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11. 发明申请

US20100005446A1 METHOD, SYSTEM AND PROGRAM PRODUCT FOR DETECTING DEVIATION FROM SOFTWARE DEVELOPMENT BEST PRACTICE RESOURCE IN A CODE SHARING SYSTEM 失效
标题翻译：用于从软件开发中检测偏差的方法，系统和程序产品代码共享系统中最佳实践资源
公开(公告)号：US20100005446A1
公开(公告)日：2010-01-07
申请号：US12556924
申请日：2009-09-10
申请人： Youssef Drissi , Tong-Haing Fin , Moon Ju Kim , Lev Koaakov , Juan Leon Rodriquez
发明人： Youssef Drissi , Tong-Haing Fin , Moon Ju Kim , Lev Koaakov , Juan Leon Rodriquez
IPC分类号： G06F9/44
CPC分类号： G06F8/36
摘要： Under the present invention, a best practice violation (BPV) engine resource is first built. This typically occurs by: receiving sets of source code from a plurality of sources; detecting a programming language of each of the sets of source code; extracting code patterns from the sets of source code; defining meta data for each code pattern that indicates a quality thereof, classifying each code pattern; and assigning a rank to each of the code pattern based on it's corresponding meta data as well as a skill level and an experience level of the developer thereof. Once the BPV engine resource is built, it will be used to detect best practice violations in other sets of source code.
摘要翻译：根据本发明，首先构建最佳实践违规（BPV）引擎资源。这通常发生在：从多个源接收源代码集合; 检测每个源代码集的编程语言; 从源代码集中提取代码模式; 为指示其质量的每个代码模式定义元数据，对每个代码模式进行分类; 并且基于其对应的元数据以及其开发者的技能水平和经验水平为每个代码模式分配等级。一旦构建了BPV引擎资源，它将被用于检测其他源代码集中的最佳实践违规。

12. 发明授权

US08356278B2 Method, system and program product for detecting deviation from software development best practice resource in a code sharing system 失效
标题翻译：用于在代码共享系统中检测与软件开发最佳实践资源的偏差的方法，系统和程序产品
公开(公告)号：US08356278B2
公开(公告)日：2013-01-15
申请号：US12556924
申请日：2009-09-10
申请人： Youssef Drissi , Tong-Haing Fin , Moon Ju Kim , Lev Koaakov , Juan Leon Rodriquez
发明人： Youssef Drissi , Tong-Haing Fin , Moon Ju Kim , Lev Koaakov , Juan Leon Rodriquez
IPC分类号： G06F9/44
CPC分类号： G06F8/36
摘要： Under the present invention, a best practice violation (BPV) engine resource is first built. This typically occurs by: receiving sets of source code from a plurality of sources; detecting a programming language of each of the sets of source code; extracting code patterns from the sets of source code; defining meta data for each code pattern that indicates a quality thereof, classifying each code pattern; and assigning a rank to each of the code pattern based on it's corresponding meta data as well as a skill level and an experience level of the developer thereof. Once the BPV engine resource is built, it will be used to detect best practice violations in other sets of source code.
摘要翻译：根据本发明，首先构建最佳实践违规（BPV）引擎资源。这通常发生在：从多个源接收源代码集合; 检测每个源代码集的编程语言; 从源代码集中提取代码模式; 为指示其质量的每个代码模式定义元数据，对每个代码模式进行分类; 并且基于其对应的元数据以及其开发者的技能水平和经验水平为每个代码模式分配等级。一旦构建了BPV引擎资源，它将被用于检测其他源代码集中的最佳实践违规。

13. 发明授权

US07143085B2 Optimization of server selection using euclidean analysis of search terms 失效
标题翻译：优化服务器选择使用欧几里得分析搜索词
公开(公告)号：US07143085B2
公开(公告)日：2006-11-28
申请号：US10209619
申请日：2002-07-31
申请人： Gregory T. Brown , Youssef Drissi , Moon Ju Kim , Lev Kozakov , Juan Leon-Rodriquez , Ricardo Vilalta
发明人： Gregory T. Brown , Youssef Drissi , Moon Ju Kim , Lev Kozakov , Juan Leon-Rodriquez , Ricardo Vilalta
IPC分类号： G06F7/00
CPC分类号： G06F17/30864 , G06F17/30657 , Y10S707/99931 , Y10S707/99932 , Y10S707/99933 , Y10S707/99936 , Y10S707/99937
摘要： Euclidean analysis is used to define queries in terms of a multi-axis query space where each of the keywords T1, T2, . . . Ti, . . . Tn is assigned an axis in that space. Sets of test queries St for each one from one of a plurality of server sources, are plotted in the query space. Clusters of the search terms are identified based on the proximity of the plotted query vectors to one another. Predominant servers are identified for each of the clusters. When a search query Ss is received, the location of its vector is determined and the servers accessed by the search query Ss are those that are predominant in the cluster which its vector may fall or is in closest proximity to.
摘要翻译：欧几里德分析用于根据多轴查询空间来定义查询，其中每个关键字T 1，T 2，...。。。。。。。。。。。。。。。。。在该空间中分配了一个轴。在多个服务器源中的一个服务器源中的每一个的测试查询集合被绘制在查询空间中。基于绘制的查询向量彼此的邻近度来识别搜索项的群集。为每个集群标识主要服务器。当接收到搜索查询S 时，确定其向量的位置，并且由搜索查询S 访问的服务器是在群集中占主导地位的那些矢量可能会下降或最接近。

14. 发明授权

US07483877B2 Dynamic comparison of search systems in a controlled environment 失效
标题翻译：在受控环境中搜索系统的动态比较
公开(公告)号：US07483877B2
公开(公告)日：2009-01-27
申请号：US10412808
申请日：2003-04-11
申请人： Moon Ju Kim , Yurdaer Nezihi Doganata , Youssef Drissi , Lev Kozakov
发明人： Moon Ju Kim , Yurdaer Nezihi Doganata , Youssef Drissi , Lev Kozakov
IPC分类号： G06F17/30
CPC分类号： G06F17/3069 , G06F17/30864 , Y10S707/99933 , Y10S707/99943 , Y10S707/99945
摘要： A random document is stripped of the relevant search terms to generate a non-relevant document. The relevant search terms are formed into grammatically correct but not necessarily technically correct sentences. The grammatically correct sentences are placed at the beginning of the random document in one pass through the system in the middle of a document in a second pass through the system and at the end of the document in a third pass through the system. A relevancy vector chart is computed using the references documents and a known relevancy algorithm relating to position of search terms. The results obtained from search engines are compared to the relevancy vector chart to determine the relative relevancy of the returned search results from the search engines.
摘要翻译：随机文件被剥离相关的搜索条件，以生成不相关的文档。相关搜索词形成为语法正确但不一定是技术上正确的句子。语法上正确的句子被放置在随机文档的开头，在通过系统的第二遍中的文档中间的一次通过系统中，并且在文档的最后通过系统的第三遍。使用参考文献和与搜索词的位置相关的已知相关性算法计算相关性矢量图。将搜索引擎获得的结果与相关矢量图进行比较，以确定搜索引擎返回的搜索结果的相对关系。

15. 发明授权

US06886009B2 Query routing based on feature learning of data sources 有权
标题翻译：基于数据源特征学习的查询路由
公开(公告)号：US06886009B2
公开(公告)日：2005-04-26
申请号：US10209112
申请日：2002-07-31
申请人： Jun-Jang Jeng , Youssef Drissi , Moon Ju Kim , Lev Kozakov , Juan Leon-Rodriquez
发明人： Jun-Jang Jeng , Youssef Drissi , Moon Ju Kim , Lev Kozakov , Juan Leon-Rodriquez
IPC分类号： G06F17/30
CPC分类号： G06F17/30864 , Y10S707/99933
摘要： Query routing is based on identifying the preeminent search systems and data sources for each of a number of information domains. This involves assigning a weight to each search system or data source for each of the information domains. The greater the weight, the more preeminent a search system or data source is in a particular information domain. These weights Wi{1=0, 1,2, . . . N] are computed through a recursive learning process employing meta processing. The meta learning process involves simultaneous interrogation of multiple search systems to take advantage of the cross correlation between the search systems and data sources. In this way, assigning a weight to a search system takes into consideration results obtained about other search systems so that the assigned weights reflect the relative strengths of each of the systems or sources in a particular information domain. In the present process, a domain dataset used as an input to query generator. The query generator extracts keywords randomly from the domain dataset. Sets of the extracted keywords constitute a domain specific search query. The query is submitted to the multiple search systems or sources to be evaluated. Initially, a random average weight is assigned to each search system or source. Then, the meta learning process recursively evaluates the search results and feeds back a weight correction dWi to be applied to each system or source server by using weight difference calculator. After a certain number of iterations, the weights Wi reach stable values. These stable values are the values assigned to the search system under evaluation. When searches are performed, the weights are used to determine search systems or sources that are interrogated.
摘要翻译：查询路由是基于为多个信息域中的每一个标识优秀的搜索系统和数据源。这涉及为每个信息域的每个搜索系统或数据源分配权重。权重越大，搜索系统或数据源在特定信息域中越是优秀。这些权重Wi {1 = 0，1,2，... 。。 N]通过使用元处理的递归学习过程来计算。元学习过程包括同时询问多个搜索系统，以利用搜索系统和数据源之间的互相关。以这种方式，向搜索系统分配权重考虑了关于其他搜索系统获得的结果，使得分配的权重反映了特定信息域中的每个系统或源的相对强度。在本过程中，用作查询生成器的输入的域数据集。查询生成器从域数据集中随机提取关键字。所提取的关键字的集合构成域特定的搜索查询。该查询被提交给要评估的多个搜索系统或源。最初，随机平均权重被分配给每个搜索系统或源。然后，元学习处理递归地评估搜索结果，并且通过使用权重差计算器反馈要应用于每个系统或源服务器的权重校正dWi。经过一定次数的迭代，重量Wi达到稳定值。这些稳定值是分配给正在评估的搜索系统的值。当执行搜索时，权重用于确定被询问的搜索系统或源。

16. 发明授权

US07249058B2 Method of promoting strategic documents by bias ranking of search results 失效
标题翻译：通过搜索结果偏好排名推广战略文件的方法
公开(公告)号：US07249058B2
公开(公告)日：2007-07-24
申请号：US10120082
申请日：2002-04-10
申请人： Moon Ju Kim , Juan-Leon Rodriguez , Yurdaer Nezihi Doganata
发明人： Moon Ju Kim , Juan-Leon Rodriguez , Yurdaer Nezihi Doganata
IPC分类号： G06Q30/00
CPC分类号： G06Q30/02 , G06Q30/0627 , G06Q30/0631 , G06Q30/0641
摘要： A method, software and apparatus are provided which enable promotion of products and services in a deterministic manner free of conflicting actions modifying raw ranking data based on merchants interests. For this purpose, an information consolidator is provided to obtain search results for a plurality of sources including directly from merchants. When a shopper enters a set of key words in an entry field in an on-screen form for a web server to obtain a list of items (products and/or services) of interest to the shopper, the documents describing the items can be prioritized by the information provided by the information source based on the web sites owner's priorities. The information consolidator receives ranking information taking the highest ranked products and obtains the product information for the most highly ranked products. The information consolidator then reranks the products using a preferred ranking algorithm to remove information sources biases in this ranking of the products. The information consolidator can add weighting factors such as those covered in the copending applications by the merchant. The weighting factors are combined with the mentioned ranking mechanisms to the documents to increase the probability that certain items come to the top when the search results are presented to the shopper. These pages could be used to promote products or otherwise direct the selection shoppers. The weighting factors are configured so as to not decrease the shoppers confidence in the ranking process.
摘要翻译：提供了一种方法，软件和装置，其能够以确定性的方式促进产品和服务，而不存在基于商家兴趣修改原始排名数据的冲突动作。为此，提供信息整合器以获得包括直接来自商家的多个源的搜索结果。当购物者在用于web服务器的屏幕上的输入字段中输入一组关键词以获得购物者感兴趣的项目（产品和/或服务）的列表时，可以对描述项目的文档进行优先化根据信息源提供的信息，根据网站所有者的优先级。信息整合者收到排名最高的产品的排名信息，并获得最高排名的产品的产品信息。信息整合者然后使用优选的排序算法重新排列产品，以消除产品排名中的信息源偏差。信息整合者可以添加加权因子，如商家在共同待审的申请中所涵盖的那些。加权因子与提到的文件排序机制结合起来，以便在将搜索结果呈现给购物者时增加某些项目到达顶部的可能性。这些页面可用于宣传产品或以其他方式指导选购顾客。加权因子被配置为不降低购物者对排名过程的置信度。

17. 发明授权

US07272573B2 Internet strategic brand weighting factor 失效
标题翻译：互联网战略品牌权重因子
公开(公告)号：US07272573B2
公开(公告)日：2007-09-18
申请号：US10054301
申请日：2001-11-13
申请人： Jane Childs , Moon Ju Kim , Susan Burlingame Levangia , Paul Nicholas Martynenko , Juan Leon-Rodriguez , Lamana Rogers
发明人： Jane Childs , Moon Ju Kim , Susan Burlingame Levangia , Paul Nicholas Martynenko , Juan Leon-Rodriguez , Lamana Rogers
IPC分类号： G06Q30/00
CPC分类号： G06Q30/02 , G06Q10/06393 , G06Q30/0256 , G06Q30/0277 , G06Q30/0601 , G06Q30/0641
摘要： A method, software and apparatus are provided which enable a merchant to promote products and services in a deterministic manner. When a shopper enters a set of key words in an entry in an on-screen form for a web server to obtain a list of items (products and/or services) of interest to the shopper, the documents describing the items are prioritized based on the web sites owner's organizational strategic decisions by the merchant providing a weighting factor for the products and services. The weighting factor is combined with existing ranking mechanisms to the documents to increase the probability that certain items come to the top when the search results are presented to the shopper. These items could be used to ensure shoppers do not overlook special offers or to reduce overstocks by promoting products with a large inventory. The weighting factor is configured so as to not decrease the shoppers confidence in the ranking process.
摘要翻译：提供了一种方法，软件和装置，其使得商家能够以确定性的方式促进产品和服务。当购物者在网络服务器的屏幕上的条目中输入一组关键词以获得购物者感兴趣的项目（产品和/或服务）的列表时，基于网站所有者的组织战略决策由商家提供产品和服务的加权因子。加权因子与文档的现有排名机制相结合，以便在将搜索结果呈现给购物者时增加某些项目达到顶部的可能性。这些物品可用于确保购物者不要忽视特别优惠，或通过推广大量库存产品来减少库存。加权因子被配置为不降低购物者对排名过程的置信度。

18. 发明申请

US20070226348A1 Computer-implemented method, system and program product for approximating resource consumption of computer system 有权
标题翻译：计算机实现的方法，系统和程序产品，用于近似计算机系统的资源消耗
公开(公告)号：US20070226348A1
公开(公告)日：2007-09-27
申请号：US11390527
申请日：2006-03-27
申请人： Genady Grabarnik , Moon Ju Kim , Lev Kozakov , Volodimir Lemberg , Larisa Shwartz
发明人： Genady Grabarnik , Moon Ju Kim , Lev Kozakov , Volodimir Lemberg , Larisa Shwartz
IPC分类号： G06F15/16
CPC分类号： G06F9/5083 , G06F2209/5019
摘要： In general, the present invention provides a method, system and program product for approximating/estimating computer resource consumption of a computer system. Specifically, under the present invention, a more efficient or reduced computer work gradient matrix (hereinafter “matrix”) is first built. This occurs by creating load measurements for a set of computer resource metrics of the computer system to analyze dependencies between different computer resource metrics. Then, a correlation matrix between the set of computer resource metrics is created based on the dependencies. The set of computer system resource metrics in the correlation matrix is thereafter clustered into a set of clusters, and a reduced matrix is built based thereon. Once the reduced matrix is built, it can be restored to a “full” matrix using linear transformation or the like.
摘要翻译：通常，本发明提供了一种用于近似/估计计算机系统的计算机资源消耗的方法，系统和程序产品。具体地说，在本发明中，首先构建了更有效率或更低的计算机工作梯度矩阵（以下称为“矩阵”）。这通过为计算机系统的一组计算机资源指标创建负载测量来分析不同计算机资源度量之间的依赖关系。然后，基于依赖关系创建该组计算机资源度量之间的相关矩阵。然后将相关矩阵中的一组计算机系统资源度量聚集成一组聚类，并基于此构建简化矩阵。一旦构建了简化矩阵，就可以使用线性变换等将其恢复到“完整”矩阵。

19. 发明授权

US07844441B2 Computer-implemented method, system and program product for approximating resource consumption of computer system 有权
标题翻译：计算机实现的方法，系统和程序产品，用于近似计算机系统的资源消耗
公开(公告)号：US07844441B2
公开(公告)日：2010-11-30
申请号：US11390527
申请日：2006-03-27
申请人： Genady Grabarnik , Moon Ju Kim , Lev Kozakov , Volodimir F. Lemberg , Larisa Shwartz
发明人： Genady Grabarnik , Moon Ju Kim , Lev Kozakov , Volodimir F. Lemberg , Larisa Shwartz
IPC分类号： G06F1/02 , G06F7/32 , G06F7/60 , G06F9/44 , G06F13/10 , G06F13/12 , G06F15/18 , G06F17/10 , G06F17/15 , G06E1/00 , G06E3/00 , G06G7/00
CPC分类号： G06F9/5083 , G06F2209/5019
摘要： In general, the present invention provides a method, system and program product for approximating/estimating computer resource consumption of a computer system. Specifically, under the present invention, a more efficient or reduced computer work gradient matrix (hereinafter “matrix”) is first built. This occurs by creating load measurements for a set of computer resource metrics of the computer system to analyze dependencies between different computer resource metrics. Then, a correlation matrix between the set of computer resource metrics is created based on the dependencies. The set of computer system resource metrics in the correlation matrix is thereafter clustered into a set of clusters, and a reduced matrix is built based thereon. Once the reduced matrix is built, it can be restored to a “full” matrix using linear transformation or the like.
摘要翻译：通常，本发明提供了一种用于近似/估计计算机系统的计算机资源消耗的方法，系统和程序产品。具体地说，在本发明中，首先构建了更有效率或更低的计算机工作梯度矩阵（以下称为“矩阵”）。这通过为计算机系统的一组计算机资源指标创建负载测量来分析不同计算机资源度量之间的依赖关系。然后，基于依赖关系创建该组计算机资源度量之间的相关矩阵。然后将相关矩阵中的一组计算机系统资源度量聚集成一组聚类，并基于此构建简化矩阵。一旦构建了简化矩阵，就可以使用线性变换等将其恢复到“完整”矩阵。

20. 发明授权

US07831976B2 Method, system and program product for predicting computer system resource consumption 失效
标题翻译：用于预测计算机系统资源消耗的方法，系统和程序产品
公开(公告)号：US07831976B2
公开(公告)日：2010-11-09
申请号：US11121828
申请日：2005-05-04
申请人： Genady Grabarnik , Moon Ju Kim , Lev Kozakov , Larisa Shwartz
发明人： Genady Grabarnik , Moon Ju Kim , Lev Kozakov , Larisa Shwartz
IPC分类号： G06F9/46 , G06F15/16 , G06F15/177
CPC分类号： G06F11/3447 , G06F9/505 , G06F9/5072 , G06F11/3404 , G06F11/3409 , G06F11/3414 , G06F11/3466 , G06F2209/5019
摘要： Under the present invention, a computer work gradient matrix is built for each computer system that is interconnected in an environment. For each computer system for which resource consumption is desired to be predicted (e.g., “target” computer systems), a transition work cocycle is generated and provided to a master computer system. A set of task work paths will be constructed for the master computer system. Thereafter, an initial resource consumption value can be computed on the master computer system based on the computer work gradient matrix and the set of task work paths for the master computer system as well as a current background loading level that is being experienced by the target computer system. Then, using the initial resource consumption and the transition work cocycle for the target computer system(s), a resource consumption of the target computer system(s) can be predicted on the master computer system.
摘要翻译：根据本发明，为在环境中互连的每个计算机系统构建计算机工作梯度矩阵。对于期望预测资源消耗的每个计算机系统（例如，“目标”计算机系统）），生成转换工作循环并将其提供给主计算机系统。将为主计算机系统构建一组任务工作路径。此后，可以在主计算机系统上基于计算机工作梯度矩阵和主计算机系统的任务工作路径集合以及目标计算机正在经历的当前后台加载水平来计算初始资源消耗值系统。然后，使用初始资源消耗和目标计算机系统的转换工作循环，可以在主计算机系统上预测目标计算机系统的资源消耗。

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