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    Co-occurrence consistency analysis method and apparatus for finding predictive variable groups
    1.
    发明授权
    Co-occurrence consistency analysis method and apparatus for finding predictive variable groups 有权
    共同一致性分析方法和装置，用于发现预测变量组

    公开(公告)号：US08355896B2

    公开(公告)日：2013-01-15

    申请号：US12202933

    申请日：2008-09-02

    申请人： Shailesh Kumar ,  Junwen Wu ,  Vesselin Diev

    发明人： Shailesh Kumar ,  Junwen Wu ,  Vesselin Diev

    IPC分类号： G06F17/50 ,  G06Q30/00 ,  G06Q99/00

    CPC分类号： G06Q10/10

    摘要： A method of modeling includes quantifying a co-operative strength value for a plurality of pairs of variables, and identifying a clique of at least three variables based on a graph of the co-operative strength values of a plurality of pairs of variables. The method also includes selecting a first pair of variables of the plurality of pairs of variables having a high co-operative strength value. A second clique may also be identified. A model of the first clique and a model of the second clique are made. The outputs of these models are combined to form a combined model which is used to make various decisions with respect to real time data.

    摘要翻译： 建模方法包括量化多对变量对的合作强度值，并且基于多对变量对的合作强度值的图来识别至少三个变量的集团。 该方法还包括选择具有高合作强度值的多对变量对的第一对变量。 也可以识别第二集团。 制作第一集团的模型和第二集团的模型。 这些模型的输出被组合以形成用于对实时数据做出各种决定的组合模型。

    CO-OCCURRENCE CONSISTENCY ANALYSIS METHOD AND APPARATUS FOR FINDING PREDICTIVE VARIABLE GROUPS
    2.
    发明申请
    CO-OCCURRENCE CONSISTENCY ANALYSIS METHOD AND APPARATUS FOR FINDING PREDICTIVE VARIABLE GROUPS 有权
    共同一致性分析方法和发现预测变量组的装置

    公开(公告)号：US20100057509A1

    公开(公告)日：2010-03-04

    申请号：US12202933

    申请日：2008-09-02

    申请人： Shailesh Kumar ,  Junwen Wu ,  Vesselin Diev

    发明人： Shailesh Kumar ,  Junwen Wu ,  Vesselin Diev

    IPC分类号： G06Q10/00

    CPC分类号： G06Q10/10

    摘要： A method of modeling includes quantifying a co-operative strength value for a plurality of pairs of variables, and identifying a clique of at least three variables based on a graph of the co-operative strength values of a plurality of pairs of variables. The method also includes selecting a first pair of variables of the plurality of pairs of variables having a high co-operative strength value. A second clique may also be identified. A model of the first clique and a model of the second clique are made. The outputs of these models are combined to form a combined model which is used to make various decisions with respect to real time data.

    摘要翻译： 建模方法包括量化多对变量对的合作强度值，并且基于多对变量对的合作强度值的图来识别至少三个变量的集团。 该方法还包括选择具有高合作强度值的多对变量对的第一对变量。 也可以识别第二集团。 制作第一集团的模型和第二集团的模型。 这些模型的输出被组合以形成用于对实时数据做出各种决定的组合模型。

    Incremental factorization-based smoothing of sparse multi-dimensional risk tables
    3.
    发明授权
    Incremental factorization-based smoothing of sparse multi-dimensional risk tables 有权
    稀疏多维风险表的增量因式分解平滑

    公开(公告)号：US08131615B2

    公开(公告)日：2012-03-06

    申请号：US12163688

    申请日：2008-06-27

    申请人： Vesselin Diev ,  Shailesh Kumar ,  Scott M. Zoldi

    发明人： Vesselin Diev ,  Shailesh Kumar ,  Scott M. Zoldi

    IPC分类号： G06Q40/00

    CPC分类号： G06Q40/02 ,  G06Q20/40 ,  G06Q40/00 ,  G06Q40/025

    摘要： A system for classifying a transaction as fraudulent includes a training component and a scoring component. The training component acts on historical data and also includes a multi-dimensional risk table component comprising one or more multidimensional risk tables each of which approximates an initial risk value for a substantially empty cell in a risk table based upon risk values in cells related to the substantially empty cell. The scoring component produces a score, based in part, on the risk tables associated with groupings of variables having values determined by the training component. The scoring component includes a statistical model that produces an output and wherein the transaction is classified as fraudulent when the output is above a selected threshold value.

    摘要翻译： 将交易分类为欺诈的系统包括训练组件和评分组件。 培训组件作用于历史数据，并且还包括多维风险表组件，其包括一个或多个多维风险表，每个风险表基于与风险表相关的单元中的风险值近似于风险表中基本上为空的单元格的初始风险值 基本上空的单元格。 评分部分产生一个分数，部分基于与具有由训练组件确定的值的变量分组相关联的风险表。 评分组件包括产生输出的统计模型，并且其中当输出高于所选择的阈值时，交易被分类为欺诈。

    INCREMENTAL FACTORIZATION-BASED SMOOTHING OF SPARSE MULTI-DIMENSIONAL RISK TABLES
    4.
    发明申请
    INCREMENTAL FACTORIZATION-BASED SMOOTHING OF SPARSE MULTI-DIMENSIONAL RISK TABLES 有权
    稀疏多维风险表的基于加速制造的平滑

    公开(公告)号：US20090327132A1

    公开(公告)日：2009-12-31

    申请号：US12163688

    申请日：2008-06-27

    申请人： Vesselin Diev ,  Shailesh Kumar ,  Scott M. Zoldi

    发明人： Vesselin Diev ,  Shailesh Kumar ,  Scott M. Zoldi

    IPC分类号： G06Q40/00

    CPC分类号： G06Q40/02 ,  G06Q20/40 ,  G06Q40/00 ,  G06Q40/025

    摘要： A system for classifying a transaction as fraudulent includes a training component and a scoring component. The training component acts on historical data and also includes a multi-dimensional risk table component comprising one or more multidimensional risk tables each of which approximates an initial risk value for a substantially empty cell in a risk table based upon risk values in cells related to the substantially empty cell. The scoring component produces a score, based in part, on the risk tables associated with groupings of variables having values determined by the training component. The scoring component includes a statistical model that produces an output and wherein the transaction is classified as fraudulent when the output is above a selected threshold value.

    摘要翻译： 将交易分类为欺诈的系统包括训练组件和评分组件。 培训组件作用于历史数据，并且还包括多维风险表组件，其包括一个或多个多维风险表，每个风险表基于与风险表相关的单元中的风险值近似于风险表中基本上为空的单元格的初始风险值 基本上空的单元格。 评分部分产生一个分数，部分基于与具有由训练组件确定的值的变量分组相关联的风险表。 评分组件包括产生输出的统计模型，并且其中当输出高于所选择的阈值时，交易被分类为欺诈。

    Event driven change injection and dynamic extensions to a business process execution language process
    6.
    发明授权
    Event driven change injection and dynamic extensions to a business process execution language process 有权
    事件驱动的更改注入和业务流程执行语言流程的动态扩展

    公开(公告)号：US08572618B2

    公开(公告)日：2013-10-29

    申请号：US12776064

    申请日：2010-05-07

    申请人： Khanderao Kand ,  Fang Liu ,  Bhagat Nainani ,  Shailesh Kumar

    发明人： Khanderao Kand ,  Fang Liu ,  Bhagat Nainani ,  Shailesh Kumar

    IPC分类号： G09F9/46

    CPC分类号： G06F9/5038 ,  G06F2209/5013

    摘要： An extensible process design provides an ability to dynamically inject changes into a running process instance, such as a BPEL instance. Using a combination of BPEL, rules and events, processes can be designed to allow flexibility in terms of adding new activities, removing or skipping activities and adding dependent activities. These changes do not require redeployment of the orchestration process and can affect the behavior of in-flight process instances. The extensible process design includes a main orchestration process, a set of task execution processes and a set of generic trigger processes. The design also includes a set of rules evaluated during execution of the tasks of the orchestration process. The design can further include three types of events: an initiate process event, a pre-task execution event and a post-task execution event. These events and rules can be used to alter the behavior of the main orchestration process at runtime.

    摘要翻译： 可扩展过程设计提供了将更改动态注入正在运行的流程实例（如BPEL实例）的功能。 使用BPEL，规则和事件的组合，可以设计流程以允许在添加新活动，删除或跳过活动以及添加依赖活动方面的灵活性。 这些更改不需要重新部署编排过程，并且可能影响飞行中流程实例的行为。 可扩展过程设计包括主要的编排过程，一组任务执行过程和一组通用触发过程。 该设计还包括在执行编排过程任务期间评估的一组规则。 该设计可以进一步包括三种类型的事件：启动过程事件，前任务执行事件和后任务执行事件。 这些事件和规则可用于在运行时改变主要业务流程的行为。

    Method and apparatus for recommendation engine using pair-wise co-occurrence consistency
    7.
    发明授权
    Method and apparatus for recommendation engine using pair-wise co-occurrence consistency 有权
    推荐引擎使用成对一致性的方法和装置

    公开(公告)号：US08015140B2

    公开(公告)日：2011-09-06

    申请号：US12857317

    申请日：2010-08-16

    申请人： Shailesh Kumar ,  Edmond D. Chow ,  Michinari Momma

    发明人： Shailesh Kumar ,  Edmond D. Chow ,  Michinari Momma

    IPC分类号： G06F17/00 ,  G06N5/02

    CPC分类号： G06Q30/06 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0204 ,  G06Q30/0224 ,  G06Q30/0605

    摘要： The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.

    摘要翻译： 本发明在本文中称为PeaCoCk，使用来自统计学，信息理论和图形理论的独特技术融合来量化和发现实体（例如产品和客户）之间的关系中的模式，如购买行为所证明的。 与传统的基于购买频率的市场篮子分析技术（如大多数产生明显和虚假关联的关联规则）相反，PeaCoCk采用信息理论的一致性和相似性概念，这使得对真实的，统计上显着的和合乎逻辑的逻辑分析 产品之间的关联。 因此，PeaCoCk可以根据购买行为进行可靠，强大的预测分析。

    Method and apparatus for recommendation engine using pair-wise co-occurrence consistency
    9.
    发明申请
    Method and apparatus for recommendation engine using pair-wise co-occurrence consistency 有权
    推荐引擎使用成对一致性的方法和装置

    公开(公告)号：US20070094066A1

    公开(公告)日：2007-04-26

    申请号：US11327822

    申请日：2006-01-06

    申请人： Shailesh Kumar ,  Edmond Chow ,  Michinari Momma

    发明人： Shailesh Kumar ,  Edmond Chow ,  Michinari Momma

    IPC分类号： G06F17/30

    CPC分类号： G06Q30/06 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0204 ,  G06Q30/0224 ,  G06Q30/0605

    摘要： The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.

    摘要翻译： 本发明在本文中称为PeaCoCk，使用来自统计学，信息理论和图形理论的独特技术融合来量化和发现实体（例如产品和客户）之间的关系中的模式，如购买行为所证明的。 与传统的基于购买频率的市场篮子分析技术（如大多数产生明显和虚假关联的关联规则）相反，PeaCoCk采用信息理论的一致性和相似性概念，这使得对真实的，统计上显着的和合乎逻辑的逻辑分析 产品之间的关联。 因此，PeaCoCk可以根据购买行为进行可靠，强大的预测分析。