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1. 发明授权

US5970464A Data mining based underwriting profitability analysis 失效
标题翻译：基于数据挖掘的承保盈利能力分析
公开(公告)号：US5970464A
公开(公告)日：1999-10-19
申请号：US926804
申请日：1997-09-10
申请人： Chidanand Vinayak Apte , Edna Grossman , Edwin Peter Dawson Pednault , Barry Kumin Rosen , Fateh Ali Tipu , Hsueh-ju Wang , Brian Frederick White
发明人： Chidanand Vinayak Apte , Edna Grossman , Edwin Peter Dawson Pednault , Barry Kumin Rosen , Fateh Ali Tipu , Hsueh-ju Wang , Brian Frederick White
IPC分类号： G06Q10/06 , G06F17/60
CPC分类号： G06Q40/08 , G06Q10/0635
摘要： A computer implemented method of underwriting profitability analysis delivers the analytic process to a wide cross section of insurance decision makers. The underwriting profitability analysis system leverages an existing investment in databases and improves underwriting business processes. Data mining techniques are applied to historical policy and claims to extract rules that describe policy holders with homogeneous claim frequency and severity characteristics. These rule sets are used to classify policy holders into distinct risk groups, each with its own set of characteristics, including pure premium. Breaking up a book of business into segments allows identification of sub-populations of policy holders that distinctly deviate from the expected normal pure premium. This identification allow the insurance business analysts to interactively adjust eligibility criteria and examine altered characteristics of the covered segments until satisfactory. The system is implemented on a client server using network centric language technology.
摘要翻译：计算机实施的盈利能力分析方法将分析过程提供给广泛的保险决策者。承销盈利能力分析系统利用现有的数据库投资，改善承销业务流程。数据挖掘技术适用于历史政策和索赔，以提取描述具有均匀索赔频率和严重性特征的策略持有人的规则。这些规则集用于将保单持有人分类为不同的风险群体，每个风险群体都有自己的一套特征，包括纯保费。将业务分成几部分，可以确定明显偏离预期的正常纯价格的保单持有人的子种群。这种识别允许保险业务分析人员交互地调整资格标准，并检查被覆盖部分的变更特征，直到令人满意。该系统在使用网络中心语言技术的客户端服务器上实现。

2. 发明申请

US20130006406A1 METHOD AND SYSTEM FOR EVALUATING A MACHINE TOOL OPERATING CHARACTERISTICS 失效
标题翻译：用于评估机床工作特性的方法和系统
公开(公告)号：US20130006406A1
公开(公告)日：2013-01-03
申请号：US13608032
申请日：2012-09-10
申请人： Ehud Aharoni , Robert J. Baseman , Ramona Kei , Oded Margalit , Kevin Mackey , Michal Rosen-Zvi , Raminderpal Singh , Noam Slomin , Hong Lin , Fateh Ali Tipu , Adam Daniel Ticknor , Timothy M. McCormack
发明人： Ehud Aharoni , Robert J. Baseman , Ramona Kei , Oded Margalit , Kevin Mackey , Michal Rosen-Zvi , Raminderpal Singh , Noam Slomin , Hong Lin , Fateh Ali Tipu , Adam Daniel Ticknor , Timothy M. McCormack
IPC分类号： G05B19/18
CPC分类号： G05B23/0216 , G05B23/0229 , G05B2219/32191 , G05B2219/32194 , G05B2219/45031 , G05B2219/50065 , Y02P90/22 , Y02P90/86 , Y10T409/309408
摘要： A method, a system and a computer program product suitable for use in a manufacturing environment comprising a multiplicity of nominally identical independent tools. A computing device generates a multi dimensional array of process trace data derived from at least one of the independent tools, wherein, the array includes data representing a first dimension comprising a list of steps in a manufacturing recipe and data representing a second dimension comprising a list of a set of sensors generating measurements from at least one of the independent tools. The computing device conducts an analysis on at least one preselected subset of the multi dimensional array for the purpose of evaluating at least one operating characteristic of at least one of the independent tools. The computing device presents results of the analysis via a set of hierarchically linked and browseable graphics.
摘要翻译：一种适用于制造环境的方法，系统和计算机程序产品，包括多个名义上相同的独立工具。计算设备生成从至少一个独立工具导出的过程跟踪数据的多维阵列，其中，所述阵列包括表示包括制造配方中的步骤列表的第一维度的数据和表示包括列表的第二维度的数据一组传感器从至少一个独立工具产生测量值。为了评估至少一个独立工具的至少一个操作特征，计算设备对多维阵列的至少一个预选子集进行分析。计算设备通过一组分层链接和可浏览的图形呈现分析结果。

3. 发明授权

US07561158B2 Method and apparatus for presenting feature importance in predictive modeling 失效
标题翻译：在预测建模中呈现特征重要性的方法和装置
公开(公告)号：US07561158B2
公开(公告)日：2009-07-14
申请号：US11329437
申请日：2006-01-11
申请人： Naoki Abe , Edwin Peter Dawson Pednault , Fateh Ali Tipu
发明人： Naoki Abe , Edwin Peter Dawson Pednault , Fateh Ali Tipu
IPC分类号： G06T11/20
CPC分类号： G06T11/206
摘要： Feature importance information available in a predictive model with correlation information among the variables is presented to facilitate more flexible choices of actions by business managers. The displayed feature importance information combines feature importance information available in a predictive model with correlational information among the variables. The displayed feature importance information may be presented as a network structure among the variables as a graph, and regression coefficients of the variables indicated on the corresponding nodes in the graph. To generate the display, a regression engine is called on a set of training data that outputs importance measures for the explanatory variables for predicting the target variable. A graphical model structural learning module is called that outputs a graph on the explanatory variables of the above regression problem representing the correlational structure among them. The feature importance measure, output by the regression engine, is displayed for each node in the graph, as an attribute, such as color, size, texture, etc, of that node in the graph output by the graphical model structural learning module.
摘要翻译：提供了具有变量之间相关性信息的预测模型中的特征重要度信息，以便企业管理者更灵活地选择行动。显示的特征重要性信息将预测模型中可用的特征重要性信息与变量之间的相关信息相结合。所显示的特征重要性信息可以作为图形中的变量之间的网络结构呈现，并且在图中的相应节点上指示的变量的回归系数。为了生成显示，在一组训练数据上调用回归引擎，该训练数据输出用于预测目标变量的解释变量的重要度量。一个图形模型结构学习模块被称为输出上述回归问题的解释变量的图表，表示它们之间的相关性结构。由图形模型结构学习模块输出的图形中的该节点的颜色，大小，纹理等属性显示图形中每个节点的回归引擎输出的特征重要性度量。

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