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    Machine-learning based multi-step engagement strategy modification
    3.
    发明授权
    Machine-learning based multi-step engagement strategy modification 有权

    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.

    Providing insights and suggestions for journeys
    6.
    发明授权
    Providing insights and suggestions for journeys 有权

    公开(公告)号：US11861636B2

    公开(公告)日：2024-01-02

    申请号：US16910357

    申请日：2020-06-24

    Applicant: ADOBE INC.

    Inventor： Pankhri Singhai ,  Piyush Gupta ,  Balaji Krishnamurthy ,  Jayakumar Subramanian ,  Nikaash Puri

    IPC: G06Q30/02 ,  G06Q30/0204 ,  G06N20/00 ,  G06Q30/0201 ,  G06Q10/0633

    CPC classification number: G06Q30/0205 ,  G06N20/00 ,  G06Q10/0633 ,  G06Q30/0201

    Abstract: Methods and systems are provided for generating and providing insights associated with a journey. In embodiments described herein, journey data associated with a journey is obtained. A journey can include journey paths indicating workflows through which audience members can traverse. The journey data can include audience member attributes (e.g., demographics) and labels indicating journey paths traversed by audience members. A set of audience segments are determined that describe a set of audience members traversing a particular journey path. The set of audience segments can be determined using the journey data to train a segmentation model and, thereafter, analyzing the segmentation model to identify patterns that indicate audience segments associated with the particular journey path. An indication of the set of audience segments that describe the set of audience members traversing the particular journey path can be provided for display.

    Machine-Learning Based Multi-Step Engagement Strategy Modification
    7.
    发明申请
    Machine-Learning Based Multi-Step Engagement Strategy Modification 有权

    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.

    Machine-Learning Based Multi-Step Engagement Strategy Generation and Visualization
    8.
    发明申请
    Machine-Learning Based Multi-Step Engagement Strategy Generation and Visualization 审中-公开

    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.

    Machine-Learning Based Multi-Step Engagement Strategy Modification
    9.
    发明申请
    Machine-Learning Based Multi-Step Engagement Strategy Modification 审中-公开

    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.

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