公开(公告)号：US20070094067A1

公开(公告)日：2007-04-26

申请号：US11355567

申请日：2006-02-15

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.

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。

公开(公告)号：US20070100680A1

公开(公告)日：2007-05-03

申请号：US11256386

申请日：2005-10-21

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。

公开(公告)号：US20100324985A1

公开(公告)日：2010-12-23

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： G06Q30/00 ,  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。

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。

公开(公告)号：US07801843B2

公开(公告)日：2010-09-21

申请号：US11327822

申请日：2006-01-06

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

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

IPC分类号： G06F15/18 ,  G06F17/00 ,  G06N5/04

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之间的关系中的模式，例如产品和客户，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。

公开(公告)号：US07685021B2

公开(公告)日：2010-03-23

申请号：US11355567

申请日：2006-02-15

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

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

IPC分类号： G06Q30/00

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。

公开(公告)号：US07672865B2

公开(公告)日：2010-03-02

申请号：US11256386

申请日：2005-10-21

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

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

IPC分类号： G06F17/30

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。

公开(公告)号：US09633959B2

公开(公告)日：2017-04-25

申请号：US14620155

申请日：2015-02-11

申请人： Shailesh Kumar ,  Vikas Garg ,  Meng Kong Lye

发明人： Shailesh Kumar ,  Vikas Garg ,  Meng Kong Lye

IPC分类号： H01L23/48 ,  H01L23/52 ,  H01L23/00 ,  H01L23/31

CPC分类号： H01L24/06 ,  H01L23/3171 ,  H01L24/02 ,  H01L24/05 ,  H01L2224/0235 ,  H01L2224/02375 ,  H01L2224/04042 ,  H01L2224/05553 ,  H01L2224/05554 ,  H01L2224/05567 ,  H01L2224/0603 ,  H01L2224/06051 ,  H01L2224/06131 ,  H01L2224/06135 ,  H01L2224/06138 ,  H01L2224/06139 ,  H01L2224/06179 ,  H01L2924/00012

摘要： An integrated circuit (IC) die has side input/output (IO) pads located along each side of the die interior. Each die corner has a corner IO pad. The side IO pads adjacent to the corner IO pads have shortened passivation regions in the top metal layer (TML) that define TML access regions. TML traces run through the TML access regions to connect the corner IO pads to the die interior. Providing corner IO pads enables an IC die to have up to four more IO pads than a comparable conventional IC die that does not have any corner IO pads, or an IC die to have the same number of IO pads within a smaller overall footprint.

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。