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1. 发明授权

US08903736B2 Fast networked based advertisement selection 有权
标题翻译：快速网络化广告选择
公开(公告)号：US08903736B2
公开(公告)日：2014-12-02
申请号：US12754422
申请日：2010-04-05
申请人： Arun Kejariwal , Amir Behroozi , Sapan Panigrahi
发明人： Arun Kejariwal , Amir Behroozi , Sapan Panigrahi
IPC分类号： G06Q30/00 , G06Q30/02
CPC分类号： G06Q30/00 , G06Q30/0251 , G06Q30/0254
摘要： A system for serving advertisements determines the frequency of occurrence for each parameter of a plurality of parameters associated with a plurality of search queries. The plurality of parameters are associated with one or more advertisements. The system stores at least some parameters to a first storage based on the frequency of occurrence of the parameters. The system stores the other parameters to a second storage that has a higher latency than the first storage. When serving advertisements, the system ranks advertisements for delivery based on the parameters stored in the first storage device and the second storage.
摘要翻译：用于服务广告的系统确定与多个搜索查询相关联的多个参数的每个参数的出现频率。多个参数与一个或多个广告相关联。该系统基于参数的发生频率将至少一些参数存储到第一存储器。该系统将其他参数存储到具有比第一存储器更高的延迟的第二存储器。当服务广告时，系统基于存储在第一存储设备和第二存储器中的参数来排列用于传送的广告。

2. 发明申请

US20110246295A1 FAST NETWORKED BASED ADVERTISEMENT SELECTION 有权
标题翻译：快速网络广告选择
公开(公告)号：US20110246295A1
公开(公告)日：2011-10-06
申请号：US12754422
申请日：2010-04-05
申请人： Arun Kejariwal , Amir Behroozi , Sapan Panigrahi
发明人： Arun Kejariwal , Amir Behroozi , Sapan Panigrahi
IPC分类号： G06Q30/00
CPC分类号： G06Q30/00 , G06Q30/0251 , G06Q30/0254
摘要： A system for serving advertisements determines the frequency of occurrence for each parameter of a plurality of parameters associated with a plurality of search queries. The plurality of parameters are associated with one or more advertisements. The system stores at least some parameters to a first storage based on the frequency of occurrence of the parameters. The system stores the other parameters to a second storage that has a higher latency than the first storage. When serving advertisements, the system ranks advertisements for delivery based on the parameters stored in the first storage device and the second storage.
摘要翻译：用于服务广告的系统确定与多个搜索查询相关联的多个参数的每个参数的出现频率。多个参数与一个或多个广告相关联。该系统基于参数的发生频率将至少一些参数存储到第一存储器。该系统将其他参数存储到具有比第一存储器更高的延迟的第二存储器。当服务广告时，系统基于存储在第一存储设备和第二存储器中的参数来排列用于传送的广告。

3. 发明申请

US20110131093A1 SYSTEM AND METHOD FOR OPTIMIZING SELECTION OF ONLINE ADVERTISEMENTS 审中-公开
标题翻译：用于优化在线广告选择的系统和方法
公开(公告)号：US20110131093A1
公开(公告)日：2011-06-02
申请号：US12628175
申请日：2009-11-30
申请人： Amir Behroozi , Arun Kejariwal , Sapan Panigrahi
发明人： Amir Behroozi , Arun Kejariwal , Sapan Panigrahi
IPC分类号： G06Q30/00 , G06F3/01 , G06F17/30
CPC分类号： G06Q30/00 , G06Q30/0241 , G06Q30/0251 , G06Q30/0254
摘要： An advanced system and method for optimizing selection of online advertisements is provided. Decision trees with expressions to evaluate feature values for advertisements may be received, and a decision tree similarity matrix of decision tree similarity values between pairs of decision trees may be generated that represent the number of common features between two decision trees. The edges of the decision tree similarity matrix may be sorted in non-increasing order by edge value, and the decision trees of each edge retrieved from the sorted order may be placed in an optimized sequence order for evaluation. In response to a request to serve advertisements, advertisements may be scored by evaluating the decision trees of advertisements in the optimized sequence order. The advertisements may then be ranked in descending order by score, and advertisement with the highest scores may be sent for display.
摘要翻译：提供了优化在线广告选择的先进系统和方法。可以接收具有用于评估广告的特征值的表达式的决策树，并且可以生成表示两个决策树之间的共同特征的数量的决策树之间的决策树相似度值的决策树相似度矩阵。决策树相似度矩阵的边缘可以按照边缘值以不增加的顺序排序，并且从排序顺序检索的每个边缘的决策树可以被放置在优化的顺序顺序中以进行评估。响应于服务广告的请求，可以通过以优化的顺序顺序评估广告的决策树来评分广告。然后可以通过分数降序排列广告，并且可以发送具有最高分数的广告以进行显示。

4. 发明申请

US20110055010A1 Enabling High Performance Ad Selection 审中-公开
标题翻译：启用高性能广告选择
公开(公告)号：US20110055010A1
公开(公告)日：2011-03-03
申请号：US12552252
申请日：2009-09-01
申请人： Amir Behroozi , Arun Kejariwal , Sapan Panigrahi
发明人： Amir Behroozi , Arun Kejariwal , Sapan Panigrahi
IPC分类号： G06Q30/00 , G06Q10/00 , G06F17/30
CPC分类号： G06Q30/0254 , G06F16/9535 , G06Q30/02
摘要： A method and a system are provided for enabling high performance ad selection. In one example, the system receives an ad. A relevance of the ad needs to be determined. The relevance is a function of one or more computational intensive functions. A computational intensive function is a function that requires more than trivial processing. The system identifies one or more arguments of the computational intensive functions that are within a fixed range. The system generates a tableau based on the one or more arguments that are within a fixed range. The tableau is configured to benefit run-time performance of an ad selection process whenever the computer uses the pre-generated tableau during run-time instead of calculating one or more computational intensive functions.
摘要翻译：提供了一种用于实现高性能广告选择的方法和系统。在一个示例中，系统接收广告。广告的相关性需要确定。相关性是一个或多个计算密集函数的函数。计算密集函数是一个需要进行简单处理的功能。系统识别处于固定范围内的计算密集函数的一个或多个参数。系统基于一个或多个固定范围内的参数生成表格。只要计算机在运行时间内使用预生成的表格，而不是计算一个或多个计算密集型函数，则该表格配置为有利于广告选择过程的运行时性能。

5. 发明授权

US08818787B2 Method and system for predicting performance of software applications on prospective hardware architecture 有权
标题翻译：用于预测软件应用程序在预期硬件架构上的性能的方法和系统
公开(公告)号：US08818787B2
公开(公告)日：2014-08-26
申请号：US13018243
申请日：2011-01-31
申请人： Arun Kejariwal , Paolo D'Alberto , Sapan Panigrahi , Rosario Cammarota
发明人： Arun Kejariwal , Paolo D'Alberto , Sapan Panigrahi , Rosario Cammarota
IPC分类号： G06F9/45 , G06F9/44
CPC分类号： G06F11/3447 , G06F11/3409 , G06F11/3419 , G06F11/3442 , G06F11/3466 , G06F2201/81 , G06F2201/83 , G06F2201/86 , G06F2201/865 , G06F2201/88
摘要： A system and method for identifying optimal system architectures for a reference application are provided. The system and method comprise executing a reference application and a plurality of test applications on a current system architecture and sampling performance data for each of the applications. The performance data is used to compute an application signature for each application. A similarity element is derived from the application signatures that illustrates the similarity between each application and every other application. Using a similarity threshold and an algorithm, a subset of test applications that are similar to the reference application are derived.
摘要翻译：提供了一种用于识别参考应用的最佳系统架构的系统和方法。该系统和方法包括在当前系统架构上执行参考应用和多个测试应用，并针对每个应用采样性能数据。性能数据用于计算每个应用程序的应用程序签名。相似性元素从应用程序签名中得出，该签名说明了每个应用程序和每个其他应用程序之间的相似性。使用相似性阈值和算法，推导出与参考应用程序相似的测试应用程序子集。

6. 发明授权

US08621424B2 Compiler based code modification for use in document ranking 有权
标题翻译：基于编译器的代码修改用于文档排名
公开(公告)号：US08621424B2
公开(公告)日：2013-12-31
申请号：US12165457
申请日：2008-06-30
申请人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
发明人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
IPC分类号： G06F9/44 , G06F9/45 , G06F13/00
CPC分类号： G06F8/41 , G06F9/3004 , G06F9/30072 , G06F9/30087 , G06F17/30864
摘要： The subject matter disclosed herein relates to alter an expression of executable instructions via a compiler component for use in ranking of electronic documents.
摘要翻译：本文公开的主题涉及通过用于电子文档排名的编译器组件来改变可执行指令的表达。

7. 发明申请

US20100023474A1 Decision Tree Coalescing for Document Ranking 有权
标题翻译：决策树合并文件排名
公开(公告)号：US20100023474A1
公开(公告)日：2010-01-28
申请号：US12178483
申请日：2008-07-23
申请人： Arun Kejariwal , Sapan Panigrahi , Girish Vaitheeswaran
发明人： Arun Kejariwal , Sapan Panigrahi , Girish Vaitheeswaran
IPC分类号： G06N5/02
CPC分类号： G06F17/30864
摘要： Machine-learned ranking algorithms, e.g. for ranking search results, often use a sequence of decision trees involving decision nodes based on threshold values of features. Modules, systems and methods of optimizing such algorithms involve analyzing threshold feature values to determine threshold intervals for each feature and grouping decision trees according to the feature used in a root decision node. Then coalescing the decision trees within each group to form a coalesced group tree for each group and finally coalescing the coalesced group trees to form a coalesced tree that implements the algorithm.
摘要翻译：机器学习排名算法，例如为了排序搜索结果，经常使用涉及基于特征阈值的决策节点的决策树序列。优化这种算法的模块，系统和方法涉及分析阈值特征值以根据根决策节点中使用的特征来确定每个特征的阈值间隔和分组决策树。然后将每个组中的决策树合并，以形成每个组的合并组树，并最终合并聚合组树以形成实现该算法的合并树。

8. 发明申请

US20110004521A1 Techniques For Use In Sorting Partially Sorted Lists 审中-公开
标题翻译：用于排序部分排序列表的技术
公开(公告)号：US20110004521A1
公开(公告)日：2011-01-06
申请号：US12498249
申请日：2009-07-06
申请人： Amir Behroozi , Kejariwal Arun , Sapan Panigrahi
发明人： Amir Behroozi , Kejariwal Arun , Sapan Panigrahi
IPC分类号： G06Q30/00 , G06F7/08 , G06F17/30
CPC分类号： G06F7/24 , G06Q30/02 , G06Q30/0251 , G06Q30/0256
摘要： Methods and systems are provided for determining whether to use a full sort sorting technique or a merge sort sorting technique to sort a partially sorted list or data set. One or more tables may be utilized to allow such a determination to be made with regard to a first partially sorted list based on parameters associated with the list including a data distribution type, a number of data items in the list, and a ratio of sorted items to unsorted items in the list.
摘要翻译：提供了方法和系统，用于确定是否使用全排序排序技术或合并排序排序技术对部分排序的列表或数据集进行排序。可以使用一个或多个表来基于与列表相关联的参数对第一部分排序的列表做出这样的确定，所述参数包括数据分发类型，列表中的数据项的数量以及排序的比率项目到列表中未排序的项目。

9. 发明授权

US08458170B2 Prefetching data for document ranking 有权
标题翻译：预取文档排名数据
公开(公告)号：US08458170B2
公开(公告)日：2013-06-04
申请号：US12165307
申请日：2008-06-30
申请人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
发明人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
IPC分类号： G06F17/30
CPC分类号： G06F17/30864
摘要： The subject matter disclosed herein relates to prefetching data for use in ranking of electronic documents via a document ranking component.
摘要翻译：本文公开的主题涉及经由文档排序组件预取用于电子文档排名的数据。

10. 发明申请

US20100070457A1 Efficient Data Layout Techniques for Fast Machine Learning-Based Document Ranking 有权
标题翻译：基于快速机器学习的文档排序的高效数据布局技术
公开(公告)号：US20100070457A1
公开(公告)日：2010-03-18
申请号：US12211636
申请日：2008-09-16
申请人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
发明人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
IPC分类号： G06N5/02
CPC分类号： G06N99/005
摘要： A computer readable medium stores a program for optimization for a search, and has sets of instructions for receiving a first decision tree. The first decision tree includes several nodes, and each node is for comparing a feature value to a threshold value. The instructions are for weighting the nodes within the first decision tree, determining the weighted frequency of a first feature within the first decision tree, and determining the weighted frequency of a second feature within the first decision tree. The instructions order the features based on the determined weighted frequencies, and store the ordering such that values of features having higher weighted frequencies are retrieved more often than values of features having lower weighted frequencies within the first decision tree.
摘要翻译：计算机可读介质存储用于搜索的优化的程序，并且具有用于接收第一决策树的指令集。第一决策树包括几个节点，每个节点用于将特征值与阈值进行比较。所述指令用于对第一决策树内的节点进行加权，确定第一决策树内的第一特征的加权频率，以及确定第一决策树内的第二特征的加权频率。指令基于确定的加权频率对特征进行排序，并存储排序，使得具有较高加权频率的特征值比第一决策树中具有较低加权频率的特征的值更频繁地检索。

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