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    Systems and methods for initial sampling in multi-objective portfolio analysis
    21.
    发明授权
    Systems and methods for initial sampling in multi-objective portfolio analysis 有权
    多目标投资组合分析初始抽样的系统和方法

    公开(公告)号：US08126795B2

    公开(公告)日：2012-02-28

    申请号：US10781898

    申请日：2004-02-20

    Applicant: Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    Inventor： Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    IPC: G06Q40/00

    CPC classification number: G06Q40/06

    Abstract: The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: generating an initial population of solutions of portfolio allocations, the generating the initial population of solutions of portfolio allocations including systematically generating the initial population of solutions to substantially cover the space defined by the competing objectives and the plurality of constraints; and generating an efficient frontier in the space based on the initial population, the efficient frontier for use in investment decisioning.

    Abstract translation: 本发明的系统和方法针对组合优化和相关技术。 例如，本发明提供了一种用于基于竞争目标和构成组合问题的多个约束的投资决策中的多目标投资组合优化的方法，所述方法包括：生成投资组合分配的初始解决方案群体， 包括投资组合分配的最初解决方案，其中包括系统地产生解决方案的初始群体，以充分覆盖竞争目标和多个约束所界定的空间; 并根据初始人口，投资决策中使用的有效前沿，在空间中产生有效的前沿。

    System and process for multivariate adaptive regression splines classification for insurance underwriting suitable for use by an automated system
    22.
    发明授权
    System and process for multivariate adaptive regression splines classification for insurance underwriting suitable for use by an automated system 有权
    适用于自动化系统使用的保险承保的多变量自适应回归样条分类系统和过程

    公开(公告)号：US07813945B2

    公开(公告)日：2010-10-12

    申请号：US10425733

    申请日：2003-04-30

    Applicant: Piero Patrone Bonissone ,  Richard Paul Messmer ,  Rajesh Venkat Subbu ,  Weizhong Yan ,  Anindya Chakraborty

    Inventor： Piero Patrone Bonissone ,  Richard Paul Messmer ,  Rajesh Venkat Subbu ,  Weizhong Yan ,  Anindya Chakraborty

    IPC: G06Q40/00

    CPC classification number: G06Q40/02 ,  G06Q40/08

    Abstract: A method and system for automating the decision-making process used in underwriting of insurance applications is described. While this approach is demonstrated for insurance underwriting, it is broadly applicable to diverse decision-making applications in business, commercial, and manufacturing processes. A structured methodology is used based on a multi-model parallel network of multivariate adaptive regression splines (“MARS”) models to identify the relevant set of variables and their parameters, and build a framework capable of providing automated decisions. The parameters of the MARS-based decision system are estimated from a database consisting of a set of applications with reference decisions against each. Cross-validation and development/hold-out combined with re-sampling techniques are used to build a robust set of models that minimize the error between the automated system's decision and the expert human underwriter. Furthermore, this model building methodology can be used periodically to update and maintain the family of models if required to assure currency.

    Abstract translation: 描述了一种自动化用于承保保险应用程序的决策过程的方法和系统。 虽然这种方法在保险承保方面得到证明，但它广泛适用于商业，商业和制造过程中的各种决策应用。 基于多变量自适应回归样条（“MARS”）模型的多模式并行网络，使用一种结构化方法来识别相关的变量及其参数集，并构建能够提供自动化决策的框架。 基于MARS的决策系统的参数是由一组数据库估算出来的，该数据库由一组应用程序组成，具有相应的参考决定。 交叉验证和开发/保留与重新采样技术相结合，用于构建一套强大的模型，以最大限度地减少自动系统决策与专家人类承保人之间的错误。 此外，如果需要确保货币，则可以定期使用此模型构建方法来更新和维护模型系列。

    Systems and methods for multi-objective portfolio optimization
    24.
    发明授权
    Systems and methods for multi-objective portfolio optimization 有权
    多目标投资组合优化的系统和方法

    公开(公告)号：US07542932B2

    公开(公告)日：2009-06-02

    申请号：US10781780

    申请日：2004-02-20

    Applicant: Kete Charles Chalermkraivuth ,  Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Michael Craig Clark ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    Inventor： Kete Charles Chalermkraivuth ,  Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Michael Craig Clark ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    IPC: G06Q40/00

    CPC classification number: G06Q40/00 ,  G06Q10/06375 ,  G06Q40/06

    Abstract: The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: generating an initial population of solutions of portfolio allocations; performing a first multi-objective process, based on the initial population and the competing objectives, to generate a first interim efficient frontier; performing a second multi-objective process, based on the initial population and the competing objectives, to generate a second interim efficient frontier; and fusing the first interim efficient frontier with the second interim efficient frontier to create an augmented efficient frontier for use in investment decisioning.

    Abstract translation: 本发明的系统和方法针对组合优化和相关技术。 例如，本发明提供了一种用于基于竞争目标和构成组合问题的多个约束的投资决策中的多目标投资组合优化的方法，所述方法包括：生成投资组合分配的最初解决方案群体; 根据初始人口和竞争目标，进行第一个多目标过程，以产生第一个临时有效的前沿; 根据初始人口和竞争目标进行第二个多目标过程，以产生第二个临时有效的边界; 并将第一个临时有效的边界与第二个临时有效的前沿融合，创造出一个扩大的投资决策的有效前沿。

    Systems and methods for multi-objective portfolio optimization
    26.
    发明申请
    Systems and methods for multi-objective portfolio optimization 有权
    多目标投资组合优化的系统和方法

    公开(公告)号：US20050187844A1

    公开(公告)日：2005-08-25

    申请号：US10781780

    申请日：2004-02-20

    Applicant: Kete Charles Chalermkraivuth ,  Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Michael Craig Clark ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    Inventor： Kete Charles Chalermkraivuth ,  Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Michael Craig Clark ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    IPC: G06Q40/00 ,  G06F17/60

    CPC classification number: G06Q40/00 ,  G06Q10/06375 ,  G06Q40/06

    Abstract: The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: generating an initial population of solutions of portfolio allocations; performing a first multi-objective process, based on the initial population and the competing objectives, to generate a first interim efficient frontier; performing a second multi-objective process, based on the initial population and the competing objectives, to generate a second interim efficient frontier; and fusing the first interim efficient frontier with the second interim efficient frontier to create an augmented efficient frontier for use in investment decisioning.

    Abstract translation: 本发明的系统和方法针对组合优化和相关技术。 例如，本发明提供了一种用于基于竞争目标和构成组合问题的多个约束的投资决策中的多目标投资组合优化的方法，所述方法包括：生成投资组合分配的最初解决方案群体; 根据初始人口和竞争目标，进行第一个多目标过程，以产生第一个临时有效的前沿; 根据初始人口和竞争目标进行第二个多目标过程，以产生第二个临时有效的边界; 并将第一个临时有效的边界与第二个临时有效的前沿融合，创造出一个扩大的投资决策的有效前沿。

    System and method for advanced condition monitoring of an asset system
    27.
    发明授权
    System and method for advanced condition monitoring of an asset system 有权
    资产系统高级状态监测系统和方法

    公开(公告)号：US08352216B2

    公开(公告)日：2013-01-08

    申请号：US12129622

    申请日：2008-05-29

    Applicant: Rajesh Venkat Subbu ,  John Erik Hershey ,  Xiao Hu ,  Robert James Mitchell, Jr. ,  Avinash Vinayak Taware ,  Piero Patrone Bonissone

    Inventor： Rajesh Venkat Subbu ,  John Erik Hershey ,  Xiao Hu ,  Robert James Mitchell, Jr. ,  Avinash Vinayak Taware ,  Piero Patrone Bonissone

    IPC: G06F11/28

    CPC classification number: G06F11/30 ,  G05B23/024

    Abstract: A method for advanced condition monitoring of an asset system includes sensing actual values of an operating condition for an operating regime of the asset system using at least one sensor; estimating sensed values of the operating condition by using an auto-associative neural network; determining a residual vector between the estimated sensed values and the actual values; and performing a fault diagnostic on the residual vector. In another method, an operating space of the asset system is segmented into operating regimes; the auto-associative neural network determines estimates of actual measured values; a residual vector is determined from the auto-associative neural network; a fault diagnostic is performed on the residual vector; and a change of the operation of the asset system is determined by analysis of the residual vector. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.

    Abstract translation: 一种用于资产系统的高级状态监测的方法包括使用至少一个传感器来感测资产系统的操作状态的操作条件的实际值; 通过使用自动关联神经网络来估计操作条件的感测值; 确定估计的感测值和实际值之间的残差矢量; 并对残差向量进行故障诊断。 在另一种方法中，资产系统的运营空间被划分为运行状态; 自相关神经网络确定实际测量值的估计值; 从自相关神经网络确定残差向量; 对残差矢量执行故障诊断; 通过分析残差向量来确定资产系统的运作变化。 必要时提供警报。 智能传感器系统包括用于执行本发明的方法的车载处理单元。

    System and process for a fusion classification for insurance underwriting suitable for use by an automated system
    28.
    发明授权
    System and process for a fusion classification for insurance underwriting suitable for use by an automated system 有权
    用于融合分类的系统和过程，适用于自动化系统使用的保险承保

    公开(公告)号：US08214314B2

    公开(公告)日：2012-07-03

    申请号：US12131545

    申请日：2008-06-02

    Applicant: Piero Patrone Bonissone ,  Kareem Sherif Aggour ,  Rajesh Venkat Subbu ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Anindya Chakraborty

    Inventor： Piero Patrone Bonissone ,  Kareem Sherif Aggour ,  Rajesh Venkat Subbu ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Anindya Chakraborty

    IPC: G06F17/00 ,  G06N5/02

    CPC classification number: G06Q40/08 ,  G06Q40/00

    Abstract: A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.

    Abstract translation: 描述用于融合用于自动保险承保系统的分类器集合和/或其质量保证的方法和系统。 具体来说，分类器的集合的输出被融合。 数据的融合通常会导致一些共识和分类器之间的一些冲突。 共识将被测量并用于估计融合决策的信心程度。 根据融合的决定和信心程度以及生产​​决策引擎的决策和决策程度，然后可以使用比较模块来识别审计案例，增加用于重新调整生产的培训/测试集的案例 决策引擎，审查案例，或者可以简单地触发其发生记录以进行跟踪。 融合可以补偿分类器之间的潜在相关性。 每个分类器的可靠性可以由静态或动态折扣因子表示，这将反映分类器的预期准确性。 静态折扣因子用于表示对分类器的可靠性的先前期望，例如，可以基于模型的平均过去精度，而使用动态贴现来表示分类器的可靠性的条件评估，例如，每当 分类器的输出基于不可靠的点数不足。

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