公开(公告)号：US20180253637A1

公开(公告)日：2018-09-06

申请号：US15446870

申请日：2017-03-01

Applicant: Microsoft Technology Licensing, LLC

Inventor： Feng Zhu ,  Xinying Song ,  Chao Zhong ,  Shijing Fang ,  Ryan Bouchard ,  Valentine N. Fontama ,  Prabhdeep Singh ,  Jianfeng Gao ,  Li Deng

IPC: G06N3/04 ,  G06N5/04 ,  H04L29/08

CPC classification number: G06N3/0445 ,  G06N3/0454 ,  G06N3/08 ,  H04L67/22

Abstract: A method to predict churn includes obtaining static features representative of a customer of a service, obtaining time series features representative of the customers interaction with the service, using a deep neural network to process the static features, using a recurrent neural network to process the time series features; and combining outputs from the deep neural network and the recurrent neural network to predict likelihood of customer churn.

公开(公告)号：US10579423B2

公开(公告)日：2020-03-03

申请号：US15943206

申请日：2018-04-02

Applicant: Microsoft Technology Licensing, LLC

Inventor： Jinchao Li ,  Yu Wang ,  Karan Srivastava ,  Jianfeng Gao ,  Prabhdeep Singh ,  Haiyuan Cao ,  Xinying Song ,  Hui Su ,  Jaideep Sarkar

IPC: G06F9/46 ,  G06F9/48 ,  G06K9/62 ,  G06F9/50 ,  G06N20/00

Abstract: Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

公开(公告)号：US10318864B2

公开(公告)日：2019-06-11

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 ,  G06Q10/06

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.

公开(公告)号：US10474950B2

公开(公告)日：2019-11-12

申请号：US14754474

申请日：2015-06-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Xiaodong He ,  Jianshu Chen ,  Brendan W L 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 ,  G06N20/00 ,  G06N3/04

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.

公开(公告)号：US10361981B2

公开(公告)日：2019-07-23

申请号：US14714137

申请日：2015-05-15

Applicant: Microsoft Technology Licensing, LLC

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

IPC: H04L12/58 ,  G06Q10/06 ,  G06Q10/10

Abstract: A system that analyses content of electronic communications may automatically extract requests or commitments from the electronic communications. In one example process, a processing component may analyze the content to determine one or more meanings of the content; query content of one or more data sources that is related to the electronic communications; and based, at least in part, on (i) the one or more meanings of the content and (ii) the content of the one or more data sources, automatically identify and extract a request or commitment from the content. Multiple actions may follow from initial recognition and extraction, including confirmation and refinement of the description of the request or commitment, and actions that assist one or more of the senders, recipients, or others to track and address the request or commitment, including the creation of additional messages, reminders, appointments, or to-do lists.

公开(公告)号：US10579430B2

公开(公告)日：2020-03-03

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: G06F9/46 ,  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：US20190303197A1

公开(公告)日：2019-10-03

申请号：US15943206

申请日：2018-04-02

Applicant: Microsoft Technology Licensing, LLC

Inventor： Jinchao Li ,  Yu Wang ,  Karan Srivastava ,  Jianfeng Gao ,  Prabhdeep Singh ,  Haiyuan Cao ,  Xinying Song ,  Hui Su ,  Jaideep Sarkar

IPC: G06F9/48 ,  G06F9/50 ,  G06K9/62 ,  G06F15/18

Abstract: Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

公开(公告)号：US20170193360A1

公开(公告)日：2017-07-06

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

CPC classification number: G06N3/08 ,  G06N3/0445 ,  G06N3/0454

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：US20160337295A1

公开(公告)日：2016-11-17

申请号：US14714137

申请日：2015-05-15

Applicant: Microsoft Technology Licensing, LLC

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

IPC: H04L12/58

CPC classification number: H04L51/18 ,  G06Q10/06 ,  G06Q10/10 ,  H04L51/04 ,  H04L51/046 ,  H04L51/10

Abstract: A system that analyses content of electronic communications may automatically extract requests or commitments from the electronic communications. In one example process, a processing component may analyze the content to determine one or more meanings of the content; query content of one or more data sources that is related to the electronic communications; and based, at least in part, on (i) the one or more meanings of the content and (ii) the content of the one or more data sources, automatically identify and extract a request or commitment from the content. Multiple actions may follow from initial recognition and extraction, including confirmation and refinement of the description of the request or commitment, and actions that assist one or more of the senders, recipients, or others to track and address the request or commitment, including the creation of additional messages, reminders, appointments, or to-do lists.

Abstract translation: 分析电子通信内容的系统可以自动从电子通信中提取请求或承诺。 在一个示例性过程中，处理组件可以分析内容以确定内容的一个或多个含义; 查询与电子通信相关的一个或多个数据源的内容; 并且至少部分地基于（i）内容的一个或多个含义，以及（ii）一个或多个数据源的内容，自动地从内容中识别和提取请求或承诺。 可以从初始识别和提取，包括对请求或承诺的描述的确认和细化，以及帮助一个或多个发件人，收件人或其他人跟踪和解决请求或承诺的行为，包括创建 附加消息，提醒，约会或待办事项列表。

公开(公告)号：US11734066B2

公开(公告)日：2023-08-22

申请号：US16737474

申请日：2020-01-08

Applicant: Microsoft Technology Licensing, LLC

Inventor： Jinchao Li ,  Yu Wang ,  Karan Srivastava ,  Jianfeng Gao ,  Prabhdeep Singh ,  Haiyuan Cao ,  Xinying Song ,  Hui Su ,  Jaideep Sarkar

IPC: G06F9/46 ,  G06F9/48 ,  G06F9/50 ,  G06N20/00 ,  G06F18/21

CPC classification number: G06F9/4887 ,  G06F9/4881 ,  G06F9/5005 ,  G06F18/21 ,  G06N20/00

Abstract: Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.