POST-INSTALL APPLICATION INTERACTION
    1.
    发明申请

    公开(公告)号:US20180018155A1

    公开(公告)日:2018-01-18

    申请号:US15642994

    申请日:2017-07-06

    Applicant: Google Inc.

    Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. Data specifying a post-install activity is received from a provider of an application. An opportunity is identified to provide third-party content to a user. A likelihood is determined that the user will perform the specified post-install activity based on one or more attributes of the user and attributes of users that have previously performed the specified post-install activity in the application. A selection value is adjusted for third-party content that identifies the application based on the determined likelihood, wherein the selection value increases as the likelihood increases. The third-party content identifying the application is selected based on the adjusted selection value. The third-party content identifying the application is distributed to a client device of the user.

    RANKING CONTENT ITEMS USING PREDICTED PERFORMANCE
    2.
    发明申请
    RANKING CONTENT ITEMS USING PREDICTED PERFORMANCE 有权
    使用预测性能排列内容项

    公开(公告)号:US20150046467A1

    公开(公告)日:2015-02-12

    申请号:US13963242

    申请日:2013-08-09

    Applicant: GOOGLE INC.

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for ranking content items. In one aspect, a method includes identifying, for a content item, a bid value specifying an amount a content item provider is willing to pay for user interaction with the content item. A predicted performance measure is identified for the content item. The predicted performance measure is adjusted based on a weighting factor for the content item. The weighting factor for the content item is indicative of confidence that the predicted performance measure will match an actual performance measure for the content item and can be different than a weighting factor for another content item identified for inclusion in a ranking with the content item. A rank score is determined for the content item using the bid value and adjusted predicted performance measure. The content item is provided based on the rank score.

    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,用于对内容项进行排名。 一方面,一种方法包括为内容项目识别指定内容项目提供者愿意为与用户与内容项目进行交互而付费的金额的出价值。 为内容项目确定预测的性能度量。 基于内容项的加权因子来调整预测的性能度量。 内容项的加权因子表示预测的性能测量值与内容项目的实际性能测量值相一致的可信度,并且可以不同于被标识为包含在与内容项目的排名中的另一内容项目的加权因子。 使用出价值和​​经调整的预测绩效度量来确定内容项的排名得分。 内容项目是根据排名得分提供的。

    Ranking content items using predicted performance
    3.
    发明授权
    Ranking content items using predicted performance 有权
    使用预测的表现排列内容项

    公开(公告)号:US09256688B2

    公开(公告)日:2016-02-09

    申请号:US13963242

    申请日:2013-08-09

    Applicant: Google Inc.

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for ranking content items. In one aspect, a method includes identifying, for a content item, a bid value specifying an amount a content item provider is willing to pay for user interaction with the content item. A predicted performance measure is identified for the content item. The predicted performance measure is adjusted based on a weighting factor for the content item. The weighting factor for the content item is indicative of confidence that the predicted performance measure will match an actual performance measure for the content item and can be different than a weighting factor for another content item identified for inclusion in a ranking with the content item. A rank score is determined for the content item using the bid value and adjusted predicted performance measure. The content item is provided based on the rank score.

    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,用于对内容项进行排名。 一方面,一种方法包括为内容项目识别指定内容项目提供者愿意为与用户与内容项目进行交互而付费的金额的出价值。 为内容项目确定预测的性能度量。 基于内容项的加权因子来调整预测的性能度量。 内容项的加权因子表示预测的性能测量值与内容项目的实际性能测量值相一致的可信度,并且可以不同于被标识为包含在与内容项目的排名中的另一内容项目的加权因子。 使用出价值和​​经调整的预测绩效度量来确定内容项的排名得分。 内容项目是根据排名得分提供的。

    Content Search Engine
    4.
    发明申请

    公开(公告)号:US20180246966A1

    公开(公告)日:2018-08-30

    申请号:US15444279

    申请日:2017-02-27

    Applicant: Google Inc.

    Abstract: Methods, systems, apparatus, including computer programs encoded on a computer storage medium, for determining whether to execute a query based on a predicted computerized rendering period The method may include actions of receiving a query, determining a set of one or more features based on the query, generating a query vector, providing the query vector to a machine learning model, receiving first data, based on the output of the machine learning model, that is indicative of whether the computerized rendering period associated with the query is likely to satisfy the predetermined threshold, determining based on the first data that the computerized rendering period available for displaying a set of one or more candidate content items that are responsive to the query is not likely to satisfy the predetermined threshold, and determining to not use a search engine to execute the received query.

    OPTIMIZED DIGITAL COMPONENTS
    5.
    发明申请

    公开(公告)号:US20180060445A1

    公开(公告)日:2018-03-01

    申请号:US15250247

    申请日:2016-08-29

    Applicant: Google Inc.

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing digital components. In one aspect, the system includes a digital component server that identifies a digital component to be presented in an electronic document. The system also includes a digital component distribution server that interacts with the digital component server to determine that the electronic document includes a particular item that identifies a same entity as the digital component that was identified to be presented in the electronic document. The system combines at least a portion of the digital component with content from the particular item to form an optimized digital component. The optimized digital component is integrated into the electronic document rather than presenting either of the particular item or the digital component individually.

    DYNAMIC REALLOCATION OF CONTENT ITEM BLOCKS
    6.
    发明申请
    DYNAMIC REALLOCATION OF CONTENT ITEM BLOCKS 审中-公开
    内容项目块的动态重组

    公开(公告)号:US20140310093A1

    公开(公告)日:2014-10-16

    申请号:US13861062

    申请日:2013-04-11

    Applicant: Google Inc.

    CPC classification number: G06Q30/0246

    Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. The method includes receiving a request for content for a block and determining a variable number of content items to be responsive to the request, including determining an efficiency for each of a number of permutations of allocations of eligible content items. The efficiency is a summation of individual values associated with content sponsors proposed to be included in the block for a given permutation. A price is established to be charged to each content sponsor associated with a permutation having a highest efficiency. The establishing includes, for each position in a determined highest efficiency permutation: determining a total increase of efficiency associated with all other content sponsors when removing a content sponsor associated with a given position. The eligible content items associated with the determined permutation are provided.

    Abstract translation: 方法,系统和装置包括在计算机可读存储介质上编码的计算机程序,包括用于提供内容的方法。 该方法包括接收对块的内容的请求,并确定可响应于该请求的可变数目的内容项,包括确定符合条件的内容项的分配排列中的每一个的效率。 效率是与建议包含在给定排列的块中的内容赞助者相关联的个体值的总和。 建立一个价格,以收取与具有最高效率的排列相关联的每个内容赞助商。 确定包括:以确定的最高效率排列中的每个位置:当移除与给定位置相关联的内容赞助商时,确定与所有其他内容赞助者相关联的效率的总增加。 提供与确定的排列相关联的符合条件的内容项。

    OPTIMIZATION OF A MULTI-CHANNEL SYSTEM USING A FEEDBACK LOOP

    公开(公告)号:US20180103093A1

    公开(公告)日:2018-04-12

    申请号:US15290940

    申请日:2016-10-11

    Applicant: Google Inc.

    Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a system that controls content distribution using a feedback loop. Content is distributed over multiple different online channels using a same initial maximum selection value for distribution over each different online channel. An observed distribution amount required for distribution of the content over the multiple different online channels is received through a feedback loop and for multiple different distributions of the content. Based on the observed distribution amount received through the feedback loop, a realized distribution amount is determined for the multiple different distributions across the multiple different online channels. The maximum selection value is adjusted based on a difference between the realized distribution amount and a reference distribution amount specified by a provider of the content. The content is distributed over the multiple different online channels using the adjusted maximum selection value.

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