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1. 发明授权

US07912773B1 Computer-implemented data storage systems and methods for use with predictive model systems 有权
标题翻译：计算机实现的数据存储系统和与预测模型系统一起使用的方法
公开(公告)号：US07912773B1
公开(公告)日：2011-03-22
申请号：US11691277
申请日：2007-03-26
申请人： Revathi Subramanian , Radu Drossu , Kevin Chaowen Chen , Paul C. Dulany
发明人： Revathi Subramanian , Radu Drossu , Kevin Chaowen Chen , Paul C. Dulany
IPC分类号： G06Q40/00
CPC分类号： G06Q40/025 , G06Q10/04 , G06Q20/4016 , G06Q30/0185 , G06Q30/0202 , G06Q30/06 , G06Q40/00 , G06Q40/02 , G06Q40/06 , G06Q40/12
摘要： Systems and methods for performing fraud detection. As an example, a system and method can be configured to contain a raw data repository for storing raw data related to financial transactions. A data store contains rules to indicate how many generations or to indicate a time period within which data items are to be stored in the raw data repository. Data items stored in the raw data repository are then accessed by a predictive model in order to perform fraud detection.
摘要翻译：用于执行欺诈检测的系统和方法。例如，可以将系统和方法配置为包含用于存储与金融交易相关的原始数据的原始数据存储库。数据存储包含用于指示多少代或指示将数据项存储在原始数据存储库中的时间段的规则。然后通过预测模型访问存储在原始数据存储库中的数据项，以执行欺诈检测。

2. 发明申请

US20090234683A1 Detecting and Measuring Risk with Predictive Models Using Content Mining 有权
标题翻译：使用内容挖掘的预测模型检测和测量风险
公开(公告)号：US20090234683A1
公开(公告)日：2009-09-17
申请号：US11867602
申请日：2007-10-04
申请人： Russell Anderson , Larry S. Peranich , Ricardo M. Dungca , Joseph P. Milana , Xuhui Shao , Paul C. Dulany , Khosrow M. Hassibi , James C. Baker
发明人： Russell Anderson , Larry S. Peranich , Ricardo M. Dungca , Joseph P. Milana , Xuhui Shao , Paul C. Dulany , Khosrow M. Hassibi , James C. Baker
IPC分类号： G06Q10/00 , G06N5/02 , G06Q30/00
CPC分类号： G06Q40/08 , G06Q20/10 , G06Q40/025 , G06Q40/12
摘要： Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.
摘要翻译：计算机实现的处理事务的方法和系统以确定事务的风险将高分类信息（例如文本数据）转换为低分类信息，例如类别或集群ID。文本数据可以是交易的商家名称或其他文本内容，或与消费者相关的数据，或任何参与交易的其他类型的实体。内容挖掘技术用于提供从高到低分类信息的转换。在操作中，将所得到的低分类信息与其他数据一起输入到统计模型中。统计模型提供交易中风险水平的输出。公开了将高分类信息转换为低分类集群，使用这种信息的方法以及使用这种集群的其它方面。

3. 发明申请

US20120317013A1 Computer-Implemented Systems And Methods For Scoring Stored Enterprise Data 审中-公开
标题翻译：计算机实现的系统和方法对存储的企业数据进行评分
公开(公告)号：US20120317013A1
公开(公告)日：2012-12-13
申请号：US13158995
申请日：2011-06-13
申请人： Ho Ming Luk , Revathi Subramanian , Brian Lee Duke , Paul C. Dulany
发明人： Ho Ming Luk , Revathi Subramanian , Brian Lee Duke , Paul C. Dulany
IPC分类号： G06Q40/00
CPC分类号： G06Q40/025 , G06Q20/4016
摘要： Systems and methods are provided for scoring transaction data representative of transactions of disparate types transaction data describing a transaction that has occurred is received. The transaction data is stored in a plurality of segments, where a segment is formatted according to a template, where the template is selected based on an attribute of the transaction, and where the attribute is a customer attribute, an activity attribute, or a channel attribute. Transaction data associated with a segment is aggregated based on a particular attribute. The aggregate transaction data is provided to a predictive model to generate a fraud score. New transaction data is received describing a new transaction, wherein the new transaction includes the particular attribute. A real-time score is provided indicating a likelihood of fraud for the new transaction, wherein the score is based at least in part on the fraud score generated using the aggregate transaction data.
摘要翻译：提供了系统和方法，用于评估表示描述已经发生的事务的不同类型的事务数据的事务的事务数据。交易数据存储在多个段中，其中根据模板格式化段，其中基于事务的属性来选择模板，并且其中属性是客户属性，活动属性或频道属性。基于特定属性来聚合与段相关联的事务数据。将总交易数据提供给预测模型以产生欺诈分数。收到描述新交易的新交易数据，其中新交易包括特定属性。提供实时分数指示新交易的欺诈可能性，其中分数至少部分地基于使用总交易数据产生的欺诈评分。

4. 发明授权

US07376618B1 Detecting and measuring risk with predictive models using content mining 有权
标题翻译：使用内容挖掘的预测模型检测和测量风险
公开(公告)号：US07376618B1
公开(公告)日：2008-05-20
申请号：US09675412
申请日：2000-09-29
申请人： Russell Anderson , Larry S Peranich , Ricardo Dungca , Joseph P Milana , Xuhui Shao , Paul C Dulany , Khosrow M Hassibi , James C Baker
发明人： Russell Anderson , Larry S Peranich , Ricardo Dungca , Joseph P Milana , Xuhui Shao , Paul C Dulany , Khosrow M Hassibi , James C Baker
IPC分类号： G06Q40/00
CPC分类号： G06Q40/08 , G06Q20/10 , G06Q40/025 , G06Q40/12
摘要： Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.
摘要翻译：计算机实现的处理事务的方法和系统以确定事务的风险将高分类信息（例如文本数据）转换为低分类信息，例如类别或集群ID。文本数据可以是交易的商家名称或其他文本内容，或与消费者相关的数据，或任何参与交易的其他类型的实体。内容挖掘技术用于提供从高到低分类信息的转换。在操作中，将所得到的低分类信息与其他数据一起输入到统计模型中。统计模型提供交易中风险水平的输出。公开了将高分类信息转换为低分类集群，使用这种信息的方法以及使用这种集群的其它方面。

5. 发明授权

US07788195B1 Computer-implemented predictive model generation systems and methods 有权
标题翻译：计算机实现的预测模型生成系统和方法
公开(公告)号：US07788195B1
公开(公告)日：2010-08-31
申请号：US11691260
申请日：2007-03-26
申请人： Revathi Subramanian , Radu Drossu , Chao-Wen (Kevin) Chen , Paul C. Dulany , Ho Ming Luk
发明人： Revathi Subramanian , Radu Drossu , Chao-Wen (Kevin) Chen , Paul C. Dulany , Ho Ming Luk
IPC分类号： G06E1/00 , G06E3/00 , G06G7/00
CPC分类号： G06N99/005
摘要： Systems and methods for performing fraud detection. As an example, a system and method can be configured to build a set of predictive models to predict credit card or debit card fraud. A first predictive model is trained using a set of training data. A partitioning criterion is used to determine how to partition the training data into partitions. Another predictive model is trained using at least one of the partitions of training data in order to generate a second predictive model. The predictive models are combined for use in predicting credit card or debit card fraud.
摘要翻译：用于执行欺诈检测的系统和方法。作为示例，系统和方法可以被配置为构建一组预测模型以预测信用卡或借记卡欺诈。使用一组训练数据训练第一预测模型。分区标准用于确定如何将训练数据分割成分区。使用训练数据的至少一个分区来训练另一个预测模型，以便产生第二预测模型。预测模型合并用于预测信用卡或借记卡欺诈。

6. 发明申请

US20120317027A1 Computer-Implemented Systems And Methods For Real-Time Scoring Of Enterprise Data 审中-公开
标题翻译：计算机实现的系统和方法用于企业数据的实时评分
公开(公告)号：US20120317027A1
公开(公告)日：2012-12-13
申请号：US13158999
申请日：2011-06-13
申请人： Ho Ming Luk , Daniel J. Dotson , Paul C. Dulany , Revathi Subramanian , Brian Lee Duke , Vijay S. Desai
发明人： Ho Ming Luk , Daniel J. Dotson , Paul C. Dulany , Revathi Subramanian , Brian Lee Duke , Vijay S. Desai
IPC分类号： G06Q40/00 , G06F15/18 , G06N5/02
CPC分类号： G06Q40/02
摘要： Systems and methods are provided for providing real-time scoring of received transaction data. Transaction data describing a particular transaction that has occurred is received. The transaction data is stored in an enterprise database, where the enterprise database is configured to store transactions of disparate types, where the transaction data is stored using a plurality of segments, where a segment is formatted according to a template, and where the template is selected based on an attribute of the transaction, wherein the attribute is a customer attribute, an activity attribute, or a channel attribute. A transaction type of the particular transaction is determined. One or more models are selected from a pool of models based on the transaction type, wherein the one or more models are configured based on a plurality of records from the enterprise database, and a score of the received transaction data is generated based on the transaction data.
摘要翻译：提供系统和方法来提供接收到的交易数据的实时评分。接收到描述发生的特定事务的事务数据。交易数据存储在企业数据库中，其中企业数据库被配置为存储不同类型的事务，其中使用多个段来存储交易数据，其中根据模板格式化段，并且模板是基于事务的属性选择，其中该属性是客户属性，活动属性或信道属性。确定特定交易的交易类型。基于事务类型从模型池中选择一个或多个模型，其中基于来自企业数据库的多个记录配置一个或多个模型，并且基于该事务生成所接收的交易数据的得分数据。

7. 发明申请

US20120317008A1 Computer-Implemented Systems And Methods For Handling And Scoring Enterprise Data 审中-公开
标题翻译：计算机实施的系统和处理和评分企业数据的方法
公开(公告)号：US20120317008A1
公开(公告)日：2012-12-13
申请号：US13158981
申请日：2011-06-13
申请人： Revathi Subramanian , Ho Ming Luk , Brian Lee Duke , Paul C. Dulany
发明人： Revathi Subramanian , Ho Ming Luk , Brian Lee Duke , Paul C. Dulany
IPC分类号： G06Q40/00 , G06F17/30
CPC分类号： G06Q30/02 , G06F16/2465 , G06Q40/00
摘要： Systems and methods for storing transaction data associated with transactions of disparate types are provided. Transaction data is received describing a transaction that has occurred, the transaction being performed by an customer of a particular customer type and the transaction being performed using a channel of a particular channel type. Transaction data about the customer is stored in an customer segment according to one of a plurality of customer templates, the one of the plurality of customer templates being selected according to the customer type. Transaction data about the channel is stored in a channel segment according to one of a plurality of channel templates, the one of the plurality of channel templates being selected according to the channel type. Data from the customer segment, the activity segment, and the channel segment for the transaction is extracted and scored by a predictive model.
摘要翻译：提供了用于存储与不同类型的事务相关联的事务数据的系统和方法。接收到描述已经发生的交易的事务数据，该事务由特定客户类型的客户执行，并且使用特定信道类型的信道执行交易。关于客户的交易数据根据多个客户模板之一存储在客户分段中，根据客户类型选择多个客户模板中的一个。关于频道的交易数据根据多个频道模板之一存储在频道段中，根据频道类型选择多个频道模板中的一个。来自客户细分的数据，活动部分和交易的通道段由预测模型提取和评分。

8. 发明授权

US08032448B2 Detecting and measuring risk with predictive models using content mining 有权
标题翻译：使用内容挖掘的预测模型检测和测量风险
公开(公告)号：US08032448B2
公开(公告)日：2011-10-04
申请号：US11867602
申请日：2007-10-04
申请人： Russell Anderson , Larry S. Peranich , Ricardo M. Dungca , Joseph P. Milana , Xuhui Shao , Paul C. Dulany , Khosrow M. Hassibi , James C. Baker
发明人： Russell Anderson , Larry S. Peranich , Ricardo M. Dungca , Joseph P. Milana , Xuhui Shao , Paul C. Dulany , Khosrow M. Hassibi , James C. Baker
IPC分类号： G06Q40/00
CPC分类号： G06Q40/08 , G06Q20/10 , G06Q40/025 , G06Q40/12
摘要： Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.
摘要翻译：计算机实现的处理事务的方法和系统以确定事务的风险将高分类信息（例如文本数据）转换为低分类信息，例如类别或集群ID。文本数据可以是交易的商家名称或其他文本内容，或与消费者相关的数据，或任何参与交易的其他类型的实体。内容挖掘技术用于提供从高到低分类信息的转换。在操作中，将所得到的低分类信息与其他数据一起输入到统计模型中。统计模型提供交易中风险水平的输出。公开了将高分类信息转换为低分类集群，使用这种信息的方法以及使用这种集群的其它方面。

9. 发明申请

US20090192957A1 Computer-Implemented Data Storage Systems And Methods For Use With Predictive Model Systems 审中-公开
公开(公告)号：US20090192957A1
公开(公告)日：2009-07-30
申请号：US12418174
申请日：2009-04-03
申请人： Revathi Subramanian , Radu Drossu , Chao-Wen (Kevin) Chen , Paul C. Dulany
发明人： Revathi Subramanian , Radu Drossu , Chao-Wen (Kevin) Chen , Paul C. Dulany
IPC分类号： G06N3/02 , G06Q50/00 , G06Q10/00 , G06N5/02
CPC分类号： G06Q40/025 , G06Q10/04 , G06Q20/4016 , G06Q30/0185 , G06Q30/0202 , G06Q30/06 , G06Q40/00 , G06Q40/02 , G06Q40/06 , G06Q40/12
摘要： Systems and methods for performing fraud detection. As an example, a system and method can be configured to contain a raw data repository for storing raw data related to financial transactions. A data store contains rules to indicate how many generations or to indicate a time period within which data items are to be stored in the raw data repository. Data items stored in the raw data repository are then accessed by a predictive model in order to perform fraud detection.

10. 发明申请

US20090192855A1 Computer-Implemented Data Storage Systems And Methods For Use With Predictive Model Systems 审中-公开
标题翻译：计算机实现的数据存储系统和使用预测模型系统的方法
公开(公告)号：US20090192855A1
公开(公告)日：2009-07-30
申请号：US12418186
申请日：2009-04-03
申请人： Revathi Subramanian , Radu Drossu , Chao-Wen (Kevin) Chen , Paul C. Dulany
发明人： Revathi Subramanian , Radu Drossu , Chao-Wen (Kevin) Chen , Paul C. Dulany
IPC分类号： G06Q10/00 , G06N5/02 , G06Q40/00
CPC分类号： G06Q40/025 , G06Q10/04 , G06Q20/4016 , G06Q30/0185 , G06Q30/0202 , G06Q30/06 , G06Q40/00 , G06Q40/02 , G06Q40/06 , G06Q40/12
摘要： Systems and methods for performing fraud detection. As an example, a system and method can be configured to contain a raw data repository for storing raw data related to financial transactions. A data store contains rules to indicate how many generations or to indicate a time period within which data items are to be stored in the raw data repository. Data items stored in the raw data repository are then accessed by a predictive model in order to perform fraud detection.
摘要翻译：用于执行欺诈检测的系统和方法。例如，可以将系统和方法配置为包含用于存储与金融交易相关的原始数据的原始数据存储库。数据存储包含用于指示多少代或指示将数据项存储在原始数据存储库中的时间段的规则。然后通过预测模型访问存储在原始数据存储库中的数据项，以执行欺诈检测。

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