公开(公告)号：US10572819B2

公开(公告)日：2020-02-25

申请号：US14812344

申请日：2015-07-29

Applicant: International Business Machines Corporation

Inventor： Tamir Klinger ,  Chandrasekhara K. Reddy ,  Ashish Sabharwal ,  Horst C. Samulowitz ,  Gerald J. Tesauro ,  Deepak S. Turaga

IPC: G06N5/04 ,  G06N20/00

Abstract: A system, method, and computer program product for automatically selecting from a plurality of analytic algorithms a best performing analytic algorithm to apply to a dataset is provided. The automatically selecting from the plurality of analytic algorithms the best performing analytic algorithm to apply to the dataset enables a training a plurality of analytic algorithms on a plurality of subsets of the dataset. Then, a corresponding prediction accuracy trend is estimated across the subsets for each of the plurality of analytic algorithms to produce a plurality of accuracy trends. Next, the best performing analytic algorithm is selected and outputted from the plurality of analytic algorithms based on the corresponding prediction accuracy trend with a highest value from the plurality of accuracy trends.

公开(公告)号：US09256838B2

公开(公告)日：2016-02-09

申请号：US13840763

申请日：2013-03-15

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Daby M. Sow ,  Deepak S. Turaga ,  Yu Zhang

IPC: G06N99/00

CPC classification number: G06N99/005

Abstract: A method of meta-learning includes receiving a prediction objective, extracting a plurality of subsets of data from a distributed dataset, generating a plurality of local predictions, wherein each local prediction is based on a different subset of the plurality of subsets of data and the prediction objective, combining the plurality of local predictions, and generating a final prediction based on the combined local predictions.

Abstract translation: 元学习的方法包括接收预测目标，从分布式数据集提取多个数据子集，生成多个局部预测，其中每个局部预测基于多个数据子集的不同子集，并且 预测目标，组合多个局部预测，以及基于组合的局部预测产生最终预测。

公开(公告)号：US20140279741A1

公开(公告)日：2014-09-18

申请号：US13840763

申请日：2013-03-15

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： DABY M. SOW ,  Deepak S. Turaga ,  Yu Zhang

IPC: G06N99/00

CPC classification number: G06N99/005

Abstract: A method of meta-learning includes receiving a prediction objective, extracting a plurality of subsets of data from a distributed dataset, generating a plurality of local predictions, wherein each local prediction is based on a different subset of the plurality of subsets of data and the prediction objective, combining the plurality of local predictions, and generating a final prediction based on the combined local predictions.

Abstract translation: 元学习的方法包括接收预测目标，从分布式数据集提取多个数据子集，生成多个局部预测，其中每个局部预测基于多个数据子集的不同子集，并且 预测目标，组合多个局部预测，以及基于组合的局部预测产生最终预测。

公开(公告)号：US11023840B2

公开(公告)日：2021-06-01

申请号：US15418377

申请日：2017-01-27

Applicant: International Business Machines Corporation

Inventor： Yuan-Chi Chang ,  Mark D. Feblowitz ,  Nagui Halim ,  Stuart S. Horn ,  Edward J. Pring ,  Anton V. Riabov ,  Edward W. Shay ,  Shirin Sohrabi Araghi ,  Deepak S. Turaga ,  Octavian Udrea ,  Fang Yuan ,  Peter Zimmer

IPC: G06Q10/06 ,  G06N5/02 ,  G06F16/951

Abstract: Techniques for scenario planning are provided. In one example, a computer-implemented method can comprise analyzing, by a device operatively coupled to a processor, content using a topic model. The content can be associated with a defined source and is related to one or more current events. The computer-implemented method can also comprise determining, by the device, one or more portions of the analyzed content that are relevant to one or more key risk drivers using a risk driver model. The computer-implemented method can also comprise aggregating, by the device, the determined one or more portions into one or more emerging storylines based on values of one or more attributes of the topic model.

公开(公告)号：US10395004B2

公开(公告)日：2019-08-27

申请号：US14990198

申请日：2016-01-07

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Alain E. Biem ,  Timothy R. Dinger ,  Nagui Halim ,  Gang Luo ,  Daby M. Sow ,  Deepak S. Turaga

IPC: G06F16/245 ,  G06N5/02 ,  G06N20/00 ,  G06F19/00 ,  G06F16/2455 ,  G16H50/20

Abstract: A method of analyzing physiological data streams. According to the method, physiological data is received into a computerized machine. The physiological data comprises numerical data and medical symptoms of a patient. Features are extracted from the physiological data based on development of the physiological data over a period of time. The features are converted into a textual representation using natural language generation. Input terms for an information retrieval system operating on the computerized machine are automatically generated based on the features. The input terms are input to the information retrieval system. A corpus of data is automatically searched to retrieve results to the input terms using the information retrieval system.

公开(公告)号：US20180348002A1

公开(公告)日：2018-12-06

申请号：US15811732

申请日：2017-11-14

Applicant: International Business Machines Corporation

Inventor： Charu C. Aggarwal ,  Saket K. Sathe ,  Deepak S. Turaga

IPC: G01C21/36 ,  G01C21/34

Abstract: Embodiments of the invention include method, systems and computer program products for providing ease-of-drive driving directions. The computer-implemented method includes receiving, by a processor, a request for a route from a starting point to a destination point. The processor calculates one or more routes from the starting point to the destination point. The processor scores the one or more calculated routes according to ease-of-drive driving criteria. The processor presents at least one of the scored calculated routes that are below a predetermined threshold.

公开(公告)号：US20160188812A1

公开(公告)日：2016-06-30

申请号：US14990198

申请日：2016-01-07

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Alain E. Biem ,  Timothy R. Dinger ,  Nagui Halim ,  Gang Luo ,  Daby M. Sow ,  Deepak S. Turaga

IPC: G06F19/00

CPC classification number: G06F19/324 ,  G06F16/24568 ,  G06N5/02 ,  G16H50/20

Abstract: A method of analyzing physiological data streams. According to the method, physiological data is received into a computerized machine. The physiological data comprises numerical data and medical symptoms of a patient. Features are extracted from the physiological data based on development of the physiological data over a period of time. The features are converted into a textual representation using natural language generation. Input terms for an information retrieval system operating on the computerized machine are automatically generated based on the features. The input terms are input to the information retrieval system. A corpus of data is automatically searched to retrieve results to the input terms using the information retrieval system.

Abstract translation: 分析生理数据流的方法。 根据该方法，将生理数据接收到计算机化机器中。 生理数据包括患者的数值数据和医学症状。 基于一段时间内生理数据的发展，从生理数据中提取特征。 使用自然语言生成将特征转换为文本表示。 基于功能自动生成在计算机化机器上运行的信息检索系统的输入项。 输入项输入到信息检索系统。 使用信息检索系统自动搜索数据语料库以将结果检索到输入项。

公开(公告)号：US20160154719A9

公开(公告)日：2016-06-02

申请号：US13972566

申请日：2013-08-21

Applicant: International Business Machines Corporation

Inventor： Wei Fan ,  Nagui Halim ,  Mark C. Johnson ,  Srinivasan Parthasarathy ,  Deepak S. Turaga ,  Olivier Verscheure

IPC: G06F11/27

CPC classification number: G06F11/27 ,  G01R31/2894

Abstract: The present invention describes a method and system for optimizing a test flow within each ATE (Automated Test Equipment) station. The test flow includes a plurality of test blocks. A test block includes a plurality of individual tests. A computing system schedule the test flow based one or more of: a test failure model, test block duration and a yield model. The failure model determines an order or sequence of the test blocks. There are at least two failure models: independent failure model and dependant failure model. The yield model describes whether a semiconductor chip is defective or not. Upon completing the scheduling, the ATE station conducts tests according to the scheduled test flow. The present invention can also be applied to software testing.

公开(公告)号：US20160110362A1

公开(公告)日：2016-04-21

申请号：US14518506

申请日：2014-10-20

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Udayan Khurana ,  Srinivasan Parthasarathy ,  Venkata N. Pavuluri ,  Deepak S. Turaga ,  Long H. Vu

IPC: G06F17/30

CPC classification number: G06F16/242 ,  G06F16/245 ,  G06F16/24553 ,  G06F16/248 ,  G06F16/3323

Abstract: Embodiments relate to analyzing dataset. A method of analyzing data is provided. The method obtains a description of a dataset. The method automatically generates a plurality of analysis options from the description of the dataset. The method generates a plurality of queries based on the analysis options. The method deploys the queries on the dataset to build a plurality of statistical models from the dataset.

Abstract translation: 实施例涉及分析数据集。 提供了一种分析数据的方法。 该方法获取数据集的描述。 该方法根据数据集的描述自动生成多个分析选项。 该方法基于分析选项生成多个查询。 该方法在数据集上部署查询以从数据集构建多个统计模型。

公开(公告)号：US20200027024A1

公开(公告)日：2020-01-23

申请号：US16587232

申请日：2019-09-30

Applicant: International Business Machines Corporation

Inventor： Charu C. Aggarwal ,  Lingtao Cao ,  Tan Hung M. Ng ,  Saket Sathe ,  Deepak S. Turaga

IPC: G06N20/00 ,  G16H50/70 ,  G16H40/63 ,  G16H40/67 ,  G16H50/20 ,  G16H10/60 ,  G16H50/50

Abstract: Embodiments for accurate temporal event predictive modeling by a processor. An average reverse event delay may be determined from one or more event delays in a time-series window. A time-series event may be predicted by applying the average reverse event delay in conjunction with one or more weighted factors in a predictive model.