-
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.
-
公开(公告)号：US12229804B2
公开(公告)日：2025-02-18
申请号：US17366910
申请日：2021-07-02
Applicant: ADOBE INC.
Inventor： Lei Zhang , Lijun Yu , Jun He , Zhenyu Yan , Wuyang Dai
IPC: G06Q30/02 , G06Q30/0272
Abstract: Methods and systems are provided for improved electronic communication campaign technologies, which can automatically balance objectives or goals of an electronic communication campaign against an overall opt-out rate for the electronic communication campaign. An electronic communications frequency optimizer can generate individual contact frequencies for individual email recipients. Embodiments can avoid unnecessary or counterproductive communications while achieving overall campaign goals, and can use processes to improve the efficiency of systems. In some cases, embodiments cluster communication recipients into different groups based on their past actions, then optimizes the communication contact frequency on different groups, to avoid performing optimization directly on millions of recipients. Some embodiments automatically self-update, for example with recipients' recent responses, to generate and/or implement campaign communication schedules on an individual level.
-
公开(公告)号：US20250036706A1
公开(公告)日：2025-01-30
申请号：US18226079
申请日：2023-07-25
Applicant: Adobe Inc.
Inventor： Yuan Yuan , Bei Huang , Lijing Wang , Yancheng Li , Bowen Wang , Jin Xu , Zhenyu Yan , Qilong Yuan
IPC: G06F16/957 , G06F9/451 , H04L67/50
Abstract: In some embodiments, a computing system extrapolates aggregated interaction data associated with users of an online platform by applying a mixed granularity model to generate extrapolated interaction data for each user in the users. The aggregated interaction data includes a total number of occurrences of a target action performed by the users with respect to the online platform. The extrapolated data includes a series of actions leading to the target action for each user. The computing system identifies an impact of each action in the series of actions for each user on leading to the target action based, at least in part, upon the extrapolating a series of actions associated with the user. User interfaces presented on the online platform can be modified based on at least the identified impacts to improve customization of the user interfaces to the users or enhance an experience of the users.
-
14.发明公开公开(公告)号：US20240311643A1
公开(公告)日：2024-09-19
申请号：US18185828
申请日：2023-03-17
Applicant: Adobe Inc.
Inventor： Bowen Wang , Yuan Yuan , Bei Huang , Lijing Wang , Yancheng Li , Jin Xu , Qilong Yuan , Zhenyu Yan
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a modified analytics prediction machine learning model using an iterative transfer learning approach. For example, the disclosed systems generate an initial version of an analytics prediction machine learning model for predicting an analytics metric according to learned parameters. In some embodiments, the disclosed systems determine expected data channel contributions for the analytics metric according to prior data. Additionally, in some cases, the disclosed systems generate a modified analytics prediction machine learning model by iteratively updating model parameters such that predicted data channel contributions are within a threshold similarity of expected data channel contributions.
-
公开(公告)号：US12021812B2
公开(公告)日：2024-06-25
申请号：US17509885
申请日：2021-10-25
Applicant: ADOBE INC.
Inventor： Lijun Yu , Wuyang Dai , Jun He , Hsiang-Yu Yang , Zhenyu Yan
IPC: G06F15/16 , G06F18/10 , G06F18/21 , G06F18/214 , G06F18/2413 , G06N20/00 , H04L51/222
CPC classification number: H04L51/222 , G06F18/10 , G06F18/214 , G06F18/217 , G06F18/24147 , G06N20/00
Abstract: Methods and systems are provided for facilitating time zone prediction using electronic communication data. Electronic message data associated with a message recipient of electronic communications is obtained. The electronic message data includes message delivery data associated with an electronic message and message response data associated with a response, by the message recipient, to a received electronic message. Using a machine learning model and based on the message delivery data and the message response data, a time-zone score is determined for a time zone. Such a time-zone score can indicate a probability the time zone corresponds with the message recipient. Based on the time-zone score, the time zone is identified as corresponding with the message recipient.
-
Patent Agency Ranking
公开(公告)号：US20220138781A1
公开(公告)日：2022-05-05
申请号：US17577818
申请日：2022-01-18
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.
-
公开(公告)号：US11263649B2
公开(公告)日：2022-03-01
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.
-
18.发明授权公开(公告)号：US11222268B2
公开(公告)日：2022-01-11
申请号：US15454799
申请日：2017-03-09
Applicant: Adobe Inc.
Inventor： Zhenyu Yan , Yang Wang , Arava Sai Kumar , Abhishek Pani
Abstract: 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.
-
19.发明申请公开(公告)号：US20200327419A1
公开(公告)日：2020-10-15
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.
-
公开(公告)号：US20200151746A1
公开(公告)日：2020-05-14
申请号：US16191289
申请日：2018-11-14
Applicant: ADOBE INC.
Inventor： Xiang Wu , Zhenyu Yan , Yi-Hong Kuo , Wuyang Dai , Polina Bartik , Abhishek Pani
Abstract: An improved analytics system generates actionable KPI-based customer segments. The analytics system determines predicted outcomes for a key performance indicator (KPI) of interest and a contribution value for each variable indicating an extent to which each variable contributes to predicted outcomes. Topics are generated by applying a topic model to the contribution values for the variables. Each topic comprises a group of variables with a contribution level for each variable that indicates the importance of each variable to the topic. User segments are generated by assigning each user to a topic based on attribution levels output by the topic model.
-
-
-
-
-
-
-
-
-
Search Results
44
records
(took 0.022 seconds)
