公开(公告)号：US20250037006A1

公开(公告)日：2025-01-30

申请号：US18225970

申请日：2023-07-25

Applicant: ADOBE INC.

Inventor： Kanak MAHADIK ,  Sungchul KIM ,  Ryan ROSSI ,  Handong ZHAO ,  Shravika MITTAL

IPC: G06N20/00

Abstract: In various examples, a ranking is generated for a set of computing instances based on predicted metrics associated with computing instances. For example, a prediction model estimates various system performance metrics based on information associated with a workload and configuration information associated with computing instances. The system performance metrics estimated by the prediction model are used to rank the set of computing instances.

公开(公告)号：US20250005691A1

公开(公告)日：2025-01-02

申请号：US18344203

申请日：2023-06-29

Applicant: Adobe Inc.

Inventor： Nedim LIPKA ,  Ryan ROSSI ,  Jianna Audrey Reyes SO ,  Franck DERNONCOURT ,  Alexa SIU

IPC: G06Q50/18

Abstract: A method includes extracting an action from a document using a machine learning model. The action is associated with an action parameter. The method further includes extracting a plurality of action events corresponding to the action from the document using the machine learning model. The method further includes generating a record associated with the document based on the extracted action. The method further includes populating the record with the action parameter. The method further includes executing an action event in the plurality of action events using the record.

公开(公告)号：US20240427995A1

公开(公告)日：2024-12-26

申请号：US18339883

申请日：2023-06-22

Applicant: Adobe Inc.

Inventor： Jiuxiang GU ,  Ryan ROSSI ,  Gaurav VERMA ,  Christopher TENSMEYER ,  Ani NENKOVA

IPC: G06F40/289 ,  G06F40/205 ,  G06T11/60

Abstract: A method includes receiving a text to be used for generating an image. The method further includes determining whether the text is a visual text using a machine learning model trained to classify whether an input text is non-visual text or visual text. The method further includes responsive to determining that the text is a visual text, generating the image using a second machine learning model based on the text. The method further includes displaying the image and the text.

公开(公告)号：US20250124235A1

公开(公告)日：2025-04-17

申请号：US18485204

申请日：2023-10-11

Applicant: ADOBE INC.

Inventor： Victor Soares BURSZTYN ,  Xiang CHEN ,  Vaishnavi MUPPALA ,  Uttaran BHATTACHARYA ,  Tong YU ,  Saayan MITRA ,  Ryan ROSSI ,  Manas GARG ,  Kenneth George RUSSELL ,  Eunyee KOH ,  Alexandru Ionut HODOROGEA

IPC: G06F40/40 ,  G06F40/279

Abstract: Methods and systems are provided for using generative artificial intelligence to evaluate fine-tuned language models. In embodiments described herein, natural language text snippets are generated via a generative language model based on corresponding data. A language model is fine-tuned into a fine-tuned language model via a language model fine-tuning component using the natural language text snippets and the corresponding data as training data. Independent natural language text snippets are generated via the generative language model based on the corresponding data. Each independent natural language text snippet is different than each corresponding natural language text snippet. An evaluation metric of the fine-tuned language model is generated via an evaluation component based on the independent natural language text snippets and the corresponding data.

公开(公告)号：US20240095440A1

公开(公告)日：2024-03-21

申请号：US18484674

申请日：2023-10-11

Applicant: Adobe Inc.

Inventor： Md Main Uddin RONY ,  Fan DU ,  Iftikhar Ahamath BURHANUDDIN ,  Ryan ROSSI ,  Niyati Himanshu CHHAYA ,  Eunyee KOH

IPC: G06F40/106 ,  G06F40/40

CPC classification number: G06F40/106 ,  G06F40/40

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating generation and presentation of insights. In one implementation, a set of data is used to generate a data visualization. A candidate insight associated with the data visualization is generated, the candidate insight being generated in text form based on a text template and comprising a descriptive insight, a predictive insight, an investigative, or a prescriptive insight. A set of natural language insights is generated, via a machine learning model. The natural language insights represent the candidate insight in a text style that is different from the text template. A natural language insight having the text style corresponding with a desired text style is selected for presenting the candidate insight and, thereafter, the selected natural language insight and data visualization are providing for display via a graphical user interface.

公开(公告)号：US20230169140A1

公开(公告)日：2023-06-01

申请号：US18061697

申请日：2022-12-05

Applicant: Adobe Inc.

Inventor： John Boaz Tsang LEE ,  Ryan ROSSI ,  Sungchul KIM ,  Eunyee KOH ,  Anup RAO

IPC: G06F17/10 ,  G06F16/901 ,  G06N3/08 ,  G06F17/16 ,  G06V10/426 ,  G06F18/21 ,  G06F18/24 ,  G06N3/047 ,  G06V10/82

CPC classification number: G06F17/10 ,  G06F16/9024 ,  G06N3/08 ,  G06F17/16 ,  G06V10/426 ,  G06F18/21 ,  G06F18/24 ,  G06N3/047 ,  G06V10/82

Abstract: Various embodiments describe techniques for making inferences from graph-structured data using graph convolutional networks (GCNs). The GCNs use various pre-defined motifs to filter and select adjacent nodes for graph convolution at individual nodes, rather than merely using edge-defined immediate-neighbor adjacency for information integration at each node. In certain embodiments, the graph convolutional networks use attention mechanisms to select a motif from multiple motifs and select a step size for each respective node in a graph, in order to capture information from the most relevant neighborhood of the respective node.

公开(公告)号：US20220070266A1

公开(公告)日：2022-03-03

申请号：US17008339

申请日：2020-08-31

Applicant: Adobe Inc.

Inventor： Ryan ROSSI ,  Tung MAI ,  Anup RAO

IPC: H04L29/08 ,  G06F16/901 ,  G06F17/18

Abstract: A system and method for fast, accurate, and scalable typed graphlet estimation. The system and method utilizes typed edge sampling and typed path sampling to estimate typed graphlet counts in large graphs in a small fraction of the computing time of existing systems. The obtained unbiased estimates of typed graphlets are highly accurate, and have applications in the analysis, mining, and predictive modeling of massive real-world networks. During operation, the system obtains a dataset indicating nodes and edges of a graph. The system samples a portion of the graph and counts a number of graph features in the sampled portion of the graph. The system then computes an occurrence frequency of a typed graphlet pattern and a total number of typed graphlets associated with the typed graphlet pattern in the graph.