公开(公告)号：US20190034591A1

公开(公告)日：2019-01-31

申请号：US15690721

申请日：2017-08-30

Applicant: Google Inc.

Inventor： Alexander Mossin ,  Alvin Rajkomar ,  Eyal Oren ,  James Wilson ,  James Wexler ,  Patrik Sundberg ,  Andrew Dai ,  Yingwei Cui ,  Gregory Corrado ,  Hector Yee ,  Jacob Marcus ,  Jeffrey Dean ,  Benjamin Irvine ,  Kai Chen ,  Kun Zhang ,  Michaela Hardt ,  Xiaomi Sun ,  Nissan Hajaj ,  Peter Liu ,  Quoc Le ,  Xiaobing Liu ,  Yi Zhang

IPC: G06F19/00 ,  G06N3/08 ,  G06N3/04

Abstract: 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.

公开(公告)号：US20170345023A1

公开(公告)日：2017-11-30

申请号：US15666378

申请日：2017-08-01

Applicant: Google Inc.

Inventor： Abhinay Sharma ,  Kai Chen

IPC: G06Q30/02

CPC classification number: G06Q30/02

Abstract: In response to a conversion action associated with an online advertisement (“ad”), an advertiser associated with the ad generates a unique identifier (e.g., a pseudo random number), which is transmitted to a payment system through a user device. In some implementations, the unique identifier can be generated by a payment system in response to a request from an advertiser. The advertiser stores the unique identifier in a secure location that is accessible by the payment system. The payment system compares the received unique identifier with the stored unique identifier. If the received unique identifier matches the stored unique identifier, and if the received unique identifier was not previously generated by the advertiser, then the payment system deems the conversion action to be valid.

公开(公告)号：US09449271B2

公开(公告)日：2016-09-20

申请号：US14834274

申请日：2015-08-24

Applicant: Google Inc.

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

IPC: G06F15/18 ,  G06E1/00 ,  G06E3/00 ,  G06G7/00 ,  G06N3/04 ,  G06N3/02 ,  G06F17/30 ,  G06N3/08 ,  G06K9/62 ,  G06F3/0484 ,  G06N7/00

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.

公开(公告)号：US09256892B2

公开(公告)日：2016-02-09

申请号：US14281370

申请日：2014-05-19

Applicant: Google Inc.

Inventor： Rob Kniaz ,  Abinhay Sharma ,  Kai Chen ,  Maxim Drobintsev

IPC: G06Q30/02

CPC classification number: G06Q30/0277 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0273

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer program product, for selecting advertisements. In one aspect, a method includes receiving publisher selections of advertisements; associating the selected advertisements with an advertisement environment in a document; generating an advertisement request code for inclusion in the document; evaluating performance metrics for the selected advertisements; and in response to determining that the selected advertisements do not meet the performance threshold, optimizing the selection of selected advertisements based on the performance metrics; substituting a selected advertisement with a candidate advertisement and causing a client device to render the candidate advertisement in the advertisement environment in the document.

Abstract translation: 方法，系统和装置，包括在计算机程序产品上编码的计算机程序，用于选择广告。 一方面，一种方法包括接收广告的发行商选择; 将所选择的广告与文档中的广告环境相关联; 生成用于包含在文档中的广告请求代码; 评估所选广告的性能指标; 并且响应于确定所选择的广告不满足所述性能阈值，基于所述性能度量优化所选择的广告的选择; 用候选广告代替所选择的广告，并使客户端设备在文档中的广告环境中呈现候选广告。

公开(公告)号：US09141916B1

公开(公告)日：2015-09-22

申请号：US13803779

申请日：2013-03-14

Applicant: Google Inc.

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

IPC: G06F15/18 ,  G06N99/00

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.

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，用于使用具有深度网络的嵌入式功能。 方法之一包括接收包括多个特征的输入，其中每个特征具有不同的特征类型; 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，其中每个嵌入功能独立于彼此嵌入功能操作，并且其中每个嵌入功能用于相应特征类型的特征; 使用深度网络处理所述数值以产生所述输入的第一替代表示，其中所述深度网络是由多个非线性操作级别组成的机器学习模型; 以及使用逻辑回归分类器处理输入的第一替代表示以预测输入的标签。

公开(公告)号：US09141906B2

公开(公告)日：2015-09-22

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: G06F15/18 ,  G06E1/00 ,  G06E3/00 ,  G06G7/00 ,  G06N3/08 ,  G06F17/30 ,  G06N3/04 ,  G06Q30/02

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合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所述资源的预测相关性。

公开(公告)号：US09037464B1

公开(公告)日：2015-05-19

申请号：US13841640

申请日：2013-03-15

Applicant: Google Inc.

Inventor： Tomas Mikolov ,  Kai Chen ,  Gregory S. Corrado ,  Jeffrey A. Dean

IPC: G10L15/00 ,  G06F17/28

CPC classification number: G06F17/2765 ,  G06F17/2785 ,  G06N99/005 ,  G10L15/06

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein training the embedding function comprises obtained trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary in the high-dimensional space; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space.

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，用于计算单词的数字表示。 一种方法包括获得一组训练数据，其中训练数据集合包括单词序列; 在训练数据集上训练分类器和嵌入函数，其中训练嵌入函数包括获得的嵌入函数参数的训练值; 使用嵌入函数根据嵌入函数参数的训练值来处理词汇表中的每个单词以产生高维空间中词汇表中每个单词的相应数值表示; 并将词汇表中的每个单词与高维空间中单词的相应数字表示相关联。