公开(公告)号：US20210319473A1

公开(公告)日：2021-10-14

申请号：US17355907

申请日：2021-06-23

申请人： Adobe Inc.

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

IPC分类号： G06Q30/02 ,  G06N20/00

摘要： 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

申请人： Adobe Inc.

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

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

摘要： 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

申请人： Adobe Inc.

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

IPC分类号： G06Q30/02 ,  G06N99/00

摘要： 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：US11631205B2

公开(公告)日：2023-04-18

申请号：US17657255

申请日：2022-03-30

申请人： Adobe Inc.

发明人： Nupur Kumari ,  Piyush Gupta ,  Akash Rupela ,  Siddarth R ,  Balaji Krishnamurthy

IPC分类号： G06T11/20 ,  G06F17/11

摘要： The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate interactive visual shape representation of digital datasets. For example, the disclosed systems can generate an augmented nearest neighbor network graph from a sampled subset of digital data points using a nearest neighbor model and witness complex model. The disclosed system can further generate a landmark network graph based on the augmented nearest neighbor network graph utilizing a plurality of random walks. The disclosed systems can also generate a loop-augmented spanning network graph based on a partition of the landmark network graph by adding community edges between communities of landmark groups based on modularity and to complete community loops. Based on the loop-augmented spanning network graph, the disclosed systems can generate an interactive visual shape representation for display on a client device.

公开(公告)号：US20210327108A1

公开(公告)日：2021-10-21

申请号：US16850677

申请日：2020-04-16

申请人： Adobe Inc.

发明人： Nupur Kumari ,  Piyush Gupta ,  Akash Rupela ,  Siddarth R ,  Balaji Krishnamurthy

IPC分类号： G06T11/20 ,  G06F17/11

摘要： The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate interactive visual shape representation of digital datasets. For example, the disclosed systems can generate an augmented nearest neighbor network graph from a sampled subset of digital data points using a nearest neighbor model and witness complex model. The disclosed system can further generate a landmark network graph based on the augmented nearest neighbor network graph utilizing a plurality of random walks. The disclosed systems can also generate a loop-augmented spanning network graph based on a partition of the landmark network graph by adding community edges between communities of landmark groups based on modularity and to complete community loops. Based on the loop-augmented spanning network graph, the disclosed systems can generate an interactive visual shape representation for display on a client device.

公开(公告)号：US20200159371A1

公开(公告)日：2020-05-21

申请号：US16193475

申请日：2018-11-16

申请人： Adobe Inc.

发明人： Harpreet Singh ,  Balaji Krishnamurthy ,  Akash Rupela

IPC分类号： G06F3/0482 ,  G06F17/22

摘要： In some embodiments, a configuration management application accesses configuration data for a multi-target website. The configuration management application provides the user interface including a timeline area and a page display area. The timeline area is configured to display timeline entries corresponding to configurations of the multi-target website. Based on a selection of a timeline entry, the page display area is configured to display a webpage configuration corresponding to the selected timeline entry. In addition, the page display area is configured to display graphical annotations indicating interaction metrics for the configured page regions. In some cases, the timeline entries, configurations, and interaction metrics are determined based on a selection of a target segment for the multi-target website.

公开(公告)号：US11816696B2

公开(公告)日：2023-11-14

申请号：US17355907

申请日：2021-06-23

申请人： Adobe Inc.

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

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

CPC分类号： G06Q30/0244 ,  G06N5/00 ,  G06N20/00 ,  G06Q30/0242 ,  G06Q30/0254 ,  G06Q30/0255 ,  G06Q30/0264

摘要： 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：US20220230369A1

公开(公告)日：2022-07-21

申请号：US17657255

申请日：2022-03-30

申请人： Adobe Inc.

发明人： Nupur Kumari ,  Piyush Gupta ,  Akash Rupela ,  Siddarth R ,  Balaji Krishnamurthy

IPC分类号： G06T11/20 ,  G06F17/11

摘要： The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate interactive visual shape representation of digital datasets. For example, the disclosed systems can generate an augmented nearest neighbor network graph from a sampled subset of digital data points using a nearest neighbor model and witness complex model. The disclosed system can further generate a landmark network graph based on the augmented nearest neighbor network graph utilizing a plurality of random walks. The disclosed systems can also generate a loop-augmented spanning network graph based on a partition of the landmark network graph by adding community edges between communities of landmark groups based on modularity and to complete community loops. Based on the loop-augmented spanning network graph, the disclosed systems can generate an interactive visual shape representation for display on a client device.

公开(公告)号：US11109084B2

公开(公告)日：2021-08-31

申请号：US16694612

申请日：2019-11-25

申请人： Adobe Inc.

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

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

摘要： 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：US10726325B2

公开(公告)日：2020-07-28

申请号：US15486862

申请日：2017-04-13

申请人： Adobe Inc.

发明人： Balaji Krishnamurthy ,  Piyush Gupta ,  Nupur Kumari ,  Akash Rupela

IPC分类号： G06N3/04 ,  G06N3/08

摘要： Disclosed systems and methods generate user-session representation vectors from data generated by user interactions with online services. A transformation application executing on a computing device receives interaction data, which is generated by user devices interacting with an online service. The transformation application separates the interaction data into session datasets. The transformation involves normalizing the session datasets by modifying the rows within each session dataset by removing event identifiers and time stamps. The application transforms each normalized session dataset into a respective user-session representation vector. The application outputs the user-session representation vectors.