公开(公告)号：US20240163238A1

公开(公告)日：2024-05-16

申请号：US18055238

申请日：2022-11-14

Applicant: Adobe Inc.

Inventor： Yeuk-yin Chan ,  Andrew Thomson ,  Caroline Kim ,  Cole Connelly ,  Eunyee Koh ,  Michelle Lee ,  Shunan Guo

IPC: H04L51/07 ,  G06F3/04842

CPC classification number: H04L51/07 ,  G06F3/04842

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates editable email components by utilizing an Answer Set Programming (ASP) model with hard and soft constraints. For instance, in one or more embodiments, the disclosed systems generate editable email components from email fragments of an email file utilizing an Answer Set Programming (ASP) model. In particular, the disclosed systems extract facts for the ASP model from the email file. In addition, the disclosed systems determine rows or columns defining cells of the email file utilizing ASP hard constraints defined by a first set of ASP atoms corresponding to the facts. Moreover, the disclosed systems determine editable email component classes for the email fragments utilizing ASP soft constraints defined by ASP classification weights and a second set of ASP atoms corresponding to the facts.

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：US20230350968A1

公开(公告)日：2023-11-02

申请号：US17661641

申请日：2022-05-02

Applicant: Adobe Inc.

Inventor： Irgelkha Mejia ,  Michele Saad ,  Eunyee Koh ,  Andrew Thomson ,  Lauren Dest ,  Dustin Ground ,  Anna Hammond ,  Arjun Athreya ,  Catherine Chiodo

IPC: G06F16/957 ,  G06F16/958 ,  G06F16/9536

CPC classification number: G06F16/9577 ,  G06F16/958 ,  G06F16/9536

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for utilizing machine learning models to extract digital signals from low-results web queries and generate item demand deficiency predictions for digital item lists corresponding to websites. In one or more embodiments, the disclosed systems identify a low-results query submitted by client devices navigating a website. The disclosed systems generate features for the low-results query and the digital item list to generate a deficiency prediction relative to demand indicated by the low-results query. In some embodiments, the disclosed system utilizes a deficiency prediction model to process the extracted signals and generate a deficiency confidence score corresponding to the low-results query. Based on the deficiency confidence score, the disclosed system can generate and provide demand notifications via one or more graphical user interfaces.

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.