专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明授权

US09514405B2 Scoring concept terms using a deep network 有权
公开(公告)号：US09514405B2
公开(公告)日：2016-12-06
申请号：US14860462
申请日：2015-09-21
申请人： Google Inc.
发明人： Kai Chen , Xiaodan Song , Gregory S. Corrado , Kun Zhang , Jeffrey A. Dean , Bahman Rabii
IPC分类号： G06F15/18 , G06E1/00 , G06E3/00 , G06G7/00 , G06N3/08 , G06F17/30 , G06N3/04 , G06Q30/02
CPC分类号： G06N3/084 , G06F17/30707 , G06F17/30864 , G06N3/04 , G06N3/0427 , G06N3/08 , G06Q30/02
摘要： 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.

2. 发明授权

US09514404B1 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
申请人： Google Inc.
发明人： Gregory S. Corrado , Kai Chen , Jeffrey A. Dean , Gary R. Holt , Julian P. Grady , Sharat Chikkerur , David W. Sculley, II
IPC分类号： G06F15/18 , G06E1/00 , G06E3/00 , G06G7/00 , G06N3/08 , G06N3/04
CPC分类号： G06N3/08 , G06N3/04 , G06N3/0454 , G06N3/084
摘要： 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.

3. 发明授权

US09269048B1 Distribution shared content based on a probability 有权
标题翻译：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
申请人： Google Inc.
发明人： Kai Chen
IPC分类号： G06N5/04 , G06N99/00
CPC分类号： G06N5/048 , G06F17/30017 , G06N99/005 , G06Q30/08
摘要： 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.
摘要翻译：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。

4. 发明申请

US20160012331A1 SCORING CONCEPT TERMS USING A DEEP NETWORK 有权
标题翻译：使用深度网络划分概念条款
公开(公告)号：US20160012331A1
公开(公告)日：2016-01-14
申请号：US14860462
申请日：2015-09-21
申请人： Google Inc.
发明人： Kai Chen , Xiaodan Song , Gregory S. Corrado , Kun Zhang , Jeffrey A. Dean , Bahman Rabii
IPC分类号： G06N3/08 , G06F17/30 , G06N3/04
CPC分类号： G06N3/084 , G06F17/30707 , G06F17/30864 , G06N3/04 , G06N3/0427 , G06N3/08 , G06Q30/02
摘要： 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.
摘要翻译：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。

5. 发明授权

US09218573B1 Training a model using parameter server shards 有权
标题翻译：使用参数服务器分片训练模型
公开(公告)号：US09218573B1
公开(公告)日：2015-12-22
申请号：US13826327
申请日：2013-03-14
申请人： Google Inc.
发明人： Gregory S. Corrado , Kai Chen , Jeffrey A. Dean , Samy Bengio , Rajat Monga , Matthieu Devin
IPC分类号： G06F15/18 , G06N99/00 , G06N7/00 , G06N5/02
CPC分类号： G06N99/005 , G06K9/6256 , G06K9/6269 , G06N3/063 , G06N3/08 , G06N5/025 , G06N7/005 , G06N7/08
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model using parameter server shards. One of the methods includes receiving, at a parameter server shard configured to maintain values of a disjoint partition of the parameters of the model, a succession of respective requests for parameter values from each of a plurality of replicas of the model; in response to each request, downloading a current value of each requested parameter to the replica from which the request was received; receiving a succession of uploads, each upload including respective delta values for each of the parameters in the partition maintained by the shard; and updating values of the parameters in the partition maintained by the parameter server shard repeatedly based on the uploads of delta values to generate current parameter values.
摘要翻译：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，用于使用参数服务器分片训练模型。其中一种方法包括在被配置为维持模型的参数的不相交分区的值的参数服务器分片上接收来自模型的多个副本中的每一个的参数值的相继请求; 响应于每个请求，将每个请求的参数的当前值下载到从其接收请求的副本; 接收连续的上传，每次上传包括由分片保存的分区中的每个参数的各自的增量值; 并且根据增量值的上载重复地更新由参数服务器分片保存的分区中的参数的值，以生成当前参数值。

6. 发明授权

US08768870B1 Training a model using parameter server shards 有权
标题翻译：使用参数服务器分片训练模型
公开(公告)号：US08768870B1
公开(公告)日：2014-07-01
申请号：US13968019
申请日：2013-08-15
申请人： Google Inc.
发明人： Gregory S. Corrado , Kai Chen , Jeffrey A. Dean , Samy Bengio , Rajat Monga , Matthieu Devin
IPC分类号： G06F15/18
CPC分类号： G06N99/005 , G06K9/6256 , G06K9/6269 , G06N3/063 , G06N3/08 , G06N5/025 , G06N7/005 , G06N7/08
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model using parameter server shards. One of the methods includes receiving, at a parameter server shard configured to maintain values of a disjoint partition of the parameters of the model, a succession of respective requests for parameter values from each of a plurality of replicas of the model; in response to each request, downloading a current value of each requested parameter to the replica from which the request was received; receiving a succession of uploads, each upload including respective delta values for each of the parameters in the partition maintained by the shard; and updating values of the parameters in the partition maintained by the parameter server shard repeatedly based on the uploads of delta values to generate current parameter values.
摘要翻译：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，用于使用参数服务器分片训练模型。其中一种方法包括在被配置为维持模型的参数的不相交分区的值的参数服务器分片上接收来自模型的多个副本中的每一个的参数值的相继请求; 响应于每个请求，将每个请求的参数的当前值下载到从其接收请求的副本; 接收连续的上传，每次上传包括由分片保存的分区中的每个参数的各自的增量值; 并且根据增量值的上载重复地更新由参数服务器分片保存的分区中的参数的值，以生成当前参数值。

7. 发明申请

US20170364822A1 OPTIMIZING CONTENT DISTRIBUTION USING A MODEL 审中-公开
公开(公告)号：US20170364822A1
公开(公告)日：2017-12-21
申请号：US15183335
申请日：2016-06-15
申请人： Google Inc.
发明人： Scott Tadashi Davis , Kai Chen , Michael Jee-Kai Wang , Wei Jiang , Maryam Tavafi , Peter Zaimis Tipton
IPC分类号： G06N99/00 , G06F17/30 , H04L29/08 , H04L29/06
CPC分类号： G06N20/00 , G06F16/22 , G06F16/435 , G06Q30/0261 , H04L67/06 , H04L67/42
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing content presentation. In one aspect, a system includes a training database that stores training data including attribute information about users and corresponding proxy metrics quantifying behavior by the users following content presentation; a content database; a model generator that accesses the training data and trains a model for content distribution; and a content distribution server that receives a content request, uses the model to select content, transmits data identifying the selected content, wherein the model: obtains a set of attributes for a user associated with the request, receives information about a given content, predicts a proxy metric based on the set of attributes and the information about the content, the predicted proxy metric providing information about subject retention or awareness; and identifies the given content for distribution if the predicted proxy metrics meet a threshold.

8. 发明授权

US09514098B1 Iteratively learning coreference embeddings of noun phrases using feature representations that include distributed word representations of the noun phrases 有权
标题翻译：使用包括名词短语的分布式单词表示的特征表示迭代地学习名词短语的嵌入式
公开(公告)号：US09514098B1
公开(公告)日：2016-12-06
申请号：US14141182
申请日：2013-12-26
申请人： Google Inc.
发明人： Amarnag Subramanya , Jingyi Liu , Fernando Carlos das Neves Pereira , Kai Chen , Jay Ponte , Rami Al-Rfou′
IPC分类号： G06F17/21
CPC分类号： G06F17/21 , G06F17/277 , G06F17/278
摘要： Methods and apparatus related to determining coreference resolution using distributed word representations. Distributed word representations, indicative of syntactic and semantic features, may be identified for one or more noun phrases. For each of the one or more noun phrases, a referring feature representation and an antecedent feature representation may be determined, where the referring feature representation includes the distributed word representation, and the antecedent feature representation includes the distributed word representation augmented by one or more antecedent features. In some implementations the referring feature representation may be augmented by one or more referring features. Coreference embeddings of the referring and antecedent feature representations of the one or more noun phrases may be learned. Distance measures between two noun phrases may be determined based on the coreference embeddings.
摘要翻译：与使用分布式字表示法确定协同解析相关的方法和设备。可以为一个或多个名词短语识别表示句法和语义特征的分布式词表示。对于一个或多个名词短语中的每个，可以确定引用特征表示和先行特征表示，其中引用特征表示包括分布式词表示，并且先行特征表示包括由一个或多个前缀增强的分布式词表示特征。在一些实现中，引用特征表示可以由一个或多个引用特征来增强。可以学习一个或多个名词短语的引用和先行特征表示的核心嵌入。两个名词短语之间的距离度量可以基于核心嵌入来确定。

9. 发明申请

US20140279774A1 Classifying Resources Using a Deep Network 有权
标题翻译：使用深度网络分类资源
公开(公告)号：US20140279774A1
公开(公告)日：2014-09-18
申请号：US13802462
申请日：2013-03-13
申请人： Google Inc.
发明人： Qingzhou Wang , Yu Liang , Ke Yang , Kai Chen
IPC分类号： G06N3/02
CPC分类号： G06N3/04 , G06F3/0484 , G06F17/3053 , G06F17/30707 , G06F17/30864 , G06K9/627 , G06N3/02 , G06N3/0427 , G06N3/084 , G06N7/005
摘要： 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.
摘要翻译：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。

10. 发明申请

US20190034589A1 System and Method for Predicting and Summarizing Medical Events from Electronic Health Records 审中-公开
公开(公告)号：US20190034589A1
公开(公告)日：2019-01-31
申请号：US15690703
申请日：2017-08-30
申请人： Google Inc.
发明人： Kai Chen , Patrik Sundberg , Alexander Mossin , Nissan Hajaj , Kurt Litsch , James Wexler , Yi Zhang , Kun Zhang , Jacob Marcus , Eyal Oren , Hector Yee , Jeffrey Dean , Michaela Hardt , Benjamin Irvine , James Wilson , Andrew Dai , Peter Liu , Xiaomi Sun , Quoc Le , Xiaobing Liu , Alvin Rajkomar , Gregory Corrado , Gerardo Flores , Yingwei Cui , Gavin Duggan
IPC分类号： G06F19/00 , G06N3/08 , G06N3/04
摘要： A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式