公开(公告)号：US11907332B2

公开(公告)日：2024-02-20

申请号：US17575533

申请日：2022-01-13

Applicant: Included Health, Inc.

Inventor： Nathaniel Freese ,  Meera Rao ,  Rick Wolf ,  Peyton Rose ,  Stephen Martin ,  Sameer Soi ,  Zachary Taylor ,  Ye Wang

IPC: G06F18/2113 ,  G06N20/00 ,  G06F16/2458 ,  G06F16/242 ,  G06F18/10 ,  G06F18/214 ,  G06Q30/0282

CPC classification number: G06F18/2113 ,  G06F16/2443 ,  G06F16/2465 ,  G06F18/10 ,  G06F18/2155 ,  G06N20/00 ,  G06Q30/0282

Abstract: Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.

公开(公告)号：US20220309286A1

公开(公告)日：2022-09-29

申请号：US17575533

申请日：2022-01-13

Applicant: Included Health, Inc.

Inventor： Nathaniel Freese ,  Meera Rao ,  Rick Wolf ,  Peyton Rose ,  Stephen Martin ,  Sameer Soi ,  Zachary Taylor ,  Ye Wang

IPC: G06K9/62 ,  G06N20/00 ,  G06F16/242 ,  G06F16/2458

Abstract: Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.

公开(公告)号：US12197878B1

公开(公告)日：2025-01-14

申请号：US18451046

申请日：2023-08-16

Applicant: Included Health, Inc.

Inventor： Peyton Rose ,  Matt Forbes ,  Susan Enneking ,  Jack Sullivan ,  Jennifer Kong

IPC: G06F40/40 ,  G06F3/0482 ,  G06Q40/08 ,  G16H20/00

Abstract: Methods, systems, and computer-readable media for the generation of real-time recommendations using natural language processing. The method receives a request for a benefit recommendation; generates at least one tag based on input data; extracts, based on the at least one tag, at least one observation and at least one action from the input data; predicts at least one recommendation based on the extracted at least one observation and the extracted at least one action in real time; and sends the at least one predicted recommendation for display to a user device.

公开(公告)号：US11783951B2

公开(公告)日：2023-10-10

申请号：US17509536

申请日：2021-10-25

Applicant: Included Health, Inc.

Inventor： Eric Carlson ,  Ramakrishna Soma ,  Molong Li ,  Jacob David Rifkin ,  Zachary Taylor ,  Peyton Rose

IPC: G16H50/70 ,  G16H20/00 ,  G16H10/60 ,  G06N20/00 ,  G06F18/214 ,  G06F18/2113

CPC classification number: G16H50/70 ,  G06F18/214 ,  G06F18/2113 ,  G06N20/00 ,  G16H10/60 ,  G16H20/00

Abstract: Methods, systems, and computer-readable media for generating a personalized action recommendation are provided. The method acquires a request for a service that is associated with a user and the user's condition. The method then identifies one or more features of the user based on stored user information. The method next assigns the user to a segment based on the identified one or more features, generates a set of one or more recommended actions for the user based on the segment, and determines an expected value of each of the one or more recommended actions. The method determines a rank of the one or more recommended actions based on the expected value of each of the one or more recommended actions, and outputs a recommended action with a highest expected value for the user in response to the request for the service.

公开(公告)号：US20240169027A1

公开(公告)日：2024-05-23

申请号：US18430480

申请日：2024-02-01

Applicant: Included Health, Inc.

Inventor： Nathaniel Freese ,  Meera Rao ,  Rick Wolf ,  Peyton Rose ,  Stephen Martin ,  Sameer Soi ,  Zachary Taylor ,  Ye Wang

IPC: G06F18/2113 ,  G06F16/242 ,  G06F16/2458 ,  G06F18/10 ,  G06F18/214 ,  G06N20/00 ,  G06Q30/0282

CPC classification number: G06F18/2113 ,  G06F16/2443 ,  G06F16/2465 ,  G06F18/10 ,  G06F18/2155 ,  G06N20/00 ,  G06Q30/0282

Abstract: Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.