公开(公告)号：US20190278378A1

公开(公告)日：2019-09-12

申请号：US15917052

申请日：2018-03-09

Applicant: Adobe Inc.

Inventor： Zhenyu Yan ,  Fnu Arava Venkata Kesava Sai Kumar ,  Chen Dong ,  Abhishek Pani ,  Ning Li

IPC: G06F3/01 ,  G06N3/08 ,  G06F3/0481 ,  H04L29/08 ,  G06F3/0484

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify and measure performance of touchpoints in digital content campaigns. For example, a deep learning attribution system trains a touchpoint attribution attention neural network using touchpoint sequences, which include user interactions with content via one or more digital media channels. In one or more embodiments, the deep learning attribution system utilizes the trained touchpoint attribution attention neural network to determine touchpoint attributions of touchpoints in a target touchpoint sequence. In addition, the deep learning attribution system can utilize the trained touchpoint attribution attention neural network to generate conversion predictions for target touchpoint sequences and to provide targeted digital content over specific digital media channels to client devices of individual users.

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

IPC: G06N7/00 ,  G06N5/04

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.

公开(公告)号：US20250068800A1

公开(公告)日：2025-02-27

申请号：US18455005

申请日：2023-08-24

Applicant: ADOBE INC.

Inventor： Lei Zhang ,  Jun He ,  Zhenyu Yan ,  Roger K. Brooks

IPC: G06F30/27

Abstract: Systems and methods for pre-deployment user journey evaluation are described. Embodiments are configured to obtain a user journey including a plurality of touchpoints; generate a simulation agent including a plurality of attributes; generate a probability score for the simulation agent for each of the plurality of touchpoints based on the plurality of attributes using a machine learning model; perform a simulation of the user journey based on the probability score; and generate a text describing the user journey based on the simulation.

公开(公告)号：US20240143941A1

公开(公告)日：2024-05-02

申请号：US18050285

申请日：2022-10-27

Applicant: Adobe Inc.

Inventor： Suofei Wu ,  Jun He ,  Zhenyu Yan

IPC: G06F40/40 ,  G06F40/295 ,  G06N3/08

CPC classification number: G06F40/40 ,  G06F40/295 ,  G06N3/08

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize machine learning to generate subject lines from subject line keywords. In one or more embodiments, the disclosed systems receive, from a client device, one or more subject line keywords. Additionally, the disclosed systems generate, utilizing a subject generation machine-learning model having learned parameters, a subject line by selecting one or more words for the subject line from a word distribution based on the one or more subject line keywords. The disclosed systems further provide, for display on the client device, the subject line.

公开(公告)号：US11710065B2

公开(公告)日：2023-07-25

申请号：US16371460

申请日：2019-04-01

Applicant: Adobe Inc.

Inventor： Jun He ,  Shiyuan Gu ,  Zhenyu Yan ,  Wuyang Dai ,  Yi-Hong Kuo ,  Abhishek Pani

IPC: G06F40/279 ,  G06N20/00 ,  G06F18/2415 ,  G06F18/214 ,  G06N7/01

CPC classification number: G06N20/00 ,  G06F18/214 ,  G06F18/24155 ,  G06F40/279 ,  G06N7/01

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times to provide electronic communications based on predicted response rates by utilizing a Bayesian approach and multi-armed bandit algorithms. For example, the disclosed systems can generate predicted response rates by training and utilizing one or more response rate prediction models to generate a weighted combination of user-specific response information and population-specific response information. The disclosed systems can further utilize a Bayes upper-confidence-bound send time model to determine send times that are more likely to elicit user responses based on the predicted response rates and further based on exploration and exploitation considerations. In addition, the disclosed systems can update the response rate prediction models and/or the Bayes upper-confidence-bound send time model based on providing additional electronic communications and receiving additional responses to modify model weights.

公开(公告)号：US11636499B2

公开(公告)日：2023-04-25

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: G06Q30/0202 ,  G06Q30/0204

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：US20220309523A1

公开(公告)日：2022-09-29

申请号：US17664601

申请日：2022-05-23

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Suofei Wu ,  Chang Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Shengyun Peng

IPC: G06Q30/02 ,  G06Q10/10 ,  H04L51/56

Abstract: Introduced here are approaches for identifying the optimal send time for messages by accounting for hidden confounders, such as the content of those messages, delivery channel, etc. These approaches use a causal inference framework to discover and then remove the impact of hidden confounders. These approaches may be employed by a marketing and analytics platform (or simply “marketing platform”) that may be used to design, implement, or review digital marketing campaigns. The marketing platform can consider the send time as a treatment and then employ machine learning (ML) models that consider the send time, features of the recipient, and hidden confounders to produce a ranked series of send times with the effect of the hidden confounders marginalized. Approaches to performing offline evaluations that mimic A/B tests using data related to existing field experiments are also introduced here.

公开(公告)号：US11288709B2

公开(公告)日：2022-03-29

申请号：US15938449

申请日：2018-03-28

Applicant: Adobe Inc.

Inventor： Zhenyu Yan ,  Chen Dong ,  Abhishek Pani ,  Yuan Yuan

IPC: G06Q30/02 ,  G06N20/00

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.

公开(公告)号：US11287894B2

公开(公告)日：2022-03-29

申请号：US15917052

申请日：2018-03-09

Applicant: Adobe Inc.

Inventor： Zhenyu Yan ,  Fnu Arava Venkata Kesava Sai Kumar ,  Chen Dong ,  Abhishek Pani ,  Ning Li

IPC: G06Q30/00 ,  G06F3/01 ,  G06N3/08 ,  G06F3/0484 ,  H04L67/50 ,  G06F3/0481

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify and measure performance of touchpoints in digital content campaigns. For example, a deep learning attribution system trains a touchpoint attribution attention neural network using touchpoint sequences, which include user interactions with content via one or more digital media channels. In one or more embodiments, the deep learning attribution system utilizes the trained touchpoint attribution attention neural network to determine touchpoint attributions of touchpoints in a target touchpoint sequence. In addition, the deep learning attribution system can utilize the trained touchpoint attribution attention neural network to generate conversion predictions for target touchpoint sequences and to provide targeted digital content over specific digital media channels to client devices of individual users.

公开(公告)号：US20210357952A1

公开(公告)日：2021-11-18

申请号：US16877385

申请日：2020-05-18

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Suofei Wu ,  Chang Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Shengyun Peng

IPC: G06Q30/02 ,  G06Q10/10 ,  H04L12/58

Abstract: Introduced here are approaches for identifying the optimal send time for messages by accounting for hidden confounders, such as the content of those messages, delivery channel, etc. These approaches use a causal inference framework to discover and then remove the impact of hidden confounders. These approaches may be employed by a marketing and analytics platform (or simply “marketing platform”) that may be used to design, implement, or review digital marketing campaigns. The marketing platform can consider the send time as a treatment and then employ machine learning (ML) models that consider the send time, features of the recipient, and hidden confounders to produce a ranked series of send times with the effect of the hidden confounders marginalized. Approaches to performing offline evaluations that mimic A/B tests using data related to existing field experiments are also introduced here.