公开(公告)号：US11341516B2

公开(公告)日：2022-05-24

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 ,  H04L51/56 ,  G06Q10/10

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：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：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：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.