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公开(公告)号：US10990889B2
公开(公告)日：2021-04-27
申请号：US15812568
申请日：2017-11-14
Applicant: Adobe Inc.
Inventor： Bo Peng , Julia Viladomat , Zhenyu Yan , Abhishek Pani
Abstract: 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.
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22.发明申请公开(公告)号：US20200027103A1
公开(公告)日：2020-01-23
申请号：US16042882
申请日：2018-07-23
Applicant: Adobe Inc.
Inventor： Jin Xu , Zhenyu Yan , Wenqing Yang , Tianyu Wang , Abhishek Pani
Abstract: Prioritization techniques and systems are described that utilize a historical purchase sequence and customer features to prioritize products and services to generate product and service recommendations. In an example, feature data describing a customer and historical purchase data for the customer is received that indicates products or services purchased by the customer. The historical purchase data further includes indicators of when the products or services were purchased by the customer. Then, probabilities of future purchases by the customer of additional products are determined by classifying the additional products using a multiclass classification. The multiclass classification is based on the historical purchase data and the feature data describing the customer. Next, a ranking of the additional products is generated based on the determined probabilities of future purchases. The ranking of the additional products is output in a user interface based on the determined probabilities.
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公开(公告)号：US20190303980A1
公开(公告)日：2019-10-03
申请号：US15938449
申请日：2018-03-28
Applicant: Adobe Inc.
Inventor： Zhenyu Yan , Chen Dong , Abhishek Pani , Yuan Yuan
Abstract: The present disclosure includes systems, methods, and non-transitory computer readable media that train and utilize multi-phase learning models to predict performance during digital content campaigns and provide digital content to client devices in a real-time bidding environment. In particular, one or more embodiments leverage organizational structure of digital content campaigns to train two learning models, utilizing different data sources, to predict performance, generate bid responses, and provide digital content to client devices. For example, the disclosed systems can train a first performance learning model in an offline mode utilizing parent-level historical data. Then, in an online mode, the disclosed systems can train a second performance learning model utilizing child-level historical data and utilize the first performance learning model and the second performance learning model to generate bid responses and bid amounts in a real-time bidding environment.
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公开(公告)号：US11645542B2
公开(公告)日：2023-05-09
申请号：US16384558
申请日：2019-04-15
Applicant: Adobe Inc.
Inventor： Lei Zhang , Jun He , Zhenyu Yan , Wuyang Dai , Abhishek Pani
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a target distribution schedule for providing electronic communications based on predicted behavior rates by utilizing a genetic algorithm and one or more objective functions. For example, the disclosed systems can generate predicted behavior rates by training and utilizing one or more behavior prediction models. Based on the predicted behavior rates, the disclosed systems can further utilize a genetic algorithm to apply objective functions to generate one or more candidate distribution schedules. In accordance with the genetic algorithm, the disclosed systems can select a target distribution schedule for a particular user/client device. The disclosed systems can thus provide one or more electronic communications to individual users based on respective target distribution schedules.
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25.发明授权公开(公告)号：US11514515B2
公开(公告)日：2022-11-29
申请号：US16037700
申请日：2018-07-17
Applicant: Adobe Inc.
Inventor： Maoqi Xu , Zhenyu Yan , Jin Xu , Abhishek Pani
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for using reject inference to generate synthetic data for modifying lead scoring models. For example, the disclosed system identifies an original dataset corresponding to an output of a lead scoring model that generates scores for a plurality of prospects to indicate a likelihood of success of prospects of the plurality of prospects. In one or more embodiments, the disclosed system selects a reject inference model by performing simulations on historical prospect data associated with the original dataset. Additionally, the disclosed system uses the selected reject inference model to generate an imputed dataset by generating synthetic outcome data representing simulated outcomes of rejected prospects in the original dataset. The disclosed system then uses the imputed dataset to modify the lead scoring model by modifying at least one parameter of the lead scoring model using the synthetic outcome data.
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Patent Agency Ranking
26.发明授权公开(公告)号：US11080764B2
公开(公告)日：2021-08-03
申请号：US15458484
申请日：2017-03-14
Applicant: ADOBE INC.
Inventor： Chen Dong , Zhenyu Yan , Pinak Panigrahi , Xiang Wu , Abhishek Pani
IPC: G06Q30/02 , G06N7/00 , G06F16/951 , G06F16/245 , G06Q30/08
Abstract: A bid management system generates estimated performance metrics at the bid unit level to facilitate bid optimization. The bid management system includes a hierarchical feature selection and prediction approach. Feature selection is performed by aggregating historical performance metrics to a higher hierarchical level and testing features for statistical significance. Features for which a significance level satisfies a significance threshold are selected for prediction analysis. The prediction analysis uses a statistical model based on selected features to generate estimated performance metrics at the bid unit level. In some implementations, the prediction analysis uses a hierarchical Bayesian smoothing method in which estimated performance metrics are calculated at the bid unit level using a posterior probability distribution derived from a prior probability distribution determined based on aggregated performance metrics and a likelihood function that takes into account historical performance metrics from the bid unit level based on the selected features.
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公开(公告)号：US10956930B2
公开(公告)日：2021-03-23
申请号：US16034232
申请日：2018-07-12
Applicant: Adobe Inc.
Inventor： Yuan Yuan , Zhenyu Yan , Yiwen Sun , Xiaojing Dong , Chen Dong , Abhishek Pani
Abstract: Dynamic Hierarchical Empirical Bayes techniques and systems are described that are implemented to control output of digital content. In one example, a system identifies splitting variables included in data. An amount of loss is then determined for each of the identified splitting variables by the system using a loss function. Based on the determined amounts of loss, the system selects at least one splitting variable from the plurality of splitting variables that are to be used to partition data in a respective node, e.g., a parent node to form a plurality of child nodes. The system, for instance, may select the splitting variable that minimizes the cost, i.e., has the lowest amount of cost. The selected splitting variable is then employed by the system to generate at least one hierarchical level of the hierarchical structure of the statistical model by partitioning data from the parent node into respective child nodes.
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公开(公告)号：US10748178B2
公开(公告)日：2020-08-18
申请号：US14874298
申请日：2015-10-02
Applicant: ADOBE INC.
Inventor： Zhenyu Yan , Xiang Wu , Chen Dong , Abhishek Pani
Abstract: In various implementations, analytics data is received that indicates performance of bid targets for historical bids made in one or more content delivery auctions. Baseline prediction models are maintained for the bid targets. The baseline prediction models use the analytics data to predict performance of the bid targets in one or more future instances of at least one content delivery auction. A presentation context factor model is maintained that provides an adjustment factor that quantifies a contribution of a subset of a plurality of presentation context factors associated with the bid targets to performance of the bid targets based on predicted values from the baseline prediction models. A contextual predicted value is computed using the adjustment factor for the subset of the plurality of presentation context factors. A performance prediction is transmitted to a user device and is based on at least the contextual predicted value.
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公开(公告)号：US20200027102A1
公开(公告)日：2020-01-23
申请号：US16042770
申请日：2018-07-23
Applicant: Adobe Inc.
Inventor： Jin Xu , Zhenyu Yan , Wenqing Yang , Tianyu Wang , Abhishek Pani
IPC: G06Q30/02
Abstract: Quantitative rating systems and techniques are described that prioritize customers by propensity to buy and buy size to generate customer ratings. In one example, a propensity model is used to determine a likelihood of a potential customer to purchase a product, and a projected timeframe buy size for the potential customer is determined. An expected value for the potential customer is generated by combining the likelihood of the potential customer to purchase the product and the projected timeframe buy size. In another example, a ratio model of annualized recurring revenue (ARR) is used to determine a timeframe buy size for an existing customer in consecutive time frames. An upsell opportunity for the existing customer is determined based on the timeframe buy size less an ARR for a current time frame for the existing customer. A rating of the potential or existing customer is output in a user interface.
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公开(公告)号：US10521828B2
公开(公告)日：2019-12-31
申请号：US15176760
申请日：2016-06-08
Applicant: ADOBE INC.
Inventor： Deepak Pai , Trung Nguyen , Sy Bor Wang , Jose Mathew , Abhishek Pani , Neha Gupta
IPC: G06Q30/02
Abstract: Systems and methods are disclosed herein for distributing online ads with electronic content according to online ad request targeting parameters. One embodiment of this technique involves placing online test ads across multiple online ad request dimensions and tracking a performance metric for the online test ads. The performance of the online ad request dimensions is estimated based on the tracking of the performance metric for the online test ads and online ad request targeting parameters are established for spending a budget of a campaign to place online ads in response to online ad requests having particular online ad request dimensions. Online ads are then distributed based on using the online ad request targeting parameters to select online ad requests.
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