公开(公告)号：US20150195079A1

公开(公告)日：2015-07-09

申请号：US14149111

申请日：2014-01-07

Applicant: Google Inc.

Inventor： Xiaohong Gong

IPC: H04L5/14

CPC classification number: H04L5/1415 ,  H04B1/56 ,  H04L5/14

Abstract: A method includes receiving, by control circuitry, a received portion of a first data unit from a remote device and determining, by the control circuitry, a first duration of the first data unit based on the received portion. The method further includes generating, by the control circuitry, a second data unit with data to be transmitted and transmitting, by the control circuitry, the second data unit to the remote device while receiving the unreceived portion of the first data unit. The second data unit has a second duration equal to the remaining duration of an unreceived portion of the first data unit.

Abstract translation: 一种方法包括由控制电路从远程设备接收第一数据单元的接收部分，并由控制电路根据接收到的部分确定第一数据单元的第一持续时间。 所述方法还包括由所述控制电路产生第二数据单元，所述第二数据单元具有要发送的数据，并且由所述控制电路将所述第二数据单元发送到所述远程设备，同时接收所述第一数据单元的未接收部分。 第二数据单元具有等于第一数据单元的未接收部分的剩余持续时间的第二持续时间。

公开(公告)号：US09858308B2

公开(公告)日：2018-01-02

申请号：US14599026

申请日：2015-01-16

Applicant: Google Inc.

Inventor： Xiaohong Gong ,  Wei Zhang ,  Nima Asgharbeygi

IPC: G06F7/00 ,  G06F17/30 ,  H04L29/08 ,  G06Q30/06 ,  H04N21/466

CPC classification number: G06F17/30345 ,  G06F17/30386 ,  G06F17/30867 ,  G06Q30/0631 ,  H04L67/30 ,  H04L67/306 ,  H04N21/4668

Abstract: System and methods of this disclosure are directed to recommending content in real-time or near real-time. The system comprises a number of pipelines updated a different frequencies that process temporally different sets of web property visit data. Within each pipeline, the system can employ different number of algorithms to process visit data to generate content recommendations. One algorithm is a content filter that filters from the visit data those determined to be unsuitable as recommendations. Another is a content analyzer that analyzes the content of each URL in the visit data by topic category and attribute. Another is an item-to-item collaborative filter that determines a correlation score for each URL based on those in the visit data in a single session Another is a device-to-item matrix factorization that determines an affinity score for each URL based on visit data, context information, and topic category.

公开(公告)号：US09264209B2

公开(公告)日：2016-02-16

申请号：US14149111

申请日：2014-01-07

Applicant: Google Inc.

Inventor： Xiaohong Gong

IPC: H04L5/14

CPC classification number: H04L5/1415 ,  H04B1/56 ,  H04L5/14

Abstract: A method includes receiving, by control circuitry, a received portion of a first data unit from a remote device and determining, by the control circuitry, a first duration of the first data unit based on the received portion. The method further includes generating, by the control circuitry, a second data unit with data to be transmitted and transmitting, by the control circuitry, the second data unit to the remote device while receiving the unreceived portion of the first data unit. The second data unit has a second duration equal to the remaining duration of an unreceived portion of the first data unit.

Abstract translation: 一种方法包括由控制电路从远程设备接收第一数据单元的接收部分，并由控制电路根据接收到的部分确定第一数据单元的第一持续时间。 所述方法还包括由所述控制电路产生第二数据单元，所述第二数据单元具有要发送的数据，并且由所述控制电路将所述第二数据单元发送到所述远程设备，同时接收所述第一数据单元的未接收部分。 第二数据单元具有等于第一数据单元的未接收部分的剩余持续时间的第二持续时间。

公开(公告)号：US10417650B1

公开(公告)日：2019-09-17

申请号：US14959494

申请日：2015-12-04

Applicant: Google Inc.

Inventor： Xiaohong Gong

IPC: G06Q30/02

Abstract: Systems, methods, and computer-readable storage media for distributed and automated prediction of future customer revenue are provided. One method involves accessing data structures, each representing a unique customer, storing a set of customer-specific characteristics, segregating the data structures into groups based on a target amount of data structures for each group, and inputting the customer-specific characteristics into a training model. The method includes generating a set of prediction model parameters for each group by applying the customer-specific characteristics to a training model. The method includes transforming the characteristics of each data structure in a first group into respective future revenue values using a first non-linear prediction model, and the characteristics of data structures in a second group into respective future revenue values using a second prediction model. A portion of the future revenue values for the groups is calculated in parallel, and the calculated values are stored in a memory.

公开(公告)号：US09668243B1

公开(公告)日：2017-05-30

申请号：US14585584

申请日：2014-12-30

Applicant: Google Inc.

Inventor： Xiaohong Gong

IPC: H04W72/04 ,  H04W74/04 ,  H04W74/00

CPC classification number: H04W72/04 ,  H04W72/0406 ,  H04W72/0453 ,  H04W74/006 ,  H04W74/04 ,  H04W74/0825

Abstract: A method includes determining a total bandwidth for a downlink communication, segmenting the downlink communication into data units and assigning the data units to channels assigned to stations of a basic service set. The method also includes transmitting a channel announce frame to stations of the basic service set. The channel announce frame indicates each station assigned to receive a corresponding data unit, and one or more channels assigned to each station for transmitting the corresponding data unit. The method further includes transmitting the corresponding data units over the assigned channels to the assigned stations in a single downlink communication.

公开(公告)号：US20160210321A1

公开(公告)日：2016-07-21

申请号：US14599026

申请日：2015-01-16

Applicant: Google Inc.

Inventor： Xiaohong Gong ,  Wei Zhang ,  Nima Asgharbeygi

IPC: G06F17/30 ,  H04L29/08

CPC classification number: G06F17/30345 ,  G06F17/30386 ,  G06F17/30867 ,  G06Q30/0631 ,  H04L67/30 ,  H04L67/306 ,  H04N21/4668

Abstract: System and methods of this disclosure are directed to recommending content in real-time or near real-time. The system comprises a number of pipelines updated a different frequencies that process temporally different sets of web property visit data. Within each pipeline, the system can employ different number of algorithms to process visit data to generate content recommendations. One algorithm is a content filter that filters from the visit data those determined to be unsuitable as recommendations. Another is a content analyzer that analyzes the content of each URL in the visit data by topic category and attribute. Another is an item-to-item collaborative filter that determines a correlation score for each URL based on those in the visit data in a single session Another is a device-to-item matrix factorization that determines an affinity score for each URL based on visit data, context information, and topic category.

Abstract translation: 本公开的系统和方法旨在实时或接近实时地推荐内容。 该系统包括多个管道更新处理时间上不同的web属性访问数据集合的不同频率。 在每个流水线中，系统可以使用不同数量的算法来处理访问数据以生成内容建议。 一种算法是从确定为不适合作为推荐的访问数据过滤的内容过滤器。 另一个是内容分析器，按照主题类别和属性分析访问数据中每个URL的内容。 另一个是项目到项目协作过滤器，其基于单个会话中的访问数据中的那些确定每个URL的相关性得分另一个是设备到项目矩阵因式分解，其基于访问确定每个URL的亲和度分数 数据，上下文信息和主题类别。