Google Inc.

Verily Life Sciences LLC

Uber Technologies, Inc.

Adobe, Inc.

Emre Demiralp

Walter Chang

Jan Blom

Tommaso Francesco Bersano-Begey

Gavin Stuart Peter Miller

Anupam J Pathak

Eoin O'Mahony

John Mark Nickels

Grayson Squier Lang

Lior Seeman

Danhua Guo

Melissa Dalis

Daicho Ito

Hongsheng Wang

Chaoxu Tong

Hyung Jin Kim

En Yu

Xiangyu Sun

Ali Shoeb

Mark Murphy

Tushar Anil Parlikar

Scott Brian Kennedy

Vishnu Srinivasan Sundaresan

Meisam Vosoughpour

Nikhil Bikhchandani

Philip John Stephens

Ameya Tayade

Mustafa Sahin

Justin Wie

Stephen LePallone

Hamid Nazerzadeh

Robert Louis Cornacchia

Cinar Kilcioglu

Kevin A Rowe

Zhengyun Sun

Introduced here are techniques for digitally characterizing the movement of a subject in order to detect the presence of a disorder or monitor the progression of the disorder. More specifically, one or more angular features can be identified that define how certain part(s) of the human body move relative to other part(s) of the human body. These angular feature(s) can be used, for example, to affirmatively diagnose instances of a disorder, eliminate a disorder as the source of symptoms experienced by a subject, generate confidence scores that can be used to assist in diagnosing a subject, monitor disorder progression due to treatment or lack thereof, etc.

Digital characterization of movement to detect and monitor disorders

A network system can receive a first request for a transport service and a second request for the transport service. The system can identify, from a plurality of service providers, a first set of service providers for the first request, and a second set of service providers for the second request. Based on a first set of predictive parameters for the first set of service providers, the system implements a multi-invite mode by transmitting a first invitation data set to service the first request to a plurality of provider devices of the first set of service providers. Based on a second set of predictive parameters for the second set of service providers, the system implements an exclusive-invite mode by transmitting a second invitation data set to a provider device of a selected service provider of the second set of service providers.

Dynamic invitation transmission and presentation mode determination for a network-based service

Systems and method for dynamically managing add-on orders within a delivery service application. For example, a computer-implemented method includes obtaining data indicative of a primary order request. The method includes selecting, ranking, and displaying menu items for add-on orders associated with a primary order. The method includes obtaining user data provided by a user through a user interface associated with a delivery service application. The method includes determining, in response to obtaining the user data, that the primary order request is eligible for an add-on order. The method includes determining merchants for the add-on order. The selected merchants can be determined from a plurality of candidate merchants based at least in part on analysis of merchant-specific data relative to the user data indicative of the primary order request. The method includes updating the user interface to display data associated with the one or more selected merchants for the add-on order.

Merchant selection model for dynamic management of add-ons for delivery service orders

A network system can communicate with user and provider devices to facilitate the provision of a network-based service. The network system can identify optimal service providers to provide services requested by users. The network can utilize context data in matching service providers with users. In particular, the network system can determine, based on context data associated with a user, whether to perform pre-request matching for that user. A service provider who is pre-request matched with the user can be directed by the network system to relocate via a pre-request relocation direction. When the user submits the service request after the pre-request match, the network system can either automatically transmit an invitation to the pre-request matched service provider or can perform post-request matching to identify an optimal service provider for the user.

Forecasting requests based on context data for a network-based service

Systems and methods of dynamically varying the intensity of providing content items in a remarketing campaign based on tracking client device interactions are provided. The system can assign an account identifier to a first segment for a pre-conversion model, responsive to receiving a first interaction associated with a content provider from a client device. The system can assign the account identifier to a second segment for the pre-conversion model, responsive to receiving a second interaction. The system can assign the account identifier to a third segment, responsive to receiving a third interaction. The third interaction can include a conversion event. The system can generate a post-conversion model based on the third segment and the pre-conversion model. The system can determine an intent index for the account identifier based on the post-conversion model. The system can store the account identifier into an interest cluster based on the intent index.

Dynamically varying remarketing based on evolving user interests

Systems and methods are provided for providing a navigation interface to access or otherwise use electronic content items. In one embodiment, an augmentation application identifies at least one entity referenced in a document. The entity can be referenced in at least two portions of the document by at least two different words or phrases. The augmentation application associates the at least one entity with at least one multimedia asset. The augmentation application generates a layout including at least some content of the document referencing the at least one entity and the at least one multimedia asset associated with the at least one entity. The augmentation application renders the layout for display.

Augmenting text with multimedia assets

Systems and methods of dynamically varying the intensity of providing content items in a remarketing campaign based on tracking client device interactions are provided. The system can assign an account identifier to a first segment for a pre-conversion model, responsive to receiving a first interaction associated with a content provider from a client device. The system can assign the account identifier to a second segment for the pre-conversion model, responsive to receiving a second interaction. The system can assign, the account identifier to a third segment, responsive to receiving a third interaction. The third interaction can include a conversion event. The system can generate a post-conversion model based on the third segment and the pre-conversion model. The system can determine an intent index for the account identifier based on the post-conversion model. The system can store the account identifier into an interest cluster based on the intent index.

In techniques for causal modeling and attribution, a causal modeling application implements a dynamical causal modeling framework. Input data is received as a representation of communications between users, such as social media interactions between social media users, and causal relationships between the users can be determined based in part on the input data that represents the communications. Influence variables, such as exogenous variables and/or endogenous variables, can also be determined that influence the causal relationships between the users. A causal relationships model is generated based on the influence variables and the causal relationships between the users, where the causal relationships model is representative of causality, influence, and attribution between the users.

Causal modeling and attribution

A system including two or more wearable devices includes at least one sensor in each device to detect signals related to the body of a wearer. User inputs, sensor outputs, or other information are used to determine an activity being performed or otherwise engaged in by the wearer and footsteps, heartbeats, discrete turns of the torso, or other events can be detected, from one or more of the sensor outputs, during a period of time that the user is engaged in the activity. A duration, amplitude, timing, mean value, or other characteristic of each such detected event can be determined, based on the sensor outputs, and used to generate a sample of clinically relevant information about the wearer. This information can then be used to determine the presence, stage, degree, progression, or other information about a disease state of the wearer or some other information about the wearer's body.

System and methods for integrating feedback from multiple wearable sensors

An apparatus, system and process for tracking and analyzing target person movements, captured while the target person is performing ordinary tasks outside of a medical context, for medical diagnosis and treatment review are described. The method may include constructing a model of a target person from three-dimensional (3D) image data of the target person performing an activity over a period of time. The method may also include tracking movement of the model of the target person in the 3D image data over the period of time, and detecting one or more motion features in the movement of the model of the target person that are relevant to diagnosis, treatment, care, or a combination thereof, of a potential chronic neurodegenerative or musculoskeletal medical condition. The method may also include computing a risk score associated with likelihood of the target person having the medical condition based on the detected motion features.

Disease and fall risk assessment using depth mapping systems

Systems and methods of rendering webpage interaction statistics data over graphical user interfaces is provided herein. A computing device can transmit a request for an interaction statistics identifying an information resource. The computing device can receive the interaction data set for the identified information resource. The computing device can calculate a similarity measure among the client devices based on the interaction data set using a clustering algorithm. The computing device can identity a subset of client devices based on the similarity measure. The computing device can calculate the interaction sequence metrics for the client devices for each selected content element. The computing device can select the content elements based on the calculated interaction sequence metrics. The computing device can generate a graph element including nodes representing each selected content element and paths representing the interaction sequence metrics. The computing device can display the graph element.

Rendering interaction statistics data for content elements of an information resource by identifying client device segments

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San Francisco, CA, United States of America

Emre Demiralp