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：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.