中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

399 records (took 0.124 seconds)

    SYSTEMS AND METHODS FOR IDENTITY DOCUMENT FRAUD DETECTION
    81.
    发明公开
    SYSTEMS AND METHODS FOR IDENTITY DOCUMENT FRAUD DETECTION 审中-公开

    公开(公告)号：US20240273538A1

    公开(公告)日：2024-08-15

    申请号：US18647158

    申请日：2024-04-26

    Applicant: Stripe, Inc.

    Inventor： Charlotte Gils ,  Efstathios Vafeias

    IPC: G06Q20/40 ,  G06V10/774 ,  G06V10/82 ,  G06V20/00 ,  G06V30/41

    CPC classification number: G06Q20/4016 ,  G06V10/774 ,  G06V10/82 ,  G06V20/95 ,  G06V30/41

    Abstract: A method and apparatus for fraud detection during transactions using identity graphs are described. The method may include receiving a document image for detecting whether an identity document depicted within the document image is fraudulent. The method may also include extracting data associated with the document image to generate extracted data. The method may also include processing, by a single machine learning model, subsets of the decoded image data used as corresponding inputs to each of a set of machine learning model backbones of the single machine learning model that generate one or more intermediate signals indicative of whether a subset of the extracted image data input into said each machine learning model backbone is associated with a fraudulent identity document. The method may also include processing, by a second machine learning model backbone that generates a final score indicative of whether the document image depicts a fraudulent identity document, at least one or more intermediate signals.

    REVERSE WEBHOOK AUTHENTICATION
    83.
    发明公开
    REVERSE WEBHOOK AUTHENTICATION 审中-公开

    公开(公告)号：US20240214227A1

    公开(公告)日：2024-06-27

    申请号：US18086626

    申请日：2022-12-21

    Applicant: Stripe, Inc.

    Inventor： Ji HUANG ,  Yu-Ting TSAI

    IPC: H04L9/32 ,  H04L9/08 ,  H04L67/147

    CPC classification number: H04L9/3297 ,  H04L9/0825 ,  H04L67/147

    Abstract: The disclosure generally describes one or more techniques for authenticating a webhook endpoint with a webhook server. Some techniques include a webhook server sending a seed with a webhook endpoint after the webhook endpoint is registered with the webhook server. In some examples, the webhook server generates the seed to send to the webhook endpoint and stores the seed with a key associated with the webhook endpoint. In such examples, the webhook server does not send data associated with the particular events to the webhook endpoint until the webhook endpoint acknowledges receipt of the seed while the seed is still valid.

    System and method for a near field communications reader device
    84.
    发明授权
    System and method for a near field communications reader device 有权

    公开(公告)号：US12008428B2

    公开(公告)日：2024-06-11

    申请号：US18202522

    申请日：2023-05-26

    Applicant: Stripe, Inc.

    Inventor： Hwai Sian Tsai ,  Chi Wah Lo

    IPC: G06K7/10

    CPC classification number: G06K7/10297 ,  G06K7/10316

    Abstract: A reader device for attachment to a smart device comprising a display, the reader comprising an antenna, processing and reading circuitry, and a communications module, wherein the antenna, the processing and reading circuitry, the processor and the communications module are coupled to each other, and whereby the antenna encloses said display. When a card is tapped on the display, the antenna receives a signal and transmits the signal to the processing and reading circuitry. The processing and reading circuitry processes the signal to produce data, and the produced data is transmitted to a device external to the reader device by the communications module.

    Using one or more networks to assess one or more metrics about an entity
    85.
    发明授权
    Using one or more networks to assess one or more metrics about an entity 有权

    公开(公告)号：US11997098B2

    公开(公告)日：2024-05-28

    申请号：US17975316

    申请日：2022-10-27

    Applicant: Stripe, Inc.

    Inventor： Pranav Dandekar ,  Peter Lofgren ,  Ashish Goel

    IPC: G06F21/50 ,  G06F15/16 ,  H04L9/40

    CPC classification number: H04L63/102 ,  G06F21/50 ,  G06F15/16 ,  H04L63/0263

    Abstract: Described herein are systems and methods for predicting a metric value for an entity associated with a query node in a graph that represents a network. In embodiments, using a user's profile as the query node, a metric about that user may be estimated based, at least in part, as a function of how well connected the query node is to a whitelist of “good” users/nodes in the network, a blacklist of “bad” users/nodes in the network, or both. In embodiments, one or more nodes or edges may be weighted when determining a final score for the query node. In embodiments, the final score regarding the metric may be used to take one or more actions relative to the query node, including accepting it into a network, allowing or rejecting a transaction, assigning a classification to the node, using the final score to compute another estimate for a node, etc.

    Machine learning-based loss forecasting model
    86.
    发明授权
    Machine learning-based loss forecasting model 有权

    公开(公告)号：US11995714B1

    公开(公告)日：2024-05-28

    申请号：US17475215

    申请日：2021-09-14

    Applicant: STRIPE, INC.

    Inventor： Mengjie Ding ,  Bo Xu ,  Yaakov Erlichman

    IPC: G06Q40/03 ,  G06Q40/06

    CPC classification number: G06Q40/03 ,  G06Q40/06

    Abstract: Systems and methods for implementing a machine learning loan portfolio loss forecasting system are provided. A current state of active loans of a loan portfolio during a first time period of a set of time periods may be determined. For each of the set of time periods, a roll rate of the active loans from each delinquency state to a subsequent delinquency state may be determined based on historical data of the loan portfolio. The machine learning model may then, iteratively, for each subsequent time period, determine a percentage of the active loans that will transition to each of the set of delinquency states during the subsequent time period based on a current state of the active loans during a previous time period and the roll rate from each delinquency state to a subsequent delinquency state for the subsequent time period.

    FRAUD DETECTION USING REAL-TIME AND BATCH FEATURES
    87.
    发明公开
    FRAUD DETECTION USING REAL-TIME AND BATCH FEATURES 审中-公开

    公开(公告)号：US20240161115A1

    公开(公告)日：2024-05-16

    申请号：US17985732

    申请日：2022-11-11

    Applicant: Stripe, Inc.

    Inventor： Ryan Drapeau ,  Logan Howard ,  Seth Hendrickson ,  Isaac Madwed

    IPC: G06Q20/40

    CPC classification number: G06Q20/4016

    Abstract: A method and system for detecting a fraudulent payment are described herein. The method can include detecting, at a server computer system, a request to perform a transaction between a merchant system and a customer of the merchant system, and in response to the detecting, determining, in real time one or more real time features corresponding to the transaction. The method may also include determining, by the server computer system, one or more batch features that correspond to one or more attributes associated with the transaction. The method may also include determining, by the server computer system, whether the payment is potentially fraudulent based on utilizing a model that includes inputs corresponding to the combination of the one or more real time features and the one or more batch features, and in response to determining that the payment is potentially fraudulent, performing, a remediation action associated with the transaction.

    DATA MANAGEMENT USING SCORE CALIBRATION AND SCALING FUNCTIONS
    88.
    发明公开
    DATA MANAGEMENT USING SCORE CALIBRATION AND SCALING FUNCTIONS 审中-公开

    公开(公告)号：US20240152923A1

    公开(公告)日：2024-05-09

    申请号：US17979985

    申请日：2022-11-03

    Applicant: Stripe, Inc.

    Inventor： Emmanuel Ameisen

    IPC: G06Q20/40

    CPC classification number: G06Q20/4016

    Abstract: Various embodiments described herein support or provide for data management operations, such as identifying an uncalibrated fraud score that corresponds to a set of transactions; using a machine learning model to generate a calibrated fraud score based on the uncalibrated fraud score; determining a calibrated fraud score distribution associated with the calibrated fraud score; identifying an uncalibrated fraud score distribution associated with the uncalibrated fraud score; using a score scaling function to generate a mapping between the calibrated fraud score distribution and the uncalibrated fraud score distribution; and generating a scaled calibrated fraud score based on the mapping.

Patent Agency Ranking