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1. 发明申请

US20190147356A1 GENERATING A PREDICTIVE BEHAVIOR MODEL FOR PREDICTING USER BEHAVIOR USING UNSUPERVISED FEATURE LEARNING AND A RECURRENT NEURAL NETWORK 审中-公开
公开(公告)号：US20190147356A1
公开(公告)日：2019-05-16
申请号：US15812568
申请日：2017-11-14
申请人： Adobe Systems Incorporated
发明人： Bo Peng , Julia Viladomat , Zhenyu Yan , Abhishek Pani
IPC分类号： G06N7/00 , G06N3/08
摘要： Certain embodiments involve a model for predicting user behavior. For example, a system accesses user behavior data indicating various users' behaviors during intervals over various periods of time and target behavior data indicating a particular user behavior. The system associates each user with a label that indicates whether a user performed a particular action during or after a time period based on the target behavior data. The system uses the user behavior data to train various deep Restricted Boltzmann Machines (“RBM”) to generate representations of each user over each period of time that indicate the user behavior over the time period. The system generates a predictive model by connecting the RBMs into a deep recurrent neural network and uses the target behavior data associated with each user, along with the representations of each user, as input data to train the deep recurrent neural network to predict user behavior.

2. 发明申请

US20180260715A1 DETERMINING ALGORITHMIC MULTI-CHANNEL MEDIA ATTRIBUTION BASED ON DISCRETE-TIME SURVIVAL MODELING 审中-公开
公开(公告)号：US20180260715A1
公开(公告)日：2018-09-13
申请号：US15454799
申请日：2017-03-09
申请人： Adobe Systems Incorporated
发明人： Zhenyu Yan , Yang Wang , Arava Sai Kumar , Abhishek Pani
IPC分类号： G06N5/02 , G06N99/00
CPC分类号： G06N5/02 , G06N20/00 , H04L67/20 , H04L67/22
摘要： The present disclosure relates to a media attribution system that improves multi-channel media attribution by employing discrete-time survival modeling. In particular, the media attribution system uses event data (e.g., interactions and conversions) to generate positive and negative conversion paths, which the media attribution system uses to train an algorithmic attribution model. The media attribution system also uses the trained algorithmic attribution model to determine attribution scores for each interaction used in the conversion paths. Generally, the attribution score for an interaction indicates the effect the interaction has in influencing a user toward conversion.

3. 发明申请

US20160292196A1 Methods and Systems for Collaborated Change Point Detection in Time Series 审中-公开
标题翻译：时间序列中协调变点检测方法与系统
公开(公告)号：US20160292196A1
公开(公告)日：2016-10-06
申请号：US14675060
申请日：2015-03-31
申请人： Adobe Systems Incorporated
发明人： Zhenyu Yan , Jie Zhang , Abhishek Pani
IPC分类号： G06F17/30 , G06Q30/02
CPC分类号： G06Q30/0246 , G06F17/18 , G06K9/00557
摘要： Systems and methods disclosed herein use one or more auxiliary time series to more accurately identify change points in a target time series. This involves receiving data for the target time series and one or more auxiliary time series, where the one or more auxiliary time series have a relationship with the target time series. A combined auxiliary time series is generated based on the relationship between the target time series and the one or more auxiliary time series and the change point is detected for the target time series based on the target time series and the combined auxiliary time series. In one embodiment, time series data is received on an on-going basis. Recent time series data for the target time series and the one or more auxiliary time series is identified and used to detect the change point. The change point can be detected without using time series data older than the recent time series data.
摘要翻译：本文公开的系统和方法使用一个或多个辅助时间序列来更精确地识别目标时间序列中的变化点。这涉及到接收目标时间序列的数据和一个或多个辅助时间序列，其中一个或多个辅助时间序列与目标时间序列具有关系。基于目标时间序列与一个或多个辅助时间序列之间的关系产生组合的辅助时间序列，并且基于目标时间序列和组合辅助时间序列来检测目标时间序列的变化点。在一个实施例中，持续地接收时间序列数据。识别目标时间序列和一个或多个辅助时间序列的最近时间序列数据，并用于检测变化点。可以在不使用比最近的时间序列数据更早的时间序列数据的情况下检测变化点。

4. 发明授权

US10108978B2 Methods and systems for collaborated change point detection in time series 有权
公开(公告)号：US10108978B2
公开(公告)日：2018-10-23
申请号：US14675060
申请日：2015-03-31
申请人： Adobe Systems Incorporated
发明人： Zhenyu Yan , Jie Zhang , Abhishek Pani
IPC分类号： G06Q30/02 , G06F17/18 , G06K9/00
摘要： Systems and methods disclosed herein use one or more auxiliary time series to more accurately identify change points in a target time series. This involves receiving data for the target time series and one or more auxiliary time series, where the one or more auxiliary time series have a relationship with the target time series. A combined auxiliary time series is generated based on the relationship between the target time series and the one or more auxiliary time series and the change point is detected for the target time series based on the target time series and the combined auxiliary time series. In one embodiment, time series data is received on an on-going basis. Recent time series data for the target time series and the one or more auxiliary time series is identified and used to detect the change point. The change point can be detected without using time series data older than the recent time series data.

5. 发明申请

US20140156379A1 Method and Apparatus for Hierarchical-Model-Based Creative Quality Scores 审中-公开
标题翻译：基于模型的创意品质分数的方法和装置
公开(公告)号：US20140156379A1
公开(公告)日：2014-06-05
申请号：US13691442
申请日：2012-11-30
申请人： ADOBE SYSTEMS INCORPORATED
发明人： Abhishek Pani , Davide Imperati , Zhenyu Yan
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0242
摘要： Performance data for online advertisement creatives may be received. A hierarchical model of the online advertisement creatives may be generated based on correlations among the online advertisement creatives. The hierarchical model may be used to estimate a respective performance value for each of at least some of the plurality of online advertisement creatives based on the received performance data. A creative quality score may be determined, for those online advertising creatives whose performance values were estimated, based on the estimated performance values.
摘要翻译：可以收到在线广告素材的效果数据。可以基于在线广告创意之间的相关性来生成在线广告创意的层次模型。可以使用层次模型来基于所接收的演奏数据来估计多个在线广告创意中的至少一些的各自的性能值。根据估计的效果值，可以确定其性能值估算的在线广告素材的创意品质分数。

6. 发明申请

US20140149205A1 Method and Apparatus for an Online Advertising Predictive Model with Censored Data 审中-公开
标题翻译：用于具有检测数据的在线广告预测模型的方法和装置
公开(公告)号：US20140149205A1
公开(公告)日：2014-05-29
申请号：US13689298
申请日：2012-11-29
申请人： ADOBE SYSTEMS INCORPORATED
发明人： Zhenyu Yan , Andrew I. Schein
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0244
摘要： A censored observation for an online advertising campaign may be received for a first given time period. It may be determined that an amount spent on the online advertising campaign met a budget constraint such that the online advertising campaign was interrupted during the first given time period. Based on the received censored observation and one or more campaign parameters for the first given time period, a predictive model for predicting the result of a new online advertising campaign may be generated.
摘要翻译：对于在线广告活动的审查观察可以在第一给定时间段内被接收。可以确定，在线广告活动上花费的金额符合预算约束，使得在第一给定时间段内在线广告活动被中断。基于收到的审查观察和第一给定时间段的一个或多个活动参数，可以生成用于预测新的在线广告活动的结果的预测模型。

7. 发明申请

US20150227964A1 Revenue Estimation through Ensemble Modeling 审中-公开
标题翻译：通过组合建模的收入估算
公开(公告)号：US20150227964A1
公开(公告)日：2015-08-13
申请号：US14177385
申请日：2014-02-11
申请人： Adobe Systems Incorporated
发明人： Zhenyu Yan , Praveen Krishnakumar , Abhishek Pani , Anil Kamath , Suman Basetty , Kunal Kumar Jain
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0244 , G06Q30/0247 , G06Q30/0277
摘要： An ensemble model is described that is usable to predict revenue metrics for one or more keywords. The ensemble model may be formed using both a historical model and a user behavior model. In one or more implementations, weights are assigned to the historical model and/or the user behavior model based on one or more criteria. Various processing techniques of the ensemble model may utilize the historical model and the user behavior model to predict revenue metrics for one or more keywords.
摘要翻译：描述了可用于预测一个或多个关键字的收入指标的综合模型。可以使用历史模型和用户行为模型来形成集合模型。在一个或多个实现中，基于一个或多个标准将权重分配给历史模型和/或用户行为模型。集合模型的各种处理技术可以利用历史模型和用户行为模型来预测一个或多个关键字的收入指标。

8. 发明申请

US20140114746A1 Selection of Creatives Based on Performance Analysis and Predictive Modeling 审中-公开
标题翻译：基于性能分析和预测建模的广告素材选择
公开(公告)号：US20140114746A1
公开(公告)日：2014-04-24
申请号：US13656497
申请日：2012-10-19
申请人： ADOBE SYSTEMS INCORPORATED
发明人： Abhishek Pani , Zhenyu Yan
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0241 , G06Q10/067
摘要： Methods and systems for testing, comparing, and optimizing creatives with multiple factors in digital advertising is presented. Experiments are designed for testing a plurality of factors combined to form a creative. Ad campaigns are launched or continue according to the design and the creatives' campaign performance data is collected. Statistical modeling and hypothesis testing are used to predict the performance of the creatives based on the performance data. The creatives are compared based on the predictions and either activated or deactivated based upon their relationship to statistical confidence levels. All the stages are executed automatically and iteratively.
摘要翻译：介绍了在数字广告中测试，比较和优化具有多个因素的广告素材的方法和系统。实验设计用于测试组合以形成创意的多个因素。根据设计推出或继续广告系列，并收集广告素材的广告系列效果数据。统计建模和假设检验用于根据绩效数据预测广告素材的性能。广告素材根据预测进行比较，并根据其与统计置信水平的关系激活或停用。所有阶段都是自动和迭代执行的。

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