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    • 2. 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。
    • 5. 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。
    • 8. 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。