,  Walter Adolf De Brouwer ,  Kartik Thakore

IPC分类号： H04L9/32 ,  H04L9/08 ,  G06N20/00 ,  G06K7/14 ,  G06N5/04

摘要： The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

公开(公告)号：US11915802B2

公开(公告)日：2024-02-27

申请号：US16985183

申请日：2020-08-04

申请人： SHARECARE AI, INC.

发明人： Brett Robert Redinger ,  Kartik Thakore ,  Sandra Ann R Steyaert ,  Walter Adolf De Brouwer ,  Srivatsa Akshay Sharma ,  Lijing Guo

IPC分类号： G16H10/40 ,  G06F16/27 ,  G16H70/60 ,  G06F9/451 ,  G16B45/00 ,  G16B50/30 ,  G06F16/13 ,  G06F9/50 ,  G06F9/48

CPC分类号： G16H10/40 ,  G06F9/451 ,  G06F9/4881 ,  G06F9/5027 ,  G06F9/5061 ,  G06F16/134 ,  G06F16/27 ,  G16B45/00 ,  G16B50/30 ,  G16H70/60 ,  G06F9/5066 ,  G06F9/5072

摘要： The technology disclosed relates to efficient tertiary analysis of genomic data. The technology disclosed includes splitting a genomic data file into a plurality of segments, and storing segments in the plurality of segments across nodes of a distributed storage system, pushing the segments from the nodes of the distributed storage system to nodes of a distributed, in-memory computing engine, distributing directives of tertiary analysis job contexts for the genomic data file across the nodes of the distributed, in-memory computing engine, directly executing the distributed directives on the segments stored on the nodes of the distributed, in-memory computing engine to cause parallel processing of the segments, and aggregating results of the parallel processing across the nodes of the distributed, in-memory computing engine to produce an output.

公开(公告)号：US11551784B2

公开(公告)日：2023-01-10

申请号：US16575278

申请日：2019-09-18

申请人： SHARECARE AI, INC.

发明人： Geert Trooskens ,  Wim Maria R. Van Criekinge

IPC分类号： G16B30/00 ,  G16B40/00 ,  G16B10/00 ,  G06F16/22 ,  G16B20/20 ,  G16B45/00 ,  G16B20/40 ,  G16B5/00 ,  G16B40/10 ,  C12Q1/6827 ,  C12Q1/6869 ,  G06F17/18 ,  G16B50/00

摘要： The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.