Ginger.io, Inc.

Uber Technologies, Inc.

Orangedot, Inc.

Karim Wahba

Sai Moturu

Anmol Madan

Shishir Dash

Gourab De

Dhruv Tyagi

Andrew Joseph Beinstein

Amritha Prasad

Audrey Lawrence

Jose Alvarez

Steve Pennington

Corin Trachtman

Joseph Patrick Sullivan

Theodore Russell Sumers

Nirveek De

Gang Su

Andrew Beinstein

A method and system for providing patient indications to an entity, the method comprising: for each of a set of patients: by way of an application executing at a mobile computing device, accessing a sensor system and a log of use of a communication application executing on the mobile computing device; receiving a survey response dataset; generating a behavioral dataset, derived from the sensor system and the log of use; generating a predictive model derived from at least one of the survey response dataset and the behavioral dataset; generating comparisons of outputs of blocks of the method to threshold conditions; producing a set of indications corresponding to the set of patients, derived from generation of, for each of the set of patients, an indication in response to the comparisons; and transmitting a portion of the set of indications to the entity according to a resource constraint of the entity.

Method for providing patient indications to an entity

A method and system for modeling behavior and depression state of an individual, the method comprising: receiving a log of use dataset associated with communication behavior of the individual during a time period; receiving a supplementary dataset characterizing activity of the individual during the time period; receiving a survey dataset including responses, to at least one of a set of depression-assessment surveys, associated with a set of time points of the time period; generating a predictive analysis of a depression-risk state of the individual associated with at least a portion of the time period, from at least one of the log of use dataset, the supplementary dataset, and the survey dataset; and generating an alert upon detection that a set of parameters from the predictive analysis of the depression-risk state satisfy a threshold condition.

Method for modeling behavior and depression state

Embodiments of a method and system for improving sleep characterization and/or a sleeping-related disorder for a user associated with a sleep session can include receiving a log of use dataset corresponding to user digital communication behavior at a mobile device, the log of use dataset associated with the sleep session; receiving a supplementary dataset characterizing activity of the user and/or mobile device, the supplementary dataset associated with the sleep session; characterizing a sleep-related parameter for the user based on at least one of the log of use dataset and the supplementary dataset; determining a sleep care plan for the user based on the sleep-related parameter, the sleep care plan including a therapeutic intervention; and promoting a therapeutic intervention to the user according to the sleep care plan.

Method and system for characterizing and/or treating poor sleep behavior

A method and system for modeling behavior and a psychotic disorder-related state of a patient, the method comprising: receiving a log of use dataset associated with communication behavior of the patient during a time period; receiving a supplementary dataset characterizing mobility-behavior of the patient during the time period; generating a predictive model based upon a passive dataset derived from the log of use dataset and the supplementary dataset; transforming at least one of the passive dataset and an output of the predictive model into an analysis of a psychotic episode-risk state of the individual associated with at least a portion of the time period; and upon detection that parameters of the psychotic episode-risk state satisfy at least one threshold condition, automatically initiating provision of a therapeutic intervention for the individual by way of at least one of the computing system and the mobile communication device.

Method for modeling behavior and psychotic disorders

A network computing system receives local device data from a mobile computing device of a person within a vehicle. The local device data may include sensor data from one or more sensors of the mobile computing device, and location data determined from a position-determination resource of the mobile computing device. The network computing system may detect a vehicle collision event based on the local device data. Additionally, the network computing system may determine a classification of the vehicle collision event based on the local device data.

Detecting vehicle collisions based on mobile computing device data

Examples provide for a computing system to obtain sensor data of one or more types, from one or more sensor components of a computing device associated with a user of the vehicle. The sensor data reflects an attribute of the vehicle's operation when the computing device is carried within or in proximity to the vehicle during the vehicle's operation. One or more characterizations may be determined for the vehicle based on the sensor data.

Vehicle monitoring system

Detecting vehicle collisions based on moble computing device data

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San Francisco, CA, United States of America

Karim Wahba