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：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：US11816272B2

公开(公告)日：2023-11-14

申请号：US17656782

申请日：2022-03-28

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 ,  G06F3/0481 ,  H04L67/50

CPC classification number: G06F3/017 ,  G06F3/0481 ,  G06F3/0484 ,  G06N3/08 ,  H04L67/535

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：US20220221939A1

公开(公告)日：2022-07-14

申请号：US17656782

申请日：2022-03-28

Applicant: Adobe Inc.

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

IPC: 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.