公开(公告)号：US11816696B2

公开(公告)日：2023-11-14

申请号：US17355907

申请日：2021-06-23

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nupur Kumari ,  Nikaash Puri ,  Mayank Singh ,  Eshita Shah ,  Balaji Krishnamurthy ,  Akash Rupela

IPC: G06Q30/00 ,  G06Q30/0242 ,  G06Q30/0251 ,  G06N20/00 ,  G06N5/00 ,  G05B19/418

CPC classification number: G06Q30/0244 ,  G06N5/00 ,  G06N20/00 ,  G06Q30/0242 ,  G06Q30/0254 ,  G06Q30/0255 ,  G06Q30/0264

Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

公开(公告)号：US11109084B2

公开(公告)日：2021-08-31

申请号：US16694612

申请日：2019-11-25

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nikaash Puri ,  Eshita Shah ,  Balaji Krishnamurthy ,  Nupur Kumari ,  Mayank Singh ,  Akash Rupela

IPC: H04N21/25 ,  H04N21/2668 ,  H04N21/258 ,  H04N21/475 ,  G06N20/00 ,  H04N21/81 ,  G06Q30/02

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.

公开(公告)号：US20210319473A1

公开(公告)日：2021-10-14

申请号：US17355907

申请日：2021-06-23

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nupur Kumari ,  Nikaash Puri ,  Mayank Singh ,  Eshita Shah ,  Balaji Krishnamurthy ,  Akash Rupela

IPC: G06Q30/02 ,  G06N20/00

Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

公开(公告)号：US20200053403A1

公开(公告)日：2020-02-13

申请号：US16057729

申请日：2018-08-07

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nikaash Puri ,  Eshita Shah ,  Balaji Krishnamurthy ,  Nupur Kumari ,  Mayank Singh ,  Akash Rupela

IPC: H04N21/25 ,  G06N99/00 ,  H04N21/475 ,  H04N21/258 ,  H04N21/2668

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.

公开(公告)号：US20200051118A1

公开(公告)日：2020-02-13

申请号：US16057743

申请日：2018-08-07

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nupur Kumari ,  Nikaash Puri ,  Mayank Singh ,  Eshita Shah ,  Balaji Krishnamurthy ,  Akash Rupela

IPC: G06Q30/02 ,  G06N99/00

Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

公开(公告)号：US11107115B2

公开(公告)日：2021-08-31

申请号：US16057743

申请日：2018-08-07

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nupur Kumari ,  Nikaash Puri ,  Mayank Singh ,  Eshita Shah ,  Balaji Krishnamurthy ,  Akash Rupela

IPC: G06Q30/00 ,  G06Q30/02 ,  G06N20/00 ,  G05B19/418

Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

公开(公告)号：US10609434B2

公开(公告)日：2020-03-31

申请号：US16057729

申请日：2018-08-07

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nikaash Puri ,  Eshita Shah ,  Balaji Krishnamurthy ,  Nupur Kumari ,  Mayank Singh ,  Akash Rupela

IPC: H04N21/25 ,  H04N21/2668 ,  H04N21/258 ,  H04N21/475 ,  G06N20/00 ,  H04N21/81 ,  G06Q30/02

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.

公开(公告)号：US20200092593A1

公开(公告)日：2020-03-19

申请号：US16694612

申请日：2019-11-25

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nikaash Puri ,  Eshita Shah ,  Balaji Krishnamurthy ,  Nupur Kumari ,  Mayank Singh ,  Akash Rupela

IPC: H04N21/25 ,  H04N21/258 ,  G06Q30/02 ,  H04N21/475 ,  H04N21/81 ,  G06N20/00 ,  H04N21/2668

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.