SYSTEMS AND METHODS FOR ENFORCING ACCESS REQUIREMENTS TO SERVICES IN A DISTRIBUTED SERVICES SYSTEM

    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.

    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
    15.
    发明授权

    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.

    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
    16.
    外观设计

    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.

    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

    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.

    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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