公开(公告)号：US20250164978A1

公开(公告)日：2025-05-22

申请号：US18513466

申请日：2023-11-17

Applicant: Adobe Inc.

Inventor： Siddarth Ramesh ,  Tarun Arora ,  Tanay Anand ,  Shreeya Singh ,  Md Anis Alam ,  Jayakumar Subramanian ,  Avinash Katiyar

IPC: G05B19/418

Abstract: Certain aspects and features of the present disclosure relate to providing contextually grounded recommendations using a large language model. For example, a method involves receiving domain specific data for a simulation and transforming the domain specific data into a labeled, natural language description of the domain specific data. The method also involves providing the labeled, natural language description and a classification task prompt with interaction history to a large language model (LLM) to generate a contextually enhanced LLM configured to produce context-aware output. The method further involves outputting, using the contextually enhanced LLM, an interactive list of scored actions corresponding to the simulation. The interactive list can be used to produce a sequence of actions to direct a process or control a machine.

公开(公告)号：US20240330682A1

公开(公告)日：2024-10-03

申请号：US18295094

申请日：2023-04-03

Applicant: Adobe Inc.

Inventor： Surgan JANDIAL ,  Siddarth Ramesh ,  Piyush Gupta ,  Gauri Gupta ,  Balaji Krishnamurthy

IPC: G06N3/08 ,  G06N3/0455

CPC classification number: G06N3/08 ,  G06N3/0455

Abstract: Systems and methods for generating synthetic tabular data for machine learning and other applications are provided. In some embodiments, a variational autoencoder is trained to learn inter-feature correlations found in tabular data collected from real data sources. The trained variational autoencoder is used to train a generator model of a Generative Adversarial Network (GAN) to generate synthetic tabular data that exhibits the inter-feature correlation distribution found in the tabular data collected from real data sources. In some embodiments, processing devices perform operations comprising: receiving a set of tabular data records, each record comprising a plurality of features; training a first machine learning model using the tabular data records to learn correlations between the plurality of features; and training a second machine learning model, using the first machine learning model, to generate a synthetic tabular data records based at least on the one or more correlations between the plurality of features.

公开(公告)号：US20240070816A1

公开(公告)日：2024-02-29

申请号：US17823582

申请日：2022-08-31

Applicant: ADOBE INC.

Inventor： Surgan Jandial ,  Siddarth Ramesh ,  Shripad Vilasrao Deshmukh ,  Balaji Krishnamurthy

IPC: G06T5/50 ,  G06T5/00 ,  G06V10/74 ,  G06V10/764 ,  G06V10/774 ,  G06V20/70

CPC classification number: G06T5/50 ,  G06T5/002 ,  G06V10/761 ,  G06V10/764 ,  G06V10/774 ,  G06V20/70 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive a reference image depicting a reference object with a target spatial attribute; generate object saliency noise based on the reference image by updating random noise to resemble the reference image; and generate an output image based on the object saliency noise, wherein the output image depicts an output object with the target spatial attribute.