公开(公告)号：US12118462B2

公开(公告)日：2024-10-15

申请号：US17148984

申请日：2021-01-14

Applicant: Visa International Service Association

Inventor： Zhongfang Zhuang ,  Michael Yeh ,  Liang Wang ,  Wei Zhang ,  Junpeng Wang

IPC: G06N3/08 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/044 ,  G06N3/045

Abstract: Described are a system, method, and computer program product for multivariate event prediction using multi-stream recurrent neural networks. The method includes receiving event data from a sample time period and generating feature vectors for each subperiod of each day. The method also includes providing the feature vectors as inputs to a set of first recurrent neural network (RNN) models and generating first outputs for each RNN node. The method further includes merging the first outputs for each same subperiod to form aggregated time-series layers. The method further includes providing the aggregated time-series layers as an input to a second RNN model and generating final outputs for each RNN node of the second RNN model.

公开(公告)号：US20240273095A1

公开(公告)日：2024-08-15

申请号：US18567717

申请日：2022-06-01

Applicant: Visa International Service Association

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

IPC: G06F16/2453 ,  G06F16/2458

CPC classification number: G06F16/24537 ,  G06F16/2465 ,  G06F16/2477

Abstract: A method is disclosed. The method comprises determining a time series, a subsequence length. The length of the time series may then be determined, and an initial matrix profile may then be computed. The method may then form a processed matrix profile for a first subsequence of the subsequence length by applying the first subsequence to the initial matrix profile. A second subsequence may then be determined from the processed matrix profile. The method may then include comparing the second subsequence to other subsequences in a dictionary and adding it to the dictionary. The subsequences in the dictionary may be used to generate a plurality of subsequence matrix profiles. The method may then include forming an approximate matrix profile using the plurality of subsequence matrix profiles and then determining one or more anomalies in the time series or another time series using the approximate matrix profile.

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：US20230143484A1

公开(公告)日：2023-05-11

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：US20240428072A1

公开(公告)日：2024-12-26

申请号：US18823865

申请日：2024-09-04

Applicant: Visa International Service Association

Inventor： Zhongfang Zhuang ,  Michael Yeh ,  Liang Wang ,  Wei Zhang ,  Junpeng Wang

IPC: G06N3/08 ,  G06N3/044 ,  G06N3/045

Abstract: Described are a system, method, and computer program product for multivariate event prediction using multi-stream recurrent neural networks. The method includes receiving event data from a sample time period and generating feature vectors for each subperiod of each day. The method also includes providing the feature vectors as inputs to a set of first recurrent neural network (RNN) models and generating first outputs for each RNN node. The method further includes merging the first outputs for each same subperiod to form aggregated time-series layers. The method further includes providing the aggregated time-series layers as an input to a second RNN model and generating final outputs for each RNN node of the second RNN model.

公开(公告)号：US20240378414A1

公开(公告)日：2024-11-14

申请号：US18692625

申请日：2022-09-20

Applicant: Visa International Service Association

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

IPC: G06N3/042

Abstract: A method performed by a server computer is disclosed. The method comprises generating a binary compositional code matrix from an input matrix. The binary compositional code matrix is then converted into an integer code matrix. Each row of the integer code matrix is input into a decoder, including plurality of codebooks, to output a summed vector for each row. The method then includes inputting a derivative of each summed vector into a downstream machine learning model to output a prediction.

公开(公告)号：US20240177071A1

公开(公告)日：2024-05-30

申请号：US18281663

申请日：2022-03-30

Applicant: Visa International Service Association

Inventor： Junpeng Wang ,  Liang Wang ,  Yan Zheng ,  Michael Yeh ,  Shubham Jain ,  Wei Zhang ,  Zhongfang Zhuang ,  Hao Yang

IPC: G06N20/20 ,  G06F18/2415

CPC classification number: G06N20/20 ,  G06F18/2415

Abstract: Systems, methods, and computer program products may compare machine learning models by identifying data instances with disagreed predictions and learning from the disagreement. Based on a model interpretation technique, differences between the compared machine learning models may be interpreted. Multiple metrics to prioritize meta-features from different perspectives may also be provided.

公开(公告)号：US20210224648A1

公开(公告)日：2021-07-22

申请号：US17148984

申请日：2021-01-14

Applicant: Visa International Service Association

Inventor： Zhongfang Zhuang ,  Michael Yeh ,  Liang Wang ,  Wei Zhang ,  Junpeng Wang

IPC: G06N3/08 ,  G06N3/04

Abstract: Described are a system, method, and computer program product for multivariate event prediction using multi-stream recurrent neural networks. The method includes receiving event data from a sample time period and generating feature vectors for each subperiod of each day. The method also includes providing the feature vectors as inputs to a set of first recurrent neural network (RNN) models and generating first outputs for each RNN node. The method further includes merging the first outputs for each same subperiod to form aggregated time-series layers. The method further includes providing the aggregated time-series layers as an input to a second RNN model and generating final outputs for each RNN node of the second RNN model.