公开(公告)号：US20240037149A1

公开(公告)日：2024-02-01

申请号：US17877469

申请日：2022-07-29

Applicant: Adobe Inc.

Inventor： Somdeb Sarkhel ,  Xiang Chen ,  Viswanathan Swaminathan ,  Swapneel Mehta ,  Saayan Mitra ,  Ryan Rossi ,  Han Guo ,  Ali Aminian ,  Kshitiz Garg

IPC: G06F16/901 ,  G06N3/04 ,  G06Q50/00

CPC classification number: G06F16/9024 ,  G06N3/0454 ,  G06Q50/01

Abstract: Techniques for recommending hashtags, including trending hashtags, are disclosed. An example method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.

公开(公告)号：US11729286B2

公开(公告)日：2023-08-15

申请号：US17902378

申请日：2022-09-02

Applicant: Adobe Inc.

Inventor： Ryan Rossi

IPC: H04L67/50 ,  H04L43/045 ,  H04L41/142 ,  H04L43/067 ,  G06F16/2458 ,  G06F16/901 ,  H04L41/14 ,  H04L67/306 ,  H04L41/082 ,  G06F16/28

CPC classification number: H04L67/535 ,  G06F16/2477 ,  G06F16/9024 ,  H04L41/142 ,  H04L43/045 ,  H04L43/067 ,  G06F16/285 ,  H04L41/082 ,  H04L41/145 ,  H04L67/306

Abstract: A system receives a temporal graph comprising nodes having respective identifiers and edges. Each of the edges has a direction pointing from a first node to a second node and indicates an association of the first node with the second node. The system generates a sequence of nodes and a sequence of edges by traversing the temporal graph. The system determines, for each node of the sequence of nodes, a respective set of feature values including an indegree, an outdegree, and a total degree. The system determines, for each edge of the sequence of edges, an edge feature comprising a sum of the total degree of a first node preceding the edge and the total degree of a second node following the edge. The system forms an edge feature values sequence for the sequence of edges and determines an edge network embedding for each edge of the sequence of edges.

公开(公告)号：US11720590B2

公开(公告)日：2023-08-08

申请号：US17091941

申请日：2020-11-06

Applicant: ADOBE INC.

Inventor： Ryan Rossi ,  Vasanthi Holtcamp ,  Tak Yeon Lee ,  Sungchul Kim ,  Sana Lee ,  Nathan Ross ,  John Anderson ,  Fan Du ,  Eunyee Koh ,  Xin Qian

IPC: G06F16/9535 ,  G06F16/26 ,  G06F3/0482 ,  G06F11/34 ,  G06F11/30 ,  G06F16/9038

CPC classification number: G06F16/26 ,  G06F3/0482 ,  G06F11/302 ,  G06F11/3438 ,  G06F16/9038

Abstract: Systems and methods for personalized visualization recommendation are described. Embodiments of the described systems and methods are configured to identify a first matrix representing user interactions with a plurality of data attributes corresponding to a plurality of datasets, a second matrix representing user interactions with a plurality of visualizations, and a third matrix representing a plurality of meta-features for each of the data attributes; compute low-dimensional embeddings representing user characteristics, the data attributes, visualization configurations, and the meta-features using joint factorization of the first matrix, the second matrix and the third matrix; generate a model for predicting visualization preference weights based on the low-dimensional embeddings; predict the visualization preference weights for a user corresponding to a plurality of candidate visualizations of dataset using the model; and generate a personalized visualization of the dataset for the user based on the predicted visualization preference weights.

公开(公告)号：US11562019B2

公开(公告)日：2023-01-24

申请号：US17161406

申请日：2021-01-28

Applicant: Adobe Inc.

Inventor： Shenyu Xu ,  Eunyee Koh ,  Fan Du ,  Tak Yeon Lee ,  Sana Malik Lee ,  Ryan Rossi

IPC: G06F16/30 ,  G06F16/738 ,  G06F16/901 ,  G06F16/783 ,  G06F16/34 ,  G06F16/9032 ,  G06F16/44

Abstract: This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that intelligently and automatically analyze input data and generate visual data stories depicting graphical visualizations from data insights determined from the input data. For example, the disclosed systems automatically extract data insights utilizing an in-depth statistical analysis of dataset groups from data-attribute categories within the input data. Based on the data insights, the disclosed systems can automatically generate exportable visual data stories to visualize the data insights, provide textual or audio-based natural language summaries of the data insights, and animate such data insights in videos. In some embodiments, the disclosed systems generate a visual-data-story graph comprising nodes representing visual data stories and edges representing similarities between the visual data stories. Based on the visual-data-story graph, the disclosed systems can select a relevant visual data story to display on a graphical user interface.

公开(公告)号：US11288541B1

公开(公告)日：2022-03-29

申请号：US17015495

申请日：2020-09-09

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Vasanthi Holtcamp ,  Tak Yeon Lee ,  Sana Lee ,  Nathan Ross ,  John Anderson ,  Fan Du ,  Eunyee Koh

IPC: G06K9/62 ,  G06F17/18 ,  G06F3/0482

Abstract: This disclosure involves generating, from a user data set, a ranked list of recommended secondary variables in a user interface field similar to primary variable selected in another user interface field. A system receives a data set having variables and corresponding sets of values. The data visualization system determines a feature vector for each variable based on statistics of a corresponding values set. The system generates a variable similarity graph having nodes representing variables and links representing degrees of similarity between feature vectors of variables. The system receives a selection of a first variable via a first field of the user interface, detects a selection of a second field, and identifies a relationship between the first field and the second field. The system generates a contextual menu of recommended secondary variables for use with the selected first variable based on similarity value of the links in the variable similarity graph.

公开(公告)号：US20210264244A1

公开(公告)日：2021-08-26

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: G06N3/08 ,  G06F16/22 ,  G06F16/901 ,  G06F16/248 ,  G06F16/2457 ,  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：US20210014124A1

公开(公告)日：2021-01-14

申请号：US16507204

申请日：2019-07-10

Applicant: Adobe Inc.

Inventor： Ryan Rossi

IPC: H04L12/24 ,  H04L12/26

Abstract: In some embodiments, a network analysis system receives network data in the form of a temporal graph that includes nodes and edges. Each node represents an entity involved in a network. An edge connects two nodes to indicate an association between the two nodes. Each edge also has a temporal value indicating a time point when the association between the two nodes was created. The network analysis system generates a sequence of nodes by traversing the nodes in the temporal graph along edges with non-decreasing temporal values or with non-increasing temporal values. The network analysis system further replaces the identifiers of the nodes in the sequence to generate a sequence of feature values. Based on the sequence of feature values, the network analysis system determines network embeddings for the nodes in the temporal graph. Using the network embeddings, the network analysis system identifies two or more of the nodes in the temporal graph that belong to the same entity.

公开(公告)号：US20200285944A1

公开(公告)日：2020-09-10

申请号：US16297024

申请日：2019-03-08

Applicant: Adobe Inc.

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

IPC: G06N3/04 ,  G06F16/901 ,  G06N3/08 ,  G06F17/16 ,  G06K9/46 ,  G06K9/62

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：US20250036858A1

公开(公告)日：2025-01-30

申请号：US18225906

申请日：2023-07-25

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Ryan Aponte ,  Shunan Guo ,  Nedim Lipka ,  Jane Hoffswell ,  Chang Xiao ,  Eunyee Koh ,  Yeuk-yin Chan

IPC: G06F40/154 ,  G06F40/117 ,  G06F40/143

Abstract: Techniques discussed herein generally relate to applying machine-learning techniques to design documents to determine relationships among the different style elements within the document. In one example, hypergraph model is trained on a corpus of hypertext markup language (HTML) documents. The trained model is utilized to identifying one or more candidate style elements for a candidate fragment and/or a candidate fragment. Each of the candidates are scored, and at least a portion of the scored candidates are presented as design options for generating a new document.

公开(公告)号：US20230267132A1

公开(公告)日：2023-08-24

申请号：US17677323

申请日：2022-02-22

Applicant: Adobe Inc.

Inventor： Yeuk-yin Chan ,  Tung Mai ,  Ryan Rossi ,  Moumita Sinha ,  Matvey Kapilevich ,  Margarita Savova ,  Fan Du ,  Charles Menguy ,  Anup Rao

IPC: G06F16/28

CPC classification number: G06F16/285

Abstract: A cluster generation system identifies data elements, from a first binary record, that each have a particular value and correspond to respective binary traits. A candidate description function describing the binary traits is generated, the candidate description function including a model factor that describes the data elements. Responsive to determining that a second record has additional data elements having the particular value and corresponding to the respective binary traits, the candidate description function is modified to indicate that the model factor describes the additional elements. The candidate description function is also modified to include a correction factor describing an additional binary trait excluded from the respective binary traits. Based on the modified candidate description function, the cluster generation system generates a data summary cluster, which includes a compact representation of the binary traits of the data elements and additional data elements.