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Patent Agency Ranking
1.发明授权公开(公告)号：US11227226B2
公开(公告)日：2022-01-18
申请号：US15783223
申请日：2017-10-13
Applicant: Adobe Inc.
Inventor： Eugene Chen , Zhenyu Yan , Xiaojing Dong
Abstract: Methods, systems, and computer readable storage media are disclosed for generating joint-probabilistic ensemble forecasts for future events based on a plurality of different prediction models for the future events. For example, in one or more embodiments the disclosed system determines error values for various predictions from a plurality of different prediction models (i.e., “forecasters”) for previous events. Moreover, in one or more embodiments the system generates an error probability density function by mapping the error values to an error space and applying a kernel density estimation. Furthermore, the system can apply the error probability density function(s) to a plurality of predictions from the forecasters for a future event to generate a likelihood function and a new prediction for the future event.
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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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3.发明申请公开(公告)号：US20190114554A1
公开(公告)日：2019-04-18
申请号：US15783223
申请日：2017-10-13
Applicant: Adobe Inc.
Inventor： Eugene Chen , Zhenyu Yan , Xiaojing Dong
Abstract: Methods, systems, and computer readable storage media are disclosed for generating joint-probabilistic ensemble forecasts for future events based on a plurality of different prediction models for the future events. For example, in one or more embodiments the disclosed system determines error values for various predictions from a plurality of different prediction models (i.e., “forecasters”) for previous events. Moreover, in one or more embodiments the system generates an error probability density function by mapping the error values to an error space and applying a kernel density estimation. Furthermore, the system can apply the error probability density function(s) to a plurality of predictions from the forecasters for a future event to generate a likelihood function and a new prediction for the future event.
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公开(公告)号：US20240386243A1
公开(公告)日：2024-11-21
申请号：US18320466
申请日：2023-05-19
Applicant: Adobe Inc.
Inventor： Hsin-Ya Lou , Xiaojing Dong , Yuting Chen
IPC: G06N3/045
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for predicting account interactions with computing applications. In particular, in one or more embodiments, the disclosed systems determine user account data associated with one or more computing applications for a user account. Additionally, in some embodiments, the disclosed systems generate, based on the user account data, a transition matrix and an emission matrix corresponding to a plurality of hidden states of a hidden Markov model to customize the hidden Markov model for the user account. Furthermore, in some implementations, the disclosed systems determine, utilizing the customized hidden Markov model, one or more predicted account interaction metrics for the user account in connection with the one or more computing applications based on the transition matrix and the emission matrix.
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公开(公告)号：US20200019984A1
公开(公告)日：2020-01-16
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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