公开(公告)号：US11170293B2

公开(公告)日：2021-11-09

申请号：US14985017

申请日：2015-12-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Jianfeng Gao ,  Li Deng ,  Xiaodong He ,  Prabhdeep Singh ,  Lihong Li ,  Jianshu Chen ,  Xiujun Li ,  Ji He

IPC: G06N3/08 ,  G06N3/04

Abstract: A processing unit can operate a first recurrent computational model (RCM) to provide first state information and a predicted result value. The processing unit can operating a first network computational model (NCM) to provide respective expectation values of a plurality of actions based at least in part on the first state information. The processing unit can provide an indication of at least one of the plurality of actions, and receive a reference result value, e.g., via a communications interface. The processing unit can train the first RCM based at least in part on the predicted result value and the reference result value to provide a second RCM, and can train the first NCM based at least in part on the first state information and the at least one of the plurality of actions to provide a second NCM.

公开(公告)号：US20190340030A1

公开(公告)日：2019-11-07

申请号：US15972968

申请日：2018-05-07

Applicant: Microsoft Technology Licensing, LLC

Inventor： Xinying Song ,  Jaideep Sarkar ,  Karan Srivastava ,  Jianfeng Gao ,  Prabhdeep Singh ,  Hui Su ,  Jinchao Li ,  Andreea Bianca Spataru

IPC: G06F9/50 ,  G06F9/48

Abstract: Generally discussed herein are devices, systems, and methods for task routing. A method can include receiving, from a resource, a request for a task, in response to receiving the request, determining whether to retrieve a new task of new tasks stored in a first queue or a backlog task of backlog tasks stored in a second queue based on a combined amount of backlog tasks and new tasks relative to a capacity of the resource or the resources, retrieving the new task or the backlog task from the determined first queue or second queue, respectively, based on the determination, and providing the retrieved task to the resource.

公开(公告)号：US20180357654A1

公开(公告)日：2018-12-13

申请号：US15617363

申请日：2017-06-08

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yifei Huang ,  Xinying Song ,  Ankit Gupta ,  Jianfeng Gao ,  Prabhdeep Singh ,  Salman Mukhtar

IPC: G06Q30/02 ,  G06N5/04 ,  G06F17/11 ,  G06F17/18 ,  G06N99/00

CPC classification number: G06Q30/0206 ,  G06F17/11 ,  G06F17/18 ,  G06N5/04 ,  G06N99/005 ,  G06Q30/0202

Abstract: Methods, systems, and computer programs are presented for evaluating the accuracy of predictive systems and quantifiable measures of incremental value. One method provides a scientific solution to test and evaluate predictive systems in a transparent, rigorous, and verifiable way to allow decision-makers to better decide whether to adopt a new predictive system. In one example, objects to be evaluated are assigned to a control group or an experiment group. The testing provides an equal or better distribution of scores in the control group for the scores obtained with the first predictor, but the method aims at maximizing the scores of objects obtained with the second predictor in the experiment group. Since the first scores are evenly distributed in both groups, any result improvements may be attributed to the better accuracy of the second predictor when the results of the experiment group are better than the results of the control group.

公开(公告)号：US20170024640A1

公开(公告)日：2017-01-26

申请号：US14808546

申请日：2015-07-24

Applicant: Microsoft Technology Licensing, LLC

Inventor： Li Deng ,  Jianfeng Gao ,  Xiaodong He ,  Prabhdeep Singh

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06Q10/067

Abstract: A deep learning network is trained to automatically analyze enterprise data. Raw data from one or more global data sources is received, and a specific training dataset that includes data exemplary of the enterprise data is also received. The raw data from the global data sources is used to pre-train the deep learning network to predict the results of a specific enterprise outcome scenario. The specific training dataset is then used to further train the deep learning network to predict the results of a specific enterprise outcome scenario. Alternately, the raw data from the global data sources may be automatically mined to identify semantic relationships there-within, and the identified semantic relationships may be used to pre-train the deep learning network to predict the results of a specific enterprise outcome scenario.

Abstract translation: 培训深入学习网络，自动分析企业数据。 接收来自一个或多个全局数据源的原始数据，并且还接收包括企业数据示例的数据的特定训练数据集。 来自全球数据源的原始数据用于预培训深度学习网络，以预测特定企业成果情景的结果。 然后将具体的培训数据集用于进一步训练深度学习网络，以预测特定企业成果情景的结果。 或者，来自全球数据源的原始数据可以被自动挖掘以识别其中的语义关系，并且所识别的语义关系可以用于预培训深度学习网络以预测特定企业成果情景的结果。

公开(公告)号：US20160379112A1

公开(公告)日：2016-12-29

申请号：US14754474

申请日：2015-06-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Xiaodong He ,  Jianshu Chen ,  Brendan WL Clement ,  Li Deng ,  Jianfeng Gao ,  Bochen Jin ,  Prabhdeep Singh ,  Sandeep P. Solanki ,  LuMing Wang ,  Hanjun Xian ,  Yilei Zhang ,  Mingyang Zhao ,  Zijian Zheng

IPC: G06N3/08 ,  G06N99/00

CPC classification number: G06N3/08 ,  G06N3/0454 ,  G06N3/049 ,  G06N20/00

Abstract: A processing unit can acquire datasets from respective data sources, each having a respective unique data domain. The processing unit can determine values of a plurality of features based on the plurality of datasets. The processing unit can modify input-specific parameters or history parameters of a computational model based on the values of the features. In some examples, the processing unit can determine an estimated value of a target feature based at least in part on the modified computational model and values of one or more reference features. In some examples, the computational model can include neural networks for several input sets. An output layer of at least one of the neural networks can be connected to the respective hidden layer(s) of one or more other(s) of the neural networks. In some examples, the neural networks can be operated to provide transformed feature value(s) for respective times.

Abstract translation: 处理单元可以从相应的数据源获取数据集，每个数据源具有相应的唯一数据域。 处理单元可以基于多个数据集来确定多个特征的值。 处理单元可以基于特征的值修改计算模型的输入特定参数或历史参数。 在一些示例中，处理单元可以至少部分地基于修改的计算模型和一个或多个参考特征的值来确定目标特征的估计值。 在一些示例中，计算模型可以包括用于多个输入集合的神经网络。 至少一个神经网络的输出层可以连接到神经网络的一个或多个其他神经网络的相应隐藏层。 在一些示例中，可以操作神经网络以在相应时间提供变换的特征值。

公开(公告)号：US20160335572A1

公开(公告)日：2016-11-17

申请号：US14714109

申请日：2015-05-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Paul Nathan Bennett ,  Nikrouz Ghotbi ,  Eric Joel Horvitz ,  Richard L. Hughes ,  Prabhdeep Singh ,  Ryen William White

IPC: G06Q10/06 ,  G06Q10/10

CPC classification number: G06Q10/06311 ,  G06Q10/107 ,  G06Q10/1095

Abstract: A system that analyses content of electronic communications may automatically detect requests or commitments from the electronic communications. In one example process, a processor may identify a request or a commitment in the content of the electronic message; based, at least in part, on the request or the commitment, determine an informal contract; and execute one or more actions to manage the informal contract, the one or more actions based, at least in part, on the request or the commitment.

Abstract translation: 分析电子通信内容的系统可以自动检测电子通信中的请求或承诺。 在一个示例过程中，处理器可以在电子消息的内容中标识请求或承诺; 至少部分地根据请求或承诺确定非正式合同; 并且执行一个或多个动作来管理非正式合同，所述一个或多个动作至少部分地基于请求或承诺。