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公开(公告)号：US12047391B2
公开(公告)日：2024-07-23
申请号：US17219318
申请日：2021-03-31
Applicant: PayPal, Inc.
Inventor： Vishal Sood , Sudhindra Murthy , Ashwin Maruti Hegde , Nitin S. Sharma , Hong Fan , Grahame Andrew Jastrebski
IPC: H04L9/40 , G06F21/55 , G06N20/00 , H04L67/133
CPC classification number: H04L63/1416 , G06F21/554 , G06N20/00 , H04L67/133
Abstract: Systems, methods, and computer program products are directed to machine learning techniques that use a separate embedding layer. This can allow for continuous monitoring of a processing system based on events that are continuously generated. Various events may have corresponding feature data associated with at least one action relating to a processing system. Embedding vectors that correspond to the features are retrieved from an embedding layer that is hosted on a separate physical device or a separate computer system from a computer that hosts the machine learning system. The embedding vectors are processed though the machine learning model, which may then make a determination (e.g. whether or not a particular user action should be allowed). Generic embedding vectors additionally enable the use of a single remote embedding layer for multiple different machine learning models, such as event driven data models.
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公开(公告)号：US11785030B2
公开(公告)日：2023-10-10
申请号：US17008323
申请日：2020-08-31
Applicant: PAYPAL, INC.
Inventor： Vishal Sood , Divakar Viswanathan , Sheena Chawla , Sudhindra Murthy , Vidya Sagar Durga , Hong Fan
CPC classification number: H04L63/1425 , G06N20/00
Abstract: This application discusses identifying data processing timeouts in live risk analysis systems. A service provider, such as an electronic transaction processor, may provide a production computing environment that includes a risk analysis system having one or more risk models, which may be machine-learning based. These risk models may be utilized in order to determine whether incoming data processing requests are fraudulent. To test these risk models using production data traffic, an audit computing environment made of a set of machines that do not service production computing environment requests, but that utilize databases and data connections as are used by the production systems. The audit computing environment may thus mirror the risk models and functionality of the production computing environment without the drawbacks of a typical fully separate testing environment. Thus, risk model performance and execution times may be monitored to determine whether any models encounter errors with production data traffic.
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公开(公告)号：US20230049611A1
公开(公告)日：2023-02-16
申请号：US17398868
申请日：2021-08-10
Applicant: PAYPAL, INC.
Inventor： Sudhindra Murthy , Divakar Viswanathan , Vishal Sood
Abstract: There are provided systems and methods for a compute platform for machine leaning model roll-out. A service provider, such as an electronic transaction processor for digital transactions, may provide intelligent decision-making through decision services that execute machine learning models. When deploying or updating machine learning models in these engines and decision services, a model package may include multiple models, each of which may have an execution graph required for model execution. When models are tested from proper execution, the models may have non-performant compute items, such as model variables, that lead to improper execution and/or decision-making. A model deployer may determine and flag these compute items as non-performant and may cause these compute items to be skipped or excluded from execution. Further, the model deployer may utilize a pre-production computing environment to generate the execution graphs for the models prior to deployment or upgrading.
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公开(公告)号：US12218926B2
公开(公告)日：2025-02-04
申请号：US17399300
申请日：2021-08-11
Applicant: PayPal, Inc.
Inventor： Vishal Sood , Yegya Narayanan Gopala Krishnan , Sudhindra Murthy , Vidya Sagar Durga , Chirag Gupta
Abstract: Techniques are disclosed for time constrained electronic request evaluation. A server system receives, from a computing device, a request submitted via an account, including a first set of characteristics associated with the request. The system executes a first machine-learning model to determine a first risk score for the request by inputting the first set of characteristics into the first model. The system generates an initial authentication decision for the request based on the first score and sends the decision to the device. The system executes a second, different machine-learning model to determine a second risk score for the request, by inputting the first set of characteristics and a second, different set of characteristics associated with the account into the second model. Based on the second score, the system determines a final authentication decision. The disclosed techniques may advantageously improve computer security and operations via identification of malicious electronic requests.
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Patent Agency Ranking
公开(公告)号：US11868756B2
公开(公告)日：2024-01-09
申请号：US17398868
申请日：2021-08-10
Applicant: PAYPAL, INC.
Inventor： Sudhindra Murthy , Divakar Viswanathan , Vishal Sood
CPC classification number: G06F8/65 , G06F11/1469 , G06F11/3688 , G06F11/3692 , G06N20/20 , G06F2201/82
Abstract: There are provided systems and methods for a compute platform for machine leaning model roll-out. A service provider, such as an electronic transaction processor for digital transactions, may provide intelligent decision-making through decision services that execute machine learning models. When deploying or updating machine learning models in these engines and decision services, a model package may include multiple models, each of which may have an execution graph required for model execution. When models are tested from proper execution, the models may have non-performant compute items, such as model variables, that lead to improper execution and/or decision-making. A model deployer may determine and flag these compute items as non-performant and may cause these compute items to be skipped or excluded from execution. Further, the model deployer may utilize a pre-production computing environment to generate the execution graphs for the models prior to deployment or upgrading.
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公开(公告)号：US20220417229A1
公开(公告)日：2022-12-29
申请号：US17399300
申请日：2021-08-11
Applicant: PayPal, Inc.
Inventor： Vishal Sood , Yegya Narayanan Gopala Krishnan , Sudhindra Murthy , Vidya Sagar Durga , Chirag Gupta
Abstract: Techniques are disclosed for time constrained electronic request evaluation. A server system receives, from a computing device, a request submitted via an account, including a first set of characteristics associated with the request. The system executes a first machine-learning model to determine a first risk score for the request by inputting the first set of characteristics into the first model. The system generates an initial authentication decision for the request based on the first score and sends the decision to the device. The system executes a second, different machine-learning model to determine a second risk score for the request, by inputting the first set of characteristics and a second, different set of characteristics associated with the account into the second model. Based on the second score, the system determines a final authentication decision. The disclosed techniques may advantageously improve computer security and operations via identification of malicious electronic requests.
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公开(公告)号：US20240168750A1
公开(公告)日：2024-05-23
申请号：US18527982
申请日：2023-12-04
Applicant: PayPal, Inc.
Inventor： Sudhindra Murthy , Divakar Viswanathan , Vishal Sood
CPC classification number: G06F8/65 , G06F11/1469 , G06F11/3688 , G06F11/3692 , G06N20/20 , G06F2201/82
Abstract: There are provided systems and methods for a compute platform for machine leaning model roll-out. A service provider, such as an electronic transaction processor for digital transactions, may provide intelligent decision-making through decision services that execute machine learning models. When deploying or updating machine learning models in these engines and decision services, a model package may include multiple models, each of which may have an execution graph required for model execution. When models are tested from proper execution, the models may have non-performant compute items, such as model variables, that lead to improper execution and/or decision-making. A model deployer may determine and flag these compute items as non-performant and may cause these compute items to be skipped or excluded from execution. Further, the model deployer may utilize a pre-production computing environment to generate the execution graphs for the models prior to deployment or upgrading.
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公开(公告)号：US20250023889A1
公开(公告)日：2025-01-16
申请号：US18750815
申请日：2024-06-21
Applicant: PayPal, Inc.
Inventor： Vishal Sood , Sudhindra Murthy , Ashwin Maruti Hegde , Nitin S. Sharma , Hong Fan , Grahame Andrew Jastrebski
IPC: H04L9/40 , G06F21/55 , G06N20/00 , H04L67/133
Abstract: Systems, methods, and computer program products are directed to machine learning techniques that use a separate embedding layer. This can allow for continuous monitoring of a processing system based on events that are continuously generated. Various events may have corresponding feature data associated with at least one action relating to a processing system. Embedding vectors that correspond to the features are retrieved from an embedding layer that is hosted on a separate physical device or a separate computer system from a computer that hosts the machine learning system. The embedding vectors are processed though the machine learning model, which may then make a determination (e.g. whether or not a particular user action should be allowed). Generic embedding vectors additionally enable the use of a single remote embedding layer for multiple different machine learning models, such as event driven data models.
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公开(公告)号：US20210400066A1
公开(公告)日：2021-12-23
申请号：US17008323
申请日：2020-08-31
Applicant: PAYPAL, INC.
Inventor： Vishal Sood , Divakar Viswanathan , Sheena Chawla , Sudhindra Murthy , Vidya Sagar Durga , Hong Fan
Abstract: This application discusses identifying data processing timeouts in live risk analysis systems. A service provider, such as an electronic transaction processor, may provide a production computing environment that includes a risk analysis system having one or more risk models, which may be machine-learning based. These risk models may be utilized in order to determine whether incoming data processing requests are fraudulent. To test these risk models using production data traffic, an audit computing environment made of a set of machines that do not service production computing environment requests, but that utilize databases and data connections as are used by the production systems. The audit computing environment may thus mirror the risk models and functionality of the production computing environment without the drawbacks of a typical fully separate testing environment. Thus, risk model performance and execution times may be monitored to determine whether any models encounter errors with production data traffic.
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