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：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.

公开(公告)号：US11038976B2

公开(公告)日：2021-06-15

申请号：US16564768

申请日：2019-09-09

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Abhishek Pani

IPC: H04L29/08 ,  G06N3/08 ,  G06N3/04 ,  G06F16/2457

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times for distributing digital content to client devices utilizing a recommendation system approach. For example, the disclosed systems can utilize a recommendation system model such as a matrix factorization model, a factorization machine model, and/or a neural network to implement collaborative filtering to generate predicted response rates for particular candidate send times. Based on the predicted response rates indicating likelihoods of receiving responses for particular send times, the disclosed system can generate a distribution schedule to provide electronic communications at one or more of the send times.

公开(公告)号：US20240168751A1

公开(公告)日：2024-05-23

申请号：US17989362

申请日：2022-11-17

Applicant: Adobe Inc.

Inventor： Luwan Zhang ,  Zhenyu Yan ,  Jun He ,  Hsiang-yu Yang ,  Cheng Zhong

IPC: G06F8/656 ,  G06N20/00

CPC classification number: G06F8/656 ,  G06N20/00

Abstract: In implementations of systems for estimating temporal occurrence of a binary state change, a computing device implements an occurrence system to compute a posterior probability distribution for temporal occurrences of binary state changes associated with client computing devices included in a group of client computing devices. The occurrence system determines probabilities of a binary state change associated with a client computing device included in the group of client computing devices based on the posterior probability distribution, and the probabilities correspond to future periods of time. A future period of time is identified based on a probability of the binary state change associated with the client computing device. The occurrence system generates a communication based on a communications protocol for transmission to the client computing device via a network at a period of time that correspond to the future period of time.

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：US20210075875A1

公开(公告)日：2021-03-11

申请号：US16564768

申请日：2019-09-09

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Abhishek Pani

IPC: H04L29/08 ,  G06F16/2457 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times for distributing digital content to client devices utilizing a recommendation system approach. For example, the disclosed systems can utilize a recommendation system model such as a matrix factorization model, a factorization machine model, and/or a neural network to implement collaborative filtering to generate predicted response rates for particular candidate send times. Based on the predicted response rates indicating likelihoods of receiving responses for particular send times, the disclosed system can generate a distribution schedule to provide electronic communications at one or more of the send times.

公开(公告)号：US20200311487A1

公开(公告)日：2020-10-01

申请号：US16371460

申请日：2019-04-01

Applicant: Adobe Inc.

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

IPC: G06K9/62 ,  G06F17/27 ,  G06N7/00

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.