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    • 1. 发明授权
    • Systems and methods for new time series model probabilistic ARMA
    • 新时间序列模型概率ARMA的系统和方法
    • US07580813B2
    • 2009-08-25
    • US10463145
    • 2003-06-17
    • Bo ThiessonChristopher A. MeekDavid M. ChickeringDavid E. Heckerman
    • Bo ThiessonChristopher A. MeekDavid M. ChickeringDavid E. Heckerman
    • G06F17/50G05B23/02
    • G06F17/18
    • The present invention utilizes a cross-prediction scheme to predict values of discrete and continuous time observation data, wherein conditional variance of each continuous time tube variable is fixed to a small positive value. By allowing cross-predictions in an ARMA based model, values of continuous and discrete observations in a time series are accurately predicted. The present invention accomplishes this by extending an ARMA model such that a first time series “tube” is utilized to facilitate or “cross-predict” values in a second time series tube to form an “ARMAxp” model. In general, in the ARMAxp model, the distribution of each continuous variable is a decision graph having splits only on discrete variables and having linear regressions with continuous regressors at all leaves, and the distribution of each discrete variable is a decision graph having splits only on discrete variables and having additional distributions at all leaves.
    • 本发明利用交叉预测方案来预测离散和连续时间观测数据的值,其中每个连续时间管变量的条件方差固定为小的正值。 通过在基于ARMA的模型中允许交叉预测,可以准确预测时间序列中连续和离散观测值。 本发明通过扩展ARMA模型来实现这一目的,使得第一时间序列“管”用于促进或“交叉预测”第二时间序列管中的值以形成“ARMAxp”模型。 一般来说,在ARMAxp模型中,每个连续变量的分布是仅在离散变量上分裂并具有在所有叶上具有连续回归的线性回归的决策图,并且每个离散变量的分布是仅分解为 离散变量,并在所有叶子上具有额外的分布。
    • 3. 发明授权
    • Anomaly detection in data perspectives
    • 数据透视异常检测
    • US07162489B2
    • 2007-01-09
    • US11299539
    • 2005-12-12
    • Allan FoltingBo ThiessonDavid E. HeckermanDavid M. ChickeringEric Barber Vigesaa
    • Allan FoltingBo ThiessonDavid E. HeckermanDavid M. ChickeringEric Barber Vigesaa
    • G06F7/00
    • G06F17/30592G06N7/00Y10S707/957Y10S707/958Y10S707/99943
    • The present invention leverages curve fitting data techniques to provide automatic detection of data anomalies in a “data tube” from a data perspective, allowing, for example, detection of data anomalies such as on-screen, drill down, and drill across data anomalies in, for example, pivot tables and/or OLAP cubes. It determines if data substantially deviates from a predicted value established by a curve fitting process such as, for example, a piece-wise linear function applied to the data tube. A threshold value can also be employed by the present invention to facilitate in determining a degree of deviation necessary before a data value is considered anomalous. The threshold value can be supplied dynamically and/or statically by a system and/or a user via a user interface. Additionally, the present invention provides an indication to a user of the type and location of a detected anomaly from a top level data perspective.
    • 本发明利用曲线拟合数据技术从数据角度提供“数据管”中的数据异常的自动检测,从而允许例如检测诸如屏幕上的数据异常,向下钻取和钻取数据异常的数据异常 例如,枢轴表和/或OLAP多维数据集。 它确定数据是否基本上偏离由曲线拟合处理(例如应用于数据管的分段线性函数)所建立的预测值。 本发明也可以采用阈值,以便在确定数据值被认为是异常之前确定所需的偏差程度。 阈值可以由系统和/或用户经由用户界面动态地和/或静态地提供。 另外,本发明从顶级数据的角度向用户提供了检测到的异常的类型和位置的指示。
    • 4. 发明授权
    • Anomaly detection in data perspectives
    • 数据透视异常检测
    • US07065534B2
    • 2006-06-20
    • US10874956
    • 2004-06-23
    • Allan FoltingBo ThiessonDavid E. HeckermanDavid M. ChickeringEric Barber Vigesaa
    • Allan FoltingBo ThiessonDavid E. HeckermanDavid M. ChickeringEric Barber Vigesaa
    • G06F7/00G06F17/00
    • G06F17/30592G06N7/00Y10S707/957Y10S707/958Y10S707/99943
    • The present invention leverages curve fitting data techniques to provide automatic detection of data anomalies in a “data tube” from a data perspective, allowing, for example, detection of data anomalies such as on-screen, drill down, and drill across data anomalies in, for example, pivot tables and/or OLAP cubes. It determines if data substantially deviates from a predicted value established by a curve fitting process such as, for example, a piece-wise linear function applied to the data tube. A threshold value can also be employed by the present invention to facilitate in determining a degree of deviation necessary before a data value is considered anomalous. The threshold value can be supplied dynamically and/or statically by a system and/or a user via a user interface. Additionally, the present invention provides an indication to a user of the type and location of a detected anomaly from a top level data perspective.
    • 本发明利用曲线拟合数据技术从数据角度提供“数据管”中的数据异常的自动检测,从而允许例如检测诸如屏幕上的数据异常,向下钻取和钻取数据异常的数据异常 例如,枢轴表和/或OLAP多维数据集。 它确定数据是否基本上偏离由曲线拟合处理(例如应用于数据管的分段线性函数)所建立的预测值。 本发明也可以采用阈值,以便在确定数据值被认为是异常之前确定所需的偏差程度。 阈值可以由系统和/或用户经由用户界面动态地和/或静态地提供。 另外,本发明从顶级数据的角度向用户提供了检测到的异常的类型和位置的指示。
    • 5. 发明授权
    • Apparatus and accompanying methods for visualizing clusters of data and hierarchical cluster classifications
    • 用于可视化数据集群和分级集群分类的装置和相关方法
    • US06742003B2
    • 2004-05-25
    • US09845151
    • 2001-04-30
    • David E. HeckermanPaul S. BradleyDavid M. ChickeringChristopher A. Meek
    • David E. HeckermanPaul S. BradleyDavid M. ChickeringChristopher A. Meek
    • G06F1730
    • G06Q30/0641G06F17/30713Y10S707/99934Y10S707/99935Y10S707/99936Y10S707/99942Y10S707/99944Y10S707/99945Y10S707/99948
    • A system that incorporates an interactive graphical user interface for visualizing clusters (categories) and segments (summarized clusters) of data. Specifically, the system automatically categorizes incoming case data into clusters, summarizes those clusters into segments, determines similarity measures for the segments, scores the selected segments through the similarity measures, and then forms and visually depicts hierarchical organizations of those selected clusters. The system also automatically and dynamically reduces, as necessary, a depth of the hierarchical organization, through elimination of unnecessary hierarchical levels and inter-nodal links, based on similarity measures of segments or segment groups. Attribute/value data that tends to meaningfully characterize each segment is also scored, rank ordered based on normalized scores, and then graphically displayed. The system permits a user to browse through the hierarchy, and, to readily comprehend segment inter-relationships, selectively expand and contract the displayed hierarchy, as desired, as well as to compare two selected segments or segment groups together and graphically display the results of that comparison. An alternative discriminant-based cluster scoring technique is also presented.
    • 一个包含交互式图形用户界面的系统,用于可视化数据的集群(类别)和分段(聚合集群)。 具体来说,系统将传入的病例数据自动分类为群集,将这些群集合成段,确定段的相似性度量,通过相似性度量对所选段进行分类,然后形成并可视地描绘这些群集的层次结构。 基于片段或段组的相似性度量,系统还可以根据需要自动和动态地减少层次组织的深度,通过消除不必要的层级和节点间链接。 倾向于对每个段进行有意义表征的属性/值数据也被划分,基于归一化分数进行排序,然后以图形方式显示。 该系统允许用户浏览层次结构,并且为了容易地理解分段相互关系,根据需要选择性地扩展和收缩所显示的层次结构,以及将两个选定的分段或分段组进行比较,并以图形方式显示 那个比较。 还提出了一种替代的基于判别式的聚类评分技术。
    • 6. 发明授权
    • Apparatus and accompanying methods for visualizing clusters of data and hierarchical cluster classifications
    • 用于可视化数据集群和分级集群分类的装置和相关方法
    • US07333998B2
    • 2008-02-19
    • US10808064
    • 2004-03-24
    • David E. HeckermanPaul S. BradleyDavid M. ChickeringChristopher A. Meek
    • David E. HeckermanPaul S. BradleyDavid M. ChickeringChristopher A. Meek
    • G06F17/30
    • G06Q30/0641G06F17/30713Y10S707/99934Y10S707/99935Y10S707/99936Y10S707/99942Y10S707/99944Y10S707/99945Y10S707/99948
    • A system that incorporates an interactive graphical user interface for visualizing clusters (categories) and segments (summarized clusters) of data. Specifically, the system automatically categorizes incoming case data into clusters, summarizes those clusters into segments, determines similarity measures for the segments, scores the selected segments through the similarity measures, and then forms and visually depicts hierarchical organizations of those selected clusters. The system also automatically and dynamically reduces, as necessary, a depth of the hierarchical organization, through elimination of unnecessary hierarchical levels and inter-nodal links, based on similarity measures of segments or segment groups. Attribute/value data that tends to meaningfully characterize each segment is also scored, rank ordered based on normalized scores, and then graphically displayed. The system permits a user to browse through the hierarchy, and, to readily comprehend segment inter-relationships, selectively expand and contract the displayed hierarchy, as desired, as well as to compare two selected segments or segment groups together and graphically display the results of that comparison. An alternative discriminant-based cluster scoring technique is also presented.
    • 一个包含交互式图形用户界面的系统,用于可视化数据的集群(类别)和分段(聚合集群)。 具体来说,系统将传入的案例数据自动分类为群集,将这些群集归纳为段,确定段的相似性度量,通过相似性度量对所选段进行分类,然后形成并可视地描绘这些群集的层次结构。 基于片段或段组的相似性度量,系统还可以根据需要自动和动态地减少层次组织的深度,通过消除不必要的层级和节点间链接。 倾向于对每个段进行有意义表征的属性/值数据也被划分,基于归一化分数进行排序,然后以图形方式显示。 该系统允许用户浏览层次结构,并且为了容易地理解分段相互关系,根据需要选择性地扩展和收缩所显示的分层结构,并且将两个选定的分段或分段组进行比较,并以图形方式显示 比较。 还提出了一种替代的基于判别式的聚类评分技术。
    • 8. 发明授权
    • Trees of classifiers for detecting email spam
    • 用于检测电子邮件垃圾邮件的分类树
    • US07930353B2
    • 2011-04-19
    • US11193691
    • 2005-07-29
    • David M. ChickeringGeoffrey J. HultenRobert L. RounthwaiteChristopher A. MeekDavid E. HeckermanJoshua T. Goodman
    • David M. ChickeringGeoffrey J. HultenRobert L. RounthwaiteChristopher A. MeekDavid E. HeckermanJoshua T. Goodman
    • G06F15/16
    • H04L51/12
    • Decision trees populated with classifier models are leveraged to provide enhanced spam detection utilizing separate email classifiers for each feature of an email. This provides a higher probability of spam detection through tailoring of each classifier model to facilitate in more accurately determining spam on a feature-by-feature basis. Classifiers can be constructed based on linear models such as, for example, logistic-regression models and/or support vector machines (SVM) and the like. The classifiers can also be constructed based on decision trees. “Compound features” based on internal and/or external nodes of a decision tree can be utilized to provide linear classifier models as well. Smoothing of the spam detection results can be achieved by utilizing classifier models from other nodes within the decision tree if training data is sparse. This forms a base model for branches of a decision tree that may not have received substantial training data.
    • 利用分类器模型填充的决策树利用电子邮件的每个功能使用单独的电子邮件分类器来提供增强的垃圾邮件检测。 这通过定制每个分类器模型提供了更高的垃圾邮件检测的概率,以便于在逐个特征的基础上更准确地确定垃圾邮件。 分类器可以基于诸如逻辑回归模型和/或支持向量机(SVM)等线性模型来构建。 分类器也可以基于决策树构建。 基于决策树的内部和/或外部节点的“复合特征”也可以用于提供线性分类器模型。 垃圾邮件检测结果的平滑可以通过使用来自决策树内的其他节点的分类器模型来实现,如果训练数据是稀疏的。 这形成了可能没有接收到大量训练数据的决策树的分支的基本模型。