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    "> Technique which utilizes a probabilistic classifier to detect
    61.
    发明授权
    Technique which utilizes a probabilistic classifier to detect "junk" e-mail by automatically updating a training and re-training the classifier based on the updated training set 失效
    利用概率分类器通过自动更新训练集并基于更新的训练集来重新训练分类器来检测“垃圾”电子邮件的技术

    公开(公告)号：US6161130A

    公开(公告)日：2000-12-12

    申请号：US102837

    申请日：1998-06-23

    申请人： Eric Horvitz ,  David E. Heckerman ,  Susan T. Dumais ,  Mehran Sahami ,  John C. Platt

    发明人： Eric Horvitz ,  David E. Heckerman ,  Susan T. Dumais ,  Mehran Sahami ,  John C. Platt

    IPC分类号： G06F17/30 ,  G06K9/62 ,  G06Q10/10 ,  H04L12/58 ,  G06F15/16 ,  G06F15/173

    CPC分类号： G06F17/30707 ,  G06K9/6269 ,  G06Q10/107 ,  H04L51/12 ,  Y10S707/99935 ,  Y10S707/99936

    摘要： A technique, specifically a method and apparatus that implements the method, which through a probabilistic classifier (370) and, for a given recipient, detects electronic mail (e-mail) messages, in an incoming message stream, which that recipient is likely to consider "junk". Specifically, the invention discriminates message content for that recipient, through a probabilistic classifier (e.g., a support vector machine) trained on prior content classifications. Through a resulting quantitative probability measure, i.e., an output confidence level, produced by the classifier for each message and subsequently compared against a predefined threshold, that message is classified as either, e.g., spam or legitimate mail, and, e.g., then stored in a corresponding folder (223, 227) for subsequent retrieval by and display to the recipient. Based on the probability measure, the message can alternatively be classified into one of a number of different folders, depicted in a pre-defined visually distinctive manner or simply discarded in its entirety.

    摘要翻译： 一种实现该方法的技术，特别是一种方法和装置，其通过概率分类器（370）以及对于给定的接收者在接收到的消息流中检测电子邮件（电子邮件）消息，该接收者可能 考虑“垃圾”。 具体地，本发明通过在先前内容分类上训练的概率分类器（例如，支持向量机）来区分该接收者的消息内容。 通过由每个消息的分类器产生的随后与预定阈值进行比较的结果定量概率测度，即输出置信水平，该消息被分类为例如垃圾邮件或合法邮件，并且例如存储在 相应的文件夹（223,227），用于随后由接收者检索和显示。 基于概率测量，该消息可替代地被分类为多个不同文件夹中的一个，以预先定义的视觉区别方式描绘，或者完全被丢弃。

    Advanced spam detection techniques
    62.
    发明授权
    Advanced spam detection techniques 有权
    高级垃圾邮件检测技术

    公开(公告)号：US08533270B2

    公开(公告)日：2013-09-10

    申请号：US10601741

    申请日：2003-06-23

    申请人： Bryan T. Starbuck ,  Robert L. Rounthwaite ,  David E. Heckerman ,  Joshua T. Goodman ,  Eliot C. Gillum ,  Nathan D. Howell ,  Kenneth R. Aldinger

    发明人： Bryan T. Starbuck ,  Robert L. Rounthwaite ,  David E. Heckerman ,  Joshua T. Goodman ,  Eliot C. Gillum ,  Nathan D. Howell ,  Kenneth R. Aldinger

    IPC分类号： G06F15/16

    CPC分类号： G06F17/3061 ,  G06Q10/107 ,  H04L51/12

    摘要： The subject invention provides for an advanced and robust system and method that facilitates detecting spam. The system and method include components as well as other operations which enhance or promote finding characteristics that are difficult or the spammer to avoid and finding characteristics in non-spam that are difficult for spammers to duplicate. Exemplary characteristics include examining origination features in pairs, analyzing character and/or number sequences, strings, and sub-strings, detecting various entropy levels of one or more character sequences, strings and/or sub-strings as well as analyzing message and/or feature sizes.

    摘要翻译： 本发明提供了一种便于检测垃圾邮件的先进且健壮的系统和方法。 该系统和方法包括增强或促进发现难以避免的特征或垃圾邮件发送者避免并且发现垃圾邮件发送者难以复制的非垃圾邮件特性的其他操作。 示例性特征包括成对检查起始特征，分析字符和/或数字序列，字符串和子串，检测一个或多个字符序列，字符串和/或子串的各种熵级，以及分析消息和/或 特征尺寸。

    Automated web-based targeted advertising with quotas
    64.
    发明授权
    Automated web-based targeted advertising with quotas 有权
    自动化基于网络的定向广告配额

    公开(公告)号：US07562064B1

    公开(公告)日：2009-07-14

    申请号：US11464402

    申请日：2006-08-14

    申请人： David Maxwell Chickering ,  David E. Heckerman

    发明人： David Maxwell Chickering ,  David E. Heckerman

    IPC分类号： G06F15/00

    CPC分类号： G06Q30/00 ,  G06Q30/0269 ,  G06Q30/0273 ,  G06Q30/0277

    摘要： The invention provides systems and methods that can be used for targeted advertising. The system determines where to present impressions, such as advertisements, to maximize an expected utility subject to one or more constraints, which can include quotas and minimum utilities for groups of one or more impression. The traditional measure of utility in web-based advertising is click-though rates, but the present invention provides a broader definition of utility, including measures of sales, profits, or brand awareness, for example. This broader definition permits advertisements to be allocated more in accordance with the actual interests of advertisers.

    摘要翻译： 本发明提供了可用于有针对性的广告的系统和方法。 该系统确定在何处呈现印象，例如广告，以最大限度地利用一个或多个约束的期望效用，其中可以包括一个或多个印象的组的配额和最小实用程序。 基于网络广告的传统效用测度是点击率，但本发明提供了更广泛的效用定义，例如销售，利润或品牌知名度等。 这种更广泛的定义允许根据广告商的实际利益更多地分配广告。

    FALSE DISCOVERY RATE FOR GRAPHICAL MODLES
    65.
    发明申请
    FALSE DISCOVERY RATE FOR GRAPHICAL MODLES 失效
    图形模型的虚拟发现率

    公开(公告)号：US20090106172A1

    公开(公告)日：2009-04-23

    申请号：US11873440

    申请日：2007-10-17

    申请人： David E. Heckerman ,  Jennifer Listgarten ,  Carl M. Kadie

    发明人： David E. Heckerman ,  Jennifer Listgarten ,  Carl M. Kadie

    IPC分类号： G06F15/18

    CPC分类号： G06N7/005

    摘要： The claimed subject matter provides systems and/or methods that determines a number of non-spurious arcs associated with a learned graphical model. The system can include devices and mechanisms that utilize learning algorithms and datasets to generate learned graphical models and graphical models associated with null permutations of the datasets, ascertaining the average number of arcs associated with the graphical models associated with null permutations of the datasets, enumerating the total number of arcs affiliated with the learned graphical model, and presenting a ratio of the average number of arcs to the total number of arcs, the ratio indicative of the number of non-spurious arcs associated the learned graphical model.

    摘要翻译： 所要求保护的主题提供确定与学习的图形模型相关联的多个非虚假​​弧的系统和/或方法。 该系统可以包括利用学习算法和数据集来生成学习图形模型和与数据集的零排列相关联的图形模型的装置和机制，确定与与数据集的零排列相关联的图形模型相关联的平均弧数，列举 与所学习的图形模型相关联的弧的总数，并且呈现平均弧数与总弧数的比率，该比率表示与所学习的图形模型相关联的非虚假弧的数量。

    Message rendering for identification of content features
    67.
    发明授权
    Message rendering for identification of content features 有权
    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。

    Cluster-based and rule-based approach for automated web-based targeted advertising with quotas
    68.
    发明授权
    Cluster-based and rule-based approach for automated web-based targeted advertising with quotas 有权
    基于群集和基于规则的自动化基于Web的定向广告配额配额方法

    公开(公告)号：US07472102B1

    公开(公告)日：2008-12-30

    申请号：US09430767

    申请日：1999-10-29

    申请人： David E. Heckerman ,  D. Maxwell Chickering ,  Daniel Rosen

    发明人： David E. Heckerman ,  D. Maxwell Chickering ,  Daniel Rosen

    IPC分类号： G06N5/00

    CPC分类号： G06Q30/02 ,  G06Q10/087

    摘要： Targeted delivery of items with inventory management using a cluster-based approach or a rule-based approach is disclosed. An example of items is advertisements. Each item is allocated to one or more clusters. The allocation is made based on a predetermined criterion accounting for at least a quota for each item and possibly a constraint for each cluster. The former can refer to the number of times an item must be shown. The latter can refer to the number of times a given group of web pages is likely to be visited by users, and hence is the number of times items can be shown in a given cluster. The invention is not limited to any particular definition of what constitutes a cluster or item.

    摘要翻译： 披露了使用基于群集的方法或基于规则的方法对库存管理进行目标交付。 项目的一个例子是广告。 每个项目被分配给一个或多个集群。 基于至少考虑每个项目的配额和可能的每个集群的约束的预定标准进行分配。 前者可以参考项目必须显示的次数。 后者可以参考给定的一组网页可能被用户访问的次数，因此是在给定的集群中可以显示项目的次数。 本发明不限于什么构成集群或项目的任何具体定义。

    T-CELL EPIOTOPE PREDICTION
    69.
    发明申请
    T-CELL EPIOTOPE PREDICTION 有权
    T细胞EPIOTOPE预测

    公开(公告)号：US20080172215A1

    公开(公告)日：2008-07-17

    申请号：US11963081

    申请日：2007-12-21

    申请人： David E. Heckerman ,  Carl M. Kadie ,  Jennifer Listgarten ,  Noah Aaron Zaitlen ,  Nebojsa Jojic

    发明人： David E. Heckerman ,  Carl M. Kadie ,  Jennifer Listgarten ,  Noah Aaron Zaitlen ,  Nebojsa Jojic

    IPC分类号： G06G7/60

    CPC分类号： G06F19/24 ,  G06F19/16 ,  G06F19/18

    摘要： Epitope prediction models are described herein. By way of example, a system for predicting epitope information relating to a epitope can include a classification model (e.g., logistic regression model). The trained classification model can illustratively operatively execute one ore logistic functions on received protein data, and incorporate one or more of hidden binary variables and shift variables that when processed represent the identification (e.g., prediction) of one or more desired epitopes. The classification model can be configured to predict the epitope information by processing data including various features of an epitope, MHC, MHC supertype, and Boolean combinations thereof.

    摘要翻译： 本文描述了表位预测模型。 作为示例，用于预测与表位相关的表位信息的系统可以包括分类模型（例如逻辑回归模型）。 经训练的分类模型可以说明性地操作地对所接收的蛋白质数据执行一个矿物物流功能，并且包含一个或多个隐藏的二进制变量和移位变量，其在被处理时表示一个或多个所需表位的识别（例如，预测）。 分类模型可以被配置为通过处理包括表位，MHC，MHC超类型和布尔组合的各种特征的数据来预测表位信息。