公开(公告)号：US20230038609A1

公开(公告)日：2023-02-09

申请号：US17968461

申请日：2022-10-18

Applicant: STRIPE, INC.

Inventor： Jeroen Antonius Egidius Habraken

IPC: G06Q30/06 ,  G06N20/00 ,  G06K9/62 ,  G06F16/957

Abstract: In an example embodiment, a method for processing payments made via an electronic payment processing system is provided. An example method includes obtaining training data from a data source. The training data relates to prior purchases made via the electronic payment processing system, wherein the data source includes, in some examples, only a checkout page in a purchase transaction funnel. Features associated with a negative user action in relation to prior purchases are identified. A machine learning algorithm produces a dynamic transactional behavior score indicative of a probability that a purchase will invoke a negative user action.

公开(公告)号：US20200258141A1

公开(公告)日：2020-08-13

申请号：US16274043

申请日：2019-02-12

Applicant: Stripe, Inc.

Inventor： Jeroen Antonius Egidius Habraken

IPC: G06Q30/06 ,  G06F16/957 ,  G06K9/62 ,  G06N20/00

Abstract: In an example embodiment, a method for processing payments made via an electronic payment processing system is provided. An example method includes obtaining training data from a data source. The training data relates to prior purchases made via the electronic payment processing system, wherein the data source includes, in some examples, only a checkout page in a purchase transaction funnel. Features associated with a negative user action in relation to prior purchases are identified. A machine learning algorithm produces a dynamic transactional behavior score indicative of a probability that a purchase will invoke a negative user action.

公开(公告)号：US11508001B2

公开(公告)日：2022-11-22

申请号：US16274043

申请日：2019-02-12

Applicant: Stripe, Inc.

Inventor： Jeroen Antonius Egidius Habraken

IPC: G06Q30/06 ,  G06F16/957 ,  G06N20/00 ,  G06K9/62

Abstract: In an example embodiment, a method for processing payments made via an electronic payment processing system is provided. An example method includes obtaining training data from a data source. The training data relates to prior purchases made via the electronic payment processing system, wherein the data source includes, in some examples, only a checkout page in a purchase transaction funnel. Features associated with a negative user action in relation to prior purchases are identified. A machine learning algorithm produces a dynamic transactional behavior score indicative of a probability that a purchase will invoke a negative user action.