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41. 发明授权

US5338421A Method of and apparatus for separation by agglomeration 失效
标题翻译：通过附聚分离的方法和装置
公开(公告)号：US5338421A
公开(公告)日：1994-08-16
申请号：US100170
申请日：1993-08-02
申请人： Naoki Abe , Fumio Kawahara , Noboru Inoue
发明人： Naoki Abe , Fumio Kawahara , Noboru Inoue
IPC分类号： B01D17/06 , B01D21/00 , C02F1/463
CPC分类号： B01D21/0009 , B01D17/06
摘要： A method of separating an aqueous colloidal solution by agglomeration into water and agglomerate of colloidal particles by applying voltage to the aqueous colloidal solution to promote the agglomeration of colloidal particles. The frequency of the voltage to be applied to the aqueous colloidal solution is increased to about 10 kHz to improve efficiency of agglomeration and suppress electrolysis of water. As the frequency is increased, hydrogen is generated earlier than the reaction of oxygen generated by the electrolysis of water with the colloidal particles, thus not oxidizing the colloidal particles. Further, as the frequency is increased, the colloidal particles tend to obtain a greater oscillation energy, thus facilitating the agglomeration thereof.
摘要翻译：通过向水性胶体溶液施加电压以促进胶体颗粒的聚集，将凝胶状水溶胶分离成水和胶体颗粒团聚体的方法。施加到水性胶体溶液的电压的频率增加到约10kHz，以提高凝聚效率并抑制水的电解。随着频率的增加，氢比电解水与胶体粒子产生的氧的反应产生得早，因此不氧化胶体粒子。此外，随着频率的增加，胶体颗粒倾向于获得更大的振荡能量，从而有助于其凝聚。

42. 发明授权

US09064364B2 Confidential fraud detection system and method 有权
标题翻译：机密欺诈检测系统及方法
公开(公告)号：US09064364B2
公开(公告)日：2015-06-23
申请号：US10690778
申请日：2003-10-22
申请人： Naoki Abe , Carl E. Abrams , Chidanand V. Apte , Bishwaranjan Bhattacharjee , Kenneth A. Goldman , Matthias Gruetzner , Matthew A. Hilbert , John Langford , Sriram K. Padmanabhan , Charles P. Tresser , Kathleen M. Troidle , Philip S. Yu
发明人： Naoki Abe , Carl E. Abrams , Chidanand V. Apte , Bishwaranjan Bhattacharjee , Kenneth A. Goldman , Matthias Gruetzner , Matthew A. Hilbert , John Langford , Sriram K. Padmanabhan , Charles P. Tresser , Kathleen M. Troidle , Philip S. Yu
IPC分类号： G06Q20/02 , G07F7/10 , G06F21/62 , G06F21/72 , G06Q20/34 , G07F19/00
CPC分类号： G06Q20/4016 , G06F21/121 , G06F21/602 , G06F21/6218 , G06F21/6245 , G06F21/72 , G06F2221/07 , G06F2221/2101 , G06Q20/341 , G06Q20/3829 , G06Q2220/00 , G07F7/1008 , G07F7/1083 , G07F19/207 , H04L63/04
摘要： Various embodiments for maintaining security and confidentiality of data and operations within a fraud detection system. Each of these embodiments utilizes a secure architecture in which: (1) access to data is limited to only approved or authorized entities; (2) confidential details in received data can be readily identified and concealed; and (3) confidential details that have become non-confidential can be identified and exposed.
摘要翻译：用于维护欺诈检测系统内的数据和操作的安全性和机密性的各种实施例。这些实施例中的每一个都利用安全架构，其中：（1）对数据的访问仅限于批准或授权的实体; （2）接收到的数据的机密细节可以很容易地识别和隐藏; 和（3）可以识别和暴露出非机密的机密细节。

43. 发明授权

US08468290B2 Method and program for file information write processing 有权
标题翻译：文件信息写入处理方法和程序
公开(公告)号：US08468290B2
公开(公告)日：2013-06-18
申请号：US12212439
申请日：2008-09-17
申请人： Naoki Abe
发明人： Naoki Abe
IPC分类号： G06F17/30
CPC分类号： G06F3/0643 , G06F3/0619 , G06F3/0676 , G06F3/0679
摘要： The file information write processing method according to the present invention is a file information write processing method wherein a computer executes a process for outputting instruction corresponding to a file information write instruction from an application to a device driver, wherein: searching clusters which are empty areas within an actual data area of a memory unit of the computer, and obtaining the search result; if clusters which are empty areas exist, writing information to overwrite to one or more clusters within the actual data area of the memory unit which is a target of the write instruction from the application, to the clusters which are empty areas; and freeing clusters which were to be overwritten by the information written to the empty area clusters.
摘要翻译：根据本发明的文件信息写入处理方法是文件信息写入处理方法，其中计算机执行用于将与文件信息写入指令相对应的指令从应用程序输出到设备驱动程序的处理，其中：搜索作为空区域的群集在计算机的存储器单元的实际数据区域内，并获得搜索结果; 如果存在作为空区域的簇，则写入信息以覆盖作为来自应用的写指令的目标的存储器单元的实际数据区域内的一个或多个簇到作为空区域的簇; 并释放被写入空区域的信息覆盖的簇。

44. 发明授权

US08255346B2 Methods and systems for variable group selection and temporal causal modeling 有权
标题翻译：可变组选择和时间因果建模的方法和系统
公开(公告)号：US08255346B2
公开(公告)日：2012-08-28
申请号：US12616534
申请日：2009-11-11
申请人： Naoki Abe , Yan Liu , Aurelie C. Lozano , Saharon Rosset , Grzegorz Swirszcz
发明人： Naoki Abe , Yan Liu , Aurelie C. Lozano , Saharon Rosset , Grzegorz Swirszcz
IPC分类号： G06F15/18 , G06F7/60 , G06F17/50
CPC分类号： G06N5/003 , G06N7/005
摘要： A “variable group selection” system and method in which constructs are based upon a training data set, a regression modeling module that takes into account information on groups of related predictor variables given as input and outputs a regression model with selected variable groups. Optionally, the method can be employed as a component in methods of temporal causal modeling, which are applied on a time series training data set, and output a model of causal relationships between the multiple times series in the data.
摘要翻译：一种“可变组选择”系统和方法，其中构造基于训练数据集，回归建模模块考虑了作为输入给出的相关预测变量组的信息，并输出具有选定变量组的回归模型。可选地，该方法可以用作时间因果建模方法中的组件，其应用于时间序列训练数据集，并且输出数据中多次序列之间的因果关系模型。

45. 发明申请

US20120116850A1 CAUSAL MODELING OF MULTI-DIMENSIONAL HIERACHICAL METRIC CUBES 审中-公开
标题翻译：多维度数公式的原理建模
公开(公告)号：US20120116850A1
公开(公告)日：2012-05-10
申请号：US12943316
申请日：2010-11-10
申请人： Naoki Abe , Jing Fu , Michael G. Gemmell , Shubir Kapoor , Floyd S. Kelly , David M. Loehr , Aurelie C. Lozano , Shilpa N. Mahatma , Bonnie K. Ray
发明人： Naoki Abe , Jing Fu , Michael G. Gemmell , Shubir Kapoor , Floyd S. Kelly , David M. Loehr , Aurelie C. Lozano , Shilpa N. Mahatma , Bonnie K. Ray
IPC分类号： G06Q10/00
CPC分类号： G06Q10/067 , G06Q10/063 , G06Q10/0639
摘要： A computing system initializes a first frontier to be a root of a multi-dimensional hierarchical data structure representing an entity. The system acquires first data corresponding to the first frontier. The system performs modeling on the first data to obtain a first model and a corresponding first statistic. The system expands a dimension of the first frontier. The system gathers second data corresponding to the expanded frontier. The system applies the data modeling on the second data to obtain a second model and a corresponding second statistic. The system compares the first statistic of the first model and the second statistic of the second model. The system sets the second model to be the first model in response to determining that the second model statistic is better than the first model statistic. The system outputs the first model.
摘要翻译：计算系统将第一边界初始化为代表实体的多维分层数据结构的根。系统获取与第一边界对应的第一数据。系统对第一个数据执行建模以获得第一个模型和相应的第一个统计量。系统扩展了第一个边界的维度。系统收集对应于扩展边界的第二个数据。该系统对第二个数据应用数据建模以获得第二个模型和相应的第二个统计量。系统比较第一模型的第一个统计量和第二个模型的第二个统计量。响应于确定第二模型统计量优于第一模型统计量，系统将第二模型设置为第一模型。系统输出第一个模型。

46. 发明授权

US07978987B2 Image forming apparatus 有权
标题翻译：图像形成装置
公开(公告)号：US07978987B2
公开(公告)日：2011-07-12
申请号：US12211889
申请日：2008-09-17
申请人： Naoki Abe
发明人： Naoki Abe
IPC分类号： G03G15/00
CPC分类号： G03G15/5008 , G03G15/0189 , G03G15/0194 , G03G2215/0141 , G03G2215/0158
摘要： In an image forming apparatus, an image forming portion forms an image on a rotator. A storage portion stores change characteristics information relevant to correction parameters corresponding to phase points of the rotator. A designating portion sequentially designates the correction parameters based on the change characteristics information. A correcting portion corrects an image forming position on the rotator based on the correction parameter designated by the designating portion. When it is determined, based on a detecting phase point of the rotator detected by a detecting portion, that the current phase of the rotator corresponds to a gradual phase point at which the correction parameter changes at a rate equal to or lower than a predetermined value, the designation by said designating portion is shifted to the correction parameter corresponding to the gradual phase point.
摘要翻译：在图像形成装置中，图像形成部在旋转体上形成图像。存储部存储与对应于旋转体的相位点的校正参数相关的变化特性信息。指定部分根据变化特征信息依次指定校正参数。校正部根据由指定部指定的校正参数来校正旋转体上的图像形成位置。当基于由检测部检测到的旋转体的检测相位点确定旋转器的当前相位对应于校正参数以等于或低于预定值的速率变化的逐渐相位点时，所述指定部分的指定被移动到对应于逐渐相位点的校正参数。

47. 发明授权

US07847810B2 Image forming apparatus 有权
标题翻译：图像形成装置
公开(公告)号：US07847810B2
公开(公告)日：2010-12-07
申请号：US12401868
申请日：2009-03-11
申请人： Naoki Abe
发明人： Naoki Abe
IPC分类号： B41J2/385
CPC分类号： H04N1/0473 , G03G15/0194 , G03G15/043 , G03G2215/0158 , H04N2201/04748 , H04N2201/04767
摘要： An image forming apparatus is provided. A second photoconductor is disposed at a downstream side of a first photoconductor in a moving direction of a medium. First and second exposure units form first and second electrostatic latent images on the first and second photoconductors line by line at first and second exposure timing intervals in first and second exposure enabling time periods based on successive lines of first and second image data, respectively. A correction unit corrects at least one of the first and second exposure timing intervals. A change unit changes the second exposure enabling time period so as to suppress a difference between the number of the successive lines of the first image data and the number of the successive lines of the second image data.
摘要翻译：提供一种图像形成装置。第二感光体在介质的移动方向上设置在第一感光体的下游侧。第一和第二曝光单元分别基于第一和第二图像数据的连续行，分别在第一和第二曝光允许时间段中的第一和第二曝光时间间隔上逐行地在第一和第二光电导体上形成第一和第二静电潜像。校正单元校正第一和第二曝光定时间隔中的至少一个。改变单元改变第二曝光使能时间段，以便抑制第一图像数据的连续行数与第二图像数据的连续行数之间的差异。

48. 发明申请

US20100042561A1 METHODS AND SYSTEMS FOR COST-SENSITIVE BOOSTING 失效
标题翻译：成本敏感性升高的方法和系统
公开(公告)号：US20100042561A1
公开(公告)日：2010-02-18
申请号：US12190325
申请日：2008-08-12
申请人： Naoki Abe , Aurelie C. Lozano
发明人： Naoki Abe , Aurelie C. Lozano
IPC分类号： G06F15/16
CPC分类号： G06N99/005
摘要： Multi-class cost-sensitive boosting based on gradient boosting with “p-norm” cost functionals” uses iterative example weighting schemes derived with respect to cost functionals, and a binary classification algorithm. Weighted sampling is iteratively applied from an expanded data set obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, and where each non-optimally labeled example is given the weight equaling a half times the original misclassification cost for the labeled example times the p−1 norm of the average prediction of the current hypotheses. Each optimally labeled example is given the weight equaling the sum of the weights for all the non-optimally labeled examples for the same instance. Component classification algorithm is executed on a modified binary classification problem. A classifier hypothesis is output, which is the average of all the hypotheses output in the respective iterations.
摘要翻译：基于使用“p范数”成本函数梯度提升的多类成本敏感性提升“使用相对于成本函数导出的迭代示例加权方案和二进制分类算法。通过增加原始数据集中的每个示例获得的扩展数据集，对任何单个实例可能的标签具有尽可能多的数据点，并且每个非最佳标记的示例给出权重等于一半的加权采样乘以标记示例的原始错误分类成本乘以当前假设的平均预测的p-1范数。给出每个最佳标记的例子，其权重等于同一实例的所有非最佳标记的示例的权重之和。对修改的二进制分类问题执行组件分类算法。输出分类器假设，它是各自迭代中输出的所有假设的平均值。

49. 发明授权

US07561158B2 Method and apparatus for presenting feature importance in predictive modeling 失效
标题翻译：在预测建模中呈现特征重要性的方法和装置
公开(公告)号：US07561158B2
公开(公告)日：2009-07-14
申请号：US11329437
申请日：2006-01-11
申请人： Naoki Abe , Edwin Peter Dawson Pednault , Fateh Ali Tipu
发明人： Naoki Abe , Edwin Peter Dawson Pednault , Fateh Ali Tipu
IPC分类号： G06T11/20
CPC分类号： G06T11/206
摘要： Feature importance information available in a predictive model with correlation information among the variables is presented to facilitate more flexible choices of actions by business managers. The displayed feature importance information combines feature importance information available in a predictive model with correlational information among the variables. The displayed feature importance information may be presented as a network structure among the variables as a graph, and regression coefficients of the variables indicated on the corresponding nodes in the graph. To generate the display, a regression engine is called on a set of training data that outputs importance measures for the explanatory variables for predicting the target variable. A graphical model structural learning module is called that outputs a graph on the explanatory variables of the above regression problem representing the correlational structure among them. The feature importance measure, output by the regression engine, is displayed for each node in the graph, as an attribute, such as color, size, texture, etc, of that node in the graph output by the graphical model structural learning module.
摘要翻译：提供了具有变量之间相关性信息的预测模型中的特征重要度信息，以便企业管理者更灵活地选择行动。显示的特征重要性信息将预测模型中可用的特征重要性信息与变量之间的相关信息相结合。所显示的特征重要性信息可以作为图形中的变量之间的网络结构呈现，并且在图中的相应节点上指示的变量的回归系数。为了生成显示，在一组训练数据上调用回归引擎，该训练数据输出用于预测目标变量的解释变量的重要度量。一个图形模型结构学习模块被称为输出上述回归问题的解释变量的图表，表示它们之间的相关性结构。由图形模型结构学习模块输出的图形中的该节点的颜色，大小，纹理等属性显示图形中每个节点的回归引擎输出的特征重要性度量。

50. 发明申请

US20080065572A1 METHODS FOR MULTI-CLASS COST-SENSITIVE LEARNING 有权
标题翻译：多级成本敏感性学习方法
公开(公告)号：US20080065572A1
公开(公告)日：2008-03-13
申请号：US11937629
申请日：2007-11-09
申请人： Naoki Abe , Bianca Zadrozny
发明人： Naoki Abe , Bianca Zadrozny
IPC分类号： G06N3/00
CPC分类号： G06N99/005 , G06K9/6256 , Y10S706/932
摘要： Methods for multi-class cost-sensitive learning are based on iterative example weighting schemes and solve multi-class cost-sensitive learning problems using a binary classification algorithm. One of the methods works by iteratively applying weighted sampling from an expanded data set, which is obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, using a weighting scheme which gives each labeled example the weight specified as the difference between the average cost on that instance by the averaged hypotheses from the iterations so far and the misclassification cost associated with the label in the labeled example in question. It then calls the component classification algorithm on a modified binary classification problem in which each example is itself already a labeled pair, and its (meta) label is 1 or 0 depending on whether the example weight in the above weighting scheme is positive or negative, respectively. It then finally outputs a classifier hypothesis which is the average of all the hypotheses output in the respective iterations.
摘要翻译：多类成本敏感学习的方法基于迭代示例加权方案，并使用二进制分类算法解决多类成本敏感学习问题。其中一种方法通过迭代地应用来自扩展数据集的加权采样来工作，该扩展数据集通过使用给出每个实例的加权方案来增强原始数据集中具有尽可能多的数据点的数据点与任何单个实例的可能标签而获得的每个示例而获得标示的重量指定为该实例的平均成本与目前为止的迭代的平均假设之间的差异以及与所标记的示例中的标签相关联的错误分类成本。然后，对修改后的二进制分类问题调用组件分类算法，其中每个示例本身已经是一个标记对，根据上述加权方案中的示例权重是正还是负，其（元）标签为1或0，分别。然后，它最终输出一个分类器假设，它是相应迭代中输出的所有假设的平均值。

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