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：US20250078200A1

公开(公告)日：2025-03-06

申请号：US18952023

申请日：2024-11-19

Applicant: Adobe Inc.

Inventor： Ruiyi Zhang ,  Yufan Zhou ,  Christopher Tensmeyer ,  Jiuxiang Gu ,  Tong Yu ,  Tong Sun

IPC: G06T3/10 ,  G06N3/04 ,  G06T11/00 ,  G10L15/22 ,  G10L15/26

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a neural network framework for interactive multi-round image generation from natural language inputs. Specifically, the disclosed systems provide an intelligent framework (i.e., a text-based interactive image generation model) that facilitates a multi-round image generation and editing workflow that comports with arbitrary input text and synchronous interaction. In particular embodiments, the disclosed systems utilize natural language feedback for conditioning a generative neural network that performs text-to-image generation and text-guided image modification. For example, the disclosed systems utilize a trained model to inject textual features from natural language feedback into a unified joint embedding space for generating text-informed style vectors. In turn, the disclosed systems can generate an image with semantically meaningful features that map to the natural language feedback. Moreover, the disclosed systems can persist these semantically meaningful features throughout a refinement process and across generated images.

公开(公告)号：US20250005289A1

公开(公告)日：2025-01-02

申请号：US18343389

申请日：2023-06-28

Applicant: Adobe Inc.

Inventor： Haoliang Wang ,  Kaige Xie ,  Tong Yu ,  Junda Wu ,  Handong Zhao ,  Ruiyi Zhang ,  Kanak Vivek Mahadik ,  Ani Nenkova

IPC: G06F40/35 ,  G06F16/34 ,  G06F16/35

Abstract: Dialogue state aware dialogue summarization techniques are described that enable generation of dialogue summaries from target domains with limited training data. A content processing system, for instance, generates one or more clusters based on training dialogues from one or more source domains. The clusters represent domain-specific features of the training dialogues and are further based on dialogue states of the training dialogues. The content processing system trains a machine learning model to generate summaries of dialogues by using the one or more clusters as prefixes in a prefix-tuning approach. The content processing system receives an input that includes a dialogue from a target domain. The content processing system generates an input prompt based on the dialogue and the one or more clusters, and the model generates a summary of the dialogue based on the input prompt.

公开(公告)号：US20240386621A1

公开(公告)日：2024-11-21

申请号：US18318921

申请日：2023-05-17

Applicant: Adobe Inc.

Inventor： Ruiyi Zhang ,  Yufan Zhou ,  Tong Yu ,  Tong Sun ,  Rajiv Jain ,  Jiuxiang Gu ,  Christopher Alan Tensmeyer

IPC: G06T11/00 ,  G06F40/40 ,  G06V10/74 ,  G06V10/774 ,  G06V10/82

Abstract: Techniques and systems for training and/or implementing a text-to-image generation model are provided. A pre-trained multimodal model is leveraged for avoiding slower and more labor-intensive methodologies for training a text-to-image generation model. Accordingly, images without associated text (i.e., bare images) are provided to the pre-trained multimodal model so that it can produce generated text-image pairs. The generated text-image pairs are provided to the text-to-image generation model for training and/or implementing the text-to-image generation model.

公开(公告)号：US12148119B2

公开(公告)日：2024-11-19

申请号：US17576091

申请日：2022-01-14

Applicant: Adobe Inc.

Inventor： Ruiyi Zhang ,  Yufan Zhou ,  Christopher Tensmeyer ,  Jiuxiang Gu ,  Tong Yu ,  Tong Sun

IPC: G06T5/00 ,  G06N3/04 ,  G06T3/10 ,  G06T5/60 ,  G06T11/00 ,  G06T11/80 ,  G10L15/22 ,  G10L15/26

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a neural network framework for interactive multi-round image generation from natural language inputs. Specifically, the disclosed systems provide an intelligent framework (i.e., a text-based interactive image generation model) that facilitates a multi-round image generation and editing workflow that comports with arbitrary input text and synchronous interaction. In particular embodiments, the disclosed systems utilize natural language feedback for conditioning a generative neural network that performs text-to-image generation and text-guided image modification. For example, the disclosed systems utilize a trained model to inject textual features from natural language feedback into a unified joint embedding space for generating text-informed style vectors. In turn, the disclosed systems can generate an image with semantically meaningful features that map to the natural language feedback. Moreover, the disclosed systems can persist these semantically meaningful features throughout a refinement process and across generated images.

公开(公告)号：US20230376356A1

公开(公告)日：2023-11-23

申请号：US17749577

申请日：2022-05-20

Applicant: ADOBE INC.

Inventor： Kanak Vivek Mahadik ,  Tong Yu

IPC: G06F9/50 ,  G06K9/62

CPC classification number: G06F9/5077 ,  G06F9/5033 ,  G06F9/5038 ,  G06K9/6262

Abstract: Systems and methods that enable the efficient and adaptive allocation of resources dedicated to a virtualized resource-based computation (e.g., one or more information processing tasks) are provided. In one embodiment, a reward model is generated based on a set of statistical distributions, for example, in response to receiving a request to launch a set of VCRs. Thereafter, an expected reward is predicting for each configuration of a set of configurations based on the reward model and one or more parameters of the corresponding configuration. The expected reward indicates an efficiency in distribution or allocation of physical computation resources to the set of VCRs. A configuration of the set of configurations is selected based on the predicted expected reward for the configuration. The set of VCRs are then configured with the selected configuration.

公开(公告)号：US20190324606A1

公开(公告)日：2019-10-24

申请号：US15957706

申请日：2018-04-19

Applicant: Adobe Inc.

Inventor： Branislav Kveton ,  Zheng Wen ,  Hung Bui ,  Tong Yu

IPC: G06F3/0483 ,  G06N99/00 ,  H04L29/08 ,  G06F17/21

Abstract: Systems and methods for customizing an interactive experience based on topics determined from an online topic model. In an example, a segmentation application executing on a computing device accesses past user interaction vectors that represent interaction data from an electronic content delivery system. The segmentation application accesses a segmentation model having parameters. The segmentation application updates the parameters by performing tensor decomposition on a tensor built from the past user interaction vectors and calculating updating values of the parameters from the tensor decomposition. The segmentation application performs a segmentation of user devices by applying the segmentation model with the updated parameters to the present user interaction vector. The segmentation assigns the user device to the user segment. The segmentation application transmits data describing the segmentation to the electronic content delivery system.