公开(公告)号：US06484163B1

公开(公告)日：2002-11-19

申请号：US09516524

申请日：2000-03-01

申请人： Andrew Martin Lawrence ,  Mark Steven Ramsey ,  David Alec Selby ,  Vincent Philip Thomas

发明人： Andrew Martin Lawrence ,  Mark Steven Ramsey ,  David Alec Selby ,  Vincent Philip Thomas

IPC分类号： G06R1730

CPC分类号： G06F17/30539 ,  G06F17/30421 ,  G06F2216/03 ,  Y10S707/954 ,  Y10S707/966 ,  Y10S707/99932 ,  Y10S707/99933 ,  Y10S707/99937

摘要： A technique for enhanced data mining of large scale relational databases is described. User defined functions (UDF) are created by a user and distributed by a managing node of a database to each node of the database. Upon the issuance of a prespecified SQL command, the UDF is executed by each node relative to the data controlled by each node. Specifically, targeted tuples in the data controlled by each node are scored based on criteria contained in the UDF. A new data field is added to each target tuple, and the score is placed therein. The score is then used to determine whether, for example, a customer which is represented by the tuple should be included in an advertising campaign or to tailor a mailing to a customer based on the score contained in the new data field.

摘要翻译： 描述了一种用于增强大型关系数据库的数据挖掘的技术。 用户定义的功能（UDF）由用户创建并由数据库的管理节点分发给数据库的每个节点。 在发出预先指定的SQL命令之后，每个节点相对于每个节点控制的数据执行UDF。 具体来说，根据UDF中包含的标准对每个节点控制的数据中的目标元组进行评分。 将新的数据字段添加到每个目标元组，并将得分置于其中。 然后，该分数用于确定例如，由元组表示的客户是否应包含在广告活动中，或者基于新数据字段中包含的分数来定制对客户的邮寄。

公开(公告)号：US20080275800A1

公开(公告)日：2008-11-06

申请号：US11743293

申请日：2007-05-02

申请人： Naoki Abe ,  James J. Bennett ,  David L. Jensen ,  Richard D. Lawrence ,  Prem Melville ,  Edwin Peter Dawson Pednault ,  Cezar Pendus ,  Chandan Karrem Reddy ,  Vincent Philip Thomas

发明人： Naoki Abe ,  James J. Bennett ,  David L. Jensen ,  Richard D. Lawrence ,  Prem Melville ,  Edwin Peter Dawson Pednault ,  Cezar Pendus ,  Chandan Karrem Reddy ,  Vincent Philip Thomas

IPC分类号： G06Q40/00

CPC分类号： G06Q40/00 ,  G06Q40/025 ,  G06Q40/12

摘要： The present invention employs data processing systems to handle debt collection by formulation the collections process as a Markov Decision Process with constrained resources, thus making it possible automatically to generate an optimal collections policy with respect to maximizing long-term expected return throughout the course of a collections process, subject to constraints on the available resources possibly in multiple organizations. This is accomplished by coupling data modeling and resource optimization within the constrained Markov Decision Process formulation and generating optimized rules based on constrained reinforcement learning process comprising applied on the basis of past historical data.

摘要翻译： 本发明采用数据处理系统来处理债务收集，通过将收集过程作为具有约束资源的马尔可夫决策过程来制定收款过程，从而使得可以自动生成关于在整个过程中最大化长期预期回报的最佳收款策略 收集过程，受到可能在多个组织中可用资源的限制。 这通过在约束马尔可夫决策过程公式中耦合数据建模和资源优化来实现，并且基于约束强化学习过程生成优化的规则，其包括基于过去的历史数据应用。

公开(公告)号：US07519553B2

公开(公告)日：2009-04-14

申请号：US11743293

申请日：2007-05-02

申请人： Naoki Abe ,  James J. Bennett ,  David L. Jensen ,  Richard D. Lawrence ,  Prem Melville ,  Edwin Peter Dawson Pednault ,  Cezar Pendus ,  Chandan Karrem Reddy ,  Vincent Philip Thomas

发明人： Naoki Abe ,  James J. Bennett ,  David L. Jensen ,  Richard D. Lawrence ,  Prem Melville ,  Edwin Peter Dawson Pednault ,  Cezar Pendus ,  Chandan Karrem Reddy ,  Vincent Philip Thomas

IPC分类号： G06Q40/00

CPC分类号： G06Q40/00 ,  G06Q40/025 ,  G06Q40/12

摘要： The present invention employs data processing systems to handle debt collection by formulation the collections process as a Markov Decision Process with constrained resources, thus making it possible automatically to generate an optimal collections policy with respect to maximizing long-term expected return throughout the course of a collections process, subject to constraints on the available resources possibly in multiple organizations. This is accomplished by coupling data modeling and resource optimization within the constrained Markov Decision Process formulation and generating optimized rules based on constrained reinforcement learning process comprising applied on the basis of past historical data.

摘要翻译： 本发明采用数据处理系统来处理债务收集，通过将收集过程作为具有约束资源的马尔可夫决策过程来制定收款过程，从而使得可以自动生成关于在整个过程中最大化长期预期回报的最佳收款策略 收集过程，受到可能在多个组织中可用资源的限制。 这通过在约束马尔可夫决策过程公式中耦合数据建模和资源优化来实现，并且基于约束强化学习过程生成优化的规则，其包括基于过去的历史数据应用。