公开(公告)号：US11853891B2

公开(公告)日：2023-12-26

申请号：US16816153

申请日：2020-03-11

Applicant: SHARECARE AI, INC.

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

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

CPC classification number: G06N3/084 ,  G06N3/08 ,  G06V10/764 ,  G06V10/809 ,  G06V10/945 ,  G06V10/95 ,  G06V10/96 ,  G16H70/60

Abstract: 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：US11145421B2

公开(公告)日：2021-10-12

申请号：US16802485

申请日：2020-02-26

Applicant: Sharecare AI, Inc.

Inventor： Walter Adolf De Brouwer ,  Srivatsa Akshay Sharma

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

Abstract: 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：US12039012B2

公开(公告)日：2024-07-16

申请号：US17508985

申请日：2021-10-23

Applicant: Sharecare AI, Inc.

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

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

CPC classification number: G06F18/2155 ,  G06F18/213 ,  G06F18/24 ,  G06N5/04

Abstract: 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

Applicant: SHARECARE AI, INC.

Inventor： 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: 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 classification number: 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

Abstract: 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

Applicant: Sharecare AI, Inc.

Inventor： 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: H04L9/32 ,  H04L9/08 ,  G06N20/00 ,  G06K7/14 ,  G06N5/04

Abstract: 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

Applicant: SHARECARE AI, INC.

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

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

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

Abstract: 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：US11430547B2

公开(公告)日：2022-08-30

申请号：US17400079

申请日：2021-08-11

Applicant: Sharecare AI, Inc.

Inventor： Srivatsa Akshay Sharma ,  Walter Adolf De Brouwer ,  Gabriel Gabra Zaccak ,  Chethan R. Sarabu ,  Devin Daniel Reich ,  Marina Titova ,  Andrés Rodríguez Esmeral

IPC: G16H10/20 ,  H04L9/08 ,  G06F7/523 ,  G06F7/58 ,  G06F7/483

Abstract: The technology disclosed relates to a system and method for assigning participants to groups in a clinical trial. The system includes a federated server configured with group assignability data specifying a plurality of groups assignable to participants in a clinical trial and group distribution data specifying distribution of the participants into groups. The groups include at least one placebo group and one or more treatment groups. The system includes an intervention server configured to generate group encryption keys for encrypting the group assignability data. The system includes edge devices of each of the participants. The edge devices are in communication with the federated server.

公开(公告)号：US11321447B2

公开(公告)日：2022-05-03

申请号：US17235876

申请日：2021-04-20

Applicant: SHARECARE AI, INC.

Inventor： 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: G06K9/00 ,  G06F21/45 ,  G06N20/00 ,  H04L9/32 ,  G16H10/60 ,  G06F21/32 ,  G06K9/62 ,  G06K7/14 ,  G06N5/04 ,  H04L9/08 ,  H04L29/06 ,  G06N3/08 ,  G06N3/04

Abstract: 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.