公开(公告)号：US20150066630A1

公开(公告)日：2015-03-05

申请号：US14105762

申请日：2013-12-13

Applicant: Google Inc.

Inventor： Rong Ge ,  Yong Sheng ,  Arthur Asuncion ,  Jonathan Michael Krafcik ,  Yiming Li

IPC: G06Q30/02

CPC classification number: G06Q30/0244 ,  G06Q30/0255

Abstract: Systems and methods for content selection with precision controls include receiving a content selection parameter value and a degree of precision specified by a content provider. A content selection parameter value for a device identifier may be predicted using a predictive model. A precision factor may be associated with the predicted content selection parameter value. Content from the provider may be selected based on a comparison between the predicted selection parameter value and precision factor for the device identifier and the selection parameter value and degree of precision specified by the content provider.

Abstract translation: 具有精确控制的内容选择的系统和方法包括接收内容选择参数值和由内容提供者指定的精度度。 可以使用预测模型来预测用于设备标识符的内容选择参数值。 精度因子可以与预测内容选择参数值相关联。 可以基于预测的选择参数值和设备标识符的精度因数与内容提供者指定的选择参数值和精度之间的比较来选择来自提供者的内容。

公开(公告)号：US09466029B1

公开(公告)日：2016-10-11

申请号：US14054196

申请日：2013-10-15

Applicant: Google Inc.

Inventor： Ruoyun Huang ,  Arthur Asuncion ,  Yong Sheng

IPC: G06N7/00 ,  G06N5/04

CPC classification number: G06Q30/00 ,  G06F17/30 ,  G06F17/30867 ,  G06N7/00

Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium for labeling user identifiers. A method includes: identifying a set of unlabeled identifiers, wherein an unlabeled identifier has an unknown classification as to a particular class in a multi-class demographic characteristic; determining for each unlabeled identifier a probability as to inclusion in a class of the multi-class demographic characteristic based on known user behavior producing a distribution of probabilities for the unlabeled identifier; for a given unlabeled identifier, adjusting the probability based on a known internet distribution of entities with respect to a given class in the multi-class demographic characteristic and distribution of the probabilities among the unlabeled identifiers; and assigning a label for a particular class in the multi-class demographic characteristic to the unlabeled identifier in accordance with the adjusting.

Abstract translation: 方法，系统和装置包括在用于标记用户标识符的计算机可读存储介质上编码的计算机程序。 一种方法包括：识别一组未标记的标识符，其中未标记的标识符对于多类人口特征中的特定类别具有未知的分类; 确定每个未标记标识符的概率，以便基于产生未标记标识符的概率分布的已知用户行为包括在多类人口特征类中; 对于给定的未标记标识符，根据多类别人口特征中的给定类别的实体的已知互联网分布和未标记标识符之间的概率分布来调整概率; 以及根据调整，将多类别人口特征中的特定类别的标签分配给未标记的标识符。

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。

公开(公告)号：US09280749B1

公开(公告)日：2016-03-08

申请号：US14048982

申请日：2013-10-08

Applicant: GOOGLE INC.

Inventor： Ian Porteous ,  Ruoyun Huang ,  Arthur Asuncion ,  Rong Ge ,  Yong Sheng ,  Jonathan Michael Krafcik ,  Xintian Yang ,  Pei Yin

IPC: G06F15/18 ,  G06N99/00

CPC classification number: G06F21/6218 ,  G06F2221/2111 ,  H04N21/251 ,  H04N21/25841 ,  H04N21/25883 ,  H04N21/4524

Abstract: A computer-implemented method for determining an attribute for an online user of a candidate computing device is provided. The method implemented uses a host computing device. The method includes identifying a first set of model data including device data from a plurality of model computing devices including location data and access data, and a plurality of categories for an attribute of a population segment including an online user. Each category defines a segment of the attribute. The method further includes training a classification model by the host computing device with at least the first set of model data and the plurality of categories. The method also includes identifying device data associated with the candidate computing device. The method further includes applying the device data of the candidate computing device to the classification model to determine a category of the plurality of categories for the online user.

Abstract translation: 提供了一种用于确定候选计算设备的在线用户的属性的计算机实现的方法。 实现的方法使用主机计算设备。 该方法包括从包括位置数据和访问数据的多个模型计算设备中识别包括设备数据的第一组模型数据，以及包括在线用户的总体段的属性的多个类别。 每个类别定义属性的一个段。 该方法还包括由主机计算设备至少训练第一组模型数据和多个类别的分类模型。 该方法还包括识别与候选计算设备相关联的设备数据。 该方法还包括将候选计算设备的设备数据应用于分类模型以确定用于在线用户的多个类别的类别。

公开(公告)号：US20150066593A1

公开(公告)日：2015-03-05

申请号：US14014898

申请日：2013-08-30

Applicant: Google Inc.

Inventor： Ruoyun Huang ,  Yong Sheng ,  Arthur Asuncion ,  Jonathan Michael Krafcik

IPC: G06Q30/02

CPC classification number: G06Q30/0202

Abstract: Systems and methods for content selection with precision controls include receiving device identifier data from multiple sources. A machine learning model may be applied to the device identifier data and content selection parameter values may be predicted. Percentiles for the predicted content selection parameter values may be analyzed to determine precision factors for the predicted content selection parameter values.

Abstract translation: 使用精密控制进行内容选择的系统和方法包括从多个来源接收设备标识符数据。 可以将机器学习模型应用于设备标识符数据，并且可以预测内容选择参数值。 可以分析预测内容选择参数值的百分位数，以确定预测内容选择参数值的精度因子。