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    SYSTEMS AND METHODS FOR SIMULATING SELECTIVE FAULT INJECTIONS INTO A NETWORK INFRASTRUCTURE
    81.
    发明申请
    SYSTEMS AND METHODS FOR SIMULATING SELECTIVE FAULT INJECTIONS INTO A NETWORK INFRASTRUCTURE 有权

    公开(公告)号：US20240430170A1

    公开(公告)日：2024-12-26

    申请号：US18341555

    申请日：2023-06-26

    Applicant: Stripe, Inc.

    Inventor： Leonardo Viccari ,  Stuart Sandine ,  Omar Eltobgy ,  Michael Succi ,  Sherif Mahmoud

    IPC: H04L41/14 ,  H04L41/06 ,  H04L41/147 ,  H04L41/22

    Abstract: A system may include a network infrastructure having a set of network component nodes, each network component node configured to communicate with at least one other network component node in accordance with a dependency protocol; and a server in communication with the network infrastructure and a fault injection server. The server can be configured to monitor outputs generated by the network infrastructure and attributes of data communication between the set of network component nodes; execute a computer model using the dependency protocol and the monitored attributes and outputs as input to predict a set of faults; in response to presenting the set of faults for display on a user interface, receive a selection of one or more of the set of faults; and instruct the fault injection server to execute a fault injection scenario simulating performance of the network infrastructure operating under the selected one or more faults.

    SYSTEMS AND METHODS FOR ENFORCING ACCESS REQUIREMENTS TO SERVICES IN A DISTRIBUTED SERVICES SYSTEM
    83.
    发明公开
    SYSTEMS AND METHODS FOR ENFORCING ACCESS REQUIREMENTS TO SERVICES IN A DISTRIBUTED SERVICES SYSTEM 审中-公开

    公开(公告)号：US20240356919A1

    公开(公告)日：2024-10-24

    申请号：US18137886

    申请日：2023-04-21

    Applicant: Stripe, Inc.

    Inventor： Suhas Hoskote Muralidhar

    IPC: H04L9/40

    CPC classification number: H04L63/0884

    Abstract: Methods and apparatus for authenticating a user by a service provider system are described. The method can include receiving, from a service of the service provider system, a user data captured at an initiation of an onboarding process for a user seeking access to the service. The method may also include retrieving an access configuration associated with the service, the access configuration defining one or more user data access requirements to enable the user to access the service. The method may then include determining whether the one or more user data access requirements of the access configuration are satisfied by the user data, and in response to determining that one or more user data access requirements are satisfied, enabling the user to access the service of the server provider system. Furthermore, the method can include transmitting, to the service, a notification indicating that the user has satisfied the access requirements to enable the user to continue the onboarding process for the user to access the service.

    Code integrator
    86.
    发明授权
    Code integrator 有权

    公开(公告)号：US12125010B2

    公开(公告)日：2024-10-22

    申请号：US17216316

    申请日：2021-03-29

    Applicant: Stripe, Inc.

    Inventor： Adrienne Dreyfus ,  Karolis Kosas ,  Jillian Wells

    IPC: G06F3/0485 ,  G06F8/34 ,  G06Q20/10 ,  G06Q20/12

    CPC classification number: G06Q20/123 ,  G06F3/0485 ,  G06F8/34 ,  G06Q20/102

    Abstract: A method comprises: displaying a first code snippet from a first code in a first panel; displaying an annotation related to the first code snippet in a second panel; receiving a code customization request; customizing the first code snippet in response to the request; displaying a preview of executing the code having the customized first code snippet in a preview window; inserting and running the code having the customized first code snippet in an application; and completing a transaction with the application running the first code with the customized first code snippet.

    Reader device dock
    87.
    外观设计
    Reader device dock 有权

    公开(公告)号：USD1043617S1

    公开(公告)日：2024-09-24

    申请号：US29891209

    申请日：2023-05-02

    Applicant: Stripe Inc.

    Designer： Conor Hayes ,  John Affaki

    Abstract: FIG. 1 illustrates a front view for a reader device dock.
    FIG. 2 illustrates a back view for the reader device dock.
    FIG. 3 illustrates a top view for the reader device dock.
    FIG. 4 illustrates a side view for the reader device dock.
    FIG. 5 illustrates a side view for the reader device dock.
    FIG. 6 illustrates a bottom view for the reader device dock.
    FIG. 7 illustrates a perspective view for the reader device dock; and,
    FIG. 8 illustrates a perspective view for the reader device dock.
    The broken line showing portions of the reader device dock illustrates unclaimed environment and forms no part of the claimed design. The claimed reader device hub is not intended to be limited to the scale illustrated in the figures.

    SHARED LEARNING ACROSS SEPARATE ENTITIES WITH PRIVATE DATA FEATURES
    89.
    发明公开
    SHARED LEARNING ACROSS SEPARATE ENTITIES WITH PRIVATE DATA FEATURES 审中-公开

    公开(公告)号：US20240303554A1

    公开(公告)日：2024-09-12

    申请号：US18664202

    申请日：2024-05-14

    Applicant: Stripe, Inc.

    Inventor： Ashish GOEL ,  Peter LOFGREN

    IPC: G06N20/20 ,  G06F18/243 ,  G06N3/084 ,  G06N5/043

    CPC classification number: G06N20/20 ,  G06F18/24323 ,  G06N3/084 ,  G06N5/043

    Abstract: Embodiments herein use transfer learning paradigms to facilitate classification across entities without requiring the entities access to the other party's sensitive data. In one or more embodiments, one entity may train a model using its own data (which may include at least some non-shared data) and shares either the scores (or an intermediate representation of the scores). One or more other parties may use the scores as a feature in its own model. The scores may be considered to act as an embedding of the features but do not reveal the features. In other embodiments, parties may be used to train part of a model or participate in generating one or more nodes of a decision tree without revealing all its features. The trained models or decision trees may then be used for classifying unlabeled events or items.

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