公开(公告)号：US20140046874A1

公开(公告)日：2014-02-13

申请号：US13569601

申请日：2012-08-08

申请人： Faming Li ,  Onno Zoeter ,  Daniel H. Greene ,  Yu-An Sun

发明人： Faming Li ,  Onno Zoeter ,  Daniel H. Greene ,  Yu-An Sun

IPC分类号： G07B15/02

CPC分类号： G07B15/02

摘要： A real time dynamic vehicle parking price management method, system and processor-readable medium. Two factors can be considered in determining the parking price: the real time occupancy level and the historic parking demand. An assured price that follows from a background schedule can be pre-determined based on a historic parking data. Future demand can be estimated based on the historic occupancy data and price and the assured price can be made proportional to the estimated demand. The assured price can be simplified to be intuitive and easy to remember. A real time parking price can be determined by an occupancy feedback control. A controller can be employed to track the occupancy and to suggest the parking price in real time based on an occupancy set point to improve economic efficiency and reduce cruising for parking.

摘要翻译： 实时动态车辆停车价格管理方法，系统和处理器可读介质。 在确定停车价格时可考虑两个因素：实时占用水平和历史停车需求。 可以根据历史性停车数据预先确定从后台进度中确定的价格。 未来的需求可以根据历史的占有率数据和价格估算，并且可靠的价格可以与估计的需求成正比。 可靠的价格可以简化为直观易记。 实时停车价格可以通过占用反馈控制来确定。 可以使用控制器来追踪占用率，并根据占用设定点实时提出停车价格，以提高经济效益，减少停车巡航。

公开(公告)号：US08538833B2

公开(公告)日：2013-09-17

申请号：US12695757

申请日：2010-01-28

申请人： Christopher R. Dance ,  Onno Zoeter ,  Yu-An Sun ,  Venkatesh Rao

发明人： Christopher R. Dance ,  Onno Zoeter ,  Yu-An Sun ,  Venkatesh Rao

IPC分类号： G06Q99/00

CPC分类号： G06Q10/00 ,  G06Q20/102 ,  G06Q20/20 ,  G06Q30/0218 ,  G06Q30/0282 ,  G06Q30/0283 ,  G06Q99/00

摘要： A method and apparatus for paying for an existing report are provided. In the method, an existing report for which a first structure of entities is identified is received. Thereafter, a set of other reports for which respective second structures of entities are identified is received. A weighting for other reports in the set of other reports is assigned, based on the respective structures. A payment for the existing report is extracted based on the weighting, a selected scoring rule, and the set of other reports.

摘要翻译： 提供了一种用于支付现有报告的方法和装置。 在该方法中，接收到识别实体的第一结构的现有报告。 此后，接收用于识别实体的各个第二结构的一组其他报告。 根据各自的结构，分配其他报告中其他报告的权重。 根据加权，选定的评分规则和其他报告集，提取现有报告的付款。

公开(公告)号：US20110184818A1

公开(公告)日：2011-07-28

申请号：US12695757

申请日：2010-01-28

申请人： Christopher R. Dance ,  Onno Zoeter ,  Yu-An Sun ,  Venkatesh Rao

发明人： Christopher R. Dance ,  Onno Zoeter ,  Yu-An Sun ,  Venkatesh Rao

IPC分类号： G06Q10/00 ,  G06Q30/00 ,  G06Q20/00 ,  G06Q99/00 ,  G06Q90/00

CPC分类号： G06Q10/00 ,  G06Q20/102 ,  G06Q20/20 ,  G06Q30/0218 ,  G06Q30/0282 ,  G06Q30/0283 ,  G06Q99/00

摘要： A method and apparatus for paying for an existing report are provided. In the method, an existing report for which a first structure of entities is identified is received. Thereafter, a set of other reports for which respective second structures of entities are identified is received. A weighting for other reports in the set of other reports is assigned, based on the respective structures. A payment for the existing report is extracted based on the weighting, a selected scoring rule, and the set of other reports.

摘要翻译： 提供了一种用于支付现有报告的方法和装置。 在该方法中，接收到识别实体的第一结构的现有报告。 此后，接收用于识别实体的各个第二结构的一组其他报告。 根据各自的结构，分配其他报告中其他报告的权重。 根据加权，选定的评分规则和其他报告集，提取现有报告的付款。

公开(公告)号：US08924315B2

公开(公告)日：2014-12-30

申请号：US13324060

申请日：2011-12-13

申请人： Cedric Archambeau ,  Shengbo Guo ,  Onno Zoeter ,  Jean-Marc Andreoli

发明人： Cedric Archambeau ,  Shengbo Guo ,  Onno Zoeter ,  Jean-Marc Andreoli

IPC分类号： G06F15/18 ,  G06F19/24

CPC分类号： G06N7/005 ,  G06N3/08

摘要： Multi-task regression or classification includes optimizing parameters of a Bayesian model representing relationships between D features and P tasks, where D≧1 and P≧1, respective to training data comprising sets of values for the D features annotated with values for the P tasks. The Bayesian model includes a matrix-variate prior having features and tasks dimensions of dimensionality D and P respectively. The matrix-variate prior is partitioned into a plurality of blocks, and the optimizing of parameters of the Bayesian model includes inferring prior distributions for the blocks of the matrix-variate prior that induce sparseness of the plurality of blocks. Values of the P tasks are predicted for a set of input values for the D features using the optimized Bayesian model. The optimizing also includes decomposing the matrix-variate prior into a product of matrices including a matrix of reduced rank in the tasks dimension that encodes correlations between tasks.

摘要翻译： 多任务回归或分类包括优化表示D特征和P任务之间的关系的贝叶斯模型的参数，其中D≥1和P≥1，分别对应于包含P任务的值所注明的D特征值的值的训练数据 。 贝叶斯模型包括先前具有维度D和P的特征和任务维度的矩阵变量。 矩阵变量先验被划分成多个块，并且贝叶斯模型的参数的优化包括推导出先前分布的矩阵变量的块，从而引起多个块的稀疏。 使用优化的贝叶斯模型，为D特征的一组输入值预测P任务的值。 优化还包括将矩阵变量分解成矩阵乘积，包括在任务维度中编码任务之间的相关性的减少秩的矩阵。

公开(公告)号：US07958004B2

公开(公告)日：2011-06-07

申请号：US12570430

申请日：2009-09-30

申请人： Christopher R. Dance ,  Onno Zoeter

发明人： Christopher R. Dance ,  Onno Zoeter

IPC分类号： G06F17/30

CPC分类号： G06Q10/087 ,  G06Q30/0202

摘要： According to various embodiments, the present teachings include inventory control policies that are defined in terms of functions of aggregate cost rates, involving thresholds Ω and an order-up-to point S. An embodiment of the present teachings includes a method. The method includes tracking an inventory position of each of the plurality of items by a logistics network and determining an item cost rate for each of the plurality of items based on the tracked inventory position. The method also includes determining an aggregate cost rate for the plurality of items based on the determined item cost rates, comparing the aggregate cost rate with a cost rate threshold Ω, and ordering the plurality of items to an order-up-to point S if the compared aggregate cost rate is greater than or equal to the cost rate threshold Ω.

摘要翻译： 根据各种实施例，本教导包括根据总成本率的功能定义的库存控制策略，涉及阈值＆OHgr; 和点到点S.本教导的实施例包括一种方法。 所述方法包括：通过物流网络跟踪所述多个物品中的每一个的库存位置，并基于跟踪的库存位置确定所述多个料品中的每一个的料品成本率。 该方法还包括基于确定的项目成本率确定多个项目的总成本率，将总成本率与成本率阈值＆amp; OHgr进行比较，并将多个项目排序到点到点S 如果比较的总成本率大于或等于成本率阈值＆OHgr;

公开(公告)号：US20100318490A1

公开(公告)日：2010-12-16

申请号：US12481802

申请日：2009-06-10

申请人： Guillaume M. Bouchard ,  Onno Zoeter

发明人： Guillaume M. Bouchard ,  Onno Zoeter

IPC分类号： G06N7/02

CPC分类号： G06F17/10 ,  G06K9/6221

摘要： A method comprises: partitioning a region of interest into a plurality of soft bin regions that span the region of interest; estimating an integral over each soft bin region of a function defined over the region of interest; and outputting a value equal to or derived from the sum of the estimated integrals over the soft bin regions spanning the region of interest. The method may further comprise: integrating a Bayesian theorem function using the partitioning, estimating, and outputting operations, and classifying an object to be classified using a classifier trained using the Bayesian machine learning. The method may further comprise performing optimal control by iteratively minimizing a controlled system cost function to determine optimized control inputs using the partitioning, estimating, and outputting with the function equal to the controlled system cost function having the selected control inputs, and controlling the controlled system using the optimized control inputs.

摘要翻译： 一种方法包括：将感兴趣区域划分成跨越感兴趣区域的多个软仓区域; 估计在感兴趣区域上定义的函数的每个软仓区域上的积分; 并且在跨越感兴趣区域的软仓区域上输出等于或从所估计的积分的总和导出的值。 该方法还可以包括：使用分区，估计和输出操作来整合贝叶斯定理函数，并且使用使用贝叶斯机器学习训练的分类器对要分类的对象进行分类。 该方法还可以包括通过迭代地最小化受控系统成本函数来执行最优控制，以使用等于具有所选择的控制输入的受控系统成本函数的功能进行分区，估计和输出来确定优化的控制输入，并且控制受控系统 使用优化的控制输入。

公开(公告)号：US08037043B2

公开(公告)日：2011-10-11

申请号：US12207315

申请日：2008-09-09

申请人： Onno Zoeter ,  Michael J. Taylor ,  Edward Lloyd Snelson ,  John P. Guiver ,  Nicholas Craswell ,  Martin Szummer

发明人： Onno Zoeter ,  Michael J. Taylor ,  Edward Lloyd Snelson ,  John P. Guiver ,  Nicholas Craswell ,  Martin Szummer

IPC分类号： G06F17/30

CPC分类号： G06F17/30675 ,  G06F17/30864

摘要： An information retrieval system is described for retrieving a list of documents such as web pages or other items from a document index in response to a user query. In an embodiment a prediction engine is used to predict both explicit relevance information such as judgment labels and implicit relevance information such as click data. In an embodiment the predicted relevance information is applied to a stored utility function that describes user satisfaction with a search session. This produces utility scores for proposed lists of documents. Using the utility scores one of the lists of documents is selected. In this way different sources of relevance information are combined into a single information retrieval system in a principled and effective manner which gives improved performance.

摘要翻译： 描述了用于响应于用户查询从文档索引检索诸如网页或其他项目的文档列表的信息检索系统。 在一个实施例中，预测引擎用于预测诸如判断标签的显式相关性信息和诸如点击数据的隐含相关性信息。 在一个实施例中，预测的相关性信息被应用于描述用户对搜索会话的满意度的存储效用函数。 这将为拟议的文件清单产生效用分数。 使用实用程序得分选择文档列表之一。 以这种方式，不同的相关信息来源以原则和有效的方式组合成单个信息检索系统，从而提高了性能。

公开(公告)号：US20100076949A1

公开(公告)日：2010-03-25

申请号：US12207315

申请日：2008-09-09

申请人： Onno Zoeter ,  Michael J. Taylor ,  Edward Lloyd Snelson ,  John P. Guiver ,  Nicholas Craswell ,  Martin Szummer

发明人： Onno Zoeter ,  Michael J. Taylor ,  Edward Lloyd Snelson ,  John P. Guiver ,  Nicholas Craswell ,  Martin Szummer

IPC分类号： G06F7/06 ,  G06F17/30

CPC分类号： G06F17/30675 ,  G06F17/30864

摘要： An information retrieval system is described for retrieving a list of documents such as web pages or other items from a document index in response to a user query. In an embodiment a prediction engine is used to predict both explicit relevance information such as judgment labels and implicit relevance information such as click data. In an embodiment the predicted relevance information is applied to a stored utility function that describes user satisfaction with a search session. This produces utility scores for proposed lists of documents. Using the utility scores one of the lists of documents is selected. In this way different sources of relevance information are combined into a single information retrieval system in a principled and effective manner which gives improved performance.

摘要翻译： 描述了用于响应于用户查询从文档索引检索诸如网页或其他项目的文档列表的信息检索系统。 在一个实施例中，预测引擎用于预测诸如判断标签的显式相关性信息和诸如点击数据的隐含相关性信息。 在一个实施例中，预测的相关性信息被应用于描述用户对搜索会话的满意度的存储效用函数。 这将为拟议的文件清单产生效用分数。 使用实用程序得分选择文档列表之一。 以这种方式，不同的相关信息来源以原则和有效的方式组合成单个信息检索系统，从而提高了性能。

公开(公告)号：US08260655B2

公开(公告)日：2012-09-04

申请号：US12792124

申请日：2010-06-02

申请人： Christopher R. Dance ,  Onno Zoeter

发明人： Christopher R. Dance ,  Onno Zoeter

IPC分类号： G06Q10/00 ,  G06Q30/00 ,  G06F15/18

CPC分类号： G06Q30/0283 ,  G06Q30/0201

摘要： A valuation optimization method includes generating offeree decision information (buyer decision information, by way of illustrative example) by presenting a sequence of mechanisms to a sequence of offerees wherein the mechanisms comprise menus of transaction offers (sale offer menus, by way of illustrative example). Actual transactions (sale transactions, by way of illustrative example) are conducted responsive to acceptances of transaction offers by buyers. At a selected time in the generating, an offeree valuation distribution belief and the current mechanism are updated optimize an offeree's utility. The offeree's utility comprises an offeree's utility function constrained by a robust learning constraint computed based on a local differential of an earlier offeree's utility function with respect to the earlier offeree's valuation.

摘要翻译： 评估优化方法包括通过向参与者序列呈现机制序列来产生违约决定信息（作为说明性示例的买方决定信息），其中机制包括交易提供的菜单（销售提供菜单，作为说明性示例） 。 实际交易（销售交易，作为说明性的例子）是响应买家对交易报价的接受。 在生成的选定时间，受委派的估值分配信念和当前机制被更新优化了受访者的效用。 受委人的实用程序包括受约束的效用函数，受到基于较早受访者效用函数的局部差异计算的强大的学习约束，相对于较早的受访者的估值。

公开(公告)号：US08190550B2

公开(公告)日：2012-05-29

申请号：US12481802

申请日：2009-06-10

申请人： Guillaume M. Bouchard ,  Onno Zoeter

发明人： Guillaume M. Bouchard ,  Onno Zoeter

IPC分类号： G06F17/10 ,  G06F17/17 ,  G06F17/18

CPC分类号： G06F17/10 ,  G06K9/6221

摘要： A method comprises: partitioning a region of interest into a plurality of soft bin regions that span the region of interest; estimating an integral over each soft bin region of a function defined over the region of interest; and outputting a value equal to or derived from the sum of the estimated integrals over the soft bin regions spanning the region of interest. The method may further comprise: integrating a Bayesian theorem function using the partitioning, estimating, and outputting operations, and classifying an object to be classified using a classifier trained using the Bayesian machine learning. The method may further comprise performing optimal control by iteratively minimizing a controlled system cost function to determine optimized control inputs using the partitioning, estimating, and outputting with the function equal to the controlled system cost function having the selected control inputs, and controlling the controlled system using the optimized control inputs.

摘要翻译： 一种方法包括：将感兴趣区域划分成跨越感兴趣区域的多个软仓区域; 估计在感兴趣区域上定义的函数的每个软仓区域上的积分; 并且在跨越感兴趣区域的软仓区域上输出等于或从所估计的积分的总和导出的值。 该方法还可以包括：使用分区，估计和输出操作来整合贝叶斯定理函数，并且使用使用贝叶斯机器学习训练的分类器对要分类的对象进行分类。 该方法还可以包括通过迭代地最小化受控系统成本函数来执行最优控制，以使用等于具有所选择的控制输入的受控系统成本函数的功能进行分区，估计和输出来确定优化的控制输入，并且控制受控系统 使用优化的控制输入。