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11.发明授权公开(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。
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公开(公告)号：US09852363B1
公开(公告)日：2017-12-26
申请号：US14987955
申请日：2016-01-05
Applicant: Google Inc.
Inventor： Jonathon Shlens , Quoc V. Le , Gregory Sean Corrado , Marc'Aurelio Ranzato
CPC classification number: G06K9/627 , G06F17/30244 , G06F17/30247 , G06F17/30268 , G06F17/3079 , G06F17/30843 , G06K9/6215 , G06K9/6255 , G06K9/6256
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating labeled images. One of the methods includes selecting a plurality of candidate videos from videos identified in a response to a search query derived from a label for an object category; selecting one or more initial frames from each of the candidate videos; detecting one or more initial images of objects in the object category in the initial frames; for each initial frame including an initial image of an object in the object category, tracking the object through surrounding frames to identify additional images of the object; and selecting one or more images from the one or more initial images and one or more additional images as database images of objects belonging to the object category.
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公开(公告)号：US20170316313A1
公开(公告)日：2017-11-02
申请号：US15595644
申请日：2017-05-15
Applicant: Google Inc.
Inventor： Gregory Sean Corrado , Jeffrey Adgate Dean
CPC classification number: G06N3/08 , G06N3/02 , G06N3/04 , G06N3/0445 , G06N3/049 , G06N3/084 , G06N3/10 , G16H50/20 , G16H50/30
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: processing each of a plurality of initial temporal sequences of health events to generate, for each of the initial temporal sequences, a respective network internal state of a recurrent neural network for each time step in the initial temporal sequence; storing, for each of the initial temporal sequences, one or more of the network internal states for the time steps in the temporal sequence in a repository; obtaining a first temporal sequence; processing the first temporal sequence using the recurrent neural network to generate a sequence internal state for the first temporal sequence; and selecting one or more initial temporal sequences that are likely to include health events that are predictive of future health events in the first temporal sequence.
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Patent Agency Ranking
公开(公告)号：US20170032243A1
公开(公告)日：2017-02-02
申请号：US14810384
申请日：2015-07-27
Applicant: Google Inc.
Inventor： Gregory Sean Corrado , Jeffrey Adgate Dean
CPC classification number: G06N3/08 , G06F19/00 , G06N3/02 , G06N3/04 , G06N3/049 , G06N3/10 , G16H50/20 , G16H50/30
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: processing each of a plurality of initial temporal sequences of health events to generate, for each of the initial temporal sequences, a respective network internal state of a recurrent neural network for each time step in the initial temporal sequence; storing, for each of the initial temporal sequences, one or more of the network internal states for the time steps in the temporal sequence in a repository; obtaining a first temporal sequence; processing the first temporal sequence using the recurrent neural network to generate a sequence internal state for the first temporal sequence; and selecting one or more initial temporal sequences that are likely to include health events that are predictive of future health events in the first 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：处理健康事件的多个初始时间序列中的每一个，以针对每个初始时间序列，为初始时间序列中的每个时间步骤生成循环神经网络的相应网络内部状态; 对于每个初始时间序列，存储在存储库中的时间序列中的时间步长的一个或多个网络内部状态; 获得第一时间序列; 使用所述循环神经网络处理所述第一时间序列以生成所述第一时间序列的序列内部状态; 以及选择可能包括在第一时间序列中预测未来健康事件的健康事件的一个或多个初始时间序列。
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公开(公告)号：US09042605B2
公开(公告)日：2015-05-26
申请号：US13768257
申请日：2013-02-15
Applicant: Google Inc.
Inventor： Richard Gossweiler , Gregory Sean Corrado
CPC classification number: G01C11/04 , G01C3/04 , G01C5/00 , G06F1/1686 , G06F1/1694 , G06F3/017 , G06F3/0481 , G06F2203/04806 , G09G5/00 , G09G2354/00
Abstract: A method, computer readable storage device, and apparatus for determining the distance a computing device is located from a user's face. An image of an individual is obtained. A first pupil location and a second pupil location are identified based on the obtained image. A first distance between the identified first and second pupil location is determined. A second distance between the individual and the computing device is determined based on the determined first distance between the identified first and second pupil locations.
Abstract translation: 一种用于确定计算设备位于用户面部的距离的方法，计算机可读存储设备和装置。 获得个人的图像。 基于获得的图像来识别第一光瞳位置和第二瞳孔位置。 确定所识别的第一和第二光瞳位置之间的第一距离。 基于所识别的第一和第二瞳孔位置之间确定的第一距离来确定个体和计算设备之间的第二距离。
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公开(公告)号：US10079785B2
公开(公告)日：2018-09-18
申请号：US14620630
申请日：2015-02-12
Applicant: Google Inc.
Inventor： Phillip Neal Sharp , Prabhakar Raghavan , Thompson Alexander Ivor Gawley , Balint Miklos , Karol Kurach , Tobias Kaufmann , Gregory Sean Corrado , László Lukács
CPC classification number: H04L51/02 , G06F16/355 , G06F17/248 , G06F17/2881 , G06N20/00 , G06Q10/107 , G06Q50/01 , H04L67/22
Abstract: Methods and apparatus related to determining reply content for a reply to an electronic communication. Some implementations are directed generally toward analyzing a corpus of electronic communications to determine relationships between one or more original message features of “original” messages of electronic communications and reply content that is included in “reply” messages of those electronic communications. Some implementations are directed generally toward providing reply text to include in a reply to a communication based on determined relationships between one or more message features of the communication and the reply text.
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公开(公告)号：US20170154262A1
公开(公告)日：2017-06-01
申请号：US14954683
申请日：2015-11-30
Applicant: Google Inc.
Inventor： David Sussillo , Gregory Sean Corrado
IPC: G06N3/08
CPC classification number: G06N3/082
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for resizing neural network layers, the method including obtaining data specifying a trained neural network, wherein the neural network comprises one or more neural network layers; reducing a size of one or more of the neural network layers to generate a resized neural network, including: selecting one or more neural network layers for resizing; for each selected neural network layer: determining an effective dimensionality reduction for the neural network layer; based on the determined effective dimensionality reduction, resizing the neural network layer; and retraining the resized neural network.
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公开(公告)号：US09652712B2
公开(公告)日：2017-05-16
申请号：US14810384
申请日：2015-07-27
Applicant: Google Inc.
Inventor： Gregory Sean Corrado , Jeffrey Adgate Dean
CPC classification number: G06N3/08 , G06F19/00 , G06N3/02 , G06N3/04 , G06N3/049 , G06N3/10 , G16H50/20 , G16H50/30
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: processing each of a plurality of initial temporal sequences of health events to generate, for each of the initial temporal sequences, a respective network internal state of a recurrent neural network for each time step in the initial temporal sequence; storing, for each of the initial temporal sequences, one or more of the network internal states for the time steps in the temporal sequence in a repository; obtaining a first temporal sequence; processing the first temporal sequence using the recurrent neural network to generate a sequence internal state for the first temporal sequence; and selecting one or more initial temporal sequences that are likely to include health events that are predictive of future health events in the first temporal sequence.
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