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    • 1. 发明申请
    • ADVERTISEMENT SPACE ALLOCATION
    • 广告空间分配
    • US20090210287A1
    • 2009-08-20
    • US12032695
    • 2008-02-18
    • David M. ChickeringYagil EngelGuy DassaJody BiggsChristopher A. Meek
    • David M. ChickeringYagil EngelGuy DassaJody BiggsChristopher A. Meek
    • G06Q30/00G06Q10/00
    • G06Q30/02G06Q30/0267G06Q30/0275
    • A user utility function is implemented in allocating advertisement space to one or more potential advertisers. The user utility function allows advertisement space to be allocated based upon, among other things, the expected utility or usefulness that a proposed advertisement will have to a user. The user utility function, for example, compares proposed advertisements to historical user actions to generate respective user utility values for advertisements (e.g., based upon user responses to advertisements for particular types of product, responses to advertisements from particular types of sellers, etc.). The user utility values can then be applied to bids submitted by advertisers for advertisement space for particular advertisements to obtain modified bids. The modified bids thus reflect, among other things, the expected utility of an advertisement to a user, and thus allow an advertisement host to allocate advertisement space accordingly.
    • 在向一个或多个潜在广告商分配广告空间中实现用户效用函数。 用户效用函数允许基于所提议的广告将对用户具有的预期效用或有用性来分配广告空间。 例如,用户效用函数将所提出的广告与历史用户动作进行比较,以生成广告的相应用户效用值(例如,基于用户对特定类型的产品的广告的响应,对来自特定类型的卖家的广告的响应等) 。 然后,可以将用户效用值应用于广告商提交的用于特定广告的广告空间的出价以获得修改的出价。 因此,修改的出价因此反映了广告对用户的期望效用,并且因此允许广告主主机相应地分配广告空间。
    • 2. 发明授权
    • Systems and methods for new time series model probabilistic ARMA
    • 新时间序列模型概率ARMA的系统和方法
    • US07580813B2
    • 2009-08-25
    • US10463145
    • 2003-06-17
    • Bo ThiessonChristopher A. MeekDavid M. ChickeringDavid E. Heckerman
    • Bo ThiessonChristopher A. MeekDavid M. ChickeringDavid E. Heckerman
    • G06F17/50G05B23/02
    • G06F17/18
    • The present invention utilizes a cross-prediction scheme to predict values of discrete and continuous time observation data, wherein conditional variance of each continuous time tube variable is fixed to a small positive value. By allowing cross-predictions in an ARMA based model, values of continuous and discrete observations in a time series are accurately predicted. The present invention accomplishes this by extending an ARMA model such that a first time series “tube” is utilized to facilitate or “cross-predict” values in a second time series tube to form an “ARMAxp” model. In general, in the ARMAxp model, the distribution of each continuous variable is a decision graph having splits only on discrete variables and having linear regressions with continuous regressors at all leaves, and the distribution of each discrete variable is a decision graph having splits only on discrete variables and having additional distributions at all leaves.
    • 本发明利用交叉预测方案来预测离散和连续时间观测数据的值,其中每个连续时间管变量的条件方差固定为小的正值。 通过在基于ARMA的模型中允许交叉预测,可以准确预测时间序列中连续和离散观测值。 本发明通过扩展ARMA模型来实现这一目的,使得第一时间序列“管”用于促进或“交叉预测”第二时间序列管中的值以形成“ARMAxp”模型。 一般来说,在ARMAxp模型中,每个连续变量的分布是仅在离散变量上分裂并具有在所有叶上具有连续回归的线性回归的决策图,并且每个离散变量的分布是仅分解为 离散变量,并在所有叶子上具有额外的分布。
    • 5. 发明申请
    • USER INTERACTION-BIASED ADVERTISING
    • 用户互动偏好广告
    • US20080114639A1
    • 2008-05-15
    • US11559992
    • 2006-11-15
    • Christopher A. MeekJody D. BiggsDavid M. Chickering
    • Christopher A. MeekJody D. BiggsDavid M. Chickering
    • G06Q30/00G06F17/40
    • G06Q30/02G06Q30/0242G06Q30/0273
    • On-line and/or off-line advertisement interactions are tracked for individual users. This information can then be utilized to adjust display parameters for an advertisement. Tracking can be accomplished via a client-side tracking mechanism and/or a server side tracking mechanism. The advertisement interactions allow advertisers to adjust their advertising campaigns to better target their advertisements. The tracked interactions can include, but are not limited to selections (clicking, etc.) and/or conversions (purchases) and the like. Some instances include a display component that can employ the user-specific interaction information to automatically adjust, for example, location, frequency, and/or to whom an advertisement is displayed. The interaction information can also be utilized for revenue generation by charging advertisers for the information and/or for adjusting their advertising campaigns and the like. Instances can be utilized with on-line and/or off-line advertising media.
    • 为个人用户追踪在线和/或离线广告交互。 然后可以利用该信息来调整广告的显示参数。 跟踪可以通过客户端跟踪机制和/或服务器端跟踪机制来实现。 广告互动允许广告客户调整他们的广告活动,以更好地定位他们的广告。 跟踪的交互可以包括但不限于选择(点击等)和/或转换(购买)等。 一些实例包括可以使用用户特定交互信息来自动调整例如位置,频率和/或广告被显示给谁的显示组件。 交互信息还可以通过向广告商收取信息和/或调整其广告活动等来用于创收。 实例可以与在线和/或离线广告媒体一起使用。
    • 6. 发明申请
    • SEARCH QUERY MONETIZATION-BASED RANKING AND FILTERING
    • 搜索查询基于功能的排序和筛选
    • US20080033797A1
    • 2008-02-07
    • US11461552
    • 2006-08-01
    • David M. ChickeringChristopher A. MeekKumar H. Chellapilla
    • David M. ChickeringChristopher A. MeekKumar H. Chellapilla
    • G06Q30/00
    • G06Q30/02G06Q30/0256G06Q30/0275G06Q30/0277
    • Advertiser monetization information is utilized to determine a search query monetization value that can be employed in web-search ranking to facilitate in ranking search results and/or in email spam filtering to reduce unsolicited emails and the like. Various methods can be employed to filter and/or rank and the like based on the search query monetization value. This can include biasing based on high values and/or low values. The search query monetization value can be determined based on, for example, independent phrases and/or bids. In other instances, personal user advertising interactions can be employed as well to facilitate search result ranking and/or email spam filtering. Employment of search query monetization value techniques can substantially reduce various types of subversive/undesired information.
    • 广告商获利信息用于确定可以在网页搜索排名中使用的搜索查询营利价值,以便于排名搜索结果和/或电子邮件垃圾邮件过滤以减少未经请求的电子邮件等。 可以使用各种方法来基于搜索查询营利值来过滤和/或排名等。 这可以包括基于高值和/或低值的偏置。 可以基于例如独立短语和/或出价来确定搜索查询营利值。 在其他情况下,也可以使用个人用户广告交互来促进搜索结果排名和/或邮件垃圾邮件过滤。 采用搜索查询营利价值技术可以大大减少各种类型的颠覆性/不需要的信息。
    • 9. 发明授权
    • Apparatus and accompanying methods for visualizing clusters of data and hierarchical cluster classifications
    • 用于可视化数据集群和分级集群分类的装置和相关方法
    • US06742003B2
    • 2004-05-25
    • US09845151
    • 2001-04-30
    • David E. HeckermanPaul S. BradleyDavid M. ChickeringChristopher A. Meek
    • David E. HeckermanPaul S. BradleyDavid M. ChickeringChristopher A. Meek
    • G06F1730
    • G06Q30/0641G06F17/30713Y10S707/99934Y10S707/99935Y10S707/99936Y10S707/99942Y10S707/99944Y10S707/99945Y10S707/99948
    • A system that incorporates an interactive graphical user interface for visualizing clusters (categories) and segments (summarized clusters) of data. Specifically, the system automatically categorizes incoming case data into clusters, summarizes those clusters into segments, determines similarity measures for the segments, scores the selected segments through the similarity measures, and then forms and visually depicts hierarchical organizations of those selected clusters. The system also automatically and dynamically reduces, as necessary, a depth of the hierarchical organization, through elimination of unnecessary hierarchical levels and inter-nodal links, based on similarity measures of segments or segment groups. Attribute/value data that tends to meaningfully characterize each segment is also scored, rank ordered based on normalized scores, and then graphically displayed. The system permits a user to browse through the hierarchy, and, to readily comprehend segment inter-relationships, selectively expand and contract the displayed hierarchy, as desired, as well as to compare two selected segments or segment groups together and graphically display the results of that comparison. An alternative discriminant-based cluster scoring technique is also presented.
    • 一个包含交互式图形用户界面的系统,用于可视化数据的集群(类别)和分段(聚合集群)。 具体来说,系统将传入的病例数据自动分类为群集,将这些群集合成段,确定段的相似性度量,通过相似性度量对所选段进行分类,然后形成并可视地描绘这些群集的层次结构。 基于片段或段组的相似性度量,系统还可以根据需要自动和动态地减少层次组织的深度,通过消除不必要的层级和节点间链接。 倾向于对每个段进行有意义表征的属性/值数据也被划分,基于归一化分数进行排序,然后以图形方式显示。 该系统允许用户浏览层次结构,并且为了容易地理解分段相互关系,根据需要选择性地扩展和收缩所显示的层次结构,以及将两个选定的分段或分段组进行比较,并以图形方式显示 那个比较。 还提出了一种替代的基于判别式的聚类评分技术。