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    Determining computing device characteristics from computer network activity
    4.
    发明授权
    Determining computing device characteristics from computer network activity 有权
    从计算机网络活动确定计算设备特性

    公开(公告)号：US09372914B1

    公开(公告)日：2016-06-21

    申请号：US14154904

    申请日：2014-01-14

    Applicant: Google Inc.

    Inventor： Arthur Asuncion ,  Johannes Christian Schuler ,  Gregory Sean Corrado ,  Kai Chen ,  Yong Sheng

    IPC: G06F17/30

    CPC classification number: G06F17/30598 ,  G06F17/30283

    Abstract: Systems and methods of determining computing device characteristics from computer network activity are provided. A data processing system can obtain data identifying a global cluster that indicates an interest category and can create a sub-cluster of the global cluster based on a characteristic common to content access computing devices. A weight indicating a correlation between the characteristic common to content access computing devices and the interest category can be assigned to the sub-cluster. Responsive to a communication between a first content access computing device and a content publisher computing device, the data processing system can identify a characteristic. The data processing system can associate the first content access computing device with the sub-cluster based on the characteristic of the first content access computing device and the characteristic common to the content access computing devices, and based on the weight can determine a status of the first content access computing device.

    Abstract translation: 提供了从计算机网络活动确定计算设备特性的系统和方法。 数据处理系统可以获得标识指示感兴趣类别的全局集群的数据，并且可以基于内容访问计算设备公用的特征来创建全局集群的子集群。 指示与内容访问计算设备共同的特征与兴趣类别之间的相关性的权重可被分配给子群集。 响应于第一内容访问计算设备和内容发布者计算设备之间的通信，数据处理系统可以识别特性。 数据处理系统可以基于第一内容访问计算设备的特性和内容访问计算设备共同的特征将第一内容访问计算设备与子群集相关联，并且基于权重可以确定 第一内容访问计算设备。

    Classifying resources using a deep network
    6.
    发明授权
    Classifying resources using a deep network 有权
    使用深层网络分类资源

    公开(公告)号：US09147154B2

    公开(公告)日：2015-09-29

    申请号：US13802462

    申请日：2013-03-13

    Applicant: Google Inc.

    Inventor： Qingzhou Wang ,  Yu Liang ,  Ke Yang ,  Kai Chen

    IPC: G06F15/18 ,  G06E1/00 ,  G06E3/00 ,  G06G7/00 ,  G06N3/02 ,  G06F17/30 ,  G06N3/04 ,  G06N3/08 ,  G06K9/62

    CPC classification number: G06N3/04 ,  G06F3/0484 ,  G06F17/3053 ,  G06F17/30707 ,  G06F17/30864 ,  G06K9/627 ,  G06N3/02 ,  G06N3/0427 ,  G06N3/084 ,  G06N7/005

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring concept terms using a deep network. One of the methods includes receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource; processing each of the features using a respective embedding function to generate one or more numeric values; processing the numeric values using one or more neural network layers to generate an alternative representation of the features, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and processing the alternative representation of the input using a classifier to generate a respective category score for each category in a pre-determined set of categories, wherein each of the respective category scores measure a predicted likelihood that the resource belongs to the corresponding category.

    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，用于使用深层网络评分概念术语。 所述方法之一包括接收包括资源的多个特征的输入，其中每个特征是所述资源的相应属性的值; 使用相应的嵌入功能处理每个特征以生成一个或多个数值; 使用一个或多个神经网络层处理所述数值以产生所述特征的替代表示，其中处理所述浮点值包括对所述浮点值应用一个或多个非线性变换; 以及使用分类器处理所述输入的替代表示以针对预定类别集合中的每个类别生成相应的类别分数，其中各个类别分数中的每一个测量所述资源属于相应类别的预测可能性。

    Scoring Concept Terms Using a Deep Network
    7.
    发明申请
    Scoring Concept Terms Using a Deep Network 有权
    使用深度网络评估概念术语

    公开(公告)号：US20140279773A1

    公开(公告)日：2014-09-18

    申请号：US13802184

    申请日：2013-03-13

    Applicant: Google Inc.

    Inventor： Kai Chen ,  Xiaodan Song ,  Gregory S. Corrado ,  Kun Zhang ,  Jeffrey A. Dean ,  Bahman Rabii

    IPC: G06N3/08

    CPC classification number: G06N3/084 ,  G06F17/30707 ,  G06F17/30864 ,  G06N3/04 ,  G06N3/0427 ,  G06N3/08 ,  G06Q30/02

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring concept terms using a deep network. One of the methods includes receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource; processing each of the features using a respective embedding function to generate one or more numeric values; processing the numeric values to generate an alternative representation of the features of the resource, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and processing the alternative representation of the input to generate a respective relevance score for each concept term in a pre-determined set of concept terms, wherein each of the respective relevance scores measures a predicted relevance of the corresponding concept term to the resource.

    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，用于使用深层网络评分概念术语。 所述方法之一包括接收包括资源的多个特征的输入，其中每个特征是所述资源的相应属性的值; 使用相应的嵌入功能处理每个特征以生成一个或多个数值; 处理所述数值以产生所述资源的特征的替代表示，其中处理所述浮点值包括将一个或多个非线性变换应用于所述浮点值; 以及处理所述输入的替代表示，以在预定概念术语集中为每个概念项产生相应的相关性得分，其中各个相关性分数中的每一个测量相应概念项与资源的预测相关性。

    Using embedding functions with a deep network
    9.
    发明授权
    Using embedding functions with a deep network 有权

    公开(公告)号：US09514404B1

    公开(公告)日：2016-12-06

    申请号：US14860497

    申请日：2015-09-21

    Applicant: Google Inc.

    Inventor： Gregory S. Corrado ,  Kai Chen ,  Jeffrey A. Dean ,  Gary R. Holt ,  Julian P. Grady ,  Sharat Chikkerur ,  David W. Sculley, II

    IPC: G06F15/18 ,  G06E1/00 ,  G06E3/00 ,  G06G7/00 ,  G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/084

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.

    Distribution shared content based on a probability
    10.
    发明授权
    Distribution shared content based on a probability 有权
    根据概率分发共享内容

    公开(公告)号：US09269048B1

    公开(公告)日：2016-02-23

    申请号：US13804744

    申请日：2013-03-14

    Applicant: Google Inc.

    Inventor： Kai Chen

    IPC: G06N5/04 ,  G06N99/00

    CPC classification number: G06N5/048 ,  G06F17/30017 ,  G06N99/005 ,  G06Q30/08

    Abstract: A system and method for distributing shared content based on a probability is provided. The system includes a shared content request unit to receive a shared content request; a bid retrieval unit to retrieve a plurality of shared content items based on the share content request, and to retrieve a plurality of bids corresponding to the plurality of shared content items, respectively; a probability retrieval unit to retrieve a plurality of likelihood values for each of the plurality of bids, respectively; a bid adjustment unit to adjust the plurality of bids based on the corresponding plurality of likelihood values; and a shared content selection unit to select shared content based on the adjusted plurality of bids.

    Abstract translation: 提供了一种基于概率分发共享内容的系统和方法。 该系统包括：共享内容请求单元，用于接收共享内容请求; 投标检索单元，基于共享内容请求检索多个共享内容项，并分别检索对应于所述多个共享内容项的多个投标; 概率检索单元，分别检索所述多个投标中的每一个的多个似然值; 投标调整单元，其基于相应的多个似然值来调整所述多个投标; 以及共享内容选择单元，用于基于所调整的多个出价来选择共享内容。

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