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公开(公告)号：US08423454B2
公开(公告)日：2013-04-16
申请号：US13344709
申请日：2012-01-06
申请人： Jie Chen , Timothy John Breault , Fernando Cela Diaz , William Anthony Nobili , Sandi Setiawan , Harsh Singhal , Agus Sudjianto , Andrea Renee Turner , Bradford Timothy Winkelman
发明人： Jie Chen , Timothy John Breault , Fernando Cela Diaz , William Anthony Nobili , Sandi Setiawan , Harsh Singhal , Agus Sudjianto , Andrea Renee Turner , Bradford Timothy Winkelman
IPC分类号： G06Q40/00
CPC分类号： G06Q10/067 , G06Q40/00
摘要： Embodiments of the present invention relate to methods and apparatuses for determining leading indicators and/or for modeling one or more time series. For example, in some embodiments, a method is provided that includes: (a) receiving first data indicating the value of a total income amount for a plurality of consumers over a period of time; (b) receiving second data indicating the value of a total debt amount for a plurality of consumers over a period of time; (c) selecting a consumer leverage time series that compares the total income amount to the total debt amount over a period of time; (d) modeling the consumer leverage time series based at least partially on the first and second data; (e) determining, using a processor, the value of the cycle component for a particular time; and (f) outputting an indication of the value of the cycle component for the particular time.
摘要翻译： 本发明的实施例涉及用于确定前导指示符和/或建模一个或多个时间序列的方法和装置。 例如，在一些实施例中，提供了一种方法，其包括：（a）在一段时间内接收指示多个消费者的总收入金额的值的第一数据; （b）在一段时间内接收表示多个消费者的总债务金额的第二数据; （c）选择消费者杠杆时间序列，将总收入与一段时间内的债务总额进行比较; （d）至少部分地基于第一和第二数据对消费者杠杆时间序列进行建模; （e）使用处理器确定特定时间的周期分量的值; 和（f）输出特定时间的循环分量的值的指示。
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公开(公告)号：US20120173399A1
公开(公告)日：2012-07-05
申请号：US13344709
申请日：2012-01-06
申请人： Jie Chen , Timothy John Breault , Fernando Cela Diaz , William Anthony Nobili , Sandi Setiawan , Harsh Singhal , Agus Sudjianto , Andrea Renee Turner , Bradford Timothy Winkelman
发明人： Jie Chen , Timothy John Breault , Fernando Cela Diaz , William Anthony Nobili , Sandi Setiawan , Harsh Singhal , Agus Sudjianto , Andrea Renee Turner , Bradford Timothy Winkelman
CPC分类号： G06Q10/067 , G06Q40/00
摘要： Embodiments of the present invention relate to methods and apparatuses for determining leading indicators and/or for modeling one or more time series. For example, in some embodiments, a method is provided that includes: (a) receiving first data indicating the value of a total income amount for a plurality of consumers over a period of time; (b) receiving second data indicating the value of a total debt amount for a plurality of consumers over a period of time; (c) selecting a consumer leverage time series that compares the total income amount to the total debt amount over a period of time; (d) modeling the consumer leverage time series based at least partially on the first and second data; (e) determining, using a processor, the value of the cycle component for a particular time; and (f) outputting an indication of the value of the cycle component for the particular time.
摘要翻译： 本发明的实施例涉及用于确定前导指示符和/或建模一个或多个时间序列的方法和装置。 例如，在一些实施例中，提供了一种方法，其包括：（a）在一段时间内接收指示多个消费者的总收入金额的值的第一数据; （b）在一段时间内接收表示多个消费者的总债务金额的第二数据; （c）选择消费者杠杆时间序列，将总收入与一段时间内的债务总额进行比较; （d）至少部分地基于第一和第二数据对消费者杠杆时间序列进行建模; （e）使用处理器确定特定时间的周期分量的值; 和（f）输出特定时间的循环分量的值的指示。
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公开(公告)号：US08577776B2
公开(公告)日：2013-11-05
申请号：US13618121
申请日：2012-09-14
申请人： Agus Sudjianto , Michael Chorba , Daniel Hudson , Sandi Setiawan , Jocelyn Sikora , Harsh Singhal , Kiran Vuppu , Kaloyan Mihaylov , Jie Chen , Timothy J. Breault , Arun R. Pinto , Naveen G. Yeri , Benhong Zhang , Zhe Zhang , Tony Nobili , Hungien Wang , Aijun Zhang
发明人： Agus Sudjianto , Michael Chorba , Daniel Hudson , Sandi Setiawan , Jocelyn Sikora , Harsh Singhal , Kiran Vuppu , Kaloyan Mihaylov , Jie Chen , Timothy J. Breault , Arun R. Pinto , Naveen G. Yeri , Benhong Zhang , Zhe Zhang , Tony Nobili , Hungien Wang , Aijun Zhang
IPC分类号： G06Q40/00
摘要： A data driven and forward looking risk and reward appetite methodology for consumer and small business is described. The methodology includes account level historical data collection for customers associated with accounts as part of a portfolio. The account level historical data is segmented into groups of customers with similar revenues and loss characteristics. Segmented data is decomposed into seasoning, vintage, and cycle effects. Statistical clusters are formed based upon the data and effects. A simulation is applied to the statistical clusters and prediction data is generated. A simulation strategy to forecast and simulate revenue and loss volatility is developed. Efficient frontier curves of risk (e.g., return volatility) and reward (e.g., expected return) are created for the current portfolio under various economic scenarios.
摘要翻译： 描述了消费者和小企业的数据驱动和前瞻性风险和奖励食欲方法。 该方法包括与帐户相关联的客户的帐户级历史数据收集作为投资组合的一部分。 账户级别的历史数据被分为具有相似收入和损失特征的客户群体。 分段数据分解为调味料，复古和循环效应。 基于数据和效果形成统计群集。 将仿真应用于统计集群，生成预测数据。 开发了一种预测和模拟收入和损失波动率的模拟策略。 在各种经济情景下为当前投资组合创建有效的边际风险曲线（例如回报波动性）和报酬（例如预期收益）。
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公开(公告)号：US08326723B2
公开(公告)日：2012-12-04
申请号：US12546807
申请日：2009-08-25
申请人： Agus Sudjianto , Michael Chorba , Daniel Hudson , Sandi Setiawan , Jocelyn Sikora , Harsh Singhal , Kiran Vuppu , Kaloyan Mihaylov , Jie Chen , Timothy J. Breault , Arun R. Pinto , Naveen G. Yeri , Benhong Zhang , Zhe Zhang , Tony Nobili , Hungien Wang , Aijun Zhang
发明人： Agus Sudjianto , Michael Chorba , Daniel Hudson , Sandi Setiawan , Jocelyn Sikora , Harsh Singhal , Kiran Vuppu , Kaloyan Mihaylov , Jie Chen , Timothy J. Breault , Arun R. Pinto , Naveen G. Yeri , Benhong Zhang , Zhe Zhang , Tony Nobili , Hungien Wang , Aijun Zhang
IPC分类号： G06Q40/00
摘要： A data driven and forward looking risk and reward appetite methodology for consumer and small business is described. The methodology includes customer segmentation to create pools of homogeneous assets in terms of revenue and loss characteristics, forward looking simulation to forecast expected values and volatilities of revenue and loss, and risk and reward optimization of the portfolio. One methodology used for modeling revenue and loss is a generalized additive effect decomposition model to fit historical data. Based on the model, a segmentation procedure is performed, which allows for creation of groups of customers with similar revenue and loss characteristics. An estimation procedure for the model is developed and a simulation strategy to forecast and simulate revenue and loss volatility is developed. Efficient frontier curves of risk (e.g., return volatility) and reward (e.g., expected return) are created for the current portfolio under various economic scenarios.
摘要翻译： 描述了消费者和小企业的数据驱动和前瞻性风险和奖励食欲方法。 该方法包括客户细分，以便在收入和损失特征方面创建同质资产池，前瞻性模拟以预测收益和损失的预期值和波动率，以及投资组合的风险和报酬优化。 用于建模收入和损失的一种方法是广义加和效应分解模型，以适应历史数据。 基于该模型，执行分割程序，其允许创建具有类似收入和损失特征的客户群体。 开发了模型的估计程序，并开发了一种预测和模拟收入和损失波动性的模拟策略。 在各种经济情景下为当前投资组合创建有效的边际风险曲线（例如回报波动性）和报酬（例如预期收益）。
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公开(公告)号：US20100293107A1
公开(公告)日：2010-11-18
申请号：US12546807
申请日：2009-08-25
申请人： Agus Sudjianto , Michael Chorba , Daniel Hudson , Sandi Setiawan , Jocelyn Sikora , Harsh Singhal , Kiran Vuppu , Kaloyan Mihaylov , Jie Chen , Timothy J. Breault , Arun R. Pinto , Naveen G. Yeri , Benhong Zhang , Zhe Zhang , Tony Nobili , Hungien Wang , Aijun Zhang
发明人： Agus Sudjianto , Michael Chorba , Daniel Hudson , Sandi Setiawan , Jocelyn Sikora , Harsh Singhal , Kiran Vuppu , Kaloyan Mihaylov , Jie Chen , Timothy J. Breault , Arun R. Pinto , Naveen G. Yeri , Benhong Zhang , Zhe Zhang , Tony Nobili , Hungien Wang , Aijun Zhang
IPC分类号： G06Q40/00
摘要： A data driven and forward looking risk and reward appetite methodology for consumer and small business is described. The methodology includes customer segmentation to create pools of homogeneous assets in terms of revenue and loss characteristics, forward looking simulation to forecast expected values and volatilities of revenue and loss, and risk and reward optimization of the portfolio. One methodology used for modeling revenue and loss is a generalized additive effect decomposition model to fit historical data. Based on the model, a segmentation procedure is performed, which allows for creation of groups of customers with similar revenue and loss characteristics. An estimation procedure for the model is developed and a simulation strategy to forecast and simulate revenue and loss volatility is developed. Efficient frontier curves of risk (e.g., return volatility) and reward (e.g., expected return) are created for the current portfolio under various economic scenarios.
摘要翻译： 描述了消费者和小企业的数据驱动和前瞻性风险和奖励食欲方法。 该方法包括客户细分，以便在收入和损失特征方面创建同质资产池，前瞻性模拟以预测收益和损失的预期值和波动率，以及投资组合的风险和报酬优化。 用于建模收入和损失的一种方法是广义加和效应分解模型，以适应历史数据。 基于该模型，执行分割程序，其允许创建具有类似收入和损失特征的客户群体。 开发了模型的估计程序，并开发了一种预测和模拟收入和损失波动性的模拟策略。 在各种经济情景下为当前投资组合创建有效的边际风险曲线（例如回报波动性）和报酬（例如预期收益）。
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公开(公告)号：US20130073481A1
公开(公告)日：2013-03-21
申请号：US13618121
申请日：2012-09-14
申请人： Agus Sudjianto , Michael Chorba , Daniel Hudson , Sandi Setiawa , Jocelyn Sikora , Harsh Singhal , Kiran Vuppo , Kaloyan Mihaylov , Jie Chen , Timothy J. Breault , Arun R. Pinto , Naveen G. Yeri , Benhong Zhang , Zhe Zhang , Tony Nobili , Hungien Wang , Aijun Zhang
发明人： Agus Sudjianto , Michael Chorba , Daniel Hudson , Sandi Setiawa , Jocelyn Sikora , Harsh Singhal , Kiran Vuppo , Kaloyan Mihaylov , Jie Chen , Timothy J. Breault , Arun R. Pinto , Naveen G. Yeri , Benhong Zhang , Zhe Zhang , Tony Nobili , Hungien Wang , Aijun Zhang
IPC分类号： G06Q40/06
摘要： A data driven and forward looking risk and reward appetite methodology for consumer and small business is described. The methodology includes account level historical data collection for customers associated with accounts as part of a portfolio. The account level historical data is segmented into groups of customers with similar revenues and loss characteristics. Segmented data is decomposed into seasoning, vintage, and cycle effects. Statistical clusters are formed based upon the data and effects. A simulation is applied to the statistical clusters and prediction data is generated. A simulation strategy to forecast and simulate revenue and loss volatility is developed. Efficient frontier curves of risk (e.g., return volatility) and reward (e.g., expected return) are created for the current portfolio under various economic scenarios.
摘要翻译： 描述了消费者和小企业的数据驱动和前瞻性风险和奖励食欲方法。 该方法包括与帐户相关联的客户的帐户级历史数据收集作为投资组合的一部分。 账户级别的历史数据被分为具有相似收入和损失特征的客户群体。 分段数据分解为调味料，复古和循环效应。 基于数据和效果形成统计群集。 将仿真应用于统计集群，生成预测数据。 开发了一种预测和模拟收入和损失波动率的模拟策略。 在各种经济情景下为当前投资组合创建有效的边际风险曲线（例如回报波动性）和报酬（例如预期收益）。
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公开(公告)号：US20120130771A1
公开(公告)日：2012-05-24
申请号：US13161291
申请日：2011-06-15
CPC分类号： G06Q10/06393 , G06Q10/06398 , G06Q30/016 , G06Q30/0201 , G06Q30/0202 , G06Q30/0203
摘要： Chat categorization uses semi-supervised clustering to provide Voice of the Customer (VOC) analytics over unstructured data via an historical understanding of topic categories discussed to derive an automated methodology of topic categorization for new data; application of semi-supervised clustering (SSC) for VOC analytics; generation of seed data for SSC; and a voting algorithm for use in the absence of domain knowledge/manual tagged data. Customer service interactions are mined and quality of these interactions is measured by “Customer's Vote” which, in turn, is determined by the customer's experience during the interaction and the quality of customer issue resolution. Key features of the interaction that drive a positive experience and resolution are automatically learned via machine learning driven algorithms based on historical data. This, in turn, is used to coach/teach the system/service representative on future interactions.
摘要翻译： 聊天分类使用半监督聚类，通过对所讨论的主题类别的历史了解，为非结构化数据提供客户声音（VOC）分析，以获得新数据的主题分类的自动化方法; 应用半监督聚类（SSC）进行VOC分析; SSC的种子数据的生成; 以及在没有域知识/手动标记数据的情况下使用的投票算法。 客户服务互动被挖掘，这些互动的质量是通过“客户的投票”衡量的，而客户的投票又由客户在交互中的经验和客户问题解决的质量决定。 通过基于历史数据的机器学习驱动的算法，自动学习促进积极体验和分辨率的交互的主要特征。 这反过来用于教授/教授系统/服务代表对未来的交互。
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公开(公告)号：US08682617B2
公开(公告)日：2014-03-25
申请号：US13187638
申请日：2011-07-21
IPC分类号： G06F15/00
CPC分类号： G06Q10/06
摘要： Methods, computer readable media, and apparatuses for evaluating models using forecast error attribution are presented. According to one or more aspects, one or more input values corresponding to one or more input variables may be forecast. One or more results of a modeling function may be calculated using the one or more forecasted input values. Thereafter, actual performance data corresponding to the modeling function may be received. One or more holdout values for the modeling function may be calculated using the actual performance data. Subsequently, a graph that includes the one or more results of the modeling function, the actual performance data, and the one or more holdout values for the modeling function may be plotted. In some arrangements, the one or more holdout values for the modeling function may be indicative of one or more assumption errors made with respect to the one or more forecasted input values.
摘要翻译： 提出了使用预测误差归因评估模型的方法，计算机可读介质和装置。 根据一个或多个方面，可以预测对应于一个或多个输入变量的一个或多个输入值。 可以使用一个或多个预测输入值来计算建模功能的一个或多个结果。 此后，可以接收与建模功能对应的实际性能数据。 可以使用实际的性能数据来计算建模功能的一个或多个保持值。 随后，可以绘制包括建模功能的一个或多个结果，实际性能数据以及建模功能的一个或多个保持值的图。 在一些布置中，用于建模功能的一个或多个保持值可以指示针对一个或多个预测输入值做出的一个或多个假设误差。
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公开(公告)号：US20130024160A1
公开(公告)日：2013-01-24
申请号：US13187638
申请日：2011-07-21
IPC分类号： G06F15/00
CPC分类号： G06Q10/06
摘要： Methods, computer readable media, and apparatuses for evaluating models using forecast error attribution are presented. According to one or more aspects, one or more input values corresponding to one or more input variables may be forecast. One or more results of a modeling function may be calculated using the one or more forecasted input values. Thereafter, actual performance data corresponding to the modeling function may be received. One or more holdout values for the modeling function may be calculated using the actual performance data. Subsequently, a graph that includes the one or more results of the modeling function, the actual performance data, and the one or more holdout values for the modeling function may be plotted. In some arrangements, the one or more holdout values for the modeling function may be indicative of one or more assumption errors made with respect to the one or more forecasted input values.
摘要翻译： 提出了使用预测误差归因评估模型的方法，计算机可读介质和装置。 根据一个或多个方面，可以预测对应于一个或多个输入变量的一个或多个输入值。 可以使用一个或多个预测输入值来计算建模功能的一个或多个结果。 此后，可以接收与建模功能对应的实际性能数据。 可以使用实际的性能数据来计算建模功能的一个或多个保持值。 随后，可以绘制包括建模功能的一个或多个结果，实际性能数据以及建模功能的一个或多个保持值的图。 在一些布置中，用于建模功能的一个或多个保持值可以指示针对一个或多个预测输入值做出的一个或多个假设误差。
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