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    Query Suggestion for Efficient Legal E-Discovery
    21.
    发明申请
    Query Suggestion for Efficient Legal E-Discovery 有权
    查询有效的法律电子发现建议

    公开(公告)号：US20120310966A1

    公开(公告)日：2012-12-06

    申请号：US13219869

    申请日：2011-08-29

    Applicant: Shailesh Kumar

    Inventor： Shailesh Kumar

    IPC: G06F17/30

    CPC classification number: G06F17/3064

    Abstract: Given a set of training documents relevant to a litigation hold, properties common to the documents relevant to the case and not common or missing in the non-relevant documents can be identified and used to generate a litigation hold query to be suggested to a user. After receiving a training set of documents, one or more common properties between the relevant training documents are identified. Based on the common properties, a suggested litigation hold query can be generated to return documents in a larger set that are representative of the relevant documents in the training set. Additionally, a query can be generated by iteratively improving a base query to return documents sharing characteristics of a training set of documents. Suggested queries may evolve as the training set evolves.

    Abstract translation: 鉴于与诉讼保持有关的一套培训文件，可以识别与非相关文件相关的文件相关文件与常见或不相关的文件共有的属性，并用于生成向用户建议的诉讼保留查询。 在收到一套文件后，确定了相关培训文件之间的一个或多个常见属性。 根据共同属性，可以生成建议的诉讼保留查询，以返回代表培训集中相关文档的较大集合中的文档。 此外，可以通过迭代地改进基本查询以返回共享文档训练集的特征的文档来生成查询。 建议的查询可能随培训集演变而发展。

    Custodian Suggestion for Efficient Legal E-Discovery
    22.
    发明申请
    Custodian Suggestion for Efficient Legal E-Discovery 有权
    托管人有效的法律电子发现建议

    公开(公告)号：US20120310951A1

    公开(公告)日：2012-12-06

    申请号：US13220013

    申请日：2011-08-29

    Applicant: Shailesh Kumar ,  Mahesh Chhaparia ,  Gaurav Agarwal

    Inventor： Shailesh Kumar ,  Mahesh Chhaparia ,  Gaurav Agarwal

    IPC: G06F17/30

    CPC classification number: G06Q10/10 ,  G06F17/30011 ,  G06Q50/18

    Abstract: Given a set of documents relevant to a litigation hold and a seed set of custodians, a second set of custodians can be generated and suggested to a user. After receiving a seed set of keywords and/or custodians, documents are identified based on their relevance. Relevant documents are searched for custodian names, and appropriate custodian names are presented to a user. Additionally, based on a first set of custodians, a suggested set of custodians can be provided to a user based on relationships between the sets of custodians.

    Abstract translation: 鉴于一系列与诉讼保持有关的文件和种子保管人，可以向用户生成并建议第二组保管人。 在收到关键字和/或保管人的种子后，根据其相关性来识别文件。 搜索相关文件的保管人姓名，并向用户显示适当的保管人姓名。 另外，根据第一套保管人，可以根据保管人组之间的关系向用户提供一组建议的保管人。

    Bond pad for semiconductor die
    23.
    发明授权
    Bond pad for semiconductor die 有权
    用于半导体管芯的焊盘

    公开(公告)号：US08242613B2

    公开(公告)日：2012-08-14

    申请号：US12874204

    申请日：2010-09-01

    Applicant: Chetan Verma ,  Shailesh Kumar ,  Meng Kong Lye

    Inventor： Chetan Verma ,  Shailesh Kumar ,  Meng Kong Lye

    IPC: H01L23/488 ,  H01L23/485 ,  H01L21/768 ,  H01L23/48

    CPC classification number: H01L24/06 ,  H01L22/32 ,  H01L24/05 ,  H01L2224/04042 ,  H01L2224/05644 ,  H01L2224/05647 ,  H01L2924/01005 ,  H01L2924/01006 ,  H01L2924/01013 ,  H01L2924/01014 ,  H01L2924/01029 ,  H01L2924/01033 ,  H01L2924/01079 ,  H01L2924/181 ,  H01L2924/00014 ,  H01L2924/00

    Abstract: A semiconductor die has rows of bond pads along the edges of a major surface. The corners of the die are designated as keep out areas, with design layout rules prohibiting a probe-able bond pad from being placed in the keep out areas so that a minimum distance may be maintained between distal ends of adjacent rows of bond pads (i.e., bond pads along adjacent edges). The bond pads of each row have IO pad areas that are aligned with each other and IO probe areas that are aligned with each other. A generally L-shaped bond pad includes a first, vertical part that extends inwardly from an edge of the semiconductor die and a second, horizontal part connected to the vertical part. The L-shaped bond pad may be placed between a last bond pad in a row and a corner keep out area, and the second part of the L-shaped bond pad extends into the corner keep out area. The first part has an IO pad area that is in alignment with the IO pad areas of the other bond pads in the same row, and the second part has an IO probe area that is in alignment with the IO probe areas of the bond pads in the adjacent row. The L-shaped bond pad does not violate design rules even though a part of the pad extends into the corner keep out area.

    Abstract translation: 半导体管芯沿着主表面的边缘具有一排接合焊盘。 模具的角被指定为保留区域，其设计布局规则禁止可探测的焊盘放置在保留区域中，使得可以在相邻排的焊盘的末端之间保持最小距离（即 ，沿着相邻边缘的接合垫）。 每行的接合焊盘具有彼此对准的IO焊盘区域和彼此对准的IO探针区域。 大致L形接合焊盘包括从半导体管芯的边缘向内延伸的第一垂直部分和连接到垂直部分的第二水平部分。 L形接合焊盘可以放置在一行中的最后接合焊盘和拐角保持区域之间，并且L形接合焊盘的第二部分延伸到角落保持区域中。 第一部分具有与同一行中的其它接合焊盘的IO焊盘区域对准的IO焊盘区域，并且第二部分具有与焊盘的IO探针区域对准的IO探针区域 相邻行。 即使垫的一部分延伸到角落保持区域，L形接合垫也不违反设计规则。

    Incremental factorization-based smoothing of sparse multi-dimensional risk tables
    24.
    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

    Applicant: Vesselin Diev ,  Shailesh Kumar ,  Scott M. Zoldi

    Inventor： Vesselin Diev ,  Shailesh Kumar ,  Scott M. Zoldi

    IPC: G06Q40/00

    CPC classification number: G06Q40/02 ,  G06Q20/40 ,  G06Q40/00 ,  G06Q40/025

    Abstract: 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.

    Abstract translation: 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。

    Method and apparatus for recommendation engine using pair-wise co-occurrence consistency
    25.
    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：US07801843B2

    公开(公告)日：2010-09-21

    申请号：US11327822

    申请日：2006-01-06

    Applicant: Shailesh Kumar ,  Edmond D. Chow ,  Michinari Momma

    Inventor： Shailesh Kumar ,  Edmond D. Chow ,  Michinari Momma

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

    CPC classification number: G06Q30/06 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0204 ,  G06Q30/0224 ,  G06Q30/0605

    Abstract: 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.

    Abstract translation: 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。

    Method for verification of file-based media content
    26.
    发明申请
    Method for verification of file-based media content 失效
    验证基于文件的媒体内容的方法

    公开(公告)号：US20080301588A1

    公开(公告)日：2008-12-04

    申请号：US11807546

    申请日：2007-05-29

    Applicant: Shailesh Kumar ,  Manik Gupta ,  Vivek Koul

    Inventor： Shailesh Kumar ,  Manik Gupta ,  Vivek Koul

    IPC: G06F3/048 ,  G06F19/00

    CPC classification number: H04N7/24 ,  H04N21/23418

    Abstract: A method for automatically verifying one or more features of file-based media content (108) is disclosed. This file-based media content includes one or more media content files. The method includes customizing (204) a test plan on the basis of the one or more features. Customizing the test plan includes creating, modifying or utilizing at least one media content check of one or more media content checks. A media content check verifies at least one feature of the one or more features of the file-based media content. Further, the method includes verifying (206) the one or more features, based on the customized test plan. The method also includes documenting (208) the results obtained from the verification of the one or more features.

    Abstract translation: 公开了一种用于自动验证基于文件的媒体内容（108）的一个或多个特征的方法。 该基于文件的媒体内容包括一个或多个媒体内容文件。 该方法包括基于一个或多个特征定制（204）测试计划。 定制测试计划包括创建，修改或利用对一个或多个媒体内容检查的至少一个媒体内容检查。 媒体内容检查验证基于文件的媒体内容的一个或多个特征的至少一个特征。 此外，该方法包括基于定制的测试计划来验证（206）一个或多个特征。 该方法还包括记录（208）从一个或多个特征的验证获得的结果。

    Method and apparatus for retail data mining using pair-wise co-occurrence consistency
    28.
    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

    Applicant: Shailesh Kumar ,  Edmond Chow ,  Michinari Momma

    Inventor： Shailesh Kumar ,  Edmond Chow ,  Michinari Momma

    IPC: G06F17/30

    CPC classification number: G06Q30/06 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0204 ,  G06Q30/0224 ,  G06Q30/0605

    Abstract: 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.

    Abstract translation: 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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