公开(公告)号：US20240386315A1

公开(公告)日：2024-11-21

申请号：US18318524

申请日：2023-05-16

Applicant: ADOBE INC.

Inventor： Thomas BOUCHER ,  Tanay ANAND ,  Stephane LECERCLE ,  Saurabh GARG ,  Pranjal PRASOON ,  Nikaash PURI ,  Mukul LAMBA ,  Milan AGGARWAL ,  Jayakumar SUBRAMANIAN ,  Francoise CORVAISIER ,  David MENDEZ ACUNA ,  Camel AISSANI ,  Balaji KRISHNAMURTHY

IPC: G06N20/00 ,  G06N5/022

Abstract: Methods and systems are provided for a transformer model for journey simulation and prediction. In embodiments described herein, training data is obtained from stored journeys. The training data for each journey indicates customer interactions with each event in the sequence of events of the journey. A machine learning model is trained using the training data to simulate customer interaction with an input journey. The trained machine learning model then generates a simulation of customer interaction with an input journey and the results of the simulation are displayed.

公开(公告)号：US20240073159A1

公开(公告)日：2024-02-29

申请号：US17897419

申请日：2022-08-29

Applicant: ADOBE INC.

Inventor： Sumit BHATIA ,  Jivat Neet KAUR ,  Rachit BANSAL ,  Milan AGGARWAL ,  Balaji KRISHNAMURTHY

IPC: H04L51/02 ,  G06F40/295 ,  G06N5/02

CPC classification number: H04L51/02 ,  G06F40/295 ,  G06N5/022

Abstract: The technology described herein receives a natural-language sequence of words comprising multiple entities. The technology then identifies a plurality of entities in the natural-language sequence. The technology generates a masked natural-language sequence by masking a first entity in the natural-language sequence. The technology retrieves, from a knowledge base, information related to a second entity in the plurality of entities. The technology then trains a natural-language model to respond to a query. The training uses a first representation of the masked natural-language sequence, a second representation of the information, and the first entity.

公开(公告)号：US20250005048A1

公开(公告)日：2025-01-02

申请号：US18345990

申请日：2023-06-30

Applicant: Adobe Inc.

Inventor： Abhinav JAVA ,  Surgan JANDIAL ,  Shripad DESHMUKH ,  Milan AGGARWAL ,  Mausoom SARKAR ,  Balaji KRISHNAMURTHY ,  Arneh JAIN

IPC: G06F16/332

Abstract: Embodiments are disclosed for one-shot document snippet search. A method of one-shot document snippet search may include obtaining a query snippet and a target document. A multi-modal snippet detection model combines first multi-modal features from the query snippet and second multi-modal features from the target document to create a feature volume. The multi-modal snippet detection model identifies one or more matching snippets from the target document based on the feature volume.

公开(公告)号：US20240153258A1

公开(公告)日：2024-05-09

申请号：US17976541

申请日：2022-10-28

Applicant: ADOBE INC.

Inventor： Puneet MANGLA ,  Milan AGGARWAL ,  Balaji KRISHNAMURTHY

IPC: G06V10/80 ,  G06F40/40 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774 ,  G06V10/82 ,  G06V10/86

CPC classification number: G06V10/811 ,  G06F40/40 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/774 ,  G06V10/82 ,  G06V10/86

Abstract: Various embodiments classify one or more portions of an image based on deriving an “intrinsic” modality. Such intrinsic modality acts as a substitute to a “text” modality in a multi-modal network. A text modality in image processing is typically a natural language text that describes one or more portions of an image. However, explicit natural language text may not be available across one or more domains for training a multi-modal network. Accordingly, various embodiments described herein generate an intrinsic modality, which is also a description of one or more portions of an image, except that such description is not an explicit natural language description, but rather a machine learning model representation. Some embodiments additionally leverage a visual modality obtained from a vision-only model or branch, which may learn domain characteristics that are not present in the multi-modal network. Some embodiments additionally fuse or integrate the intrinsic modality with the visual modality for better generalization.

公开(公告)号：US20240086457A1

公开(公告)日：2024-03-14

申请号：US17944502

申请日：2022-09-14

Applicant: ADOBE INC.

Inventor： Yaman KUMAR ,  Vaibhav AHLAWAT ,  Ruiyi ZHANG ,  Milan AGGARWAL ,  Ganesh Karbhari PALWE ,  Balaji KRISHNAMURTHY ,  Varun KHURANA

IPC: G06F16/58 ,  G06F16/55

CPC classification number: G06F16/58 ,  G06F16/55

Abstract: A content analysis system provides content understanding for a content item using an attention aware multi-modal model. Given a content item, feature extractors extract features from content components of the content item in which the content components comprise multiple modalities. A cross-modal attention encoder of the attention aware multi-modal model generates an embedding of the content item using features extracted from the content components. A decoder of the attention aware multi-modal model generates an action-reason statement using the embedding of the content item from the cross-modal attention encoder.

公开(公告)号：US20230143777A1

公开(公告)日：2023-05-11

申请号：US17454445

申请日：2021-11-10

Applicant: ADOBE INC.

Inventor： Pinkesh BADJATIYA ,  Tanay ANAND ,  Simra SHAHID ,  Nikaash PURI ,  Milan AGGARWAL ,  S Sejal NAIDU ,  Sharat Chandra RACHA

IPC: G06F16/9536 ,  G06F16/9538 ,  G06F40/20

CPC classification number: G06F16/9536 ,  G06F16/9538 ,  G06F40/20

Abstract: A method of finding online relevant conversing posts, comprises receiving, by a web server serving an online forum, a query post from an inquirer using the online forum, computing a contextual similarity score between each conversing post of a set of conversing posts with a query post, wherein the contextual similarity score is computed between the body of each of conversing posts and of the query post, wherein N1 conversing posts with a highest contextual similarity score are selected; computing a fine grained similarity score between the subject of the query post and of each of the N1 conversing posts, wherein N2 conversing posts with a highest fine grained similarity score are selected; and boosting the fine grained similarity score of the N2 conversing posts based on relevance metrics, wherein N3 highest ranked conversing posts are selected as a list of conversing posts most relevant to the query post.