公开(公告)号：US20190303964A1

公开(公告)日：2019-10-03

申请号：US16439523

申请日：2019-06-12

Applicant: Adobe Inc.

Inventor： Antoine Barbier ,  Greg Collison ,  Albert Lim ,  Nikhil Belsare ,  Alexander R. Hood

IPC: G06Q30/02 ,  H04N21/24 ,  H04N21/81

Abstract: Systems and methods are disclosed for enabling incremental reach for an advertising campaign, across multiple screens/channels. In some embodiments, a base TV/media plan is uploaded, and targeted exposed viewers are monitored. Unexposed target viewers are identified. Additional media channels that unexposed viewers use are found. Unexposed viewers are matched with pricing and media avails from one or more media directories. Media avails are then analyzed along with incremental on-target impressions. Targeted avails for unexposed viewers are then determined, based on lowest incremental cost and largest incremental reach are added to a current cross-screen plan. An analysis to find new unexposed viewers is then re-run to determine the next-most cost effective avails. The above steps are repeated or looped until a selected advertisement budget has been allocated. User interface embodiments for these methods are also disclosed.

公开(公告)号：US10949873B2

公开(公告)日：2021-03-16

申请号：US16439523

申请日：2019-06-12

Applicant: Adobe Inc.

Inventor： Antoine Barbier ,  Greg Collison ,  Albert Lim ,  Nikhil Belsare ,  Alexander R. Hood

IPC: G06Q30/00 ,  G06Q30/02 ,  H04N21/81 ,  H04N21/24

Abstract: Systems and methods are disclosed for enabling incremental reach for an advertising campaign, across multiple screens/channels. In some embodiments, a base TV/media plan is uploaded, and targeted exposed viewers are monitored. Unexposed target viewers are identified. Additional media channels that unexposed viewers use are found. Unexposed viewers are matched with pricing and media avails from one or more media directories. Media avails are then analyzed along with incremental on-target impressions. Targeted avails for unexposed viewers are then determined, based on lowest incremental cost and largest incremental reach are added to a current cross-screen plan. An analysis to find new unexposed viewers is then re-run to determine the next-most cost effective avails. The above steps are repeated or looped until a selected advertisement budget has been allocated. User interface embodiments for these methods are also disclosed.

公开(公告)号：US20230030341A1

公开(公告)日：2023-02-02

申请号：US17383114

申请日：2021-07-22

Applicant: Adobe Inc.

Inventor： Eunyee Koh ,  Tak Yeon Lee ,  Andrew Thomson ,  Vasanthi Holtcamp ,  Ryan Rossi ,  Fan Du ,  Caroline Kim ,  Tong Yu ,  Shunan Guo ,  Nedim Lipka ,  Shriram Venkatesh Shet Revankar ,  Nikhil Belsare

IPC: G06N3/08 ,  H04L12/26 ,  G06F40/186 ,  G06N3/04

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a dynamic user interface and machine learning tools to generate data-driven digital content and multivariate testing recommendations for distributing digital content across computer networks. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to generate digital recommendations at multiple development stages of digital communications that are targeted on particular performance metrics. For example, the disclosed systems utilize historical information and recipient profile data to generate recommendations for digital communication templates, fragment variants of content fragments, and content variants of digital content items. Ultimately, the disclosed systems generate multivariate testing recommendations incorporating selected fragment variants to intelligently narrow multivariate testing candidates and generate more meaningful and statistically significant multivariate testing results.

公开(公告)号：US20220405314A1

公开(公告)日：2022-12-22

申请号：US17354954

申请日：2021-06-22

Applicant: ADOBE INC.

Inventor： Fan Du ,  Zening Qu ,  Vasanthi Swaminathan Holtcamp ,  Tak Yeon Lee ,  Sungchul Kim ,  Saurabh Mahapatra ,  Sana Malik Lee ,  Ryan A. Rossi ,  Nikhil Belsare ,  Eunyee Koh ,  Andrew Thomson ,  Sumit Shekhar

IPC: G06F16/33 ,  G06F16/338 ,  G06N5/04

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data visualization generation. In one implementation, dataset intent data, visual design intent data, and insight intent data determined from a user input natural language query are obtained. A set of candidate intent recommendations is generated using various combinations of the dataset intent data, visual design intent data, and insight intent data. Each of the candidate intent recommendations is incorporated into a set of visualization templates to determine eligibility of the candidate intent recommendations. For eligible candidate intent recommendations, a score associated with a corresponding visualization template is determined. Based on the scores, a candidate intent recommendation and corresponding visualizations template is selected to use as a visual recommendation for presenting a data visualization.

公开(公告)号：US11995048B2

公开(公告)日：2024-05-28

申请号：US17036453

申请日：2020-09-29

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Yikun Xian ,  Sungchul Kim ,  Tak Yeon Lee ,  Nikhil Belsare ,  Shashi Kant Rai ,  Vasanthi Holtcamp ,  Thomas Jacobs ,  Duy-Trung T Dinh ,  Caroline Jiwon Kim

IPC: G06F16/00 ,  G06F16/21 ,  G06F18/2115 ,  G06F18/214 ,  G06F18/2431 ,  G06N3/08 ,  G06V30/262

CPC classification number: G06F16/213 ,  G06F18/2115 ,  G06F18/2148 ,  G06F18/2431 ,  G06N3/08 ,  G06V30/274

Abstract: Systems and methods for lifelong schema matching are described. The systems and methods include receiving data comprising a plurality of information categories, classifying each information category according to a schema comprising a plurality of classes, wherein the classification is performed by a neural network classifier trained based on a lifelong learning technique using a plurality of exemplar training sets, wherein each of the exemplar training sets includes a plurality of examples corresponding to one of the classes, and wherein the examples are selected based on a metric indicating how well each of the examples represents the corresponding class, and adding the data to a database based on the classification, wherein the database is organized according to the schema.

公开(公告)号：US11960517B2

公开(公告)日：2024-04-16

申请号：US17383051

申请日：2021-07-22

Applicant: Adobe Inc.

Inventor： Piyush Gupta ,  Binit Kumar Sinha ,  Eunyee Koh ,  Fan Du ,  Gaurav Makkar ,  Silky Kedawat ,  Subrahmanya Kumar Giliyaru ,  Vasanthi Holtcamp ,  Nikhil Belsare

IPC: G06F16/33 ,  G06F16/338 ,  G06F40/40

CPC classification number: G06F16/3344 ,  G06F16/3346 ,  G06F16/338 ,  G06F40/40

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate a dynamic cross-platform ask interface and utilize a cross-platform language processing model to provide platform-specific, contextually based responses to natural language digital text queries. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to extract registered intents from digital text queries to identify platform-specific configurations associated with the registered intents. Utilizing the platform-specific configurations, the disclosed systems can generate tailored platform-specific requests for information, as well as customized end-user search results that cause client devices to efficiently, accurately, and flexibly render platform-specific search results.

公开(公告)号：US10445766B1

公开(公告)日：2019-10-15

申请号：US15282992

申请日：2016-09-30

Applicant: Adobe Inc.

Inventor： Antoine Barbier ,  Greg Collison ,  Albert Lim ,  Nikhil Belsare ,  Alexander R. Hood

IPC: G06Q30/00 ,  G06Q30/02 ,  H04N21/81 ,  H04N21/24

Abstract: Systems and methods are disclosed for enabling incremental reach for an advertising campaign, across multiple screens/channels. In some embodiments, a base TV/media plan is uploaded, and targeted exposed viewers are monitored. Unexposed target viewers are identified. Additional media channels that unexposed viewers use are found. Unexposed viewers are matched with pricing and media avails from one or more media directories. Media avails are then analyzed along with incremental on-target impressions. Targeted avails for unexposed viewers are then determined, based on lowest incremental cost and largest incremental reach are added to a current cross-screen plan. An analysis to find new unexposed viewers is then re-run to determine the next-most cost effective avails. The above steps are repeated or looped until a selected advertisement budget has been allocated. User interface embodiments for these methods are also disclosed.

公开(公告)号：US11836172B2

公开(公告)日：2023-12-05

申请号：US17354954

申请日：2021-06-22

Applicant: ADOBE INC.

Inventor： Fan Du ,  Zening Qu ,  Vasanthi Swaminathan Holtcamp ,  Tak Yeon Lee ,  Sungchul Kim ,  Saurabh Mahapatra ,  Sana Malik Lee ,  Ryan A. Rossi ,  Nikhil Belsare ,  Eunyee Koh ,  Andrew Thomson ,  Sumit Shekhar

IPC: G06F16/33 ,  G06N5/046 ,  G06F16/338

CPC classification number: G06F16/3344 ,  G06F16/338 ,  G06F16/3346 ,  G06N5/046

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data visualization generation. In one implementation, dataset intent data, visual design intent data, and insight intent data determined from a user input natural language query are obtained. A set of candidate intent recommendations is generated using various combinations of the dataset intent data, visual design intent data, and insight intent data. Each of the candidate intent recommendations is incorporated into a set of visualization templates to determine eligibility of the candidate intent recommendations. For eligible candidate intent recommendations, a score associated with a corresponding visualization template is determined. Based on the scores, a candidate intent recommendation and corresponding visualizations template is selected to use as a visual recommendation for presenting a data visualization.

公开(公告)号：US20230021797A1

公开(公告)日：2023-01-26

申请号：US17383051

申请日：2021-07-22

Applicant: Adobe Inc.

Inventor： Piyush Gupta ,  Binit Kumar Sinha ,  Eunyee Koh ,  Fan Du ,  Gaurav Makkar ,  Silky Kedawat ,  Subrahmanya Kumar Giliyaru ,  Vasanthi Holtcamp ,  Nikhil Belsare

IPC: G06F16/33 ,  G06F40/40 ,  G06F16/338

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate a dynamic cross-platform ask interface and utilize a cross-platform language processing model to provide platform-specific, contextually based responses to natural language digital text queries. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to extract registered intents from digital text queries to identify platform-specific configurations associated with the registered intents. Utilizing the platform-specific configurations, the disclosed systems can generate tailored platform-specific requests for information, as well as customized end-user search results that cause client devices to efficiently, accurately, and flexibly render platform-specific search results.

公开(公告)号：US20220100714A1

公开(公告)日：2022-03-31

申请号：US17036453

申请日：2020-09-29

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Yikun Xian ,  Sungchul Kim ,  Tak Yeon Lee ,  Nikhil Belsare ,  Shashi Kant Rai ,  Vasanthi Holtcamp ,  Thomas Jacobs ,  Duy-Trung T. Dinh ,  Caroline Jiwon Kim

IPC: G06F16/21 ,  G06K9/62 ,  G06N3/08 ,  G06K9/72

Abstract: Systems and methods for lifelong schema matching are described. The systems and methods include receiving data comprising a plurality of information categories, classifying each information category according to a schema comprising a plurality of classes, wherein the classification is performed by a neural network classifier trained based on a lifelong learning technique using a plurality of exemplar training sets, wherein each of the exemplar training sets includes a plurality of examples corresponding to one of the classes, and wherein the examples are selected based on a metric indicating how well each of the examples represents the corresponding class, and adding the data to a database based on the classification, wherein the database is organized according to the schema.