公开(公告)号：US11995547B2

公开(公告)日：2024-05-28

申请号：US17823390

申请日：2022-08-30

申请人： Adobe Inc.

发明人： Fan Du ,  Sungchul Kim ,  Shunan Guo ,  Sana Lee ,  Eunyee Koh

IPC分类号： G06N3/08 ,  G06N3/04 ,  G06N3/045 ,  G06N5/02 ,  G06N7/01 ,  G06N20/10

CPC分类号： G06N3/08 ,  G06N3/045 ,  G06N5/02 ,  G06N7/01 ,  G06N20/10

摘要： Disclosed systems and methods predict and visualize outcomes based on past events. For example, an analysis application encodes a sequence of events into a feature vector that includes, for each event, a numerical representation of a respective category and a respective timestamp. The application applies a time-aware recurrent neural network to the feature vector, resulting in one or more of (i) a set of future events in which each event is associated with a probability and a predicted duration and (ii) a sequence embedding that contains information about predicted outcomes and temporal patterns observed in the sequence of events. The application applies a support vector model classifier to the sequence embedding. The support vector model classifier computes a likelihood of a categorical outcome for each of the events in the probability distribution. The application modifies interactive content according to the categorical outcomes and probability distribution.

公开(公告)号：US20240152771A1

公开(公告)日：2024-05-09

申请号：US17979843

申请日：2022-11-03

申请人： Adobe Inc.

发明人： Can Qin ,  Sungchul Kim ,  Tong Yu ,  Ryan A. Rossi ,  Handong Zhao

IPC分类号： G06N5/02

CPC分类号： G06N5/02

摘要： Tabular data machine-learning model techniques and systems are described. In one example, common-sense knowledge is infused into training data through use of a knowledge graph to provide external knowledge to supplement a tabular data corpus. In another example, a dual-path architecture is employed to configure an adapter module. In an implementation, the adapter module is added as part of a pre-trained machine-learning model for general purpose tabular models. Specifically, dual-path adapters are trained using the knowledge graphs and semantically augmented trained data. A path-wise attention layer is applied to fuse a cross-modality representation of the two paths for a final result.

公开(公告)号：US20230143721A1

公开(公告)日：2023-05-11

申请号：US17524282

申请日：2021-11-11

申请人： ADOBE INC.

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

IPC分类号： G06F40/295 ,  G06N20/00

CPC分类号： G06F40/295 ,  G06N20/00

摘要： 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：US20220405314A1

公开(公告)日：2022-12-22

申请号：US17354954

申请日：2021-06-22

申请人： ADOBE INC.

发明人： Fan Du ,  Zening Qu ,  Vasanthi Swaminathan Holtcamp ,  Tak Yeon Lee ,  Sungchul Kim ,  Saurabh Mahapatra ,  Sana Malik Lee ,  Ryan A. Rossi ,  Nikhil Belsare ,  Eunyee Koh ,  Andrew Thomson ,  Sumit Shekhar

IPC分类号： G06F16/33 ,  G06F16/338 ,  G06N5/04

摘要： Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data visualization generation. In one implementation, dataset intent data, visual design intent data, and insight intent data determined from a user input natural language query are obtained. A set of candidate intent recommendations is generated using various combinations of the dataset intent data, visual design intent data, and insight intent data. Each of the candidate intent recommendations is incorporated into a set of visualization templates to determine eligibility of the candidate intent recommendations. For eligible candidate intent recommendations, a score associated with a corresponding visualization template is determined. Based on the scores, a candidate intent recommendation and corresponding visualizations template is selected to use as a visual recommendation for presenting a data visualization.

公开(公告)号：US20220244815A1

公开(公告)日：2022-08-04

申请号：US17161770

申请日：2021-01-29

申请人： Adobe Inc.

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

IPC分类号： G06F3/0482 ,  G06F3/0484 ,  G06F17/15

摘要： 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：US20210342345A1

公开(公告)日：2021-11-04

申请号：US17373281

申请日：2021-07-12

申请人： ADOBE INC.

发明人： Di Jin ,  Ryan A. Rossi ,  Eunyee Koh ,  Sungchul Kim ,  Anup Rao

IPC分类号： G06F16/2458 ,  G06F16/901 ,  G06F16/26 ,  G06F16/215 ,  G06F16/28

摘要： Embodiments of the present invention provide systems, methods, and computer storage media for latent summarization of a graph. Structural features can be captured from feature vectors associated with each node of the graph by applying base functions on the feature vectors and iteratively applying relational operators to successive feature matrices to derive deeper inductive relational functions that capture higher-order structural information in different subgraphs of increasing size (node separations). Heterogeneity can be summarized by performing capturing features in appropriate subgraphs (e.g., node-centric neighborhoods associated with each node type, edge direction, and/or edge type). Binning and/or dimensionality reduction can be applied to the resulting feature matrices. The resulting set of relational functions and multi-level feature matrices can form a latent summary that can be used to perform a variety of graph-based tasks, including node classification, node clustering, link prediction, entity resolution, anomaly and event detection, and inductive learning tasks.

公开(公告)号：US11113293B2

公开(公告)日：2021-09-07

申请号：US16252169

申请日：2019-01-18

申请人： ADOBE INC.

发明人： Di Jin ,  Ryan A. Rossi ,  Eunyee Koh ,  Sungchul Kim ,  Anup Rao

IPC分类号： G06F16/24 ,  G06F16/2458 ,  G06F16/901 ,  G06F16/26 ,  G06F16/215 ,  G06F16/28

摘要： Embodiments of the present invention provide systems, methods, and computer storage media for latent summarization of a graph. Structural features can be captured from feature vectors associated with each node of the graph by applying base functions on the feature vectors and iteratively applying relational operators to successive feature matrices to derive deeper inductive relational functions that capture higher-order structural information in different subgraphs of increasing size (node separations). Heterogeneity can be summarized by performing capturing features in appropriate subgraphs (e.g., node-centric neighborhoods associated with each node type, edge direction, and/or edge type). Binning and/or dimensionality reduction can be applied to the resulting feature matrices. The resulting set of relational functions and multi-level feature matrices can form a latent summary that can be used to perform a variety of graph-based tasks, including node classification, node clustering, link prediction, entity resolution, anomaly and event detection, and inductive learning tasks.

公开(公告)号：US20200167690A1

公开(公告)日：2020-05-28

申请号：US16203263

申请日：2018-11-28

申请人： Adobe Inc.

发明人： Handong Zhao ,  Zheng Wen ,  Sungchul Kim ,  Sheng Li ,  Branislav Kveton

IPC分类号： G06N20/00 ,  G06N3/08 ,  G06N7/00 ,  G06K9/62 ,  G06F9/48

摘要： Systems and techniques for multi-task equidistant embedding are described that process categorical feature data to explore feature interactions. A digital analytics system enforces an equidistant relationship among features within a category while extracting high-order feature interactions by punishing both positive correlations and negative correlations among low-dimensional representations of different features. By enforcing an equidistant embedding, information is retained and accuracy is increased while higher order feature interactions are determined. Further, the digital analytics system shares knowledge among different tasks by connecting a shared network representation common to multiple tasks with exclusive network representations specific to particular tasks.

公开(公告)号：US11544535B2

公开(公告)日：2023-01-03

申请号：US16297024

申请日：2019-03-08

申请人： Adobe Inc.

发明人： John Boaz Tsang Lee ,  Ryan Rossi ,  Sungchul Kim ,  Eunyee Koh ,  Anup Rao

IPC分类号： G06N3/04 ,  G06F16/901 ,  G06N3/08 ,  G06K9/62 ,  G06F17/16 ,  G06V10/426

摘要： 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：US20220138557A1

公开(公告)日：2022-05-05

申请号：US17089157

申请日：2020-11-04

申请人： Adobe Inc.

发明人： Ryan A. Rossi ,  Hongjie Chen ,  Kanak Vivek Mahadik ,  Sungchul Kim

IPC分类号： G06N3/08 ,  G06N3/04 ,  G06F16/2458

摘要： In implementations of deep hybrid graph-based forecasting systems, a computing device implements a forecast system to receive time-series data describing historic computing metric values for a plurality of processing devices. The forecast system determines dependency relationships between processing devices of the plurality of processing devices based on time-series data of the processing devices. Time-series data of each processing device is represented as a node of a graph and the nodes are connected based on the dependency relationships. The forecast system generates an indication of a future computing metric value for a particular processing device by processing a first set of the time-series data using a relational global model and processing a second set of the time-series data using a relational local model. The first and second sets of the time-series data are determined based on a structure of the graph.