公开(公告)号：US20240185565A1

公开(公告)日：2024-06-06

申请号：US18550356

申请日：2021-09-23

Applicant: Visa International Service Association

Inventor： Huiyuan Chen ,  Yu-San Lin ,  Fei Wang ,  Hao Yang

IPC: G06V10/74 ,  G06V10/80 ,  G06V10/82

CPC classification number: G06V10/761 ,  G06V10/80 ,  G06V10/82

Abstract: A method includes determining a set of regions for each of a first plurality of images of a first item type, a second plurality of images of a second item type, and a third plurality of images of a third item type. The method also includes for each region in each set of regions of the images, generating, by the processing computer, a vector representing the region, and then generating a plurality of aggregated messages using the vectors corresponding to combinations of images of different types of items, the images being from the first, second, and third plurality of images. Then, unified embeddings are generated for the images in the first, second, and third plurality of images, respectively, using aggregated messages in the plurality of aggregated messages. Matching scores associated with combinations of the images are created using the unified embeddings and a machine learning model.

公开(公告)号：US20230308464A1

公开(公告)日：2023-09-28

申请号：US18202405

申请日：2023-05-26

Applicant: Visa International Service Association

Inventor： Bo Dong ,  Yuhang Wu ,  Yu-San Lin ,  Michael Yeh ,  Hao Yang

IPC: H04L9/40

CPC classification number: H04L63/1425 ,  H04L63/1416 ,  H04L63/1475

Abstract: Disclosed are a system, method, and computer program product for user network activity anomaly detection. The method includes generating a multilayer graph from network resource data, and generating an adjacency matrix associated with each layer of the multilayer graph to produce a plurality of adjacency matrices. The method further includes assigning a weight to each adjacency matrix to produce a plurality of weights, and generating a merged single layer graph by merging the plurality of layers based on a weighted sum of the plurality of adjacency matrices using the plurality of weights. The method further includes generating a set of anomaly scores by generating, for each node in the merged single layer graph, an anomaly score. The method further includes determining a set of anomalous users based on the set of anomaly scores, detecting fraudulent network activity based on the set of anomalous users, and executing a fraud mitigation process.

公开(公告)号：US20240428142A1

公开(公告)日：2024-12-26

申请号：US18830191

申请日：2024-09-10

Applicant: Visa International Service Association

Inventor： Linyun He ,  Shubham Agrawal ,  Yu-San Lin ,  Yuhang Wu ,  Ishita Bindlish ,  Chiranjeet Chetia ,  Fei Wang

IPC: G06N20/20 ,  G06N5/04

Abstract: Systems, methods, and computer program products for multi-domain ensemble learning based on multivariate time sequence data are provided. A method may include receiving multivariate sequence data. At least a portion of the multivariate sequence data may be inputted into a plurality of anomaly detection models to generate a plurality of scores. The multivariate sequence data may be combined with the plurality of scores to generate combined intermediate data. The combined intermediate data may be inputted into a combined ensemble model to generate an output score. In response to determining that the output score satisfies a threshold, at least one of an alert may be communicated to a user device, the multivariate sequence data may be inputted into the feature-domain ensemble model to generate a feature importance vector, or at least one of a model-domain, a time-domain, a feature-domain, or the combined ensemble model may be updated.

公开(公告)号：US20240062120A1

公开(公告)日：2024-02-22

申请号：US18268465

申请日：2022-10-20

Applicant: Visa International Service Association

Inventor： Linyun He ,  Shubham Agrawal ,  Yu-San Lin ,  Yuhang Wu ,  Ishita Bindlish ,  Chiranjeet Chetia ,  Fei Wang

IPC: G06N20/20

CPC classification number: G06N20/20

Abstract: Systems, methods, and computer program products for multi-domain ensemble learning based on multivariate time sequence data are provided. A method may include receiving multivariate sequence data. At least a portion of the multivariate sequence data may be inputted into a plurality of anomaly detection models to generate a plurality of scores. The multivariate sequence data may be combined with the plurality of scores to generate combined intermediate data. The combined intermediate data may be inputted into a combined ensemble model to generate an output score. In response to determining that the output score satisfies a threshold, at least one of an alert may be communicated to a user device, the multivariate sequence data may be inputted into the feature-domain ensemble model to generate a feature importance vector, or at least one of a model-domain, a time-domain, a feature-domain, or the combined ensemble model may be updated.

公开(公告)号：US11481485B2

公开(公告)日：2022-10-25

申请号：US16737367

申请日：2020-01-08

Applicant: Visa International Service Association

Inventor： Yuhang Wu ,  Yanhong Wu ,  Hossein Hamooni ,  Yu-San Lin ,  Hao Yang

IPC: G06F21/00 ,  G06F21/55 ,  G06Q50/26 ,  G06F16/28 ,  G06Q10/10

Abstract: Methods for detecting insider threats are disclosed. A method includes collecting server access data and application access data, based on the server access data and the application access data, determining nearest neighbors of an employee, and based on the nearest neighbors of the employee, determining a peer group of the employee, determining an average rank distance (ARD) of the nearest neighbors based on a ranking of the nearest neighbors in a plurality of time periods, identifying ARD gaps between the nearest neighbors, and generating scores corresponding to the ARD gaps between the nearest neighbors. One or more employees are identified that represent an internal threat to an organization based on the scores corresponding to the ARD gaps.

公开(公告)号：US20240152735A1

公开(公告)日：2024-05-09

申请号：US18280727

申请日：2022-06-10

Applicant: Visa International Service Association

Inventor： Lan Wang ,  Yu-San Lin ,  Yuhang Wu ,  Huiyuan Chen ,  Fei Wang ,  Hao Yang

IPC: G06N3/0464

CPC classification number: G06N3/0464

Abstract: Provided is a system for detecting an anomaly in a multivariate time series that includes at least one processor programmed or configured to receive a dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, determine a set of target data instances based on the dataset, determine a set of historical data instances based on the dataset, generate, based on the set of target data instances, a true value matrix, a true frequency matrix, and a true correlation matrix, generate a forecast value matrix, a forecast frequency matrix, and a forecast correlation matrix based on the set of target data instances and the set of historical data instances, determine an amount of forecasting error, and determine whether the amount of forecasting error corresponds to an anomalous event associated with the dataset of data instances. Methods and computer program products are also provided.

公开(公告)号：US20220407879A1

公开(公告)日：2022-12-22

申请号：US17763282

申请日：2021-10-18

Applicant: Visa International Service Association

Inventor： Bo Dong ,  Yuhang Wu ,  Yu-San Lin ,  Michael Yeh ,  Hao Yang

IPC: H04L9/40

Abstract: Described are a system, method, and computer program product for user network activity anomaly detection. The method includes receiving network resource data associated with network resource activity of a plurality of users and generating a plurality of layers of a multilayer graph from the network resource data. Each layer of the plurality of layers may include a plurality of nodes, which are associated with users, connected by a plurality of edges, which are representative of node interdependency. The method also includes generating a plurality of adjacency matrices from the plurality of layers and generating a merged single layer graph based on a weighted sum of the plurality of adjacency matrices. The method further includes generating anomaly scores for each node in the merged single layer graph and determining a set of anomalous users based on the anomaly scores.

公开(公告)号：US20220398466A1

公开(公告)日：2022-12-15

申请号：US17836249

申请日：2022-06-09

Applicant: Visa International Service Association

Inventor： Yuhang Wu ,  Linyun He ,  Mengting Gu ,  Lan Wang ,  Shubham Agrawal ,  Yu-San Lin ,  Ishita Bindlish ,  Fei Wang ,  Hao Yang

IPC: G06N5/02

Abstract: Provided is a system for event forecasting using a graph-based machine-learning model that includes at least one processor programmed or configured to receive a dataset of data instances, where each data instance comprises a time series of data points, detect a plurality of motifs representing a plurality of events in the dataset of data instances using a matrix profile-based motif detection technique, generate a bipartite graph representation of the plurality of motifs in a time sequence, and generate a machine-learning model based on the bipartite graph representation of the plurality of motifs in the time sequence, where the machine-learning model is configured to provide an output and the output includes a prediction of whether an event will occur during a specified time interval. Methods and computer program products are also provided.

公开(公告)号：US20210390606A1

公开(公告)日：2021-12-16

申请号：US17270632

申请日：2019-08-26

Applicant: Visa International Service Association

Inventor： Maryam Moosaei ,  Yu-San Lin ,  Hao Yang

IPC: G06Q30/06 ,  G06Q30/02 ,  G06N3/04

Abstract: Systems, methods, and computer program products for predicting user preference of items in an image process image data associated with a single image with a first branch of a neural network to produce an image embedding, the single image including a set of multiple items; process a user identifier of a user with a second branch of the neural network to produce a user embedding; concatenate the image embedding with the user embedding to produce a concatenated embedding; process the concatenated embedding with the neural network to produce a joint embedding; and generate a user preference score for the set of multiple items from the neural network based on the joint embedding, the user preference score including a prediction of whether the user prefers the set of multiple items.

公开(公告)号：US20240412065A1

公开(公告)日：2024-12-12

申请号：US18702382

申请日：2022-09-30

Applicant: Visa International Service Association

Inventor： Huiyuan Chen ,  Yu-San Lin ,  Menghai Pan ,  Lan Wang ,  Michael Yeh ,  Fei Wang ,  Hao Yang

IPC: G06N3/08

Abstract: Described are a system, method, and computer program product for denoising sequential machine learning models. The method includes receiving data associated with a plurality of sequences and training a sequential machine learning model based on the data associated with the plurality of sequences to produce a trained sequential machine learning model. Training the sequential machine learning model includes denoising a plurality of sequential dependencies between items in the plurality of sequences using at least one trainable binary mask. The method also includes generating an output of the trained sequential machine learning model based on the denoised sequential dependencies. The method further includes generating a prediction of an item associated with a sequence of items based on the output of the trained sequential machine learning model.