公开(公告)号：US11423441B2

公开(公告)日：2022-08-23

申请号：US16698548

申请日：2019-11-27

申请人： Google LLC

发明人： Michael Kleber ,  Gang Wang ,  Daniel Ramage ,  Charlie Harrison ,  Josh Karlin ,  Moti Yung

IPC分类号： G06F7/02 ,  G06F16/00 ,  G06Q30/02 ,  G06F16/951 ,  H04L9/32 ,  H04L9/08

摘要： The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.

公开(公告)号：US20230222542A9

公开(公告)日：2023-07-13

申请号：US17892699

申请日：2022-08-22

申请人： Google LLC

发明人： Michael Kleber ,  Gang Wang ,  Daniel Ramage ,  Charlie Harrison ,  Josh Karlin ,  Moti Yung

IPC分类号： G06Q30/02 ,  G06F16/951 ,  H04L9/32 ,  H04L9/08

CPC分类号： G06Q30/0255 ,  G06F16/951 ,  H04L9/32 ,  H04L9/085 ,  H04L2209/42

摘要： The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.

公开(公告)号：US11954705B2

公开(公告)日：2024-04-09

申请号：US17892699

申请日：2022-08-22

申请人： Google LLC

发明人： Michael Kleber ,  Gang Wang ,  Daniel Ramage ,  Charlie Harrison ,  Josh Karlin ,  Moti Yung

IPC分类号： G06F7/02 ,  G06F16/00 ,  G06F16/951 ,  G06Q30/0251 ,  H04L9/08 ,  H04L9/32

CPC分类号： G06Q30/0255 ,  G06F16/951 ,  H04L9/085 ,  H04L9/32 ,  H04L2209/42

摘要： The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.

公开(公告)号：US20240211996A1

公开(公告)日：2024-06-27

申请号：US18588659

申请日：2024-02-27

申请人： Google LLC

发明人： Michael Kleber ,  Gang Wang ,  Daniel Ramage ,  Charlie Harrison ,  Josh Karlin ,  Moti Yung

IPC分类号： G06Q30/0251 ,  G06F16/951 ,  H04L9/08 ,  H04L9/32

CPC分类号： G06Q30/0255 ,  G06F16/951 ,  H04L9/085 ,  H04L9/32 ,  H04L2209/42

摘要： The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.

公开(公告)号：US20210042787A1

公开(公告)日：2021-02-11

申请号：US16698548

申请日：2019-11-27

申请人： Google LLC

发明人： Michael Kleber ,  Gang Wang ,  Daniel Ramage ,  Charlie Harrison ,  Josh Karlin ,  Moti Yung

IPC分类号： G06Q30/02 ,  H04L9/32 ,  H04L9/08 ,  G06F16/951

摘要： The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.