利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

50 条记录 (耗时 0.033 秒)

    Method and apparatus for retail data mining using pair-wise co-occurrence consistency
    32.
    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。

    Phrase identification in a sequence of words
    37.
    发明授权
    Phrase identification in a sequence of words 有权
    短语识别在一系列单词中

    公开(公告)号：US08892422B1

    公开(公告)日：2014-11-18

    申请号：US13544323

    申请日：2012-07-09

    申请人： Shailesh Kumar

    发明人： Shailesh Kumar

    IPC分类号： G06F17/27 ,  G06F17/20 ,  G06F17/21

    CPC分类号： G06F17/277 ,  G06F17/2775

    摘要： Methods and apparatus related to phrase identification. Methods are provided for determining co-occurrence consistencies for positional word pairings of a plurality of sequences of words in a corpus that may be utilized in identifying a phrase; determining a phrase coherence of a sequence of words based on the co-occurrence consistencies for positional word pairings in the sequence of words; and determining one or more phrase boundaries in a sequence of words.

    摘要翻译： 与短语识别相关的方法和设备。 提供了用于确定可用于识别短语的语料库中的多个单词序列的位置词对的同现一致性的方法; 基于所述单词序列中的位置字对的同现一致性来确定单词序列的短语一致性; 以及确定单词序列中的一个或多个短语边界。

    Identifying and redacting privileged information
    38.
    发明授权
    Identifying and redacting privileged information 有权
    识别和修改特权信息

    公开(公告)号：US08752204B2

    公开(公告)日：2014-06-10

    申请号：US13564032

    申请日：2012-08-01

    申请人： Pamela H. Davis ,  Theresa Hughes Beaumont ,  Shailesh Kumar

    发明人： Pamela H. Davis ,  Theresa Hughes Beaumont ,  Shailesh Kumar

    IPC分类号： H04N7/16

    CPC分类号： G06Q10/10 ,  G06Q10/107

    摘要： Systems and methods for identifying and redacting privileged information automatically in electronic discovery documents such as emails are disclosed. In reviewing documents for discovery review, some documents have both privileged and non-privileged information. Significant cost savings are achieved in conducting electronic discovery by automatically identifying and redacting privileged content while preserving non-privileged information for production.

    摘要翻译： 公开了在诸如电子邮件的电子发现文档中自动识别和修改特权信息的系统和方法。 在审查发现审查文件时，一些文件具有特权和非特权信息。 通过自动识别和修改特权内容，同时保留用于生产的非特权信息，进行电子发现可大大节省成本。

    EVENT DRIVEN CHANGE INJECTION AND DYNAMIC EXTENSIONS TO A BPEL PROCESS
    39.
    发明申请
    EVENT DRIVEN CHANGE INJECTION AND DYNAMIC EXTENSIONS TO A BPEL PROCESS 有权
    事件驱动更改注入和动态扩展到BPEL流程

    公开(公告)号：US20110276968A1

    公开(公告)日：2011-11-10

    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： G06F9/46

    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
    40.
    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

    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。