公开(公告)号：US09501497B1

公开(公告)日：2016-11-22

申请号：US14457793

申请日：2014-08-12

Applicant: Google Inc.

Inventor： Niyati Yagnik

IPC: G06F17/30 ,  G06F7/00

CPC classification number: G06F17/30241 ,  G06F17/3087

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing location queries. In one aspect, a method includes obtaining a location search profile for a user. The location search profile specifies, for each geographic location, a set of references to location resources that were previously requested through user interaction, by the user, with previous search results that were provided in response to a previous location query. A current location query is received from a user device that is associated with the user. In response to receiving the current location query a reference to at least one of the location resources from the set of references and search results responsive to the current location query are selected. In turn, data that cause presentation of the selected reference and the search results are provided.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于处理位置查询。 一方面，一种方法包括获得用户的位置搜索简档。 位置搜索简档为每个地理位置指定一组对先前通过用户交互通过用户交互请求的对位置资源的引用，以及响应于先前位置查询提供的先前搜索结果。 从与用户相关联的用户设备接收当前位置查询。 响应于接收到当前位置查询，选择响应于当前位置查询的来自参考集合和搜索结果中的至少一个位置资源的引用。 反过来，提供了导致所选参考的呈现和搜索结果的数据。

公开(公告)号：US20170061528A1

公开(公告)日：2017-03-02

申请号：US14836537

申请日：2015-08-26

Applicant: Google Inc.

Inventor： 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: G06Q30/08 ,  G06N99/00 ,  G06F17/30

CPC classification number: G06Q30/08 ,  G06F17/3053 ,  G06F17/30864 ,  G06F17/3089 ,  G06N99/005

Abstract: 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.

Abstract translation: 本公开内容基于反馈来选择第三方内容。 选择器识别包括响应于请求的第一和第二内容项（或更多）的多个内容项。 机器学习引擎确定第一内容项目的第一特征，第二内容项目的第二特征以及网页的第三特征或与请求相关联的设备。 机器学习引擎响应于第一特征和第三特征，基于通过从电子反馈接口形成​​的元数据信道从设备接收的历史信号生成的机器学习模型来确定第一内容项的第一分数。 机器学习引擎响应于第二特征和第三特征确定第二内容项目的第二分数。 投标模块基于第一和第二分数来确定第一内容项目的价格。

公开(公告)号：US10223742B2

公开(公告)日：2019-03-05

申请号：US14836537

申请日：2015-08-26

Applicant: Google Inc.

Inventor： 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: G06Q30/00 ,  G06F17/30 ,  G06Q30/08 ,  G06N99/00

Abstract: 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.