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：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：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：US06728695B1

公开(公告)日：2004-04-27

申请号：US09589180

申请日：2000-05-26

申请人： Anu K. Pathria ,  Krishna M. Gopinathan ,  Theodore J. Crooks ,  Edmond D. Chow ,  Mark A. Laffoon ,  Dayne B. Freitag

发明人： Anu K. Pathria ,  Krishna M. Gopinathan ,  Theodore J. Crooks ,  Edmond D. Chow ,  Mark A. Laffoon ,  Dayne B. Freitag

IPC分类号： G06F1730

CPC分类号： G06F17/2775 ,  G06F17/2715 ,  G06F17/279 ,  G06F17/30616 ,  Y10S707/99932 ,  Y10S707/99935 ,  Y10S707/99945 ,  Y10S707/99954

摘要： A method and apparatus is disclosed for making predictions about entities represented in documents and for information analysis of text documents or the like, from a large number of such documents. Predictive models are executed responsive to variables derived from canonical documents to determine documents containing desired attributes or characteristics. The canonical documents are derived from standardized documents, which, in turn, are derived from original documents.

摘要翻译： 公开了一种方法和装置，用于从大量这样的文档中对文档中表示的实体进行预测，以及对文本文档等的信息分析。 响应于从规范文档导出的变量来确定包含所需属性或特征的文档来执行预测模型。 标准文件来源于标准化文件，而这些文件反过来源于原始文件。