公开(公告)号：US20240311623A1

公开(公告)日：2024-09-19

申请号：US18183387

申请日：2023-03-14

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Eunyee Koh ,  Jane Hoffswell ,  Nedim Lipka ,  Shunan Guo ,  Sudhanshu Chanpuriya ,  Sungchul Kim ,  Tong Yu

IPC: G06N3/049

CPC classification number: G06N3/049

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for building time-decayed line graphs from temporal graph networks for efficiently and accurately generating time-aware recommendations. For example, the time-decayed line graph system creates a line graph of the temporal graph network by deriving interaction nodes from temporal edges (e.g., timed interactions) and connecting interactions that share an endpoint node. Then, the time-decayed line graph system determines the edge weights in the line graph based on differences in time between interactions, with interactions that occur closer together in time being connected with higher weights. Notably, by using this method, the derived time-decayed line graph directly represents topological proximity and temporal proximity. Upon generating the time-decayed line graphs, the system performs downstream predictive modeling such as predicted edge classifications and/or temporal link predictions.

公开(公告)号：US12093322B2

公开(公告)日：2024-09-17

申请号：US17654933

申请日：2022-03-15

Applicant: Adobe Inc.

Inventor： Fayokemi Ojo ,  Ryan Rossi ,  Jane Hoffswell ,  Shunan Guo ,  Fan Du ,  Sungchul Kim ,  Chang Xiao ,  Eunyee Koh

IPC: G06F16/904 ,  G06N3/02

CPC classification number: G06F16/904 ,  G06N3/02

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.

公开(公告)号：US11995403B2

公开(公告)日：2024-05-28

申请号：US17524282

申请日：2021-11-11

Applicant: ADOBE INC.

Inventor： Sungchul Kim ,  Subrata Mitra ,  Ruiyi Zhang ,  Rui Wang ,  Handong Zhao ,  Tong Yu

IPC: G06F40/295 ,  G06N20/00

CPC classification number: G06F40/295 ,  G06N20/00

Abstract: Embodiments of the technology described herein describe a machine classifier capable of continually learning new classes through a continual few-shot learning approach. A natural language processing (NLP) machine classifier may initially be trained to identify a plurality of other classes through a conventional training process. In order to learn a new class, natural-language training data for a new class is generated. The training data for the new class may be few-shot training data. The training also uses synthetic training data that represents each of the plurality of other classes. The synthetic training data may be generated through a model inversion of the original classifier. The synthetic training data and the natural-language training data are used to retrain the NLP classifier to identify text in the plurality of other classes and the new class using.

公开(公告)号：US11995048B2

公开(公告)日：2024-05-28

申请号：US17036453

申请日：2020-09-29

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Yikun Xian ,  Sungchul Kim ,  Tak Yeon Lee ,  Nikhil Belsare ,  Shashi Kant Rai ,  Vasanthi Holtcamp ,  Thomas Jacobs ,  Duy-Trung T Dinh ,  Caroline Jiwon Kim

IPC: G06F16/00 ,  G06F16/21 ,  G06F18/2115 ,  G06F18/214 ,  G06F18/2431 ,  G06N3/08 ,  G06V30/262

CPC classification number: G06F16/213 ,  G06F18/2115 ,  G06F18/2148 ,  G06F18/2431 ,  G06N3/08 ,  G06V30/274

Abstract: Systems and methods for lifelong schema matching are described. The systems and methods include receiving data comprising a plurality of information categories, classifying each information category according to a schema comprising a plurality of classes, wherein the classification is performed by a neural network classifier trained based on a lifelong learning technique using a plurality of exemplar training sets, wherein each of the exemplar training sets includes a plurality of examples corresponding to one of the classes, and wherein the examples are selected based on a metric indicating how well each of the examples represents the corresponding class, and adding the data to a database based on the classification, wherein the database is organized according to the schema.

公开(公告)号：US20240160890A1

公开(公告)日：2024-05-16

申请号：US18052463

申请日：2022-11-03

Applicant: ADOBE INC.

Inventor： Namyong Park ,  Ryan A. Rossi ,  Eunyee Koh ,  Iftikhar Ahamath Burhanuddin ,  Sungchul Kim ,  Fan Du

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/04 ,  G06N3/08

Abstract: Systems and methods for contrastive graphing are provided. One aspect of the systems and methods includes receiving a graph including a node; generating a node embedding for the node based on the graph using a graph neural network (GNN); computing a contrastive learning loss based on the node embedding; and updating parameters of the GNN based on the contrastive learning loss.

公开(公告)号：US20240152769A1

公开(公告)日：2024-05-09

申请号：US18050607

申请日：2022-10-28

Applicant: ADOBE INC.

Inventor： Ryan A. Rossi ,  Kanak Mahadik ,  Mustafa Abdallah ElHosiny Abdallah ,  Sungchul Kim ,  Handong Zhao

IPC: G06N3/0985 ,  G06Q10/04

CPC classification number: G06N3/0985 ,  G06Q10/04

Abstract: Systems and methods for automatic forecasting are described. Embodiments of the present disclosure receive a time-series dataset; compute a time-series meta-feature vector based on the time-series dataset; generate a performance score for a forecasting model using a meta-learner machine learning model that takes the time-series meta-feature vector as input; select the forecasting model from a plurality of forecasting models based on the performance score; and generate predicted time-series data based on the time-series dataset using the selected forecasting model.

公开(公告)号：US11782576B2

公开(公告)日：2023-10-10

申请号：US17161770

申请日：2021-01-29

Applicant: Adobe Inc.

Inventor： Camille Harris ,  Zening Qu ,  Sana Lee ,  Ryan Rossi ,  Fan Du ,  Eunyee Koh ,  Tak Yeon Lee ,  Sungchul Kim ,  Handong Zhao ,  Sumit Shekhar

IPC: G06F3/0482 ,  G06F17/15 ,  G06F3/04845

CPC classification number: G06F3/0482 ,  G06F3/04845 ,  G06F17/15

Abstract: In some embodiments, a data visualization system detects insights from a dataset and computes insight scores for respective insights. The data visualization system further computes insight type scores, from the insight scores, for insight types in the detected insights. The data visualization system determines a selected insight type for the dataset having a higher insight type score than unselected insight types and determines, for the selected insight type, a set of selected insights that have higher insight scores than unselected insights. The data visualization system determines insight visualizations for the set of selected insights and generates, for inclusion in a user interface of the data visualization system, selectable interface elements configured for invoking an editing tool for updating the determined insight visualizations from the dataset. The selectable interface elements are arranged in the user interface according to the insight scores of the set of selected insights.

公开(公告)号：US20230244926A1

公开(公告)日：2023-08-03

申请号：US17592186

申请日：2022-02-03

Applicant: ADOBE INC.

Inventor： Sungchul Kim ,  Sejoon Oh ,  Ryan A. Rossi

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: A data augmentation framework enhances the prediction accuracy of tensor completion methods. An array having a set of cells associated with a set of entities is received. Influence metrics of cells from the array are determined based on an influence of the cells on minimizing loss while training a machine learning model. An entity-importance metric is generated for each entity of the set of entities based on the influence metrics. A cell from the array for which to augment the array with a predicted value is identified. The cell is identified based on a sampling of the set of entities that is weighted by the entity-importance metric for each entity of the set of entities.

公开(公告)号：US11645523B2

公开(公告)日：2023-05-09

申请号：US16796681

申请日：2020-02-20

Applicant: Adobe Inc.

Inventor： Yikun Xian ,  Tak Yeon Lee ,  Sungchul Kim ,  Ryan Rossi ,  Handong Zhao

IPC: G06F16/22 ,  G06F16/2457 ,  G06F16/248 ,  G06F16/901 ,  G06N3/08 ,  G06N5/02

CPC classification number: G06N3/08 ,  G06F16/221 ,  G06F16/248 ,  G06F16/24578 ,  G06F16/9024 ,  G06N5/02

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for generating generate explanatory paths for column annotations determined using a knowledge graph and a deep representation learning model. For instance, the disclosed systems can utilize a knowledge graph to generate an explanatory path for a column label determination from a deep representation learning model. For example, the disclosed systems can identify a column and determine a label for the column using a knowledge graph (e.g., a representation of a knowledge graph) that includes encodings of columns, column features, relational edges, and candidate labels. Then, the disclosed systems can determine a set of candidate paths between the column and the determined label for the column within the knowledge graph. Moreover, the disclosed systems can generate an explanatory path by ranking and selecting paths from the set of candidate paths using a greedy ranking and/or diversified ranking approach.

公开(公告)号：US11562234B2

公开(公告)日：2023-01-24

申请号：US16751755

申请日：2020-01-24

Applicant: Adobe Inc.

Inventor： Yikun Xian ,  Tak Yeon Lee ,  Sungchul Kim ,  Ryan Rossi ,  Handong Zhao

IPC: G06N3/08

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for dynamically determining schema labels for columns regardless of information availability within the columns. For example, the disclosed systems can identify a column that contains an arbitrary amount of information (e.g., a header-only column, a cell-only column, or a whole column). Additionally, the disclosed systems can generate a vector embedding for an arbitrary input column by selectively using a header neural network and/or a cell neural network based on whether the column includes a header label and/or whether the column includes a populated column cell. Furthermore, the disclosed systems can compare the column vector embedding to schema vector embeddings of candidate schema labels in a d-dimensional space to determine a schema label for the column.