Google Inc.

Michael Eugene Aiello

Yi Zhang

Bahman Rabii

Garth Shoemaker

Gaofeng Zhao

Niyati Yagnik

Manohar Karkal Prabhu

Timothy S Lipus

Creighton Thomas

Ajay Kumar Bangla

Shilpa Arora

Yingwei Cui

Colin Craig McCulloch

Colin McCulloch

Andrew Swerdlow

Dan Fredinburg

Yingweii Cui

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

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 for guided user actions are described, including gathering information associated with a detected first action; applying a predictive model to suggest a second action based on an application of machine learning to the first action and the information; calculating a first confidence level associated with the application of the machine learning to the first action and the information; for the first confidence level exceeding a threshold, providing the second action, and performing the second action or a third action based on user input.

Systems and methods for guided user actions on a computing device

Systems and methods for selecting third party content based on feedback

Systems and methods for guided user actions are described, including detecting a first action performed by a user; gathering information associated with the first action; retrieving a predictive model based on the information; determining an applicability level of the predictive model to the first action, the predictive model suggests a second action; providing the second action in a user interface when the applicability level meets a threshold level; and receiving input from the user selecting the second action or a third action.

Systems and methods for guided user actions

Identity protection and management for electronic communication is described, including receiving a request to provide data from a first address to a second address, the request including an attribute associated with the second address, determining a risk value of the second address based on the attribute associated with the second address and a risk score, based on the risk value of the second address generating a third address that is associated with the first address, and providing the data and the third address to the second address without providing the first address to the second address.

Identity protection and management for electronic communication

User settings management using external sources is described, including providing a user interface for a user to configure one or more settings that affect functions of an application; the user interface allows the one or more settings to be manipulated by the user, and allows the one or more settings to be configured based on setting data from a third-party entity; receiving input from the user to configure at least a portion of the one or more settings based on the setting data from the entity; identifying that the setting data from the entity includes the at least the portion of the one or more settings; and configuring the at least the portion of the one or more settings based on the setting data from the entity.

User settings management using external sources

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Mountain View, CA, United States of America

Michael Eugene Aiello