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1. 发明申请

US20120278354A1 USER ANALYSIS THROUGH USER LOG FEATURE EXTRACTION 审中-公开
标题翻译：用户分析通过用户日志功能提取
公开(公告)号：US20120278354A1
公开(公告)日：2012-11-01
申请号：US13097277
申请日：2011-04-29
申请人： Shengquan Yan , Zhenghao Wang , Xiao Huang , Yu Chen , An Yan , Jeffrey Eric Larsson , Michael Kiogora Kinoti , Peng Yu , Zijian Zheng
发明人： Shengquan Yan , Zhenghao Wang , Xiao Huang , Yu Chen , An Yan , Jeffrey Eric Larsson , Michael Kiogora Kinoti , Peng Yu , Zijian Zheng
IPC分类号： G06F17/30
CPC分类号： G06Q10/063
摘要： Systems, methods, and computer media for efficiently processing user log data are provided. A received user log data analysis request specifies: target user log features that identify users in a target user group, analysis user log features that identify data associated with the users in the target user group, and an analysis to perform on the identified data associated with the users in the target user group. Occurrences of specified features are extracted from user logs and stored. Users associated with an occurrence of each of the extracted and stored target user log features are identified as users in the target user group. Occurrences of the analysis user log features that are associated with a user in the target user group are extracted and reformatted for the analysis specified in the analysis request.
摘要翻译：提供了用于有效处理用户日志数据的系统，方法和计算机媒体。接收到的用户日志数据分析请求指定：标识目标用户组中的用户的目标用户日志功能，分析用于识别与目标用户组中的用户相关联的数据的用户日志功能，以及对与目标用户组中的用户。指定功能的发生从用户日志中提取并存储。与提取和存储的每个目标用户日志特征中的每一个相关联的用户被标识为目标用户组中的用户。与目标用户组中的用户相关联的分析用户日志功能的出现被提取并重新格式化以用于分析请求中指定的分析。

2. 发明授权

US08806451B2 Monad based cloud computing 有权
标题翻译：基于Monad的云计算
公开(公告)号：US08806451B2
公开(公告)日：2014-08-12
申请号：US13161787
申请日：2011-06-16
申请人： Zijian Zheng , Shengquan Yan , Peng Yu
发明人： Zijian Zheng , Shengquan Yan , Peng Yu
IPC分类号： G06F9/45 , G06F17/30 , G06F7/00 , G06F9/46
CPC分类号： G06F17/30539
摘要： Systems and methods are provided for using monads to facilitate complex computation tasks in a cloud computing environment. In particular, monads can be employed to facilitate creation and execution of data mining jobs for large data sets. Monads can allow for improved error handling for complex computation tasks. Monads can also facilitate identification of opportunities for improving the efficiency of complex computations.
摘要翻译：提供了系统和方法，以便在云计算环境中使用单极化来促进复杂的计算任务。特别地，可以采用单子来促进大数据集的数据挖掘作业的创建和执行。 Monads可以允许改进复杂计算任务的错误处理。 Monads也可以促进识别提高复杂计算效率的机会。

3. 发明申请

US20090319456A1 MACHINE-BASED LEARNING FOR AUTOMATICALLY CATEGORIZING DATA ON PER-USER BASIS 有权
标题翻译：基于机器的学习，在每个用户基础上自动分类数据
公开(公告)号：US20090319456A1
公开(公告)日：2009-12-24
申请号：US12142747
申请日：2008-06-19
申请人： Ashish Consul , Harvey Rook , Rajasi Saha , Shengquan Yan
发明人： Ashish Consul , Harvey Rook , Rajasi Saha , Shengquan Yan
IPC分类号： G06F15/18
CPC分类号： H04L51/12 , G06N99/005
摘要： Architecture that employs machine-based learning to automatically categorize data on a per-user basis. Auto-tagging reduces the burden on infoworkers by creating a machine learning model to learn from user tagging behavior or preferences. Once this information is obtained, a trained model for this specific user is used to assign tags to incoming data, such as emails. The architecture finds particular applicability to compliance and message retention policies that otherwise would mandate extra work for the infoworker. The architecture learns the tagging behavior of a user and uses this learned behavior to automatically tag data based on the user's prior tagging habits. A regression algorithm is employed to process the training data according to an n-dimensional framework for prediction and application of the tag(s) to the incoming messages.
摘要翻译：采用基于机器学习的架构可以在每个用户的基础上自动分类数据。自动标记可以通过创建机器学习模型来减轻对信息收集者的负担，从而吸取用户标签行为或偏好。一旦获得了这个信息，就可以使用这个特定用户的经过训练的模型来为传入的数据（如电子邮件）分配标签。该体系结构特别适用于合规性和邮件保留策略，否则这些策略将为infoworker增加额外的工作。架构学习用户的标记行为，并使用此学习行为根据用户的先前标记习惯自动标记数据。采用回归算法根据n维框架来处理训练数据，用于对输入消息进行标签的预测和应用。

4. 发明申请

US20100306147A1 Boosting to Determine Indicative Features from a Training Set 有权
标题翻译：促进确定培训集的指示性特征
公开(公告)号：US20100306147A1
公开(公告)日：2010-12-02
申请号：US12471841
申请日：2009-05-26
申请人： John Platt , Harvey Rook , Shengquan Yan , Rajasi Saha
发明人： John Platt , Harvey Rook , Shengquan Yan , Rajasi Saha
IPC分类号： G06N5/02 , G06F15/18
CPC分类号： G06F17/30705
摘要： Determining indicative features may be provided. First, a first set of features may be determined using a document frequency process. Then a second set of features may be determined using a boosting process. Using the boosting process may comprise using an approximation for a one-dimensional optimization. The approximation may include an upper bound. Next, the first set of features and the second set of features may be combined into a combined set of features. The combined set of features may comprise a union of the first set of features and the second set of features. At least one document may then be classified based on the combined set of features.
摘要翻译：可以提供确定指示特征。首先，可以使用文档频率处理来确定第一组特征。然后可以使用升压过程来确定第二组特征。使用升压过程可以包括对一维优化使用近似。近似值可以包括上界。接下来，第一组特征和第二组特征可以组合成组合的特征集合。组合的特征集合可以包括第一组特征和第二组特征的联合。然后可以基于组合的一组特征来分类至少一个文档。

5. 发明授权

US08200601B2 Boosting to determine indicative features from a training set 有权
标题翻译：促进确定训练集的指示性特征
公开(公告)号：US08200601B2
公开(公告)日：2012-06-12
申请号：US12471841
申请日：2009-05-26
申请人： John Platt , Harvey Rook , Shengquan Yan , Rajasi Saha
发明人： John Platt , Harvey Rook , Shengquan Yan , Rajasi Saha
IPC分类号： G06F17/00 , G06N5/02
CPC分类号： G06F17/30705
摘要： Determining indicative features may be provided. First, a first set of features may be determined using a document frequency process. Then a second set of features may be determined using a boosting process. Using the boosting process may comprise using an approximation for a one-dimensional optimization. The approximation may include an upper bound. Next, the first set of features and the second set of features may be combined into a combined set of features. The combined set of features may comprise a union of the first set of features and the second set of features. At least one document may then be classified based on the combined set of features.
摘要翻译：可以提供确定指示特征。首先，可以使用文档频率处理来确定第一组特征。然后可以使用升压过程来确定第二组特征。使用升压过程可以包括对一维优化使用近似。近似值可以包括上界。接下来，第一组特征和第二组特征可以组合成组合的特征集合。组合的特征集合可以包括第一组特征和第二组特征的联合。然后可以基于组合的一组特征来分类至少一个文档。

6. 发明授权

US08682819B2 Machine-based learning for automatically categorizing data on per-user basis 有权
标题翻译：基于机器的学习，可以根据用户自动对数据进行分类
公开(公告)号：US08682819B2
公开(公告)日：2014-03-25
申请号：US12142747
申请日：2008-06-19
申请人： Ashish Consul , Harvey Rook , Rajasi Saha , Shengquan Yan
发明人： Ashish Consul , Harvey Rook , Rajasi Saha , Shengquan Yan
IPC分类号： G06F15/18
CPC分类号： H04L51/12 , G06N99/005
摘要： Architecture that employs machine-based learning to automatically categorize data on a per-user basis. Auto-tagging reduces the burden on infoworkers by creating a machine learning model to learn from user tagging behavior or preferences. Once this information is obtained, a trained model for this specific user is used to assign tags to incoming data, such as emails. The architecture finds particular applicability to compliance and message retention policies that otherwise would mandate extra work for the infoworker. The architecture learns the tagging behavior of a user and uses this learned behavior to automatically tag data based on the user's prior tagging habits. A regression algorithm is employed to process the training data according to an n-dimensional framework for prediction and application of the tag(s) to the incoming messages.
摘要翻译：采用基于机器学习的架构可以在每个用户的基础上自动分类数据。自动标记可以通过创建机器学习模型来减轻对信息收集者的负担，从而吸取用户标签行为或偏好。一旦获得了这个信息，就可以使用这个特定用户的经过训练的模型来为传入的数据（如电子邮件）分配标签。该体系结构特别适用于合规性和邮件保留策略，否则这些策略将为infoworker增加额外的工作。架构学习用户的标记行为，并使用此学习行为根据用户的先前标记习惯自动标记数据。采用回归算法根据n维框架来处理训练数据，用于对输入消息进行标签的预测和应用。

7. 发明申请

US20100146056A1 Searching An Email System Dumpster 审中-公开
标题翻译：搜索邮件系统垃圾箱
公开(公告)号：US20100146056A1
公开(公告)日：2010-06-10
申请号：US12328410
申请日：2008-12-04
申请人： Ashish Consul , Suryanarayana M. Gorti , Shengquan Yan , Andrei Marinescu , Julian Alexander Zbogar-Smith
发明人： Ashish Consul , Suryanarayana M. Gorti , Shengquan Yan , Andrei Marinescu , Julian Alexander Zbogar-Smith
IPC分类号： G06F15/82
CPC分类号： H04L51/22 , H04L51/24
摘要： A method is presented for searching for email messages that on a server computer. A request is received on the server computer to search for one or more email messages in one or more mailboxes on the server computer. Each of the one or more mailboxes includes a dumpster folder. The request includes search criteria including a parameter indicating whether the dumpster folder associated with a mailbox should be searched. The dumpster folder stores one or more email messages that have been deleted from a deleted items folder in the mailbox. One or more mailboxes that satisfy the search criteria in the request are identified. If the parameter indicates that the dumpster folder should be searched, the dumpster folder of each of the identified mailboxes that satisfy the search criteria is queried and any email messages in each dumpster folder that satisfy the search criteria are identified.
摘要翻译：提出了一种在服务器计算机上搜索电子邮件的方法。在服务器计算机上收到请求，以在服务器计算机上的一个或多个邮箱中搜索一个或多个电子邮件。每个一个或多个邮箱都包含一个垃圾箱文件夹。请求包括搜索条件，包括指示是否应搜索与邮箱关联的垃圾箱文件夹的参数。垃圾箱文件夹存储从邮箱中的已删除邮件文件夹中删除的一个或多个电子邮件。标识满足请求中搜索条件的一个或多个邮箱。如果参数指示应该搜索dumpster文件夹，则会查询每个符合搜索条件的标识邮箱的dumpster文件夹，并标识每个dumpster文件夹中满足搜索条件的任何电子邮件。

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