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1.发明申请METHOD AND APPARATUS FOR RECOMMENDATION ENGINE USING PAIR-WISE CO-OCCURRENCE CONSISTENCY 有权使用配对联合一致性推荐发动机的方法和装置公开(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
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。
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2.发明授权公开(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
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。
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3.发明授权公开(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
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。
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4.发明授权Method and apparatus for initiating a transaction based on a bundle-lattice space of feasible product bundles 有权用于基于可行产品束的束格空间发起事务的方法和装置公开(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。
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5.发明授权Method and apparatus for retail data mining using pair-wise co-occurrence consistency 有权零售数据挖掘的方法和装置，使用成对的同现一致性公开(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。
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6.发明申请Method and apparatus for retail data mining using pair-wise co-occurrence consistency 有权零售数据挖掘的方法和装置，使用成对的同现一致性公开(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。
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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.
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8.发明申请公开(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。
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9.发明申请ACTIVE METRIC LEARNING DEVICE, ACTIVE METRIC LEARNING METHOD, AND ACTIVE METRIC LEARNING PROGRAM 有权主动公制学习设备，主动学习方法和主动学习方案公开(公告)号：US20110231350A1
公开(公告)日：2011-09-22
申请号：US13130950
申请日：2009-11-24
IPC分类号： G06F15/18
CPC分类号： G06N99/005 , G06F17/30943 , G06K9/6218
摘要： An active metric learning device includes a metric application data analysis unit, a metric optimization unit, and an attribute clustering unit. The metric application data analysis unit is formed with a metric applying module for calculating the distance between data to be analyzed, a data analyzing module for analyzing the data using a predetermined function and the distances between the data to be analyzed and outputting the result of the data analysis, and an analysis result storage unit for storing the result of the data analysis. The metric optimization unit is formed with a feedback converting module for creating side information according to the command of feedback from the user and a metric learning module for generating a metric matrix optimized under a predetermined condition using the created side information. The attribute clustering unit clusters the metric matrix optimized by the metric optimization unit and structuralizes the attributes.
摘要翻译： 活动度量学习装置包括度量应用数据分析单元，度量优化单元和属性聚类单元。 度量应用数据分析单元形成有用于计算要分析的数据之间的距离的度量应用模块，用于使用预定函数分析数据的数据分析模块和要分析的数据之间的距离，并输出 数据分析以及用于存储数据分析结果的分析结果存储单元。 度量优化单元由反馈转换模块形成，用于根据来自用户的反馈命令创建侧信息，以及度量学习模块，用于使用创建的侧信息生成在预定条件下优化的度量矩阵。 属性聚类单元聚合由度量优化单元优化的度量矩阵，并对属性进行结构化。
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公开(公告)号：US20100318334A1
公开(公告)日：2010-12-16
申请号：US12866828
申请日：2009-02-09
申请人： Michinari Momma
发明人： Michinari Momma
IPC分类号： G06G7/48
CPC分类号： G06K9/6269
摘要： The data analysis apparatus (100) of the present invention includes a control unit (180) that, upon input of a plurality of data that are the object of analysis, sets constraints that take as version space a space that is enclosed by planes that contain these data and moreover that are perpendicular to each of the plurality of data in model parameter space, maximizes the size of a shape that is inscribed in a plurality of planes that enclose the version space, and finds the center of the shape.
摘要翻译： 本发明的数据分析装置（100）包括：控制单元（180），当输入作为分析对象的多个数据时，设定作为版本空间的约束，该空间包含由 这些数据，而且在模型参数空间中垂直于多个数据中的每一个，使包围该版本空间的多个平面中内接的形状的大小最大化，并且找到该形状的中心。
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