公开(公告)号：US20230281736A1

公开(公告)日：2023-09-07

申请号：US18316967

申请日：2023-05-12

Applicant: Included Health, Inc.

Inventor： Nathaniel Freese ,  Jyotiwardhan Patil ,  Ramakrishna Soma ,  William Roller ,  Eric Carlson

IPC: G06Q50/14 ,  G06N20/00 ,  G06Q30/0204

CPC classification number: G06Q50/14 ,  G06N20/00 ,  G06Q30/0205

Abstract: Methods, systems, and computer-readable media for generating a virtual based on location. The method acquires a request for a service based on a type of service and is associated with a user, the user's location, and user preferences. The method then acquires a set of service providers based on the type of service and the user's location who are filtered from a larger set of service providers using user preferences. The method in the next step acquires a machine learning model that is based on stored information associated with other users travel patterns and with service providers providing the service and the geographical information associated with the user's location. The method executed the identified machine learning model to aggregate a subset of service providers based on output from the machine learning model. The machine learning model is inputted the set of service providers, the user's location, the user's preferences, and the geographical information.

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：US20230244726A1

公开(公告)日：2023-08-03

申请号：US18297516

申请日：2023-04-07

Applicant: Included Health, Inc.

Inventor： Nathaniel Freese ,  Derek Macklin ,  Ramkrishna Soma ,  Eric Carlson ,  Stephen Martin

IPC: G06F16/9532 ,  G06F16/9537 ,  G06N20/00 ,  G06F16/9032 ,  G06F16/9538

CPC classification number: G06F16/9532 ,  G06F16/9537 ,  G06N20/00 ,  G06F16/90324 ,  G06F16/9538

Abstract: Methods, systems, and computer-readable media for updating a machine learning model utilized in a search engine operation. The method identifies a set of search queries in stored search queries corresponding to a service and apply the identified set of search queries to the search engine to generate one or more search results for the service. Each search result has an assigned aggregate based on values of a set of parameters associated with the service. The method then analyzes the values of the set of parameters to determine a tradeoff point of each parameter to determine one or more weights to apply to the machine learning model based on the tradeoff points. The method stores the determined one or more weights and applies them to the machine learning model for a search query corresponding to the service.

公开(公告)号：US20240266012A1

公开(公告)日：2024-08-08

申请号：US18637307

申请日：2024-04-16

Applicant: Included Health, Inc.

Inventor： Eric Carlson ,  Molong Li ,  Zachary Taylor ,  Carolina Marquez

IPC: G16H10/40

CPC classification number: G16H10/40

Abstract: Methods, systems, and computer-readable media for multi-domain, multi-modal data segmentation, and automatically generating and refining hypotheses. The method receives data from a plurality of data sources; synthesizing the receive data; identifying trigger event data based on the synthesized data; generating an episode based on a segmentation of the synthesized data and trigger event data; and identifying at least one set of observational features associated with the episode based on the synthesized data and a relevancy metric. The method also includes iteratively generating a hypothesis based on the observational features using machine learning, predicting an outcome based on the hypothesis using machine learning, generating an outcome measure, and validating the hypothesis based on the outcome measure. The method also includes determining an optimal hypothesis upon reaching the threshold value; analyzing coefficients associated with the optimal hypothesis; and identifying a set of factors associated based on the analyzed coefficients.

公开(公告)号：US12051125B2

公开(公告)日：2024-07-30

申请号：US18316967

申请日：2023-05-12

Applicant: Included Health, Inc.

Inventor： Nathaniel Freese ,  Jyotiwardhan Patil ,  Ramakrishna Soma ,  William Roller ,  Eric Carlson

IPC: G06Q50/14 ,  G06N20/00 ,  G06Q30/0204

CPC classification number: G06Q50/14 ,  G06N20/00 ,  G06Q30/0205

Abstract: Methods, systems, and computer-readable media for generating a virtual based on location. The method acquires a request for a service based on a type of service and is associated with a user, the user's location, and user preferences. The method then acquires a set of service providers based on the type of service and the user's location who are filtered from a larger set of service providers using user preferences. The method in the next step acquires a machine learning model that is based on stored information associated with other users travel patterns and with service providers providing the service and the geographical information associated with the user's location. The method executed the identified machine learning model to aggregate a subset of service providers based on output from the machine learning model. The machine learning model is inputted the set of service providers, the user's location, the user's preferences, and the geographical information.

公开(公告)号：US12019693B2

公开(公告)日：2024-06-25

申请号：US18297516

申请日：2023-04-07

Applicant: Included Health, Inc.

Inventor： Nathaniel Freese ,  Derek Macklin ,  Ramkrishna Soma ,  Eric Carlson ,  Stephen Martin

IPC: G06F16/9532 ,  G06F16/9032 ,  G06F16/9537 ,  G06F16/9538 ,  G06N20/00

CPC classification number: G06F16/9532 ,  G06F16/90324 ,  G06F16/9537 ,  G06F16/9538 ,  G06N20/00

Abstract: Methods, systems, and computer-readable media for updating a machine learning model utilized in a search engine operation. The method identifies a set of search queries in stored search queries corresponding to a service and apply the identified set of search queries to the search engine to generate one or more search results for the service. Each search result has an assigned aggregate based on values of a set of parameters associated with the service. The method then analyzes the values of the set of parameters to determine a tradeoff point of each parameter to determine one or more weights to apply to the machine learning model based on the tradeoff points. The method stores the determined one or more weights and applies them to the machine learning model for a search query corresponding to the service.

公开(公告)号：US11978539B2

公开(公告)日：2024-05-07

申请号：US17752715

申请日：2022-05-24

Applicant: Included Health, Inc.

Inventor： Eric Carlson ,  Molong Li ,  Zachary Taylor ,  Carolina Marquez

IPC: G16H10/40

CPC classification number: G16H10/40

Abstract: Methods, systems, and computer-readable media for multi-domain, multi-modal data segmentation, and automatically generating and refining hypotheses. The method receives data from a plurality of data sources; synthesizing the receive data; identifying trigger event data based on the synthesized data; generating an episode based on a segmentation of the synthesized data and trigger event data; and identifying at least one set of observational features associated with the episode based on the synthesized data and a relevancy metric. The method also includes iteratively generating a hypothesis based on the observational features using machine learning, predicting an outcome based on the hypothesis using machine learning, generating an outcome measure, and validating the hypothesis based on the outcome measure. The method also includes determining an optimal hypothesis upon reaching the threshold value; analyzing coefficients associated with the optimal hypothesis; and identifying a set of factors associated based on the analyzed coefficients.

公开(公告)号：US20240232236A1

公开(公告)日：2024-07-11

申请号：US18584675

申请日：2024-02-22

Applicant: Included Health, Inc.

Inventor： Jyotiwardhan PATIL ,  Eric Carlson ,  Cole Leahy ,  Bradley S. Tofel ,  Vinay Goel ,  Nicholas Gorski

IPC: G06F16/28 ,  G06F11/14 ,  G06F16/18 ,  G06F16/25 ,  G06N20/00

CPC classification number: G06F16/288 ,  G06F11/1451 ,  G06F16/1873 ,  G06F16/258 ,  G06N20/00 ,  G06F2201/80 ,  G06F2201/84

Abstract: Methods, systems, and computer-readable media for linking multiple data entities. The method collects a snapshot of data from one or more data sources and converts it into a canonical representation of records expressing relationships between data elements in the records. The method next cleans the records to generate output data of entities by grouping chunks of records using a machine learning model. The method next ingests the output data of entities to generate a versioned data store of the entities and optimizes versioned data store for real-time data lookup. The method then receives a request for data pertaining to a real-world entity and presenting relevant data from the versioned data store of entities.

公开(公告)号：US11921758B2

公开(公告)日：2024-03-05

申请号：US17735060

申请日：2022-05-02

Applicant: Included Health, Inc.

Inventor： Jyotiwardhan Patil ,  Eric Carlson ,  Cole Leahy ,  Bradley S. Tofel ,  Vinay Goel ,  Nicholas Gorski

IPC: G06F16/00 ,  G06F11/14 ,  G06F16/18 ,  G06F16/25 ,  G06F16/28 ,  G06N20/00

CPC classification number: G06F16/288 ,  G06F11/1451 ,  G06F16/1873 ,  G06F16/258 ,  G06N20/00 ,  G06F2201/80 ,  G06F2201/84

Abstract: Methods, systems, and computer-readable media for linking multiple data entities. The method collects a snapshot of data from one or more data sources and converts it into a canonical representation of records expressing relationships between data elements in the records. The method next cleans the records to generate output data of entities by grouping chunks of records using a machine learning model. The method next ingests the output data of entities to generate a versioned data store of the entities and optimizes versioned data store for real-time data lookup. The method then receives a request for data pertaining to a real-world entity and presenting relevant data from the versioned data store of entities.

公开(公告)号：US20230386621A1

公开(公告)日：2023-11-30

申请号：US17752715

申请日：2022-05-24

Applicant: Included Health, Inc.

Inventor： Eric Carlson ,  Molong Li ,  Zachary Taylor ,  Carolina Marquez

IPC: G16H10/40

CPC classification number: G16H10/40

Abstract: Methods, systems, and computer-readable media for multi-domain, multi-modal data segmentation, and automatically generating and refining hypotheses. The method receives data from a plurality of data sources; synthesizing the receive data; identifying trigger event data based on the synthesized data; generating an episode based on a segmentation of the synthesized data and trigger event data; and identifying at least one set of observational features associated with the episode based on the synthesized data and a relevancy metric. The method also includes iteratively generating a hypothesis based on the observational features using machine learning, predicting an outcome based on the hypothesis using machine learning, generating an outcome measure, and validating the hypothesis based on the outcome measure. The method also includes determining an optimal hypothesis upon reaching the threshold value; analyzing coefficients associated with the optimal hypothesis; and identifying a set of factors associated based on the analyzed coefficients.