公开(公告)号：US12288237B2

公开(公告)日：2025-04-29

申请号：US17743360

申请日：2022-05-12

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Aravind Reddy Talla ,  Zhao Song ,  Anup Rao ,  Tung Mai ,  Nedim Lipka ,  Gang Wu ,  Eunyee Koh

IPC: G06Q30/0601

Abstract: Embodiments provide systems, methods, and computer storage media for a Nonsymmetric Determinantal Point Process (NDPPs) for compatible set recommendations in a setting where data representing entities (e.g., items) arrives in a stream. A stream representing compatible sets of entities is received and used to update a latent representation of the entities and a compatibility distribution indicating likelihood of compatibility of subsets of the entities. The probability distribution is accessed in a single sequential pass to predict a compatible complete set of entities that completes an incomplete set of entities. The predicted complete compatible set is provided a recommendation for entities that complete the incomplete set of entities.

公开(公告)号：US12174907B2

公开(公告)日：2024-12-24

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 ,  G06F17/16 ,  G06F18/21 ,  G06F18/24 ,  G06N3/047 ,  G06N3/08 ,  G06V10/426 ,  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：US11544535B2

公开(公告)日：2023-01-03

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 ,  G06K9/62 ,  G06F17/16 ,  G06V10/426

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：US11343325B2

公开(公告)日：2022-05-24

申请号：US17008339

申请日：2020-08-31

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Tung Mai ,  Anup Rao

IPC: G06F15/173 ,  H04L67/141 ,  G06F17/18 ,  G06F16/901

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.

公开(公告)号：US20220148015A1

公开(公告)日：2022-05-12

申请号：US17096255

申请日：2020-11-12

Applicant: Adobe Inc.

Inventor： Tung Mai ,  Iftikhar Ahamath Burhanuddin ,  Georgios Theocharous ,  Anup Rao

IPC: G06Q30/02 ,  G06F7/08 ,  G06Q10/10

Abstract: Techniques are provided for analyzing user actions that have occurred over a time period. The user actions can be, for example, with respect to the user's navigation of content or interaction with an application. Such user data is provided in an action string, which is converted into a highly searchable format. As such, the presence and frequency of particular user actions and patterns of user actions within an action string of a particular user, as well as among multiple action strings of multiple users, are determinable. Subsequences of one or more action strings are identified and both the number of action strings that include a particular subsequence and the frequency that a particular subsequence is present in a given action string are determinable. The conversion involves breaking that string into a sorted list of locations for the actions within that string. Queries can be readily applied against the sorted list.

公开(公告)号：US20220138218A1

公开(公告)日：2022-05-05

申请号：US17090556

申请日：2020-11-05

Applicant: Adobe Inc.

Inventor： Anup Rao ,  Tung Mai ,  Matvey Kapilevich

IPC: G06F16/26 ,  G06F16/28 ,  G06T11/20

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that estimate the overlap between sets of data samples. In particular, in one or more embodiments, the disclosed systems utilize a sketch-based sampling routine and a flexible, accurate estimator to determine the overlap (e.g., the intersection) between sets of data samples. For example, in some implementations, the disclosed systems generate a sketch vector—such as a one permutation hashing vector—for each set of data samples. The disclosed systems further compare the sketch vectors to determine an equal bin similarity estimator, a lesser bin similarity estimator, and a greater bin similarity estimator. The disclosed systems utilize one or more of the determined similarity estimators in generating an overlap estimation for the sets of data samples.

公开(公告)号：US11170048B2

公开(公告)日：2021-11-09

申请号：US16451956

申请日：2019-06-25

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Aldo Gael Carranza ,  David Arbour ,  Anup Rao ,  Sungchul Kim ,  Eunyee Koh

IPC: G06F16/00 ,  G06F16/901 ,  G06F17/18

Abstract: A system is disclosed for identifying and counting typed graphlets in a heterogeneous network. A methodology implementing techniques for the disclosed system according to an embodiment includes identifying typed k-node graphlets occurring between any two selected nodes of a heterogeneous network, wherein the nodes are connected by one or more edges. The identification is based on combinatorial relationships between (k−1)-node typed graphlets occurring between the two selected nodes of the heterogeneous network. Identification of 3-node typed graphlets is based on computation of typed triangles, typed 3-node stars, and typed 3-paths associated with each edge connecting the selected nodes. The method further includes maintaining a count of the identified k-node typed graphlets and storing those graphlets with non-zero counts. The identified graphlets are employed for applications including visitor stitching, user profiling, outlier detection, and link prediction.

公开(公告)号：US12175366B2

公开(公告)日：2024-12-24

申请号：US17210157

申请日：2021-03-23

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Tung Mai ,  Nedim Lipka ,  Jiong Zhu ,  Anup Rao ,  Viswanathan Swaminathan

IPC: G06N3/08 ,  G06F16/901 ,  G06F18/2132 ,  G06F18/2413 ,  G06N5/02

Abstract: Techniques are provided for training graph neural networks with heterophily datasets and generating predictions for such datasets with heterophily. A computing device receives a dataset including a graph data structure and processes the dataset using a graph neural network. The graph neural network defines prior belief vectors respectively corresponding to nodes of the graph data structure, executes a compatibility-guided propagation from the set of prior belief vectors and using a compatibility matrix. The graph neural network predicts predicting a class label for a node of the graph data structure based on the compatibility-guided propagations and a characteristic of at least one node within a neighborhood of the node. The computing device outputs the graph data structure where it is usable by a software tool for modifying an operation of a computing environment.

公开(公告)号：US11720592B2

公开(公告)日：2023-08-08

申请号：US17818974

申请日：2022-08-10

Applicant: Adobe Inc.

Inventor： Anup Rao ,  Tung Mai ,  Matvey Kapilevich

IPC: G06F16/00 ,  G06F16/26 ,  G06T11/20 ,  G06F16/28

CPC classification number: G06F16/26 ,  G06F16/285 ,  G06F16/288 ,  G06T11/206

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that estimate the overlap between sets of data samples. In particular, in one or more embodiments, the disclosed systems utilize a sketch-based sampling routine and a flexible, accurate estimator to determine the overlap (e.g., the intersection) between sets of data samples. For example, in some implementations, the disclosed systems generate a sketch vector—such as a one permutation hashing vector—for each set of data samples. The disclosed systems further compare the sketch vectors to determine an equal bin similarity estimator, a lesser bin similarity estimator, and a greater bin similarity estimator. The disclosed systems utilize one or more of the determined similarity estimators in generating an overlap estimation for the sets of data samples.

公开(公告)号：US11526907B2

公开(公告)日：2022-12-13

申请号：US16688700

申请日：2019-11-19

Applicant: ADOBE INC.

Inventor： Alireza Farhadi ,  Ryan A. Rossi ,  Tung Mai ,  Anup Rao

IPC: G06F16/2455 ,  G06F16/735 ,  G06F16/901 ,  G06Q30/02

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for determining an increased matching for large graphs in which an increased matching is generated for the graph by leveraging an initial matching for a small fraction of edges of the large graph. An initial matching for a random subset of edges of an input graph is leveraged to generate alternating paths based on the initially matched edges and the remaining edges, not included in the random subset. An increased matching for the entire graph includes the alternating paths without the initial matched edges, thus increasing the number of matched edges in the increased matching by at least one for every initially matched edge. Graph-based tasks may then be triggered based on the increased matching.