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    Aligning Information Technology with Business Objectives Through Automated Feedback Control
    2.
    发明申请
    Aligning Information Technology with Business Objectives Through Automated Feedback Control 有权
    通过自动反馈控制将信息技术与业务目标对齐

    公开(公告)号：US20070168914A1

    公开(公告)日：2007-07-19

    申请号：US11164035

    申请日：2005-11-08

    申请人： Ashok Reddy

    发明人： Ashok Reddy

    IPC分类号： G06F9/44

    CPC分类号： G06Q90/00 ,  G06Q10/00

    摘要： A method of aligning development of an information technology system with business objectives can include obtaining a plurality of metrics relating to a development process for the information technology system and comparing the plurality of metrics with at least one quantified business objective relating to the information technology system to determine a delta between the plurality of metrics and the quantified business objective(s). The method further can include automatically performing a programmatic action, wherein the programmatic action regulates, according to the delta, at least one resource available for use in the development process for the information technology system.

    摘要翻译： 将信息技术系统的开发与业务目标相一致的方法可以包括获得与信息技术系统的开发过程相关的多个度量，并将多个度量与至少一个与信息技术系统有关的量化业务目标进行比较 确定所述多个度量和所述量化的业务目标之间的差值。 所述方法还可以包括自动执行编程动作，其中所述编程动作根据所述增量来调节可用于所述信息技术系统的开发过程中的至少一个资源。

    Process for producing anhydride-containing polymers for radiation sensitive compositions
    3.
    发明授权
    Process for producing anhydride-containing polymers for radiation sensitive compositions 失效
    用于产生用于辐射敏感组合物的含酸酐聚合物的方法

    公开(公告)号：US06803434B2

    公开(公告)日：2004-10-12

    申请号：US10376037

    申请日：2003-02-27

    申请人： Stephanie Dilocker ,  Sanjay Malik ,  Binod De ,  Ashok Reddy

    发明人： Stephanie Dilocker ,  Sanjay Malik ,  Binod De ,  Ashok Reddy

    IPC分类号： C08F12204

    CPC分类号： C08F122/04 ,  C08F2/06 ,  C08F6/003 ,  C08F222/04 ,  C08F222/06 ,  G03F7/0758 ,  C08L37/00

    摘要： A synthetic process comprising the following steps: providing a reaction mixture comprising: an ethylenically unsaturated anhydride monomer, at least one ethylenically unsaturated non-anhydride monomer, a free radical initiator, and an alkyl substituted THF solvent having the general structure of formula 1: where R1, R2, R3, and R4 are independently chosen from the group hydrogen and C1-C4 linear or branched alkyl with the proviso that at least one of R1, R2, R3, and R4 is not H; polymerizing the reaction mixture; and removing, by distillation, unreacted monomers, the alkyl substituted THF solvent, and any low boiling volatile reaction products.

    摘要翻译： 一种合成方法，包括以下步骤：提供反应混合物，其包含：烯属不饱和酸酐单体，至少一种烯键式不饱和非酸酐单体，自由基引发剂和具有通式1的通式结构的烷基取代的THF溶剂：其中 R 1，R 2，R 3和R 4独立地选自氢和C 1 -C 4直链或支链烷基，条件是R 1，R 2， R 3，R 3，R 4不是H; 聚合反应混合物; 并通过蒸馏除去未反应的单体，烷基取代的THF溶剂和任何低沸点的挥发性反应产物。

    METHOD AND SYSTEM FOR AUTOMATED MODEL BUILDING
    4.
    发明申请
    METHOD AND SYSTEM FOR AUTOMATED MODEL BUILDING 审中-公开

    公开(公告)号：US20170330078A1

    公开(公告)日：2017-11-16

    申请号：US15652281

    申请日：2017-07-18

    申请人： ASHOK REDDY

    发明人： PRAVEEN KODURU

    IPC分类号： G06N3/12 ,  G06N99/00

    CPC分类号： G06N3/126 ,  G06N5/003 ,  G06N20/00

    摘要： The various embodiments herein provide a method and system for automated model building, validation and selection of best performing models. The method comprises of selecting a dataset available for modeling from one or more external data sources, dividing the dataset into at least three parts, selecting one or more modeling methods along with associated parameters ranges based on the model to be built, identifying one or more fitness functions against which the models need to be evaluated, generating a plurality of model building experiment variation that can be run utilizing a first part of the dataset, obtaining values of the fitness function for the different modeling method experiments on the first part of the dataset, obtaining a second fitness value by re-evaluating the generated models from the different experiments on a second part of the dataset to evaluate the model performance on unseen data during training, selecting, one or more best performing models by comparing the first fitness value and the second fitness value, generating fitness values by an algorithm processing module using selected one or more best performing models on the remaining datasets, and selecting the best model from the conducted evaluation.