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    • 57. 发明授权
    • Systems and methods for tracking parcel data acquisition
    • 跟踪包裹数据采集的系统和方法
    • US09105070B2
    • 2015-08-11
    • US13961369
    • 2013-08-07
    • CORELOGIC SOLUTIONS, LLC
    • Brett T. PearcyMatthew E. KarliCharles P. ReynoldsHugo A. Tagle
    • G06F17/30G06Q50/16G06Q10/06G06Q10/10
    • G06Q50/16G06F17/30073G06F17/30241G06F17/30477G06Q10/06G06Q10/10G06Q50/165
    • In some embodiments, scripts may be used to perform parcel data acquisition, conversion, and clean-up/repair in an automated manner and/or through graphical user interfaces. The scripts may be used, for example, to repair geometries of new parcel data, convert multi-part parcel geometries to single part parcel geometries (explode), eliminate duplicate parcel geometries, append columns, create feature classes, and append feature classes. These scripts may be executed in a predetermined manner to increase efficiency. In some embodiments, different combinations of attributes may be appended to stored parcel data. In some embodiments, a tracking application may be used to track information about sources of data. In some embodiments, a tracking application may be used to track which system users are assigned to specific tasks (e.g., in a data acquisition project).
    • 在一些实施例中,脚本可以用于以自动方式和/或通过图形用户界面来执行包裹数据采集,转换和清理/修复。 例如,可以使用脚本来修复新的宗地数据的几何,将多部分宗地几何体转换为单个零件包裹几何(爆炸),消除重复的包裹几何,附加列,创建要素类和附加要素类。 这些脚本可以以预定的方式执行以提高效率。 在一些实施例中,可以将不同的属性组合附加到所存储的包裹数据。 在一些实施例中,可以使用跟踪应用来跟踪关于数据源的信息。 在一些实施例中,可以使用跟踪应用来跟踪哪些系统用户被分配给特定任务(例如,在数据获取项目中)。
    • 59. 发明申请
    • DATA ANALYTICS MODEL FOR LOAN TREATMENT
    • 用于贷款处理的数据分析模型
    • US20150026035A1
    • 2015-01-22
    • US14286039
    • 2014-05-23
    • CoreLogic Solutions LLC
    • Thomas SHOWALTER
    • G06Q40/02
    • G06Q40/025G06Q40/02G06Q40/06
    • Data analytics are provided in loan treatment. Various sources of data may be used to optimize or predict value for a loan. Using machine-learning and/or statistical analysis, loans or treatment best suited for a particular borrower may be determined. Due to the large amounts of data available, borrower behavior may be learned from previous behavior of others and mapped to a predictive model. Machine-learning indicates the most relevant factors in loan treatment, providing a matrix for predicting loan value or treatment success. A given borrower may be classified into one of many classes of borrower based on credit information, property information, desired loan information, real estate market information, and/or other data. Tens, hundreds, or even thousands of variables may be used to predict the optimum treatment.
    • 数据分析在贷款处理中提供。 可以使用各种数据来源来优化或预测贷款的价值。 使用机器学习和/或统计分析,可以确定最适合于特定借款人的贷款或待遇。 由于大量数据可用,借用者的行为可能会从其他人的先前行为中学到,并映射到预测模型。 机器学习表明贷款处理中最相关的因素,为预测贷款价值或治疗成功提供了一个矩阵。 基于信用信息,财产信息,期望的贷款信息,房地产市场信息和/或其他数据,给定的借款人可以被分类为许多类别的借款人之一。 可以使用十万,甚至数千个变量来预测最佳治疗。
    • 60. 发明申请
    • SYSTEMS AND METHODS FOR QUANTIFYING FLOOD RISK
    • 用于量化风险的系统和方法
    • US20140229420A1
    • 2014-08-14
    • US14179018
    • 2014-02-12
    • CORELOGIC SOLUTIONS, LLC
    • Mark Charles GREENLee Jason SearsWei DuJeff C. HimmelrightKevin Eugene Madden
    • G06Q40/08G06N5/04
    • G06Q40/08G06N5/048G06Q50/16G06Q90/00Y02A10/46Y02A10/48
    • In various embodiments, a flood risk score may be determined for a property point that provides a comprehensive assessment of the property point's risk of flooding. Determining the flood risk score may include determining a flood risk characteristic for the property point and assigning a flood risk score that corresponds to the flood risk characteristic. In some embodiments, flood risk characteristics may include a difference in elevation between the elevation of the property point and an elevation of a calculated point (e.g., on a known flood risk zone boundary). Flood risk characteristics may also include a flood zone determination for the property point and/or proximity of the property point to a known flood risk zone boundary or a flood source. In some embodiments, flood risk scores may be provided on flood risk score reports.
    • 在各种实施例中,可以为提供对特征点的洪水风险的综合评估的属性点确定洪水风险评分。 确定洪水风险评分可能包括确定物业点的洪水风险特征,并分配与洪水风险特征相对应的洪水风险评分。 在一些实施例中,洪水风险特征可以包括属性点的升高与计算点的高程之间的高度差(例如,在已知的洪水风险区域边界上)。 洪水风险特征还可包括洪水区确定物业点和/或属性点与已知洪水风险区域边界或洪水源的接近度。 在一些实施例中,可以在洪水风险评分报告上提供洪水风险评分。