Patents by Inventor Michael Yeh
Michael Yeh has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240160854Abstract: Described are a system, method, and computer program product for debiasing embedding vectors of machine learning models. The method includes receiving embedding vectors and generating two clusters thereof. The method includes determining a first mean vector of the first cluster and a second mean vector of the second cluster. The method includes determining a bias associated with each of a plurality of first candidate vectors and replacing the first mean vector with a first candidate vector based on the bias. The method includes determining a bias associated with each of a plurality of second candidate vectors and replacing the second mean vector with a second candidate vector based on the bias. The method includes repeatedly replacing the first and second mean vectors until an extremum of the bias score is reached, and debiasing the embedding vectors by linear projection using a direction defined by the first and second mean vectors.Type: ApplicationFiled: March 30, 2022Publication date: May 16, 2024Inventors: Sunipa Dev, Yan Zheng, Michael Yeh, Junpeng Wang, Wei Zhang, Archit Rathore
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Publication number: 20240152499Abstract: Provided is a system for analyzing features associated with entities using an embedding tree, the system including at least one processor programmed or configured to receive a dataset associated with a plurality of entities, wherein the dataset comprises a plurality of data instances for a plurality of entities. The processor may be programmed or configured to generate at least two embeddings based on the dataset and determine split criteria for partitioning an embedding space of at least one embedding tree associated with the dataset based on feature data associated with an entity and embedding data associated with the at least two embeddings. The processor may be programmed or configured to generate at least one embedding tree having a plurality of nodes based on the split criteria. Methods and computer program products are also provided.Type: ApplicationFiled: June 9, 2022Publication date: May 9, 2024Inventors: Yan Zheng, Wei Zhang, Michael Yeh, Liang Wang, Junpeng Wang, Shubham Jain, Zhongfang Zhuang
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Publication number: 20240134599Abstract: Provided is a method for normalizing embeddings for cross-embedding alignment. The method may include applying mean centering to the at least one embedding set, applying spectral normalization to the at least one embedding set, and/or applying length normalization to the at least one embedding set. Spectral normalization may include decomposing the at least one embedding set, determining an average singular value of the at least one embedding set, determining a respective substitute singular value for each respective singular value of a diagonal matrix, and/or replacing the at least one embedding set with a product of the at least one embedding set, a right singular vector, and an inverse of the substitute diagonal matrix. The mean centering, spectral normalization, and/or length normalization may be iteratively repeated for a configurable number of iterations. A system and computer program product are also disclosed.Type: ApplicationFiled: December 6, 2023Publication date: April 25, 2024Inventors: Yan Zheng, Michael Yeh, Junpeng Wang, Wei Zhang, Liang Wang, Hao Yang, Prince Osei Aboagye
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Patent number: 11966832Abstract: A method includes receiving a first data set comprising embeddings of first and second types, generating a fixed adjacency matrix from the first dataset, and applying a first stochastic binary mask to the fixed adjacency matrix to obtain a first subgraph of the fixed adjacency matrix. The method also includes processing the first subgraph through a first layer of a graph convolutional network (GCN) to obtain a first embedding matrix, and applying a second stochastic binary mask to the fixed adjacency matrix to obtain a second subgraph of the fixed adjacency matrix. The method includes processing the first embedding matrix and the second subgraph through a second layer of the GCN to obtain a second embedding matrix, and then determining a plurality of gradients of a loss function, and modifying the first stochastic binary mask and the second stochastic binary mask using at least one of the plurality of gradients.Type: GrantFiled: July 2, 2021Date of Patent: April 23, 2024Assignee: Visa International Service AssociationInventors: Huiyuan Chen, Yu-San Lin, Lan Wang, Michael Yeh, Fei Wang, Hao Yang
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Publication number: 20240127035Abstract: 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.Type: ApplicationFiled: February 1, 2022Publication date: April 18, 2024Applicant: VISA INTERNATIONAL SERVICE ASSOCIATIONInventors: Michael Yeh, Zhongfang Zhuang, Junpeng Wang, Yan Zheng, Javid Ebrahimi, Liang Wang, Wei Zhang
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Publication number: 20240085449Abstract: An electronic device may include a housing and a display in the housing. The display may be used as an anemometer to measure the speed of ambient air in the device's environment. In particular, the display may be monitored by a temperature sensor until it reaches an equilibrium temperature, at which point it may be heated by increasing the brightness of the display or using a separate heater. After heating, a cooling response of the display may be measured, and the ambient air speed may be calculated based on the cooling response of the display. Instead of measuring the air speed using the display, other components, such as a pressure sensor, may be used to measure the air speed by heating the components and measuring a cooling response of the components. Multiple temperature sensors may be incorporated into the device to determine a wind direction in addition to air speed.Type: ApplicationFiled: July 11, 2023Publication date: March 14, 2024Inventors: David MacNeil, Michael J. Glickman, John P. Bergen, Richard Yeh
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Publication number: 20240086422Abstract: 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.Type: ApplicationFiled: November 15, 2023Publication date: March 14, 2024Inventors: Michael Yeh, Liang Gou, Wei Zhang, Dhruv Gelda, Zhongfang Zhuang, Yan Zheng
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Publication number: 20240078416Abstract: 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.Type: ApplicationFiled: January 30, 2023Publication date: March 7, 2024Applicant: Visa International Service AssociationInventors: Jiarui Sun, Mengting Gu, Michael Yeh, Liang Wang, Wei Zhang
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Patent number: 11922290Abstract: 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.Type: GrantFiled: May 24, 2022Date of Patent: March 5, 2024Assignee: Visa International Service AssociationInventors: Zhongfang Zhuang, Michael Yeh, Wei Zhang, Mengting Gu, Yan Zheng, Liang Wang
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Publication number: 20240046075Abstract: A method includes receiving a first data set comprising embeddings of first and second types, generating a fixed adjacency matrix from the first dataset, and applying a first stochastic binary mask to the fixed adjacency matrix to obtain a first subgraph of the fixed adjacency matrix. The method also includes processing the first subgraph through a first layer of a graph convolutional network (GCN) to obtain a first embedding matrix, and applying a second stochastic binary mask to the fixed adjacency matrix to obtain a second subgraph of the fixed adjacency matrix. The method includes processing the first embedding matrix and the second subgraph through a second layer of the GCN to obtain a second embedding matrix, and then determining a plurality of gradients of a loss function, and modifying the first stochastic binary mask and the second stochastic binary mask using at least one of the plurality of gradients.Type: ApplicationFiled: July 2, 2021Publication date: February 8, 2024Applicant: Visa International Service AssociationInventors: Huiyuan Chen, Yu-San Lin, Lan Wang, Michael Yeh, Fei Wang, Hao Yang
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Patent number: 11861324Abstract: Provided is a method for normalizing embeddings for cross-embedding alignment. The method may include applying mean centering to the at least one embedding set, applying spectral normalization to the at least one embedding set, and/or applying length normalization to the at least one embedding set. Spectral normalization may include decomposing the at least one embedding set, determining an average singular value of the at least one embedding set, determining a respective substitute singular value for each respective singular value of a diagonal matrix, and/or replacing the at least one embedding set with a product of the at least one embedding set, a right singular vector, and an inverse of the substitute diagonal matrix. The mean centering, spectral normalization, and/or length normalization may be iteratively repeated for a configurable number of iterations. A system and computer program product are also disclosed.Type: GrantFiled: May 25, 2022Date of Patent: January 2, 2024Assignee: Visa International Service AssociationInventors: Yan Zheng, Michael Yeh, Junpeng Wang, Wei Zhang, Liang Wang, Hao Yang, Prince Osei Aboagye
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Patent number: 11836159Abstract: 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.Type: GrantFiled: October 9, 2020Date of Patent: December 5, 2023Assignee: Visa International Service AssociationInventors: Michael Yeh, Liang Gou, Wei Zhang, Dhruv Gelda, Zhongfang Zhuang, Yan Zheng
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Publication number: 20230308464Abstract: 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.Type: ApplicationFiled: May 26, 2023Publication date: September 28, 2023Inventors: Bo Dong, Yuhang Wu, Yu-San Lin, Michael Yeh, Hao Yang
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Publication number: 20230252557Abstract: Systems, methods, and computer program products train a residual neural network including a first fully connected layer, a first recurrent neural network layer, and at least one skip connection for anomaly detection. The at least one skip connection directly connects at least one of (i) an output of the first fully connected layer to a first other layer downstream of the first recurrent neural network layer in the residual neural network and (ii) an output of the first recurrent neural network layer to a second other layer downstream of a second recurrent neural network layer in the residual neural network.Type: ApplicationFiled: June 22, 2021Publication date: August 10, 2023Inventors: Zhongfang Zhuang, Michael Yeh, Wei Zhang, Javid Ebrahimi
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Patent number: 11711391Abstract: 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.Type: GrantFiled: October 18, 2021Date of Patent: July 25, 2023Assignee: Visa International Service AssociationInventors: Bo Dong, Yuhang Wu, Yu-San Lin, Michael Yeh, Hao Yang
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Publication number: 20230214177Abstract: Provided is a method for normalizing embeddings for cross-embedding alignment. The method may include applying mean centering to the at least one embedding set, applying spectral normalization to the at least one embedding set, and/or applying length normalization to the at least one embedding set. Spectral normalization may include decomposing the at least one embedding set, determining an average singular value of the at least one embedding set, determining a respective substitute singular value for each respective singular value of a diagonal matrix, and/or replacing the at least one embedding set with a product of the at least one embedding set, a right singular vector, and an inverse of the substitute diagonal matrix. The mean centering, spectral normalization, and/or length normalization may be iteratively repeated for a configurable number of iterations. A system and computer program product are also disclosed.Type: ApplicationFiled: May 25, 2022Publication date: July 6, 2023Inventors: Yan Zheng, Michael Yeh, Junpeng Wang, Wei Zhang, Liang Wang, Hao Yang, Prince Osei Aboagye
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Publication number: 20230186078Abstract: A method for evaluating a RNN-based deep learning model includes: receiving model data generated by the RNN-based model, the model data including a plurality of events associated with a plurality of states; generating a first GUI based on the events and states including a chart visually representing a timeline for the events in relation to a parameter value; generating a second GUI including a point chart visually representing a two-dimensional projection of the multi-dimensional intermediate data, each point of the point chart representing a time step and an event from the time step, based on multi-dimensional intermediate data between transformations in the model that connect a state to an event; and perturbing the environment at a time step based on user interaction with at least one of the first and second GUIs.Type: ApplicationFiled: April 30, 2021Publication date: June 15, 2023Inventors: Junpeng Wang, Wei Zhang, Hao Yang, Michael Yeh, Liang Wang
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Publication number: 20230153870Abstract: Embodiments for training a recommendation system to provide merchant recommendations comprise receiving, by a processor, raw merchant embeddings and raw user embeddings generated from payment transaction records, wherein the raw merchant embeddings include a plurality of embedded features. A generative adversarial network (GAN) is trained to generate modified merchant embeddings from the raw merchant embeddings, where the modified embeddings remove a location feature. Subsequent to training and responsive to receiving a request for merchant recommendations in the target location for the target user, the GAN and a trained preference model are used to generate a list of merchant rankings based on a new set of modified merchant embeddings, past preferences of a target user, and the target location to recommend merchants in the target location.Type: ApplicationFiled: December 20, 2022Publication date: May 18, 2023Applicant: Visa International Service AssociationInventors: Yan ZHENG, Yuwei WANG, Wei ZHANG, Michael YEH, Liang WANG
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Publication number: 20230143484Abstract: 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.Type: ApplicationFiled: May 24, 2022Publication date: May 11, 2023Inventors: Zhongfang Zhuang, Michael Yeh, Wei Zhang, Mengting Gu, Yan Zheng, Liang Wang
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Patent number: D999291Type: GrantFiled: October 7, 2022Date of Patent: September 19, 2023Assignee: Tecla, IncInventors: Andrew Erickson, Michael Yeh