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Patent Agency Ranking
公开(公告)号：US20170017986A1
公开(公告)日：2017-01-19
申请号：US14801638
申请日：2015-07-16
Applicant: Adobe Systems Incorporated
Inventor： Craig M. Mathis , Vikas Yadav
CPC classification number: G06Q30/0242 , G06F16/9535 , G06F16/955
Abstract: Methods and systems for analyzing usage and performance of digital design assets for asset selection. In particular, one or more embodiments maintain a digital design asset repository containing a plurality of digital design assets available for use in marketing campaigns. One or more embodiments assign asset identifiers to the digital design assets. One or more embodiments then track usage of and interactions with a first digital design asset in a plurality of marketing campaigns. One or more embodiments aggregate analytics data for the first digital design asset based on the tracked usage and interactions, and provide the aggregated analytics data with the first digital design asset in the digital design asset repository.
Abstract translation: 分析资产选择的数字设计资产的使用和性能的方法和系统。 特别地，一个或多个实施例维护包含可用于营销活动的多个数字设计资产的数字设计资产库。 一个或多个实施例将资产标识符分配给数字设计资产。 一个或多个实施例然后跟踪多个营销活动中的第一数字设计资产的使用和与之相互作用。 一个或多个实施例基于跟踪的使用和交互来聚合用于第一数字设计资产的分析数据，并且在数字设计资产库中提供聚合的分析数据与第一数字设计资产。
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公开(公告)号：US09407651B2
公开(公告)日：2016-08-02
申请号：US14850854
申请日：2015-09-10
Applicant: Adobe Systems Incorporated
Inventor： Craig M. Mathis
CPC classification number: H04L63/1425 , H04L41/145 , H04L41/147 , H04L43/08
Abstract: Methods and apparatus for anomaly detection in network-site metrics using predictive modeling are described. A method comprises obtaining time-series data for a given time range, wherein the time-series data comprises values for a network-site analytics metric for each of a plurality of sequential time steps across the given time range. The method includes generating a predictive model for the network-site analytics metric based on at least a segment of the time-series data. The method includes using the predictive model to predict an expected value range for the network-site analytics metric for a next time step after the segment and, based on the expected value range, determining whether an actual value for the network-site analytics metric for the next time step is an anomalous value.
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公开(公告)号：US20150381648A1
公开(公告)日：2015-12-31
申请号：US14850854
申请日：2015-09-10
Applicant: Adobe Systems Incorporated
Inventor： Craig M. Mathis
IPC: H04L29/06
CPC classification number: H04L63/1425 , H04L41/145 , H04L41/147 , H04L43/08
Abstract: Methods and apparatus for anomaly detection in network-site metrics using predictive modeling are described. A method comprises obtaining time-series data for a given time range, wherein the time-series data comprises values for a network-site analytics metric for each of a plurality of sequential time steps across the given time range. The method includes generating a predictive model for the network-site analytics metric based on at least a segment of the time-series data. The method includes using the predictive model to predict an expected value range for the network-site analytics metric for a next time step after the segment and, based on the expected value range, determining whether an actual value for the network-site analytics metric for the next time step is an anomalous value.
Abstract translation: 描述了使用预测建模的网站测量中异常检测的方法和装置。 一种方法包括获得给定时间范围的时间序列数据，其中所述时间序列数据包括在给定时间范围内的多个连续时间步骤中的每一个的网站分析度量的值。 该方法包括基于时间序列数据的至少一个段来生成针对网站分析度量的预测模型。 所述方法包括使用所述预测模型来预测所述段之后的下一时间步长的所述网站分析度量的期望值范围，并且基于所述预期值范围来确定所述网站分析度量的实际值是否为 下一个时间步长是一个异常的价值。
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公开(公告)号：US09197511B2
公开(公告)日：2015-11-24
申请号：US13651176
申请日：2012-10-12
Applicant: Adobe Systems Incorporated
Inventor： Craig M. Mathis
CPC classification number: H04L63/1425 , H04L41/145 , H04L41/147 , H04L43/08
Abstract: Methods and apparatus for anomaly detection in network-site metrics using predictive modeling are described. A method comprises obtaining time-series data for a given time range, wherein the time-series data comprises values for a network-site analytics metric for each of a plurality of sequential time steps across the given time range. The method includes generating a predictive model for the network-site analytics metric based on at least a segment of the time-series data. The method includes using the predictive model to predict an expected value range for the network-site analytics metric for a next time step after the segment and, based on the expected value range, determining whether an actual value for the network-site analytics metric for the next time step is an anomalous value.
Abstract translation: 描述了使用预测建模的网站测量中异常检测的方法和装置。 一种方法包括获得给定时间范围的时间序列数据，其中所述时间序列数据包括在给定时间范围内的多个连续时间步骤中的每一个的网站分析度量的值。 该方法包括基于时间序列数据的至少一个段来生成针对网站分析度量的预测模型。 所述方法包括使用所述预测模型来预测所述段之后的下一时间步长的所述网站分析度量的期望值范围，并且基于所述预期值范围来确定所述网站分析度量的实际值是否为 下一个时间步长是一个异常的价值。
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公开(公告)号：US20140108640A1
公开(公告)日：2014-04-17
申请号：US13651176
申请日：2012-10-12
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Craig M. Mathis
IPC: G06F15/173
CPC classification number: H04L63/1425 , H04L41/145 , H04L41/147 , H04L43/08
Abstract: Methods and apparatus for anomaly detection in network-site metrics using predictive modeling are described. A method comprises obtaining time-series data for a given time range, wherein the time-series data comprises values for a network-site analytics metric for each of a plurality of sequential time steps across the given time range. The method includes generating a predictive model for the network-site analytics metric based on at least a segment of the time-series data. The method includes using the predictive model to predict an expected value range for the network-site analytics metric for a next time step after the segment and, based on the expected value range, determining whether an actual value for the network-site analytics metric for the next time step is an anomalous value.
Abstract translation: 描述了使用预测建模的网站测量中异常检测的方法和装置。 一种方法包括获得给定时间范围的时间序列数据，其中所述时间序列数据包括在给定时间范围内的多个连续时间步骤中的每一个的网站分析度量的值。 该方法包括基于时间序列数据的至少一个段来生成针对网站分析度量的预测模型。 所述方法包括使用所述预测模型来预测所述段之后的下一时间步长的所述网站分析度量的期望值范围，并且基于所述预期值范围来确定所述网站分析度量的实际值是否为 下一个时间步长是一个异常的价值。
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