Included Health, Inc.

Grand Rounds, Inc.

Chevron U.s.a. Inc.

Ramakrishna Soma

Eric Carlson

Nathaniel Freese

Zachary Taylor

Jyotiwardhan Patil

Morteza Sayarpour

Peyton Rose

Nariman Fathi Najafabadi

Lokendra Jain

Tiantian Zhang

Molong Li

William Roller

Irina V Prestwood

Carmen C Hinds

Jacob David Rifkin

Omer Izgec

Pavarit Trakulhoon

Jorge L Landa

Maryam Davari

Donna T Auzenne

Carman C Hinds

Jain Lokendra

Frederick Paul Duke

Irina Prestwood

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.

Systems and methods for travel optimization

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.

Systems and methods for generating predictive data models using large data sets to provide personalized action recommendations

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Fremont, CA, United States of America

Ramakrishna Soma