公开(公告)号：US20250111011A1

公开(公告)日：2025-04-03

申请号：US18374799

申请日：2023-09-29

Applicant: Visa International Service Association

Inventor： Junpeng Wang ,  Minghua Xu ,  Shubham Jain ,  Yan Zheng ,  Michael Yeh ,  Liang Wang ,  Wei Zhang

IPC: G06F17/18

Abstract: Methods, systems, and computer program products are provided for coordinated analysis of output scores and input features of machine learning models in different environments. An example method includes receiving a plurality of first data records and a plurality of second data records. A first plot is generated based on a first score generated by a machine learning model for each first data record and a second score generated by the machine learning model for each second data record. The first plot is displayed. A plurality of second plots associated with at least a subset of the plurality of features are generated. Each respective second plot is generated based on a respective first field associated with a respective feature from the first data records and a respective second field associated with the respective feature from the second data records. The second plots are displayed.

公开(公告)号：US20250103884A1

公开(公告)日：2025-03-27

申请号：US18889563

申请日：2024-09-19

Applicant: Visa International Service Association

Inventor： Yujie Fan ,  Jiarui Sun ,  Michael Yeh ,  Wei Zhang

IPC: G06N3/08 ,  G06N3/0455

Abstract: Methods, systems, and computer program products are provided for spatial-temporal prediction using trained spatial-temporal masked autoencoders. An example system includes a processor configured to determine a structural dependency graph associated with a networked system. The processor is also configured to receive multivariate time-series data from a first time period associated with the networked system. The processor is further configured to mask the plurality of edges of the structural dependency graph and mask the multivariate time-series data. The processor is further configured to train a spatial-temporal autoencoder based on the masked structural representation and the masked temporal representation. The processor is further configured to generate a prediction using a spatial-temporal machine learning model including the trained spatial-temporal autoencoder, the prediction associated with an attribute of the networked system in a second time period subsequent to the first time period.

公开(公告)号：US12217157B2

公开(公告)日：2025-02-04

申请号：US18271301

申请日：2023-01-30

Applicant: Visa International Service Association

Inventor： Jiarui Sun ,  Mengting Gu ,  Michael Yeh ,  Liang Wang ,  Wei Zhang

IPC: G06N3/04 ,  G06F17/16 ,  G06N3/049

Abstract: Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot. The method includes generating and propagating initial node representations across information propagation layers using the adaptive information transition matrix and classifying a node of the discrete time dynamic graph subsequent to the first time period based on final node representations.

公开(公告)号：US12205044B2

公开(公告)日：2025-01-21

申请号：US17273275

申请日：2019-09-24

Applicant: Visa International Service Association

Inventor： Aravind Sankar ,  Yanhong Wu ,  Liang Gou ,  Wei Zhang ,  Hao Yang

IPC: G06N5/022 ,  G06F16/901 ,  G06F18/214 ,  G06N3/08 ,  G06N20/10

Abstract: A method includes extracting, by an analysis computer, a plurality of first datasets from a plurality of graph snapshots using a structural self-attention module. The analysis computer can then extract at least a second dataset from the plurality of first datasets using a temporal self-attention module across the plurality of graph snapshots. The analysis computer can then perform graph context prediction with at least the second dataset.

公开(公告)号：US20240289613A1

公开(公告)日：2024-08-29

申请号：US18656024

申请日：2024-05-06

Applicant: Visa International Service Association

Inventor： Haoyu Li ,  Junpeng Wang ,  Liang Wang ,  Yan Zheng ,  Wei Zhang

IPC: G06N3/08 ,  G06N3/0455

CPC classification number: G06N3/08 ,  G06N3/0455

Abstract: A method, system, and computer program product is provided for embedding compression and reconstruction. The method includes receiving embedding vector data comprising a plurality of embedding vectors. A beta-variational autoencoder is trained based on the embedding vector data and a loss equation. The method includes determining a respective entropy of a respective mean and a respective variance of each respective dimension of a plurality of dimensions. A first subset of the plurality of dimensions is determined based on the respective entropy of the respective mean and the respective variance for each respective dimension of the plurality of dimensions. A second subset of the plurality of dimensions is discarded based on the respective entropy of the respective mean and the respective variance for each respective dimension of the plurality of dimensions. The method includes generating a compressed representation of the embedding vector data based on the first subset of dimensions.

公开(公告)号：US20240127035A1

公开(公告)日：2024-04-18

申请号：US18275598

申请日：2022-02-01

Applicant: VISA INTERNATIONAL SERVICE ASSOCIATION

Inventor： Michael Yeh ,  Zhongfang Zhuang ,  Junpeng Wang ,  Yan Zheng ,  Javid Ebrahimi ,  Liang Wang ,  Wei Zhang

IPC: G06N3/0455

CPC classification number: G06N3/0455

Abstract: A method performed by a computer is disclosed. The method comprises receiving interaction data between electronic devices of a plurality of entities. The interaction data is used to form an entity interaction vector containing a number of interactions between the electronic devices of a chosen entity and an entity time series containing a plurality of metrics per unit time of the interactions. An interaction encoder of the computer can generate an interaction hidden representation of the entity interaction vector using embeddings of the plurality of entities. A temporal encoder of the computer can generate a temporal hidden representation of the entity time series. The interaction hidden representation and the temporal hidden representation can be used to generate a predicted scale and a shape estimation of a target interaction metric. The computer can then generate an estimated interaction metric of a time period using the predicted scale and the shape estimation.

公开(公告)号：US20240086422A1

公开(公告)日：2024-03-14

申请号：US18509465

申请日：2023-11-15

Applicant: Visa International Service Association

Inventor： Michael Yeh ,  Liang Gou ,  Wei Zhang ,  Dhruv Gelda ,  Zhongfang Zhuang ,  Yan Zheng

IPC: G06F16/28 ,  G06F16/23 ,  G06N3/08

CPC classification number: G06F16/284 ,  G06F16/2379 ,  G06N3/08

Abstract: Provided are systems for analyzing a relational database using embedding learning that may include at least one processor programmed or configured to generate one or more entity-relation matrices from a relational database and perform, for each entity-relation matrix of the one or more entity-relation matrices, an embedding learning process on an embedding associated with an entity. When performing the embedding learning process on the embedding associated with the entity, the at least one processor is programmed or configured to generate an updated embedding associated with the entity. Computer-implemented methods and computer program products are also provided.

公开(公告)号：US11922290B2

公开(公告)日：2024-03-05

申请号：US17919898

申请日：2022-05-24

Applicant: Visa International Service Association

Inventor： Zhongfang Zhuang ,  Michael Yeh ,  Wei Zhang ,  Mengting Gu ,  Yan Zheng ,  Liang Wang

IPC: G06N3/0464 ,  G06F17/14

CPC classification number: G06N3/0464 ,  G06F17/142

Abstract: Provided is a system for analyzing a multivariate time series that includes at least one processor programmed or configured to receive a time series of historical data points, determine a historical time period, determine a contemporary time period, determine a first time series of data points associated with a historical transaction metric from the historical time period, determine a second time series of data points associated with a historical target transaction metric from the historical time period, determine a third time series of data points associated with a contemporary transaction metric from the contemporary time period, and generate a machine learning model, wherein the machine learning model is configured to provide an output that comprises a predicted time series of data points associated with a contemporary target transaction metric. Methods and computer program products are also provided.

公开(公告)号：US11783436B2

公开(公告)日：2023-10-10

申请号：US16891993

申请日：2020-06-03

Applicant: VISA INTERNATIONAL SERVICE ASSOCIATION

Inventor： Dhruv Gelda ,  Konik Kothari ,  Wei Zhang ,  Hao Yang

IPC: G06Q50/14 ,  G06Q40/12 ,  G06Q30/0201 ,  G06F16/23 ,  G06T11/00 ,  G06F16/248 ,  G06N5/04 ,  G06N20/00 ,  G06F16/22

CPC classification number: G06Q50/14 ,  G06F16/22 ,  G06F16/2379 ,  G06F16/248 ,  G06N5/04 ,  G06N20/00 ,  G06Q30/0201 ,  G06Q40/12 ,  G06T11/00

Abstract: A dynamic next-stop or next-item recommendation system that is built entirely from raw card transaction data logs. These data logs contain rich transaction data between cardholders and merchants. A query network approach is constructed for geometrical expressivity and automatically learns the inherent class-hierarchy. To ensure scalability and interpretability of the approach, merchants or entities are grouped into interpretable categories and propose a quadtree-based spatial decomposition of the underlying geography. A two-step recommendation process initiates: (1) predict next-merchant quadtree-box and category combination (2) recommend merchants within the predicted combination. This novel neural architecture may handle the hierarchical classification task in the first part of the recommendation system and compare the methods to previous state-of-the-art approaches in related areas.

公开(公告)号：US20230306024A1

公开(公告)日：2023-09-28

申请号：US17907996

申请日：2022-03-24

Applicant: Visa International Service Association

Inventor： Mangesh Bendre ,  Robert Brian Christensen ,  Yan Zheng ,  Wei Zhang ,  Fei Wang ,  Hao Yang

IPC: G06F16/2453 ,  G06F7/08 ,  G06F16/2458 ,  G06F16/27

CPC classification number: G06F16/24537 ,  G06F7/08 ,  G06F16/2477 ,  G06F16/27

Abstract: Described are a system, method, and computer program product for efficiently joining time-series data tables. The method includes loading a first table and a second table into a memory and generating a set of first key-value pairs based on a set of first time-series records and a set of second key-value pairs based on a set of second time-series records. The method also includes sorting the set of first key-value pairs and the set of second key-value pairs. The method further includes interleaving the set of first key-value pairs with the set of second key-value pairs and sequentially matching the sets of time-series records to form a joined table. The method further includes, in response to matching each respective second time-series record with the respective first time-series record, removing the respective second time-series record from the at least one memory.