公开(公告)号：US20160210126A1

公开(公告)日：2016-07-21

申请号：US14597320

申请日：2015-01-15

Applicant: XEROX CORPORATION

Inventor： Atul Singh ,  Vaibhav Rajan ,  Koustuv Dasgupta ,  Nischal Murthy Piratla

IPC: G06F9/44 ,  G06F11/36 ,  H04L12/18

CPC classification number: G06F8/34 ,  G06F8/70 ,  G06F9/46 ,  G06F11/3688 ,  G06Q10/06 ,  G06Q10/101 ,  H04L12/1822

Abstract: The disclosed embodiments illustrate methods and systems for crowdsourcing a software development project. The method includes segregating the software development project into one or more modules based on at least one configuration file. The at least one configuration file is deterministic of at least a set of dependencies between the one or more modules. Further, a task corresponding to at least one module from the one or more modules is created. The task is crowdsourced to one or more crowdworkers. Thereafter, a source code of the at least one module, received as a response for the task, is integrated with one or more source codes of remaining of the one or more modules to generate an integrated software package based on said at least one configuration file. Further, the integrated software package is validated by performing integration testing of the integrated software package.

Abstract translation: 所公开的实施例说明了用于使软件开发项目众包的方法和系统。 该方法包括基于至少一个配置文件将软件开发项目隔离成一个或多个模块。 所述至少一个配置文件是所述一个或多个模块之间的至少一组依赖性的确定性。 此外，创建与来自一个或多个模块的至少一个模块对应的任务。 这个任务是挤满了一个或者更多的人群。 此后，将作为任务的响应接收的至少一个模块的源代码与一个或多个模块的剩余的源代码集成，以基于所述至少一个配置文件生成集成的软件包 。 此外，集成软件包通过执行集成软件包的集成测试来验证。

公开(公告)号：US09383976B1

公开(公告)日：2016-07-05

申请号：US14597320

申请日：2015-01-15

Applicant: XEROX CORPORATION

Inventor： Atul Singh ,  Vaibhav Rajan ,  Koustuv Dasgupta ,  Nischal Murthy Piratla

IPC: G06F9/44 ,  H04L12/18 ,  G06F11/36 ,  G06Q10/06

CPC classification number: G06F8/34 ,  G06F8/70 ,  G06F9/46 ,  G06F11/3688 ,  G06Q10/06 ,  G06Q10/101 ,  H04L12/1822

Abstract: The disclosed embodiments illustrate methods and systems for crowdsourcing a software development project. The method includes segregating the software development project into one or more modules based on at least one configuration file. The at least one configuration file is deterministic of at least a set of dependencies between the one or more modules. Further, a task corresponding to at least one module from the one or more modules is created. The task is crowdsourced to one or more crowdworkers. Thereafter, a source code of the at least one module, received as a response for the task, is integrated with one or more source codes of remaining of the one or more modules to generate an integrated software package based on said at least one configuration file. Further, the integrated software package is validated by performing integration testing of the integrated software package.

Abstract translation: 所公开的实施例说明了用于使软件开发项目众包的方法和系统。 该方法包括基于至少一个配置文件将软件开发项目隔离成一个或多个模块。 所述至少一个配置文件是所述一个或多个模块之间的至少一组依赖性的确定性。 此外，创建与来自一个或多个模块的至少一个模块对应的任务。 这个任务是挤满了一个或者更多的人群。 此后，将作为任务的响应接收的至少一个模块的源代码与一个或多个模块的剩余的源代码集成，以基于所述至少一个配置文件生成集成的软件包 。 此外，集成软件包通过执行集成软件包的集成测试来验证。

公开(公告)号：US20170055916A1

公开(公告)日：2017-03-02

申请号：US14841812

申请日：2015-09-01

Applicant: XEROX CORPORATION

Inventor： Sakyajit Bhattacharya ,  Vaibhav Rajan ,  Harsh Shrivastava

IPC: A61B5/00 ,  A61B5/145 ,  A61B5/0205

CPC classification number: A61B5/7275 ,  A61B5/02055 ,  A61B5/021 ,  A61B5/024 ,  A61B5/0816 ,  A61B5/14542 ,  G16H10/60 ,  G16H50/20 ,  G16H50/30

Abstract: Disclosed are embodiments of methods and systems for predicting mortality of a first patient. The method comprises categorizing a historical data into a first category and a second category. The method further comprises determining a first test parameter and a second test parameter based on at least one of a sample data of a first patient and the historical data corresponding to at least one of the first category and the second category. The method further comprises determining a probability score based on a cumulative distribution of at least one of the first test parameter and the second test parameter. The method further comprises categorizing the sample data in one of the first category and the second category based on the probability score. Further, the method comprises predicting the mortality of the first patient based on at least the categorization of the sample data of the first patient.

Abstract translation: 公开了用于预测第一患者的死亡率的方法和系统的实施例。 该方法包括将历史数据分类为第一类别和第二类别。 该方法还包括基于第一患者的样本数据和对应于第一类别和第二类别中的至少一个的历史数据中的至少一个来确定第一测试参数和第二测试参数。 该方法还包括基于第一测试参数和第二测试参数中的至少一个的累积分布来确定概率得分。 该方法还包括基于概率分数将样本数据分类为第一类别和第二类别之一。 此外，该方法包括至少基于第一患者的样本数据的分类来预测第一患者的死亡率。

公开(公告)号：US20150228015A1

公开(公告)日：2015-08-13

申请号：US14179775

申请日：2014-02-13

Applicant: Xerox Corporation

Inventor： Sakyajit Bhattacharya ,  Vaibhav Rajan

IPC: G06Q40/02

CPC classification number: G06Q40/025

Abstract: Disclosed are the embodiments for creating a model capable of identifying one or more clusters in a financial data. An input is received pertaining to a range of numbers. Each number in the range of numbers is representative of a number of clusters in the financial data. For a cluster, one or more first parameters of a distribution associated with the cluster are estimated. Thereafter, a threshold value is determined based on the one or more first parameters. An inverse cumulative distribution of each of one or more n-dimensional variables in the financial data is determined. The one or more first parameters are updated to generate one or more second parameters based on the estimated inverse cumulative distribution. A model is created for each number in the range of numbers based on the one or more second parameters.

Abstract translation: 公开了用于创建能够识别财务数据中的一个或多个集群的模型的实施例。 接收与数字范围有关的输入。 数字范围内的每个数字代表财务数据中的多个集群。 对于集群，估计与集群相关联的分发的一个或多个第一参数。 此后，基于一个或多个第一参数来确定阈值。 确定财务数据中的一个或多个n维变量中的每一个的逆累积分布。 更新一个或多个第一参数以基于估计的反向累积分布生成一个或多个第二参数。 基于一个或多个第二参数，为数字范围内的每个数字创建模型。

公开(公告)号：US20150220871A1

公开(公告)日：2015-08-06

申请号：US14171793

申请日：2014-02-04

Applicant: XEROX CORPORATION

Inventor： Vaibhav Rajan ,  Sakyajit Bhattacharya ,  Koustuv Dasgupta ,  Nischal Murthy Piratla ,  Laura Elisa Celis ,  Deepthi Chander ,  Saraschandra Karanam

IPC: G06Q10/06

CPC classification number: G06Q10/063112

Abstract: The disclosed embodiments illustrate methods and systems for scheduling a batch of tasks on one or more crowdsourcing platforms. The method includes generating one or more forecast models for each of the one or more crowdsourcing platforms based on historical data associated with each of the one or more crowdsourcing platforms and a robustness parameter. Thereafter, for a forecast model, from the one or more forecast models, associated with each of the one or more crowdsourcing platforms, a schedule is generated based on the forecast model and one or more parameters associated with the batch of tasks. Further, the schedule is executed on each of the one or more forecasts models associated with the one or more crowdsourcing platforms to determine a performance score of the schedule on each of the one or more forecast models. Finally, the schedule is recommended to a requestor based on the performance score.

Abstract translation: 所公开的实施例示出了用于在一个或多个众包平台上调度一批任务的方法和系统。 该方法包括基于与一个或多个众包平台中的每一个相关联的历史数据和鲁棒性参数为一个或多个众包平台中的每一个生成一个或多个预测模型。 此后，对于与一个或多个众包平台中的每一个相关联的一个或多个预测模型的预测模型，基于预测模型和与该批次任务相关联的一个或多个参数来生成调度。 此外，对与一个或多个众包平台相关联的一个或多个预测模型中的每一个执行日程表，以确定该一个或多个预测模型中的每个预测模型上的日程表的绩效分数。 最后，根据绩效分数向请求者推荐日程表。

公开(公告)号：US20170293856A1

公开(公告)日：2017-10-12

申请号：US15093302

申请日：2016-04-07

Applicant: Xerox Corporation

Inventor： Sakyajit Bhattacharya ,  Vaibhav Rajan ,  Asim Anand

IPC: G06N99/00 ,  G06F17/30

CPC classification number: G06F16/285 ,  G06N7/005

Abstract: LASSO constraints can lead to a Gaussian mixture copula model that is more robust, better conditioned, and more reflective of the actual clusters in the training data. These qualities of the GMCM have been shown with data obtained from: digital images of fine needle aspirates of breast tissue for detecting cancer; email for detecting spam; two dimensional terrain data for detecting hills and valleys; and video sequences of hand movements to detect gestures. Using training data, a GMCM estimate can be produced and iteratively refined to maximize a penalized log likelihood estimate until sequential iterations are within a threshold value of one another. The GMCM estimate can then be used to classify further samples. The LASSO constraints help keep the analysis tractibe such that useful results can be found and used while the result is still useful.

公开(公告)号：US20170286569A1

公开(公告)日：2017-10-05

申请号：US15083466

申请日：2016-03-29

Applicant: Xerox Corporation

Inventor： Abhishek Sengupta ,  Prathosh Aragulla Prasad ,  Satya Narayan Shukla ,  Vaibhav Rajan ,  Katerina Sinclair ,  Stephen Fullerton

IPC: G06F17/50 ,  G06N99/00 ,  G06N5/04 ,  G06F17/18

CPC classification number: G06F17/5009 ,  G06F17/18 ,  G06F2217/10 ,  G06K9/00536 ,  G06N5/04 ,  G06N20/00

Abstract: Systems and methods of modeling irregularly sampled time series signals with unknown temporal dynamics are disclosed wherein a temporal difference variable (TDV) is introduced to model irregular time differences between subsequent measurements. A hierarchical model is designed comprising two linear dynamical systems that model the effects of evolving TDV on temporal observations. All the parameters of the model, including the temporal dynamics are statistically estimated using historical data.

公开(公告)号：US20170262597A1

公开(公告)日：2017-09-14

申请号：US15065432

申请日：2016-03-09

Applicant: Xerox Corporation

Inventor： Vijay Huddar ,  Bhupendra Solanki ,  Vaibhav Rajan ,  Sakyajit Bhattacharya

IPC: G06F19/00 ,  G06N99/00

CPC classification number: G16H50/20 ,  G06N20/00

Abstract: A method, non-transitory computer readable medium and apparatus for predicting mortality of a current patient are disclosed. For example, the method includes receiving data associated with a plurality of different patients with known mortality outcomes, wherein the data includes a subset of data for each one of a plurality of different measurement timepoints for each one of the plurality of different patients, calculating n number of classifiers, wherein n is equal to a number of the plurality of different measurement timepoints, receiving data associated with the current patient at an i-th measurement timepoint, predicting the current patient has a high mortality risk based on an output of the i-th classifier of the n number of classifiers and transmitting a signal to a health administration server to cause an alarm to be generated in response to the high mortality risk that is predicted.

公开(公告)号：US20160210839A1

公开(公告)日：2016-07-21

申请号：US14597303

申请日：2015-01-15

Applicant: XEROX CORPORATION

Inventor： Kuldeep Yadav ,  Vaibhav Rajan ,  Abhishek Kumar ,  Nischal Murthy Piratla

IPC: G08B21/04 ,  H04W4/00 ,  H04N7/18

CPC classification number: G08B21/0492 ,  G08B13/19613 ,  G08B21/0461 ,  G08B25/08 ,  H04W4/38

Abstract: Embodiments of a computer-implemented method for monitoring a physical activity of a user are disclosed. The method includes receiving position or orientation data of a portable computing device; receiving an indication of an input device being operated by the user and a video captured by an imaging unit, the input device, and the imaging unit being operationally coupled to a stationary computing device. The portable computing device, the input device and the imaging unit are triggered by a data aggregator module based on a predefined sequence. The method also includes determining an activity pattern data of the user over a predefined time interval based on the position or orientation data, the received indication, and the video including an image of the user; and correlating the determined activity pattern data with health data of the user to monitor the physical activity of the user.

Abstract translation: 公开了一种用于监视用户身体活动的计算机实现的方法的实施例。 该方法包括接收便携式计算设备的位置或取向数据; 接收由用户操作的输入设备的指示和由成像单元捕获的视频，输入设备和成像单元可操作地耦合到固定计算设备。 基于预定义的顺序，便携式计算设备，输入设备和成像单元由数据聚合器模块触发。 该方法还包括基于所述位置或取向数据，所接收的指示和包括用户的图像的视频在预定时间间隔上确定用户的活动模式数据; 以及将确定的活动模式数据与用户的健康数据相关联以监视用户的身体活动。

公开(公告)号：US20150302155A1

公开(公告)日：2015-10-22

申请号：US14253941

申请日：2014-04-16

Applicant: Xerox Corporation

Inventor： Vaibhav Rajan ,  Sakyajit Bhattacharya ,  Ranjan Shetty K ,  Amith Sitaram ,  Vivek G. Raman

IPC: G06F19/00

CPC classification number: G16H50/20 ,  G16H50/30

Abstract: Disclosed are the methods and systems for classifying one or more patients in one or more categories. A distribution of one or more physiological parameters associated with the one or more patients is determined based on a patient dataset. The one or more physiological parameters correspond to at least a stroke scale score. One or more parameters associated with a copula are estimated by the one or more processors. In an embodiment, the copula defines a joint distribution of the one or more physiological parameters. A classifier is created based on the one or more parameters, wherein the classifier classifies the one or more patients in the one or more categories. The one or more categories correspond to a range of the stroke scale score.

Abstract translation: 公开了用于对一个或多个类别中的一个或多个患者进行分类的方法和系统。 基于患者数据集确定与一个或多个患者相关联的一个或多个生理参数的分布。 所述一个或多个生理参数对应于至少中风量表得分。 与一个或多个处理器估计与copula相关联的一个或多个参数。 在一个实施例中，所述copula限定所述一个或多个生理参数的联合分布。 基于一个或多个参数创建分类器，其中分类器对一个或多个类别中的一个或多个患者进行分类。 一个或多个类别对应于笔画刻度分数的范围。