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公开(公告)号：US20170308787A1
公开(公告)日：2017-10-26
申请号：US15588535
申请日：2017-05-05
Applicant: Google Inc.
Inventor： Gregory Sean Corrado , Ilya Sutskever , Jeffrey Adgate Dean
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting likelihoods of conditions being satisfied using recurrent neural networks. One of the systems is configured to process a temporal sequence comprising a respective input at each of a plurality of time steps and comprises: one or more recurrent neural network layers; one or more logistic regression nodes, wherein each of the logistic regression nodes corresponds to a respective condition from a predetermined set of conditions, and wherein each of the logistic regression nodes is configured to, for each of the plurality of time steps: receive the network internal state for the time step; and process the network internal state for the time step in accordance with current values of a set of parameters of the logistic regression node to generate a future condition score for the corresponding condition for the time step.
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公开(公告)号：US20170206556A1
公开(公告)日：2017-07-20
申请号：US15478767
申请日：2017-04-04
Applicant: Google Inc.
Inventor： Jeffrey Adgate Dean , Georges Harik , Paul Buchheit
CPC classification number: G06Q30/0256 , G06F16/24565 , G06Q30/02 , G06Q30/0254 , G06Q30/0261 , G06Q30/0269 , G06Q30/0277
Abstract: Targeting information (also referred to as ad “serving constraints”) or candidate targeting information for an advertisement is identified. Targeting information may be identified by extracting topics or concepts from, and/or generating topics or concepts based on, ad information, such as information from a Web page to which an ad is linked (or some other Web page of interest to the ad or advertiser). The topics or concepts may be relevant queries associated with the Web page of interest, clusters, etc.
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公开(公告)号：US09652695B2
公开(公告)日：2017-05-16
申请号：US14135816
申请日：2013-12-20
Applicant: Google Inc.
Inventor： Samy Bengio , Jeffrey Adgate Dean , Quoc Le , Jonathon Shlens , Yoram Singer
CPC classification number: G06K9/723 , G06K9/00677 , G06K9/6212 , G06K9/72 , G06K2009/6213 , G06N7/005 , G06N99/005
Abstract: Systems and techniques for labeling objects within an image. The objects may be labeled by selecting an option from a plurality of options such that each option is a potential label for the object. An option may have an option score associated with. Additionally, a relation score may be calculated for a first option and a second option corresponding to a second object in an image. The relation score may be based on a frequency, probability, or observance corresponding to the co-occurrence of text associated with the first option and the second option in a text corpus such as the World Wide Web. An option may be selected as a label for an object based on a global score calculated based at least on an option score and relation score associated with the option.
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24.发明申请公开(公告)号：US20170032242A1
公开(公告)日：2017-02-02
申请号：US15150091
申请日：2016-05-09
Applicant: Google Inc.
Inventor： Gregory Sean Corrado , Ilya Sutskever , Jeffrey Adgate Dean
CPC classification number: G06N3/0472 , G06F19/00 , G06N3/02 , G06N3/0427 , G06N3/0445 , G06N3/063 , G16H50/20
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting likelihoods of conditions being satisfied using recurrent neural networks. One of the systems is configured to process a temporal sequence comprising a respective input at each of a plurality of time steps and comprises: one or more recurrent neural network layers; one or more logistic regression nodes, wherein each of the logistic regression nodes corresponds to a respective condition from a predetermined set of conditions, and wherein each of the logistic regression nodes is configured to, for each of the plurality of time steps: receive the network internal state for the time step; and process the network internal state for the time step in accordance with current values of a set of parameters of the logistic regression node to generate a future condition score for the corresponding condition for the time step.
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，用于预测利用循环神经网络满足条件的可能性。 系统中的一个被配置为在多个时间步长中的每一个处理包括相应输入的时间序列，并且包括：一个或多个循环神经网络层; 一个或多个逻辑回归节点，其中每个逻辑回归节点对应于来自预定条件集合的相应条件，并且其中每个逻辑回归节点被配置为针对多个时间步骤中的每一个：接收网络 内部状态为时间步; 并根据逻辑回归节点的一组参数的当前值处理时间步长的网络内部状态，以生成时间步长相应条件的未来条件分数。
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公开(公告)号：US20170032241A1
公开(公告)日：2017-02-02
申请号：US14810368
申请日：2015-07-27
Applicant: Google Inc.
Inventor： Gregory Sean Corrado , Jeffrey Adgate Dean , Ilya Sutskever
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using recurrent neural networks to analyze health events. One of the methods includes obtaining a first temporal sequence of health events, wherein the first temporal sequence comprises respective health-related data associated with a particular patient at each of a plurality of time steps; processing the first temporal sequence of health events using a recurrent neural network to generate a neural network output for the first temporal sequence; and generating, from the neural network output for the first temporal sequence, health analysis data that characterizes future health events that may occur after a last time step in the temporal sequence.
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，用于使用循环神经网络来分析健康事件。 所述方法之一包括获得健康事件的第一时间序列，其中所述第一时间序列在多个时间步骤中的每一个步骤包括与特定患者相关联的各个健康相关数据; 使用循环神经网络处理健康事件的第一时间序列以生成用于第一时间序列的神经网络输出; 以及从用于第一时间序列的神经网络输出生成表征可能在时间序列中的最后时间步长之后发生的未来健康事件的健康分析数据。
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Patent Agency Ranking
26.发明授权Predicting likelihoods of conditions being satisfied using recurrent neural networks 有权使用循环神经网络预测条件满足的可能性公开(公告)号：US09336482B1
公开(公告)日：2016-05-10
申请号：US14810381
申请日：2015-07-27
Applicant: Google Inc.
Inventor： Gregory Sean Corrado , Ilya Sutskever , Jeffrey Adgate Dean
CPC classification number: G06N3/0472 , G06F19/00 , G06N3/02 , G06N3/0427 , G06N3/0445 , G06N3/063 , G16H50/20
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting likelihoods of conditions being satisfied using recurrent neural networks. One of the systems is configured to process a temporal sequence comprising a respective input at each of a plurality of time steps and comprises: one or more recurrent neural network layers; one or more logistic regression nodes, wherein each of the logistic regression nodes corresponds to a respective condition from a predetermined set of conditions, and wherein each of the logistic regression nodes is configured to, for each of the plurality of time steps: receive the network internal state for the time step; and process the network internal state for the time step in accordance with current values of a set of parameters of the logistic regression node to generate a future condition score for the corresponding condition for the time step.
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，用于预测利用循环神经网络满足条件的可能性。 系统中的一个被配置为在多个时间步长中的每一个处理包括相应输入的时间序列，并且包括：一个或多个循环神经网络层; 一个或多个逻辑回归节点，其中每个逻辑回归节点对应于来自预定条件集合的相应条件，并且其中每个逻辑回归节点被配置为针对多个时间步骤中的每一个：接收网络 内部状态为时间步; 并根据逻辑回归节点的一组参数的当前值处理时间步长的网络内部状态，以生成时间步长相应条件的未来条件分数。
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公开(公告)号：US20150178383A1
公开(公告)日：2015-06-25
申请号：US14576907
申请日：2014-12-19
Applicant: Google Inc.
Inventor： Gregory Sean Corrado , Tomas Mikolov , Samy Bengio , Yoram Singer , Jonathon Shlens , Andrea L. Frome , Jeffrey Adgate Dean , Mohammad Norouzi
IPC: G06F17/30
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. One of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.
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，用于对数据对象进行分类。 其中一种方法包括获得将术语词汇中的每个术语与该术语的相应高维表示相关联的数据; 获取数据对象的分类数据，其中分类数据包括多个类别中的每一个的相应分数，并且其中每个类别与相应的分类标签相关联; 从与类别和相应分数相关联的类别标签的高维表示中计算数据对象的聚合高维表示; 识别具有最接近聚合高维表示的高维表示的术语词汇表中的第一项; 并选择第一项作为数据对象的类别标签。
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公开(公告)号：US20150046525A1
公开(公告)日：2015-02-12
申请号：US14525072
申请日：2014-10-27
Applicant: Google Inc.
Inventor： Jeffrey Adgate Dean
CPC classification number: H04L67/10 , G06F9/4881 , G06F9/5027 , G06F2209/5013 , H04L67/42
Abstract: A method of processing a request, performed by a respective server, is provided in which a request is received from a client. After receiving the request, a determination is made as to whether at least a first predefined number of other servers have a task-processing status for the request indicating that the other servers have undertaken performance of a task-processing operation for the request. When less than the first number of other servers in the set of other servers have the task-processing status for the request, a processing-status message is sent to one or more of the servers in the set of other servers indicating that the respective server is performing the task-processing operation. Upon completion of the task-processing, a result of the processing is sent to the client contingent upon a status of the other servers in the set of other servers.
Abstract translation: 提供一种处理由相应服务器执行的请求的方法，其中从客户端接收请求。 在接收到请求之后，确定至少第一预定数量的其他服务器是否具有用于请求的任务处理状态，指示其他服务器已经执行了对该请求的任务处理操作的执行。 当小于其他服务器集合中的第一数量的其他服务器具有用于请求的任务处理状态时，处理状态消息被发送到该组其他服务器中的一个或多个服务器，指示相应的服务器 正在执行任务处理操作。 完成任务处理后，根据其他服务器组中的其他服务器的状态，将处理结果发送给客户端。
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公开(公告)号：US20140025899A1
公开(公告)日：2014-01-23
申请号：US13910059
申请日：2013-06-04
Applicant: Google Inc.
Inventor： Jeffrey Adgate Dean , Sanjay Ghemawat , Andrew Fikes
IPC: G06F12/12
CPC classification number: G06F12/121 , G06F17/30345 , G06F17/30368
Abstract: A method of storing data is disclosed. The method is performed on a data storage server having one or more processors and memory storing one or more programs for execution by the one or more processors. The data storage server receives a first and second data request, the requests including a first and second range of one or more keys and an associated first and second value respectively. The data storage server identifies one or more overlap points associated with the first range and the second range. For each of the overlap points, the data storage server then creates data items including ranges of keys, the ranges of each data item including one or more keys that are either: (a) the keys between a terminal key of the first or second range and the overlap point, or (b) the keys between two adjacent overlap points.
Abstract translation: 公开了存储数据的方法。 该方法在具有一个或多个处理器的数据存储服务器和存储一个或多个程序的存储器中执行，以供一个或多个处理器执行。 数据存储服务器接收第一和第二数据请求，所述请求分别包括一个或多个密钥的第一和第二范围以及关联的第一和第二值。 数据存储服务器识别与第一范围和第二范围相关联的一个或多个重叠点。 对于每个重叠点，数据存储服务器然后创建包括密钥范围的数据项，每个数据项的范围包括一个或多个密钥，它们是：（a）第一或第二范围的终端密钥之间的密钥 和重叠点，或（b）两个相邻重叠点之间的键。
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公开(公告)号：US20170220906A1
公开(公告)日：2017-08-03
申请号：US15488041
申请日：2017-04-14
Applicant: Google Inc.
Inventor： Samy Bengio , Jeffrey Adgate Dean , Quoc V. Le , Jonathon Shlens , Yoram Singer
CPC classification number: G06K9/723 , G06K9/00677 , G06K9/6212 , G06K9/72 , G06K2009/6213 , G06N7/005 , G06N20/00
Abstract: Systems and techniques are disclosed for labeling objects within an image. The objects may be labeled by selecting an option from a plurality of options such that each option is a potential label for the object. An option may have an option score associated with. Additionally, a relation score may be calculated for a first option and a second option corresponding to a second object in an image. The relation score may be based on a frequency, probability, or observance corresponding to the co-occurrence of text associated with the first option and the second option in a text corpus such as the World Wide Web. An option may be selected as a label for an object based on a global score calculated based at least on an option score and relation score associated with the option.
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