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101. 发明授权

US07483947B2 Message rendering for identification of content features 有权
标题翻译：消息渲染用于识别内容功能
公开(公告)号：US07483947B2
公开(公告)日：2009-01-27
申请号：US10428649
申请日：2003-05-02
申请人： Bryan T. Starbuck , Robert L. Rounthwaite , David E. Heckerman , Joshua T. Goodman
发明人： Bryan T. Starbuck , Robert L. Rounthwaite , David E. Heckerman , Joshua T. Goodman
IPC分类号： G06F15/16
CPC分类号： G06Q10/107 , H04L51/12
摘要： Architecture for detecting and removing obfuscating clutter from the subject and/or body of a message, e.g., e-mail, prior to filtering of the message, to identify junk messages commonly referred to as spam. The technique utilizes the powerful features built into an HTML rendering engine to strip the HTML instructions for all non-substantive aspects of the message. Pre-processing includes pre-rendering of the message into a final format, which final format is that which is displayed by the rendering engine to the user. The final format message is then converted to a text-only format to remove graphics, color, non-text decoration, and spacing that cannot be rendered as ASCII-style or Unicode-style characters. The result is essentially to reduce each message to its common denominator essentials so that the junk mail filter can view each message on an equal basis.
摘要翻译：用于在过滤消息之前检测和去除来自主体和/或消息主体（例如电子邮件）的模糊杂波的体系结构，以识别通常被称为垃圾邮件的垃圾邮件。该技术利用内置于HTML呈现引擎中的强大功能来剥离消息的所有非实质性方面的HTML指令。预处理包括将消息预渲染成最终格式，最终格式是由呈现引擎向用户显示的最终格式。最终格式化消息然后转换为纯文本格式以删除不能以ASCII样式或Unicode风格字符呈现的图形，颜色，非文本装饰和间距。结果基本上是将每个消息减少到其公分要素，以便垃圾邮件过滤器可以在平等的基础上查看每个消息。

102. 发明授权

US07464264B2 Training filters for detecting spasm based on IP addresses and text-related features 有权
标题翻译：培训过滤器，用于根据IP地址和文本相关功能检测痉挛
公开(公告)号：US07464264B2
公开(公告)日：2008-12-09
申请号：US10809163
申请日：2004-03-25
申请人： Joshua T. Goodman , Robert L. Rounthwaite , Geoffrey J. Hulten , Wen-tau Yih
发明人： Joshua T. Goodman , Robert L. Rounthwaite , Geoffrey J. Hulten , Wen-tau Yih
IPC分类号： H04L9/00 , G06F21/00
CPC分类号： H04L51/12 , G06Q10/107
摘要： The subject invention provides for an intelligent quarantining system and method that facilitates detecting and preventing spam. In particular, the invention employs a machine learning filter specifically trained using origination features such as an IP address as well as destination feature such as a URL. Moreover, the system and method involve training a plurality of filters using specific feature data for each filter. The filters are trained independently each other, thus one feature may not unduly influence another feature in determining whether a message is spam. Because multiple filters are trained and available to scan messages either individually or in combination (at least two filters), the filtering or spam detection process can be generalized to new messages having slightly modified features (e.g., IP address). The invention also involves locating the appropriate IP addresses or URLs in a message as well as guiding filters to weigh origination or destination features more than text-based features.
摘要翻译：本发明提供了一种便于检测和防止垃圾邮件的智能隔离系统和方法。特别地，本发明采用使用诸如IP地址之类的发起特征以及目的地特征（例如URL）专门训练的机器学习滤波器。此外，该系统和方法涉及使用针对每个滤波器的特定特征数据来训练多个滤波器。滤波器被彼此独立地训练，因此在确定消息是否是垃圾邮件时，一个特征可能不会不适当地影响另一特征。由于多个过滤器被训练并可用于单独或组合扫描消息（至少两个过滤器），因此过滤或垃圾邮件检测过程可以推广到具有稍微修改的特征（例如，IP地址）的新消息。本发明还涉及在消息中定位适当的IP地址或URL，以及引导过滤器比基于文本的特征更重要的起始或目的地特征。

103. 发明申请

US20080201434A1 Context-Sensitive Searches and Functionality for Instant Messaging Applications 审中-公开
标题翻译：即时消息应用程序的上下文敏感搜索和功能
公开(公告)号：US20080201434A1
公开(公告)日：2008-08-21
申请号：US11675787
申请日：2007-02-16
申请人： John S. Holmes , Heather Ferguson , Adam C. Czeisler , Joshua T. Goodman
发明人： John S. Holmes , Heather Ferguson , Adam C. Czeisler , Joshua T. Goodman
IPC分类号： G06F15/16
CPC分类号： H04L51/04 , G06F16/951 , G06Q10/10
摘要： In one or more embodiments, in the context of an instant messaging application, a conversation is analyzed and contextually or textually relevant keywords and/or phrases are identified. These keywords or phrases are then highlighted in a visually-identifiable manner for selection by an individual participating in the conversation. Once selected by an individual, a user interface is presented and exposes the individual or individuals in the conversation to various contextually- or textually-relevant material or functionality that pertains to the selected word or phrase. In one or more embodiments, an individual can also manually select a word or phrase to access the user interface that exposes contextually or textually-relevant material or functionality. In the various embodiments described below, at least some of this relevant material or functionality is presented to the user in the context of the instant messaging application and in a manner in which it can be consumed by the individual within the instant messaging application itself.
摘要翻译：在一个或多个实施例中，在即时消息收发应用的上下文中，分析对话并识别上下文或文本相关的关键字和/或短语。这些关键字或短语然后以视觉上可识别的方式突出显示，以供参与对话的个人选择。一旦被个人选择，就呈现用户界面，并将会话中的个人或个人公开到与所选择的单词或短语相关的各种与内容或文本相关的材料或功能。在一个或多个实施例中，个人还可以手动地选择单词或短语来访问暴露内容或文本相关材料或功能的用户界面。在下面描述的各种实施例中，在即时消息收发应用的上下文中以这种方式可以将该相关材料或功能中的至少一些呈现给用户，并且其方式可以由即时消息收发应用本身内的个人消费。

104. 发明申请

US20080140781A1 SPAM FILTRATION UTILIZING SENDER ACTIVITY DATA 有权
标题翻译：垃圾邮件过滤利用SENDER活动数据
公开(公告)号：US20080140781A1
公开(公告)日：2008-06-12
申请号：US11567632
申请日：2006-12-06
申请人： Alexei V. Bocharov , Joshua T. Goodman
发明人： Alexei V. Bocharov , Joshua T. Goodman
IPC分类号： G06F15/16
CPC分类号： G06Q10/107
摘要： Spam is identified by computing sender reputation derived from historical activity data across counts for various categories. A spam filter or machine learning system can be trained utilizing pre-categorized data in conjunction with activity data associated with a sender aggregated across at least one time period. This sender activity filter can be employed alone or in combination with other filters to facilitate classification of messages as spam or non-spam.
摘要翻译：通过计算来自各种类别的历史活动数据的发送者信誉来识别垃圾邮件。可以使用预先分类的数据结合在至少一个时间段内聚集的发送者的活动数据来训练垃圾邮件过滤器或机器学习系统。该发件人活动过滤器可以单独使用或与其他过滤器组合使用，以便于将邮件分类为垃圾邮件或非垃圾邮件。

105. 发明申请

US20070294402A1 Extensible Email 审中-公开
标题翻译：可扩展电子邮件
公开(公告)号：US20070294402A1
公开(公告)日：2007-12-20
申请号：US11564659
申请日：2006-11-29
申请人： Eliot C. Gillum , Joshua T. Goodman
发明人： Eliot C. Gillum , Joshua T. Goodman
IPC分类号： G06F15/173
CPC分类号： G06Q10/109 , G06Q10/107
摘要： An email server and systems and methods for providing requested data are provided. The email server is configured to automatically determine capabilities or version information of connecting client software on per account basis. The e-mail server is also configured to store the determined capabilities or version information, and to transmit the determined capabilities or version information.
摘要翻译：提供了一种电子邮件服务器以及用于提供所请求的数据的系统和方法。电子邮件服务器被配置为自动确定每个帐户连接客户端软件的功能或版本信息。电子邮件服务器还被配置为存储确定的能力或版本信息，并且发送所确定的能力或版本信息。

106. 发明授权

US07219035B2 Exponential priors for maximum entropy models 失效
标题翻译：最大熵模型的指数先验
公开(公告)号：US07219035B2
公开(公告)日：2007-05-15
申请号：US11186318
申请日：2005-07-21
申请人： Joshua T. Goodman
发明人： Joshua T. Goodman
IPC分类号： G06F15/00
CPC分类号： G06K9/6217 , G06N99/005
摘要： The subject invention provides for systems and methods that facilitate optimizing one or mores sets of training data by utilizing an Exponential distribution as the prior on one or more parameters in connection with a maximum entropy (maxent) model to mitigate overfitting. Maxent is also known as logistic regression. More specifically, the systems and methods can facilitate optimizing probabilities that are assigned to the training data for later use in machine learning processes, for example. In practice, training data can be assigned their respective weights and then a probability distribution can be assigned to those weights.
摘要翻译：本发明提供了通过利用指数分布作为与最大熵（maxent）模型相结合的一个或多个参数之前的指数分布来优化一个或多个训练数据组的系统和方法，以减轻过拟合。 Maxent也被称为逻辑回归。更具体地，系统和方法可以有助于优化分配给训练数据的概率，以备以后在机器学习过程中使用。实际上，训练数据可以分配它们各自的权重，然后将概率分布分配给这些权重。

107. 发明授权

US07184929B2 Exponential priors for maximum entropy models 有权
公开(公告)号：US07184929B2
公开(公告)日：2007-02-27
申请号：US10766348
申请日：2004-01-28
申请人： Joshua T. Goodman
发明人： Joshua T. Goodman
IPC分类号： G06F15/00
CPC分类号： G06K9/6217 , G06N99/005
摘要： The subject invention provides for systems and methods that facilitate optimizing one or mores sets of training data by utilizing an Exponential distribution as the prior on one or more parameters in connection with a maximum entropy (maxent) model to mitigate overfitting. Maxent is also known as logistic regression. More specifically, the systems and methods can facilitate optimizing probabilities that are assigned to the training data for later use in machine learning processes, for example. In practice, training data can be assigned their respective weights and then a probability distribution can be assigned to those weights.

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