Google Inc.

Ian Porteous

Hao Chen

Arthur Asuncion

Jonathan Michael Krafcik

Surbhi Maheshwari

Yong Sheng

Rong Ge

Ruoyun Huang

Jane Huang

Xintian Yang

Daniel Zink

Pei Yin

Li He

Systems, methods and computer-readable storage media utilized to train a machine-learning architecture. One method includes receiving, by one or more processing circuits, a data set. The method further includes determining, by the one or more processing circuits, a first portion of the data set associated with a plurality of entities. The method further includes training, by the one or more processing circuits and utilizing the first portion of the data set, an entity model. The method further includes determining, by the one or more processing circuits, a second portion of the data set associated with a first subset of entities and determining a second subset of entities. The method further includes freezing, by the one or more processing circuits, one or more parameters associated with the second subset of entities and training, utilizing the second portion of the data set, the entity model.

Personalized data model utilizing closed data

A method is disclosed for providing, for display to a content provider, a user interface comprising an option to view new audiences to be added to a plurality of users currently designated to receive content of the content provider, receiving a user selection of the option, and causing display of information identifying the new audiences, the information identifying the new audiences comprises, for each new audience, an audience identifier, an indication of an estimated number of user actions related to the content of the content provider, and an option to request that a corresponding audience be added to the plurality of users currently designated to receive the content of the content provider.

Method for identifying new audiences for content of a content provider

A computer-implemented method for determining an attribute for an online user of a candidate computing device is provided. The method implemented uses a host computing device. The method includes identifying a first set of model data including device data from a plurality of model computing devices including location data and access data, and a plurality of categories for an attribute of a population segment including an online user. Each category defines a segment of the attribute. The method further includes training a classification model by the host computing device with at least the first set of model data and the plurality of categories. The method also includes identifying device data associated with the candidate computing device. The method further includes applying the device data of the candidate computing device to the classification model to determine a category of the plurality of categories for the online user.

Determining an attribute of an online user using user device data

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Mercer Island, WA, United States of America

Ian Porteous