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1. 发明授权

US06260038B1 Clustering mixed attribute patterns 失效
标题翻译：聚合混合属性模式
公开(公告)号：US06260038B1
公开(公告)日：2001-07-10
申请号：US09394883
申请日：1999-09-13
申请人： David C. Martin , Dharmendra Shantilal Modha , Shivakumar Vaithyanathan
发明人： David C. Martin , Dharmendra Shantilal Modha , Shivakumar Vaithyanathan
IPC分类号： G06F1730
CPC分类号： G06F17/30598 , G06K9/6218 , Y10S707/99932 , Y10S707/99935 , Y10S707/99936 , Y10S707/99937 , Y10S707/99942 , Y10S707/99943
摘要： A technique for clustering data points in a data set that is arranged as a matrix having n objects and m attributes. Each categorical attribute of the data set is converted to a 1-of-p representation of the categorical attribute. A converted data set A is formed based on the data set and the 1-of-p representation for each categorical attribute. The converted data set A is compressed using, for example, a Goal Directed Projection compression technique or a Singular Value Decomposition compression technique, to obtain q basis vectors, with q being defined to be at least m+1. The transformed data set is projected onto the q basis vectors to form a data matrix having at least one vector, with each vector having q dimensions. Lastly, a clustering technique is performed on the data matrix having vectors having q dimensions.
摘要翻译：一种用于聚类数据集中的数据点的技术，其被布置为具有n个对象和m个属性的矩阵。将数据集的每个分类属性转换为分类属性的1-p表示。基于数据集和每个分类属性的1-p表示形成转换数据集A. 转换的数据集合A使用目标定向投影压缩技术或奇异值分解压缩技术进行压缩，以获得q个基本向量，其中q被定义为至少m + 1。经变换的数据集被投影到q基向量上以形成具有至少一个向量的数据矩阵，其中每个向量具有q维度。最后，对具有q维的向量的数据矩阵执行聚类技术。

2. 发明申请

US20090198646A1 SYSTEMS, METHODS AND COMPUTER PROGRAM PRODUCTS FOR AN ALGEBRAIC APPROACH TO RULE-BASED INFORMATION EXTRACTION 审中-公开
标题翻译：用于基于规则信息提取的代数方法的系统，方法和计算机程序产品
公开(公告)号：US20090198646A1
公开(公告)日：2009-08-06
申请号：US12023479
申请日：2008-01-31
申请人： Rajasekar Krishnamurthy , Sriram Raghavan , Frederick R. Reiss , Shivakumar Vaithyanathan , Huaiyu Zhu
发明人： Rajasekar Krishnamurthy , Sriram Raghavan , Frederick R. Reiss , Shivakumar Vaithyanathan , Huaiyu Zhu
IPC分类号： G06F17/30
CPC分类号： G06F16/36
摘要： Systems, methods and computer program products for an algebraic approach to rule-based information extraction. Exemplary embodiments include a method for rule-based information extraction, the method including specifying an annotator using algebraic operators, wherein each algebraic operator describes annotations identification from text documents.
摘要翻译：用于基于规则的信息提取的代数方法的系统，方法和计算机程序产品。示例性实施例包括用于基于规则的信息提取的方法，所述方法包括使用代数运算符指定注释器，其中每个代数运算符从文本文档描述注释标识。

3. 发明授权

US07433820B2 Asynchronous Hidden Markov Model method and system 失效
标题翻译：异步隐马尔可夫模型的方法和系统
公开(公告)号：US07433820B2
公开(公告)日：2008-10-07
申请号：US10844093
申请日：2004-05-12
申请人： Ashutosh Garg , Sreeram V. Balakrishnan , Shivakumar Vaithyanathan
发明人： Ashutosh Garg , Sreeram V. Balakrishnan , Shivakumar Vaithyanathan
IPC分类号： G10L15/14
CPC分类号： G10L15/144 , G06K9/6297
摘要： A system, method and program storage device implementing a method for modeling a data generating process, wherein the modeling comprises observing a data sequence comprising irregularly sampled data, obtaining an observation sequence based on the observed data sequence, assigning a time index sequence to the data sequence, obtaining a hidden state sequence of the data sequence, and decoding the data sequence based on a combination of the time index sequence and the hidden state sequence to model the data sequence. The method further comprises assigning a probability distribution over time stamp values of the observation sequence, wherein the decoding comprises using a Hidden Markov Model. The method further comprises using an expectation maximization methodology to learn the Hidden Markov Model.
摘要翻译：一种实现数据生成过程建模方法的系统，方法和程序存储设备，其中所述建模包括观察包含不规则采样数据的数据序列，基于观察到的数据序列获得观测序列，向数据分配时间索引序列获取数据序列的隐藏状态序列，并且基于时间索引序列和隐藏状态序列的组合对数据序列进行解码以建模数据序列。该方法还包括分配观测序列的时间戳值的概率分布，其中解码包括使用隐马尔科夫模型。该方法还包括使用期望最大化方法来学习隐马尔可夫模型。

4. 发明授权

US5857179A Computer method and apparatus for clustering documents and automatic generation of cluster keywords 失效
标题翻译：用于聚类文档和自动生成集群关键字的计算机方法和装置
公开(公告)号：US5857179A
公开(公告)日：1999-01-05
申请号：US709755
申请日：1996-09-09
申请人： Shivakumar Vaithyanathan , Mark R. Adler , Christopher G. Hill
发明人： Shivakumar Vaithyanathan , Mark R. Adler , Christopher G. Hill
IPC分类号： G06F17/30
CPC分类号： G06F17/3071 , G06F17/30719 , Y10S707/99932
摘要： A computer method and apparatus determines keywords of documents. An initial document by term matrix is formed, each document being represented by a respective M dimensional vector, where M represents the number of terms or words in a predetermined domain of documents. The dimensionality of the initial matrix is reduced to form resultant vectors of the documents. The resultant vectors are then clustered such that correlated documents are grouped into respective clusters. For each cluster, the terms having greatest impact on the documents in that cluster are identified. The identified terms represent key words of each document in that cluster. Further, the identified terms form a cluster summary indicative of the documents in that cluster.
摘要翻译：计算机方法和装置确定文档的关键字。形成按术语矩阵的初始文档，每个文档由相应的M维向量表示，其中M表示文档的预定域中的术语或单词的数量。减少初始矩阵的维数以形成文档的合成向量。然后将合成的向量聚类，使得相关文档被分组成各自的簇。对于每个集群，确定对该集群中的文档具有最大影响的术语。识别的术语表示该集群中每个文档的关键词。此外，所识别的术语形成指示该集群中的文档的集群摘要。

5. 发明申请

US20100205201A1 User-Guided Regular Expression Learning 有权
标题翻译：用户引导正则表达式学习
公开(公告)号：US20100205201A1
公开(公告)日：2010-08-12
申请号：US12369216
申请日：2009-02-11
申请人： Rajasekar Krishmamurthy , Yunyao Li , Sriram Raghavan , Shivakumar Vaithyanathan
发明人： Rajasekar Krishmamurthy , Yunyao Li , Sriram Raghavan , Shivakumar Vaithyanathan
IPC分类号： G06F17/30
CPC分类号： G06F17/30985 , G06F17/30648
摘要： A method, device, and computer program product are provided for regular expression learning is provided. An initial regular expression may be received from a user. The initial regular expression is executed over a database. Positive matches and negative matches are labeled. The initial regular expression and the labeled positive and negative matches are input in a transformation process. The transformation process may iteratively execute character class restrictions, quantifier restrictions, negative lookaheads on the initial regular expression to transform the initial regular expression into the pool of candidate regular expressions. The transformation process may execute, one at a time, the character class restrictions, quantifier restrictions, the negative lookaheads. A candidate regular expression is selected from the pool of candidate regular expressions, where the selected candidate regular expression has a best F-Measure out of the pool of candidate regular expressions.
摘要翻译：提供了一种用于正则表达式学习的方法，设备和计算机程序产品。可以从用户接收到初始正则表达式。初始正则表达式通过数据库执行。正面比赛和负面比赛被标记。在转换过程中输入初始正则表达式和标记的正和负匹配。转换过程可以迭代地执行字符类限制，量词限制，初始正则表达式的负面前瞻，以将初始正则表达式转换为候选正则表达式的池。转换过程可以一次一个地执行字符类限制，量词限制，否定前瞻。从候选正则表达式的池中选择候选正则表达式，其中所选择的候选正则表达式在候选正则表达式池中具有最佳的F-Measure。

6. 发明申请

US20080215299A1 Asynchronous Hidden Markov Model Method and System 审中-公开
标题翻译：异步隐马尔科夫模型的方法与系统
公开(公告)号：US20080215299A1
公开(公告)日：2008-09-04
申请号：US12105430
申请日：2008-04-18
申请人： Ashutosh Garg , Sreeram V. Balakrishnan , Shivakumar Vaithyanathan
发明人： Ashutosh Garg , Sreeram V. Balakrishnan , Shivakumar Vaithyanathan
IPC分类号： G06F17/18
CPC分类号： G10L15/144 , G06K9/6297
摘要： A system, method and program storage device implementing a method for modeling a data generating process, wherein the modeling comprises observing a data sequence comprising irregularly sampled data, obtaining an observation sequence based on the observed data sequence, assigning a time index sequence to the data sequence, obtaining a hidden state sequence of the data sequence, and decoding the data sequence based on a combination of the time index sequence and the hidden state sequence to model the data sequence. The method further comprises assigning a probability distribution over time stamp values of the observation sequence, wherein the decoding comprises using a Hidden Markov Model. The method further comprises using an expectation maximization methodology to learn the Hidden Markov Model.
摘要翻译：一种实现数据生成过程建模方法的系统，方法和程序存储设备，其中所述建模包括观察包含不规则采样数据的数据序列，基于观察到的数据序列获得观测序列，向数据分配时间索引序列获取数据序列的隐藏状态序列，并且基于时间索引序列和隐藏状态序列的组合对数据序列进行解码以建模数据序列。该方法还包括分配观测序列的时间戳值的概率分布，其中解码包括使用隐马尔科夫模型。该方法还包括使用期望最大化方法来学习隐马尔可夫模型。

7. 发明申请

US20070233651A1 Online analytic processing in the presence of uncertainties 审中-公开
标题翻译：在线分析处理存在不确定性
公开(公告)号：US20070233651A1
公开(公告)日：2007-10-04
申请号：US11395403
申请日：2006-03-31
申请人： Prasad Deshpande , Jayram Thathachar , Shivakumar Vaithyanathan , Douglas Burdick
发明人： Prasad Deshpande , Jayram Thathachar , Shivakumar Vaithyanathan , Douglas Burdick
IPC分类号： G06F17/30
CPC分类号： G06F16/24556
摘要： Disclosed are embodiments of a method for online analytic processing of queries and, and more particularly, of a method that extends the on-line analytic processing (OLAP) data model to represent data ambiguity, such as imprecision and uncertainty, in data values. Specifically, the embodiments of the method incorporate a statistical model that allows for uncertain measures to be modeled as conditional probabilities. Additionally, an embodiment of the method further identifies natural query properties (e.g., consistency and faithfulness) and uses them to shed light on alternative query semantics. Lastly, an embodiment of the method further introduces an allocation-based approach to the semantics of aggregation queries over such data.
摘要翻译：公开了用于查询的在线分析处理的方法的实施例，并且更具体地，扩展在线分析处理（OLAP）数据模型以表示数据值中的数据模糊性，诸如不精确性和不确定性的方法的实施例。具体地说，该方法的实施例包括允许将不确定度量建模为条件概率的统计模型。此外，该方法的一个实施例进一步标识自然查询属性（例如，一致性和忠实性），并使用它们来阐明替代查询语义。最后，该方法的一个实施例进一步引入基于分配的方法来处理关于这种数据的聚合查询的语义。

8. 发明申请

US20070168380A1 System and method for storing text annotations with associated type information in a structured data store 有权
标题翻译：用于在结构化数据存储中存储具有关联类型信息的文本注释的系统和方法
公开(公告)号：US20070168380A1
公开(公告)日：2007-07-19
申请号：US11334255
申请日：2006-01-17
申请人： Krishna Chitrapura , Rahul Gupta , Rajasekar Krishnamurthy , Sriram Raghavan , Shivakumar Vaithyanathan , Hualyu Zhu
发明人： Krishna Chitrapura , Rahul Gupta , Rajasekar Krishnamurthy , Sriram Raghavan , Shivakumar Vaithyanathan , Hualyu Zhu
IPC分类号： G06F7/00
CPC分类号： G06F17/30616 , Y10S707/99933 , Y10S707/99934 , Y10S707/99942 , Y10S707/99943
摘要： A text annotation structured storage system stores text annotations with associated type information in a structured data store. The present system persists or stores annotations in a structured data store in an indexable and queryable format. Exemplary structured data stores comprise XML databases and relational databases. The system exploits type information in a type system to develop corresponding schemas in a structured data model. The system comprises techniques for mapping annotations to an XML data model and a relational data model. The system captures various features of the type system, such as complex types and inheritance, in the schema for the persistent store. In particular, the repository provides support for path navigation over the hierarchical type system starting at any type.
摘要翻译：文本注释结构化存储系统将具有关联类型信息的文本注释存储在结构化数据存储中。本系统以可索引和可查询的格式将批注持久存储在结构化数据存储中。示例性结构化数据存储包括XML数据库和关系数据库。系统利用类型系统中的类型信息来开发结构化数据模型中的相应模式。该系统包括用于将注释映射到XML数据模型和关系数据模型的技术。系统在持久存储的架构中捕获类型系统的各种功能，例如复杂类型和继承。特别地，存储库提供对从任何类型开始的分层式系统的路径导航的支持。

9. 发明授权

US07130777B2 Method to hierarchical pooling of opinions from multiple sources 有权
标题翻译：从多个来源层次分组意见的方法
公开(公告)号：US07130777B2
公开(公告)日：2006-10-31
申请号：US10723471
申请日：2003-11-26
申请人： Ashutosh Garg , Jayram S. Thathachar , Shivakumar Vaithyanathan , Huaiyu Zhu
发明人： Ashutosh Garg , Jayram S. Thathachar , Shivakumar Vaithyanathan , Huaiyu Zhu
IPC分类号： G06F17/10
CPC分类号： G06Q30/02 , G06Q30/0282
摘要： Disclosed is a system, method, and program storage device of aggregating opinions comprising consolidating a plurality of expressed opinions on various dimensions of topics as discrete probability distributions, generating an aggregate opinion as a single point probability distribution by minimizing a sum of weighted divergences between a plurality of the discrete probability distributions, and presenting the aggregate opinion as a Bayesian network, wherein the divergences comprise Kullback-Liebler distance divergences, and wherein the expressed opinions are generated by experts and comprise opinions on sentiments of products and services. Moreover, the aggregate opinion predicts success of the products and services. Furthermore, the experts are arranged in a hierarchy of knowledge, wherein the knowledge comprises the various dimensions of topics for which opinions may be expressed upon.
摘要翻译：公开了一种集合意见的系统，方法和程序存储装置，包括将关于主题的各个维度的多个表达的意见合并为离散概率分布，通过最小化一个点概率分布的加权差异之和来生成聚合意见作为单点概率分布多个离散概率分布，并将总体意见呈现为贝叶斯网络，其中分歧包括Kullback-Liebler距离差异，并且其中所表达的意见由专家产生并且包括对产品和服务的感觉的意见。此外，总体意见预测产品和服务的成功。此外，专家们被安排在知识层次中，其中知识包括可以表达意见的主题的各个维度。

10. 发明授权

US06338066B1 Surfaid predictor: web-based system for predicting surfer behavior 有权
标题翻译： Surfaid预测器：用于预测冲浪者行为的基于网络的系统
公开(公告)号：US06338066B1
公开(公告)日：2002-01-08
申请号：US09160828
申请日：1998-09-25
申请人： David Charles Martin , Hansel Joseph Miranda , Mark Paul Plutowski , William Scott Spangler , Shivakumar Vaithyanathan , Kevin Wheeler , David Hilton Wolpert
发明人： David Charles Martin , Hansel Joseph Miranda , Mark Paul Plutowski , William Scott Spangler , Shivakumar Vaithyanathan , Kevin Wheeler , David Hilton Wolpert
IPC分类号： G06F15163
CPC分类号： G06Q30/02 , G06F17/3089 , Y10S707/99935 , Y10S707/99936 , Y10S707/99943
摘要： Given a log of previous web-surfer behavior listing the order in which each surfer downloaded specific items at the web site, and given a meaningful classification of those same items, future surfer behavior is predicted by the present invention. The algorithm utilizes a quantitative model relating items downloaded prior to some specified event to items downloaded after that same event. When the model is applied to a new surfer's session prior to an analogous event, the present invention predicts the likely behavior of the surfer subsequent to that event. The predicted behavior is then further analyzed to derive a quantitative value for the utility of the expected behavior. By randomly selecting sample sessions from a web log, multiple models of surfer behavior can be generated. The multiple models can then be applied to a new surfer's session to produce a predicted behavior/utility distribution and thus a confidence interval for the predicted behavior/utility.
摘要翻译：给出以前的网络冲浪者行为的日志，列出每个冲浪者在网站上下载特定项目的顺序，并给出对这些相同项目的有意义的分类，本发明预测未来的冲浪者行为。该算法使用定量模型，将与某个指定事件之前下载的项目相关联的项目与相同事件之后下载的项目相关联。当模型在类似事件之前应用于新的冲浪者的会话时，本发明预测该事件之后的冲浪者的可能行为。然后进一步分析预测行为，以获得预期行为效用的定量值。通过从Web日志中随机选择样本会话，可以生成多种冲突行为模型。然后可以将多个模型应用于新的冲浪者会话，以产生预测的行为/效用分布，并因此产生预测行为/效用的置信区间。

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