公开(公告)号：US20190180357A1

公开(公告)日：2019-06-13

申请号：US16280716

申请日：2019-02-20

申请人： Google LLC

发明人： Shilpa Arora ,  Colin McCulloch ,  Niyati Yagnik ,  Creighton Thomas ,  Manohar Prabhu ,  Timothy Lipus ,  Michael Eugene Aiello ,  Yi Zhang ,  Ajay Kumar Bangla ,  Bahman Rabii ,  Gaofeng Zhao ,  Yingweii Cui

IPC分类号： G06Q30/08 ,  G06N20/00 ,  G06F16/2457 ,  G06F16/951 ,  G06F16/958

CPC分类号： G06Q30/08 ,  G06F16/24578 ,  G06F16/951 ,  G06F16/958 ,  G06N20/00

摘要： 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.

公开(公告)号：US10817931B2

公开(公告)日：2020-10-27

申请号：US16280716

申请日：2019-02-20

申请人： Google LLC

发明人： Shilpa Arora ,  Colin McCulloch ,  Niyati Yagnik ,  Creighton Thomas ,  Manohar Prabhu ,  Timothy Lipus ,  Michael Eugene Aiello ,  Yi Zhang ,  Ajay Kumar Bangla ,  Bahman Rabii ,  Gaofeng Zhao ,  Yingwei Cui

IPC分类号： G06Q30/00 ,  G06Q30/08 ,  G06N20/00 ,  G06F16/951 ,  G06F16/958 ,  G06F16/2457

摘要： 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.

公开(公告)号：US20240354839A1

公开(公告)日：2024-10-24

申请号：US18762422

申请日：2024-07-02

申请人： GOOGLE LLC

发明人： Shilpa Arora ,  Colin McCulloch ,  Niyati Yagnik ,  Creighton Thomas ,  Manohar Prabhu ,  Timothy Lipus ,  Michael Eugene Aiello ,  Yi Zhang ,  Ajay Kumar Bangla ,  Bahman Rabii ,  Gaofeng Zhao ,  Yingwei Cui

IPC分类号： G06Q30/08 ,  G06F16/2457 ,  G06F16/951 ,  G06F16/958 ,  G06N20/00

CPC分类号： G06Q30/08 ,  G06F16/24578 ,  G06F16/951 ,  G06F16/958 ,  G06N20/00

摘要： 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.

公开(公告)号：US20210035207A1

公开(公告)日：2021-02-04

申请号：US17074132

申请日：2020-10-19

申请人： Google LLC

发明人： Shilpa Arora ,  Colin McCulloch ,  Niyati Yagnik ,  Creighton Thomas ,  Manohar Prabhu ,  Timothy Lipus ,  Michael Eugene Aiello ,  Yi Zhang ,  Ajay Kumar Bangla ,  Bahman Rabii ,  Gaofeng Zhao ,  Yingwei Cui

IPC分类号： G06Q30/08 ,  G06F16/2457 ,  G06N20/00 ,  G06F16/958 ,  G06F16/951

摘要： 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.

公开(公告)号：US12062085B2

公开(公告)日：2024-08-13

申请号：US17074132

申请日：2020-10-19

申请人： Google LLC

发明人： Shilpa Arora ,  Colin McCulloch ,  Niyati Yagnik ,  Creighton Thomas ,  Manohar Prabhu ,  Timothy Lipus ,  Michael Eugene Aiello ,  Yi Zhang ,  Ajay Kumar Bangla ,  Bahman Rabii ,  Gaofeng Zhao ,  Yingwei Cui

IPC分类号： G06Q30/00 ,  G06F16/2457 ,  G06F16/951 ,  G06F16/958 ,  G06N20/00 ,  G06Q30/08

CPC分类号： G06Q30/08 ,  G06F16/24578 ,  G06F16/951 ,  G06F16/958 ,  G06N20/00

摘要： 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.