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1. 发明授权

US5704017A Collaborative filtering utilizing a belief network 失效
标题翻译：利用信念网络进行协同过滤
公开(公告)号：US5704017A
公开(公告)日：1997-12-30
申请号：US602238
申请日：1996-02-16
申请人： David E. Heckerman , John S. Breese , Eric Horvitz , David Maxwell Chickering
发明人： David E. Heckerman , John S. Breese , Eric Horvitz , David Maxwell Chickering
IPC分类号： G06Q30/02 , G06F17/00
CPC分类号： H04N21/252 , G06Q30/02
摘要： The disclosed system provides an improved collaborative filtering system by utilizing a belief network, which is sometimes known as a Bayesian network. The disclosed system learns a belief network using both prior knowledge obtained from an expert in a given field of decision making and a database containing empirical data obtained from many people. The empirical data contains attributes of users as well as their preferences in the field of decision making. After initially learning the belief network, the belief network is relearned at various intervals when additional attributes are identified as having a causal effect on the preferences and data for these additional attributes can be gathered. This relearning allows the belief network to improve its accuracy at predicting preferences of a user. Upon each iteration of relearning, a cluster model is automatically generated that best predicts the data in the database. After relearning the belief network a number of times, the belief network is used to predict the preferences of a user using probabilistic inference. In performing probabilistic inference, the known attributes of a user are received and the belief network is accessed to determine the probability of the unknown preferences of the user given the known attributes. Based on these probabilities, the preference most likely to be desired by the user can be predicted.
摘要翻译：所公开的系统通过利用有时被称为贝叶斯网络的置信网络来提供改进的协同过滤系统。所公开的系统使用从给定的决策领域的专家获得的现有知识和包含从许多人获得的经验数据的数据库来学习信念网络。实证数据包含用户的属性以及决策领域的偏好。在最初学习信念网络之后，当附加属性被识别为对偏好具有因果影响并且可以收集这些附加属性的数据时，信念网络以不同的间隔被重新学习。这种再学习允许信念网络在预测用户的偏好时提高其准确性。在重新学习的每次迭代之后，自动生成最能预测数据库中的数据的集群模型。在重新学习信念网络多次之后，信念网络用于使用概率推理来预测用户的偏好。在执行概率推理时，接收用户的已知属性，并且访问置信网络以确定给定已知属性的用户的未知偏好的概率。基于这些概率，可以预测用户最可能希望的偏好。

2. 发明授权

US07562064B1 Automated web-based targeted advertising with quotas 有权
标题翻译：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。

3. 发明授权

US06216134B1 Method and system for visualization of clusters and classifications 失效
标题翻译：集群和分类可视化的方法和系统
公开(公告)号：US06216134B1
公开(公告)日：2001-04-10
申请号：US09104751
申请日：1998-06-25
申请人： David E. Heckerman , David Maxwell Chickering , Usama M. Fayyad , Christopher A. Meek
发明人： David E. Heckerman , David Maxwell Chickering , Usama M. Fayyad , Christopher A. Meek
IPC分类号： G06F1730
CPC分类号： G06F17/30713 , G06Q30/0643 , Y10S707/956 , Y10S707/99934 , Y10S707/99935 , Y10S707/99944 , Y10S707/99945
摘要： A system that provides for the graphic visualization of the categories of a collection of records. The graphic visualization is referred to as “category graph.” The system optionally displays the category graph as a “similarity graph” or a “hierarchical map.” When displaying a category graph, the system displays a graphic representation of each category. The system displays the category graph as a similarity graph or a hierarchical map in a way that visually illustrates the similarity between categories. The display of a category graph allows a data analyst to better understand the similarity and dissimilarity between categories. A similarity graph includes a node for each category and an arc connecting nodes representing categories whose similarity is above a threshold. A hierarchical map is a tree structure that includes a node for each base category along with nodes representing combinations of similar categories.
摘要翻译：提供记录集合的类别的图形可视化的系统。图形可视化被称为“类别图”。系统可选地将类别图显示为“相似图”或“分层图”。当显示类别图时，系统显示每个类别的图形表示。系统以类似的方式显示类别图作为相似图或分层图，以视觉方式显示类别之间的相似性。类别图的显示允许数据分析人员更好地了解类别之间的相似性和不相似性。相似图包括用于每个类别的节点和连接表示相似度高于阈值的类别的节点的弧。层次映射是树结构，其包括每个基本类别的节点以及表示类似类别的组合的节点。

4. 发明授权

US07389201B2 System and process for automatically providing fast recommendations using local probability distributions 有权
标题翻译：使用本地概率分布自动提供快速建议的系统和过程
公开(公告)号：US07389201B2
公开(公告)日：2008-06-17
申请号：US09681742
申请日：2001-05-30
申请人： David Maxwell Chickering , David E. Heckerman , Robert Rounthwaite
发明人： David Maxwell Chickering , David E. Heckerman , Robert Rounthwaite
IPC分类号： G06F19/00
CPC分类号： G06Q10/04 , G06Q30/02 , G06Q40/025
摘要： The system and method of the present invention automatically extracts the top k recommendations of objects, such as topics, items, products, books, movies, food, drinks, etc., from a local probabilistic recommendation system. Unlike prior systems, the present invention accomplishes the extraction of the top k recommendations of objects without examining a probability for every object that can be recommended. Further, the system and method of the present invention is capable of being implemented using probabilistic recommendation systems based on any conventional type of probabilistic distribution or machine learning technique, including, for example, decision trees and Bayesian networks.
摘要翻译：本发明的系统和方法从本地概率推荐系统自动提取诸如主题，项目，产品，书籍，电影，食品，饮料等的物体的顶部k建议。与现有系统不同，本发明完成了对对象的顶部k建议的提取，而不检查可推荐的每个对象的概率。此外，本发明的系统和方法能够使用基于任何常规类型的概率分布或机器学习技术的概率推荐系统来实现，包括例如决策树和贝叶斯网络。

5. 发明授权

US07158959B1 Automated web-based targeted advertising with quotas 有权
标题翻译：自动化基于网络的定向广告配额
公开(公告)号：US07158959B1
公开(公告)日：2007-01-02
申请号：US11047276
申请日：2005-01-31
申请人： David Maxwell Chickering , David E. Heckerman
发明人： David Maxwell Chickering , David E. Heckerman
IPC分类号： G06N706/045
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。

6. 发明授权

US07660705B1 Bayesian approach for learning regression decision graph models and regression models for time series analysis 有权
标题翻译：用于学习回归决策图模型的贝叶斯方法和时间序列分析的回归模型
公开(公告)号：US07660705B1
公开(公告)日：2010-02-09
申请号：US10102116
申请日：2002-03-19
申请人： Christopher A. Meek , David E. Heckerman , Robert L. Rounthwaite , David Maxwell Chickering , Bo Thiesson
发明人： Christopher A. Meek , David E. Heckerman , Robert L. Rounthwaite , David Maxwell Chickering , Bo Thiesson
IPC分类号： G06F17/10
CPC分类号： G06K9/6297
摘要： Methods and systems are disclosed for learning a regression decision graph model using a Bayesian model selection approach. In a disclosed aspect, the model structure and/or model parameters can be learned using a greedy search algorithm applied to grow the model so long as the model improves. This approach enables construction of a decision graph having a model structure that includes a plurality of leaves, at least one of which includes a non-trivial linear regression. The resulting model thus can be employed for forecasting, such as for time series data, which can include single or multi-step forecasting.
摘要翻译：公开了使用贝叶斯模型选择方法学习回归决策图模型的方法和系统。在公开的方面，只要模型改进，可以使用应用于增长模型的贪心搜索算法来学习模型结构和/或模型参数。该方法能够构建具有包括多个叶子的模型结构的决策图，其中至少一个包括非平凡的线性回归。因此，所得到的模型可以用于预测，例如用于时间序列数据，其可以包括单步或多步预测。

7. 发明授权

US06831663B2 System and process for automatically explaining probabilistic predictions 有权
标题翻译：自动解释概率预测的系统和过程
公开(公告)号：US06831663B2
公开(公告)日：2004-12-14
申请号：US09681709
申请日：2001-05-24
申请人： David Maxwell Chickering , David E. Heckerman , Robert Rounthwaite
发明人： David Maxwell Chickering , David E. Heckerman , Robert Rounthwaite
IPC分类号： G09G500
CPC分类号： G06Q30/02 , G06Q10/02 , Y10S707/99932 , Y10S707/99933 , Y10S707/99934 , Y10S707/99943
摘要： The system and method of the present invention automatically assigns “scores” to the predictor/variable value pairs of a conventional probabilistic model to measure the relative impact or influence of particular elements of a set of topics, items, products, etc. in making specific predictions using the probabilistic model. In particular, these scores measure the relative impact, either positive or negative, that the value of each individual predictor variable has on the posterior distribution of the target topic, item, product, etc., for which a probability is being determined. These scores are useful for understanding why each prediction is make, and how much impact each predictor has on the prediction. Consequently, such scores are useful for explaining why a particular prediction or recommendation was made.
摘要翻译：本发明的系统和方法自动地将“分数”分配给常规概率模型的预测值/变量值对，以测量一组主题，项目，产品等的特定元素对制作具体的相对影响或影响使用概率模型的预测。特别地，这些分数测量相对影响，无论是正面还是负面，每个单个预测变量的值对于其中确定概率的目标主题，项目，产品等的后验分布。这些分数对于了解每个预测的原因以及每个预测者对预测的影响有多大。因此，这些分数对于解释为什么进行特定的预测或建议是有用的。

8. 发明授权

US07143075B1 Automated web-based targeted advertising with quotas 有权
公开(公告)号：US07143075B1
公开(公告)日：2006-11-28
申请号：US09799269
申请日：2001-03-06
申请人： David Maxwell Chickering , David E. Heckerman
发明人： David Maxwell Chickering , David E. Heckerman
IPC分类号： G06N5/00
CPC分类号： G06Q30/02
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.

9. 发明授权

US07908328B1 Identification of email forwarders 有权
标题翻译：电子邮件转发器的识别
公开(公告)号：US07908328B1
公开(公告)日：2011-03-15
申请号：US11023293
申请日：2004-12-27
申请人： Geoffrey J Hulten , Anthony P. Penta , David Maxwell Chickering , Eliot C. Gillum , Gopalakrishnan Seshadrinathan , Jay T. Buckingham , Joshua T. Goodman , Paul S Rehfuss , Robert L. Rounthwaite , Ryan C Colvin
发明人： Geoffrey J Hulten , Anthony P. Penta , David Maxwell Chickering , Eliot C. Gillum , Gopalakrishnan Seshadrinathan , Jay T. Buckingham , Joshua T. Goodman , Paul S Rehfuss , Robert L. Rounthwaite , Ryan C Colvin
IPC分类号： G06F15/16
CPC分类号： H04L51/12
摘要： Identification of email forwarders is described. In an implementation, a method includes using heuristics to identify email forwarders for use in a reputation system for locating spammers. In another implementation, a method includes determining a likelihood that a particular Internet Protocol (IP) address corresponds to an email forwarder and processing email originating from the particular IP address based on the determined likelihood. In a further implementation, a method includes collecting heuristic data that describes characteristics of emails sent from one or more Internet Protocol (IP) addresses and constructing a model from the heuristic data for identifying whether at least one of the IP address is an email forwarder. In yet a further implementation, a method includes identifying that a particular Internet Protocol (IP) address likely corresponds to an email forwarder and processing email from the particular IP address based on an implied sender of the email.
摘要翻译：描述电子邮件转发器的识别。在一个实现中，一种方法包括使用启发式方法来识别在信誉系统中用于定位垃圾邮件发送者的电子邮件转发器。在另一实施方式中，一种方法包括确定特定因特网协议（IP）地址对应于电子邮件转发器的可能性，以及基于所确定的可能性处理来自该特定IP地址的电子邮件。在另一实现中，一种方法包括收集启发式数据，该启发式数据描述从一个或多个因特网协议（IP）地址发送的电子邮件的特征，并根据启发式数据构建模型，用于识别IP地址中的至少一个是电子邮件转发器。在又一个实现中，一种方法包括识别特定的因特网协议（IP）地址可能对应于电子邮件转发器，并且基于电子邮件的隐含发送者从特定IP地址处理电子邮件。

10. 发明授权

US06336108B1 Speech recognition with mixtures of bayesian networks 有权
标题翻译：语音识别与贝叶斯网络的混合
公开(公告)号：US06336108B1
公开(公告)日：2002-01-01
申请号：US09220197
申请日：1998-12-23
申请人： Bo Thiesson , Christopher A. Meek , David Maxwell Chickering , David Earl Heckerman , Fileno A. Alleva , Mei-Yuh Hwang
发明人： Bo Thiesson , Christopher A. Meek , David Maxwell Chickering , David Earl Heckerman , Fileno A. Alleva , Mei-Yuh Hwang
IPC分类号： G06F1518
CPC分类号： G06K9/6296 , G06N5/025 , Y10S707/99945 , Y10S707/99948
摘要： The invention performs speech recognition using an array of mixtures of Bayesian networks. A mixture of Bayesian networks (MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs) having possibly hidden and observed variables. A common external hidden variable is associated with the MBN, but is not included in any of the HSBNs. The number of HSBNs in the MBN corresponds to the number of states of the common external hidden variable, and each HSBN models the world under the hypothesis that the common external hidden variable is in a corresponding one of those states. In accordance with the invention, the MBNs encode the probabilities of observing the sets of acoustic observations given the utterance of a respective one of said parts of speech. Each of the HSBNs encodes the probabilities of observing the sets of acoustic observations given the utterance of a respective one of the parts of speech and given a hidden common variable being in a particular state. Each HSBN has nodes corresponding to the elements of the acoustic observations. These nodes store probability parameters corresponding to the probabilities with causal links representing dependencies between ones of said nodes.
摘要翻译：本发明使用贝叶斯网络混合的阵列来执行语音识别。贝叶斯网络（MBN）的混合由多个具有隐藏和观察变量的假设特定贝叶斯网络（HSBN）组成。常见的外部隐藏变量与MBN相关联，但不包括在任何HSBN中。 MBN中的HSBN的数量对应于共同外部隐藏变量的状态数，并且每个HSBN在假设下共同的外部隐藏变量处于相应的一个状态的假设下对世界进行建模。根据本发明，MBN编码了考虑到所述话音部分中的相应一个的话语来观察声学观测组的概率。每个HSBN编码观察给定语音相应的一个语音的发音并给出隐藏的公共变量处于特定状态的声学观察组的概率。每个HSBN具有对应于声学观测元素的节点。这些节点存储对应于概率的概率参数，其中因果链接表示所述节点之间的依赖关系。

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