公开(公告)号：US09147154B2

公开(公告)日：2015-09-29

申请号：US13802462

申请日：2013-03-13

Applicant: Google Inc.

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

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

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.

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; 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。

公开(公告)号：US08498864B1

公开(公告)日：2013-07-30

申请号：US13628138

申请日：2012-09-27

Applicant: Google Inc.

Inventor： Yu Liang ,  Xiaotao Duan

IPC: G10L15/00

CPC classification number: G10L15/22 ,  G06F17/2223 ,  G06F17/276 ,  G10L15/24 ,  G10L2015/221 ,  G10L2015/226

Abstract: Methods and systems for predicting a text are described. In an example, a computing device may be configured to receive one or more typed characters that compose a portion of a text; and receive, a voice input corresponding to a spoken utterance of at least a portion of the text. The computing device may be configured to determine, based on the one or more typed characters and the voice input, one or more candidate texts predicting the text. Further, the computing device may be configured to provide the one or more candidate texts.

Abstract translation: 描述用于预测文本的方法和系统。 在一个示例中，计算设备可以被配置为接收构成文本的一部分的一个或多个类型字符; 并且接收对应于所述文本的至少一部分的说出话语的语音输入。 计算设备可以被配置为基于一个或多个类型字符和语音输入来确定预测文本的一个或多个候选文本。 此外，计算设备可以被配置为提供一个或多个候选文本。

公开(公告)号：US20140279774A1

公开(公告)日：2014-09-18

申请号：US13802462

申请日：2013-03-13

Applicant: Google Inc.

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

IPC: G06N3/02

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.

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; 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。

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：US20160048754A1

公开(公告)日：2016-02-18

申请号：US14834274

申请日：2015-08-24

Applicant: Google Inc.

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

IPC: G06N3/04 ,  G06F17/30 ,  G06N7/00 ,  G06F3/0484

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.

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; 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。