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11. 发明授权

US07085348B2 Leaf sequencing method and system 失效
标题翻译：叶片测序方法和系统
公开(公告)号：US07085348B2
公开(公告)日：2006-08-01
申请号：US10736023
申请日：2003-12-15
申请人： Srijit Kamath , Sartaj Sahni , Jantinder Palta , Sanjay Ranka , Jonathan G. Li
发明人： Srijit Kamath , Sartaj Sahni , Jantinder Palta , Sanjay Ranka , Jonathan G. Li
IPC分类号： A61N5/10
CPC分类号： A61N5/103 , A61N5/1036 , A61N5/1042
摘要： A method of delivering radiation treatment using multi-leaf collimation includes the step of providing a radiation fluence map which includes an intensity profile. The fluence map is converted into a preliminary leaf sequence, wherein the preliminary leaf sequence minimizes machine on-time and is generated without leaf movement constraints. The leaf movement constraint is imposed on the preliminary leaf sequence. At least one constraint elimination algorithm is then applied, the algorithm adjusting the preliminary leaf sequence to minimize violations of the constraint while providing the desired fluence map and minimized radiation on-time. The method can be applied to SMLC and DLMC systems, and can include adjustment for the tongue-and-groove effect.
摘要翻译：使用多叶准直进行放射治疗的方法包括提供包括强度分布的辐射注量图的步骤。注量图被转换成初步的叶子序列，其中初步叶子序列使机器准时最小化，并且不产生叶片运动限制。叶片运动约束对初步叶序列施加。然后应用至少一个约束消除算法，该算法调整初步叶序列以最小化对约束的违反，同时提供期望的通量图和最小化的辐射on-time。该方法可以应用于SMLC和DLMC系统，并且可以包括对榫槽效应的调整。

12. 发明申请

US20110238487A1 System and Method for Improving the Performance of Electronic Media Advertising Campaigns Through Multi-Attribute Analysis and Optimization 审中-公开
标题翻译：通过多属性分析和优化提高电子媒体广告业务绩效的系统和方法
公开(公告)号：US20110238487A1
公开(公告)日：2011-09-29
申请号：US13099608
申请日：2011-05-03
申请人： E. Diane Chang , Richard E. Chatwin , Sachin Kumar , Sanjay Ranka , James R. Weisinger , Jason Lenderman
发明人： E. Diane Chang , Richard E. Chatwin , Sachin Kumar , Sanjay Ranka , James R. Weisinger , Jason Lenderman
IPC分类号： G06Q30/00
CPC分类号： G06Q10/06375 , G06Q10/06393 , G06Q30/02 , G06Q30/0203 , G06Q30/0204 , G06Q30/0207 , G06Q30/0241 , G06Q30/0242 , G06Q30/0243 , G06Q30/0244 , G06Q30/0277
摘要： Automated system, methods, algorithms, procedures, and computer software programs and computer program products for improving and optimizing the performance of messaging campaigns, particularly for marketing campaigns in which advertisements or other messages are distributed over an interactive measurable medium such as the Internet. Analysis and Optimization method and procedure, an automated system, and system and method that exploit the underlying multi-attribute structure, as well as other features and advantages. Optimization procedures allocate the ad alternatives or other message to the customer population to optimize business objectives such as maximizing the number of positive responses received. Procedure for generating message allocations that improve and attempt to optimize the campaign performance. Methods ensure that campaign constraints are not violated. Methods can be implemented on a computer that is programmed to retrieve message performance information and to generate recommended message allocations for each stage in a multi-stage messaging campaign to achieve messaging goals.
摘要翻译：自动化系统，方法，算法，程序，计算机软件程序和计算机程序产品，用于改善和优化消息传播活动的性能，特别是对于通过互动可测量介质（如互联网）分发广告或其他消息的营销活动。分析和优化方法和过程，自动化系统，以及利用底层多属性结构以及其他特征和优点的系统和方法。优化程序将广告替代品或其他消息分配给客户群体，以优化业务目标，例如最大限度地获得收到的积极响应数量。生成消息分配的过程，用于改进和尝试优化广告系列的效果。方法确保不违反活动约束。方法可以在被编程为检索消息性能信息的计算机上实现，并且为了实现消息传送目标而在多阶段消息传送活动中为每个阶段生成推荐的消息分配。

13. 发明申请

US20080306810A1 Method, algorithm, and computer program for optimizing the performance of messages including advertisements in an interactive measurable medium 有权
标题翻译：方法，算法和计算机程序，用于优化包括交互式可测量介质中的广告的消息的性能
公开(公告)号：US20080306810A1
公开(公告)日：2008-12-11
申请号：US12193163
申请日：2008-08-18
申请人： Sanjay Ranka , Jason S. Lenderman , James Weisinger
发明人： Sanjay Ranka , Jason S. Lenderman , James Weisinger
IPC分类号： G06Q30/00 , G06Q10/00
CPC分类号： G06Q30/02 , G06Q10/06315 , G06Q10/0639 , G06Q30/0201 , G06Q30/0204 , G06Q30/0205 , G06Q30/0241 , G06Q30/0242 , G06Q30/0244 , G06Q30/0256 , G06Q30/0277 , G06Q30/0631 , G06Q30/0633
摘要： Method, procedure, algorithm, system, and computer program for improving and attempting to optimize the performance of messaging campaigns, particularly to marketing campaigns in which advertisements or other messages are distributed over an interactive measurable medium such as the Internet. A goal is to allocate the message alternatives to the customer population to optimize business objectives. The includes reading prior stage message state data pertaining to a prior stage including a cumulative number of trials and a cumulative number of successes for a particular message. Message performance results representing message trials and message successes from the previous stage based on the prior-stage state are then read, and a current message state is computed. A current message allocation is generated based on the current message state. Desirably the cumulative number of trials and the cumulative number of successes are discounted based on the age of the information. This inventive procedure may be applied to various message types including, for example, web site banner advertisements, electronic advertisements generally, email messages and promotions, and the like. In one embodiment, the messages are internet web site banner ads, trials are impressions of the ads presented to the user, and successes are measured by clickthroughs from the banner ad to the web site associated with the banner ad. Computer system, computer program, and computer program product associated with the inventive method and procedures are also provided.
摘要翻译：方法，程序，算法，系统和计算机程序，用于改进和尝试优化消息传播活动的性能，特别是在诸如因特网的交互式可测量介质上分发广告或其他消息的营销活动。目标是为客户群体分配消息替代品，以优化业务目标。这包括读取与前一阶段相关的前级消息状态数据，其包括累积的试验次数和针对特定消息的累积成功次数。然后读取表示基于前一级状态的前一级的消息试验和消息成功的消息性能结果，并且计算当前消息状态。基于当前消息状态生成当前消息分配。理想的是，根据信息的年龄，累积的试验次数和累积的成功次数将被打折扣。本发明的过程可以应用于各种消息类型，包括例如网站横幅广告，通常的电子广告，电子邮件消息和促销等。在一个实施例中，消息是互联网网站横幅广告，试验是向用户呈现的广告的印象，成功通过从横幅广告到与横幅广告相关联的网站的点击来衡量。还提供了与本发明的方法和程序相关联的计算机系统，计算机程序和计算机程序产品。

14. 发明申请

US20050148841A1 Leaf sequencing method and system 失效
标题翻译：叶片测序方法和系统
公开(公告)号：US20050148841A1
公开(公告)日：2005-07-07
申请号：US10736023
申请日：2003-12-15
申请人： Srijit Kamath , Sartaj Sahni , Jatinder Palta , Sanjay Ranka , Jonathan Li
发明人： Srijit Kamath , Sartaj Sahni , Jatinder Palta , Sanjay Ranka , Jonathan Li
IPC分类号： A61B5/05 , A61N5/10
CPC分类号： A61N5/103 , A61N5/1036 , A61N5/1042
摘要： A method of delivering radiation treatment using multi-leaf collimation includes the step of providing a radiation fluence map which includes an intensity profile. The fluence map is converted into a preliminary leaf sequence, wherein the preliminary leaf sequence minimizes machine on-time and is generated without leaf movement constraints. The leaf movement constraint is imposed on the preliminary leaf sequence. At least one constraint elimination algorithm is then applied, the algorithm adjusting the preliminary leaf sequence to minimize violations of the constraint while providing the desired fluence map and minimized radiation on-time. The method can be applied to SMLC and DLMC systems, and can include adjustment for the tongue-and-groove effect.
摘要翻译：使用多叶准直进行放射治疗的方法包括提供包括强度分布的辐射注量图的步骤。注量图被转换成初步的叶子序列，其中初步叶子序列使机器准时最小化，并且不产生叶片运动限制。叶片运动约束对初步叶序列施加。然后应用至少一个约束消除算法，该算法调整初步叶序列以最小化对约束的违反，同时提供期望的通量图和最小化的辐射on-time。该方法可以应用于SMLC和DLMC系统，并且可以包括对榫槽效应的调整。

15. 发明授权

US5987468A Structure and method for efficient parallel high-dimensional similarity join 失效
标题翻译：高效平行高维相似性联合的结构与方法
公开(公告)号：US5987468A
公开(公告)日：1999-11-16
申请号：US989847
申请日：1997-12-12
申请人： Vineet Singh , Khaled Alsabti , Sanjay Ranka
发明人： Vineet Singh , Khaled Alsabti , Sanjay Ranka
IPC分类号： G06F15/173 , G06F12/00 , G06F17/30
CPC分类号： G06F17/30327 , G06F17/30333 , Y10S707/99936 , Y10S707/99937
摘要： Multidimensional similarity join finds pairs of multi-dimensional points that are within some small distance of each other. Databases in domains such as multimedia and time-series can require a high number of dimensions. The .epsilon.-k-d-B tree has been proposed as a data structure that scales better as number of dimensions increases compared to previous data structures such as the R-tree (and variations), grid-file, and k-d-B tree. We present a cost model of the .epsilon.-k-d-B tree and use it to optimize the leaf size. This new leaf size is shown to be better in most situations compared to previous work that used a constant leaf size. We present novel parallel procedures for the .epsilon.-k-d-B tree. A load-balancing strategy based on equi-depth histograms is shown to work well for uniform or low-skew situations, whereas another based on weighted, equi-depth histograms works far better for high-skew datasets. The latter strategy is only slightly slower than the former strategy for low skew datasets. The weights for the latter strategy are based on the same cost model that is used to determine optimal leaf sizes.
摘要翻译：多维相似联合找到彼此在一些小距离内的多维点对。域中的数据库（如多媒体和时间序列）可能需要大量的维度。已经提出了epsilon -k-d-B树作为数据结构，与先前的数据结构（如R-tree（和变体），网格文件和k-d-B树）相比，维度数量的增加更好。我们提出了一个eps-k-d-B树的成本模型，并用它来优化叶尺寸。与使用恒定叶尺寸的以前的工作相比，在大多数情况下，新叶尺寸显示出更好。我们提出了epsilon -k-d-B树的新颖并行程序。基于等深度直方图的负载平衡策略被显示为均匀或低偏移情况下工作良好，而基于加权的等深度直方图的另一个对于高偏斜数据集的工作更好。后一种策略仅比低偏差数据集的前一种策略略慢。后一种策略的权重基于用于确定最佳叶尺寸的相同成本模型。

16. 发明申请

US20130179253A1 Method, algorithm, and computer program for targeting messages including advertisements in an interactive measurable medium 失效
标题翻译：方法，算法和计算机程序，用于定位包含交互式可测量介质中的广告的消息
公开(公告)号：US20130179253A1
公开(公告)日：2013-07-11
申请号：US13601595
申请日：2012-08-31
申请人： Sanjay Ranka , E. Diane Chang , Daniel Veiner
发明人： Sanjay Ranka , E. Diane Chang , Daniel Veiner
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0244 , G06Q30/0241
摘要： Techniques for improving and optimizing or attempting to optimize performance of messaging campaigns, particularly to marketing campaigns in which advertisements or other messages are distributed over an interactive measurable medium. When message is an advertisement, campaign involves a list of ad alternatives and a target customer population. Goal of message manager or marketing manager is to allocate ad alternatives to customer population to optimize business objectives such as maximizing the number of positive responses received. This is achieved by segmenting customer population into segments and then finding best allocation of ad alternatives for each segment. The number of segments and grouping of a customer population changes at different stages of the campaign. When the message is other than an advertisement, goal is to allocate messages to optimize analogous business or campaign objectives, typically measured by the number of successes or successful responses.
摘要翻译：用于改进和优化或尝试优化消息传播活动的功能的技术，特别是在广告或其他消息通过交互式可测量媒体分发的营销活动中。当消息是广告时，广告系列涉及广告替代品和目标客户群体的列表。消息管理员或营销经理的目标是为客户群体分配广告替代品，以优化业务目标，例如最大限度地获得收到的积极响应数量。这是通过将客户群体细分为几个部分，然后为每个细分市场找到最佳的广告替代方案。客户群体的数量和分组在广告系列的不同阶段发生变化。当消息不是广告时，目标是分配消息以优化类似的业务或活动目标，通常由成功数量或成功响应来衡量。

17. 发明授权

US07406434B1 System and method for improving the performance of electronic media advertising campaigns through multi-attribute analysis and optimization 有权
公开(公告)号：US07406434B1
公开(公告)日：2008-07-29
申请号：US10024449
申请日：2001-12-17
申请人： E. Diane Chang , Richard E. Chatwin , Sachin Kumar , Sanjay Ranka , James R. Weisinger , Jason Lenderman
发明人： E. Diane Chang , Richard E. Chatwin , Sachin Kumar , Sanjay Ranka , James R. Weisinger , Jason Lenderman
IPC分类号： G06Q99/00
CPC分类号： G06Q10/06375 , G06Q10/06393 , G06Q30/02 , G06Q30/0203 , G06Q30/0204 , G06Q30/0207 , G06Q30/0241 , G06Q30/0242 , G06Q30/0243 , G06Q30/0244 , G06Q30/0277
摘要： Automated system, methods, algorithms, procedures, and computer software programs and computer program products for improving and optimizing the performance of messaging campaigns, particularly for marketing campaigns in which advertisements or other messages are distributed over an interactive measurable medium such as the Internet. Analysis and Optimization method and procedure, an automated system, and system and method that exploit the underlying multi-attribute structure, as well as other features and advantages. Optimization procedures allocate the ad alternatives or other message to the customer population to optimize business objectives such as maximizing the number of positive responses received. Procedure for generating message allocations that improve and attempt to optimize the campaign performance. Methods ensure that campaign constraints are not violated. Methods can be implemented on a computer that is programmed to retrieve message performance information and to generate recommended message allocations for each stage in a multi-stage messaging campaign to achieve messaging goals.

18. 发明授权

US07142635B2 Field splitting for intensity modulated fields of large size 失效
标题翻译：大尺寸强度调制场的场分割
公开(公告)号：US07142635B2
公开(公告)日：2006-11-28
申请号：US11102083
申请日：2005-04-08
申请人： Srijit Kamath , Sartaj Sahni , Jonathan Gang Li , Jatinder Palta , Sanjay Ranka
发明人： Srijit Kamath , Sartaj Sahni , Jonathan Gang Li , Jatinder Palta , Sanjay Ranka
IPC分类号： A61N5/10
CPC分类号： A61N5/1042 , A61N5/1036
摘要： A method of delivering intensity modulated radiation therapy (IMRT) is disclosed. An intensity profile for the treatment of a patient is provided which spans a prescribed field width and includes a discrete profile having intensity values at each of a plurality of sample points bounded by the prescribed width. The prescribed width is compared to a maximum field width provided by the radiation treatment system. The intensity profile is split into a plurality of intensity profile portions, each having respective widths less than the maximum width if the prescribed width is greater than the maximum width. The prescribed field is also divided into a plurality of different profile portion split arrangements. A monitor unit (MU) efficiency is calculated for each of the arrangements. One of the arrangements is selected for delivery by the system using a leaf sequencing method.
摘要翻译：公开了一种递送强度调制放射治疗（IMRT）的方法。提供了用于治疗患者的强度分布，其跨越规定的场宽度并且包括在由规定宽度限定的多个采样点中的每一个处具有强度值的离散轮廓。将规定的宽度与辐射处理系统提供的最大场宽进行比较。如果规定的宽度大于最大宽度，则强度分布被分成多个强度分布部分，每个强度分布部分各自具有小于最大宽度的宽度。规定的场也被分成多个不同的轮廓部分割装置。针对每个布置计算监视器单元（MU）的效率。选择其中一种安排用于通过系统使用叶片测序方法递送。

19. 发明申请

US20050254623A1 Field splitting for intensity modulated fields of large size 失效
标题翻译：大尺寸强度调制场的场分割
公开(公告)号：US20050254623A1
公开(公告)日：2005-11-17
申请号：US11102083
申请日：2005-04-08
申请人： Srijit Kamath , Sartaj Sahni , Jonathan Li , Jatinder Palta , Sanjay Ranka
发明人： Srijit Kamath , Sartaj Sahni , Jonathan Li , Jatinder Palta , Sanjay Ranka
IPC分类号： A61N5/10
CPC分类号： A61N5/1042 , A61N5/1036
摘要： A method of delivering intensity modulated radiation therapy (IMRT) is disclosed. An intensity profile for the treatment of a patient is provided which spans a prescribed field width and includes a discrete profile having intensity values at each of a plurality of sample points bounded by the prescribed width. The prescribed width is compared to a maximum field width provided by the radiation treatment system. The intensity profile is split into a plurality of intensity profile portions, each having respective widths less than the maximum width if the prescribed width is greater than the maximum width. The prescribed field is also divided into a plurality of different profile portion split arrangements. A monitor unit (MU) efficiency is calculated for each of the arrangements. One of the arrangements is selected for delivery by the system using a leaf sequencing method.
摘要翻译：公开了一种递送强度调制放射治疗（IMRT）的方法。提供了用于治疗患者的强度分布，其跨越规定的场宽度并且包括在由规定宽度限定的多个采样点中的每一个处具有强度值的离散轮廓。将规定的宽度与辐射处理系统提供的最大场宽进行比较。如果规定的宽度大于最大宽度，则强度分布被分成多个强度分布部分，每个强度分布部分各自具有小于最大宽度的宽度。规定的场也被分成多个不同的轮廓部分割装置。针对每个布置计算监视器单元（MU）的效率。选择其中一种安排用于通过系统使用叶片测序方法递送。

20. 发明授权

US06563952B1 Method and apparatus for classification of high dimensional data 失效
标题翻译：高维数据分类方法和装置
公开(公告)号：US06563952B1
公开(公告)日：2003-05-13
申请号：US09420252
申请日：1999-10-18
申请人： Anurag Srivastava , G. D. Ramkumar , Vineet Singh , Sanjay Ranka
发明人： Anurag Srivastava , G. D. Ramkumar , Vineet Singh , Sanjay Ranka
IPC分类号： G06K962
CPC分类号： G06K9/6276
摘要： The present invention is an apparatus and method for classifying high-dimensional sparse datasets. A raw data training set is flattened by converting it from categorical representation to a boolean representation. The flattened data is then used to build a class model on which new data not in the training set may be classified. In one embodiment, the class model takes the form of a decision tree, and large itemsets and cluster information are used as attributes for classification. In another embodiment, the class model is based on the nearest neighbors of the data to be classified. An advantage of the invention is that, by flattening the data, classification accuracy is increased by eliminating artificial ordering induced on the attributes. Another advantage is that the use of large itemsets and clustering increases classification accuracy.
摘要翻译：本发明是用于对高维稀疏数据集进行分类的装置和方法。原始数据训练集通过将其从分类表示转换为布尔表示而被平坦化。然后，使用平坦化的数据来构建一个类别模型，在该类模型中，不在训练集中的新数据可以被分类。在一个实施例中，类模型采用决策树的形式，并且使用大的项目集和集群信息作为分类的属性。在另一个实施例中，类模型基于要分类的数据的最近邻。本发明的优点在于，通过平坦化数据，通过消除对属性引起的人为排序来增加分类精度。另一个优点是使用大项集和聚类提高了分类精度。

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