Google Inc.

Yingwei Cui

Bahman Rabii

Gaofeng Zhao

Jeffrey Adgate Dean

Deepak Jindal

Narayanan Shivakumar

Xiaodan Song

Yi Zhang

Kun Zhang

Georges R Harik

Wei Chai

Hui Tan

Darrell Anderson

Alexander Paul Carobus

Paul Thomas Buchheit

Jian Chen

Tao Huang

Fong Shen

Andrew M Dai

Niyati Yagnik

Manohar Karkal Prabhu

Timothy S Lipus

Michael Eugene Aiello

Creighton Thomas

Eyal Oren

Ajay Kumar Bangla

Patrik Sundberg

Shilpa Arora

Kai Chen

Quoc V Le

James Wilson

Gregory Sean Corrado

Rama Ranganath

Nissan Hajaj

Alvin Rishi Rajkomar

Peter Junteng Liu

Willa Angel Chen

Benjamin Irvine

Kevan Newton

James Wexler

Kenneth W Bronson

Hector Yee

Jason Liebman

William Martin Halpin, Jr

Michaela Hardt

Xiaomi Sun

Xiaobing Liu

Gerardo Flores

Alexander Mossin

Gavin Duggan

Jacob Marcus

Kurt Litsch

Tobias Maurer

Colin McCulloch

Dengyong Zhou

Colin Craig McCulloch

Nan Du

Paul Buchhcit

Yuan Xue

Zhen Xu

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for distributing content are disclosed. In one aspect, a method includes storing, in a data structure, data specifying a future live event. An opportunity to provide the specified content to a user at a user device is identified. It is determined that (i) a time of the opportunity is between a start time and an end time for the live event, (ii) that a user device is located in a same geographic region as the live event based on geographical data provided by the user device, and (iii) the user is interested in the live event based on attributes of the user matching attributes of other users that were identified as interested in the live event (e.g., based on evaluation of online search data). The content is provided for display at the user device.

Event-based content distribution

A method and system for adjusting ads auction using predicted user responses to an in-ad survey is provided. The method includes (1) providing a content item associated with an actionable object, which when selected, causes a computing device to present a plurality of interactive elements each corresponding to a different one of a plurality of reasons for restricting the content item; (2) receiving, from the computing device, data indicating a particular reason, of the plurality of reasons, for restricting the content item, and the particular reason corresponding to a particular interactive element, of the plurality of interactive elements, that was selected by the user; and (3) updating, using the received data, a content selection model for selecting content items, wherein the content selection model is associated with the user.

Adjust ads auction using predicted user response to an in-ad survey

A deep state space generative model is augmented with intervention prediction. The state space model provides a principled way to capture the interactions among observations, interventions, critical event occurrences, true states, and associated uncertainty. The state space model can include a discrete-time hazard rate model that provides flexible fitting of general survival time distributions. The state space model can output a joint prediction of event risk, observation and intervention trajectories based on patterns in temporal progressions, and correlations between past measurements and interventions.

Machine-learned state space model for joint forecasting

The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Systems and methods of evaluating information in a computer network environment are provided. A data processing system can obtain or receive a content placement criterion, such as a keyword, associated with a content item and can determine a quality metric of the content placement criterion. The data processing system can identify a candidate content placement criterion and expand placement criteria associated with the content item to include the content placement criterion and the candidate content placement criterion based at least in part on an evaluation of the quality metric of the content placement criterion. The data processing system can expand placement criteria based in part on a throttling parameter. The data processing system can identify a correlation between a document and the placement criteria to identify appropriate content items for the document.

Expansion of high performing placement criteria

A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.

System and method for predicting and summarizing medical events from electronic health records

Systems and methods of providing information via a computer network are provided. A data processing system can identify a cluster that includes a plurality of online content items having a semantic or user similarity. The data processing system determines a plurality of cluster placement criteria of the cluster, and receives content configured for display with a web page. The content can be associated with the cluster based on the semantic or user similarity. A cluster placement criterion of the plurality of cluster placement criteria can be selected based on a quality metric of the selected cluster placement criterion, and the selected cluster placement criterion can be provided as a supplemental criterion used to select the content for display with the web page.

Content placement criteria expansion

Method and systems for displaying content items on an information resource include identifying a supplemental content item to append to a DOM tree of the information resource and identifying a first content item displayed in a first container of the information resource. The methods also includes monitoring a position of a predetermined portion of the first content item relative to a viewport of the application and determining, responsive to monitoring the position of the predetermined portion of the first content item, that the first content item is visible within the viewport of the application. The method also includes appending the supplemental content item in a second container at a first position between the first container and an end of the information resource and displaying the supplemental content item within the viewport of the application responsive to detecting a scroll action towards the end of the information resource.

Systems and methods for dynamically appending supplemental content to an information resource responsive to scroll activity

Systems and methods for selecting third party content based on feedback

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Los Altos, CA, United States of America

Yingwei Cui