Patent Applications by Stripe, Inc. on May 29th, 2025

Stripe, Inc.: 4 patent applications

Stripe, Inc. has applied for patents in the areas of G06F16/2379 ({Updates performed during online database operations; commit processing}, 1), G06Q20/42 (Confirmation, e.g. check or permission by the legal debtor of payment, 1), G06Q40/00 (Finance; Insurance; Tax strategies; Processing of corporate or income taxes, 1), H04L63/1408 ({by monitoring network traffic (monitoring network traffic per se )}, 1)

With keywords such as: method, data, router, updating, apparatus, nodes, distributed, storage, system, described in patent application abstracts.

Top Inventors:

Hemal Shah of Sunnyvale CA US (1 patents)
Shashi Madappa of Campbell CA US (1 patents)
Karan Vohra of Sammamish WA US (1 patents)
Alexander THIEMANN of South San Francisco CA US (1 patents)
Ji HUANG of South San Francisco CA US (1 patents)

Patent Applications by Stripe, Inc.

20250173327. SYSTEMS METHODS ZERO DOWNTIME TOPOLOGY UPDATES DISTRIBUTED DATA STORAGE (Stripe, .)

Abstract: a method and apparatus for updating data router nodes in a distributed storage system are described. the method can include querying, by a first database agent of a database node, a cache data store for health status metrics of the cache data store. the method can also include storing, by the first database agent of the database node, the health status metrics in a data repository. furthermore, the method can include obtaining, by a second database agent of a router node, the health status metrics from the data repository, and updating, by the second database agent of the router node, a database topology file based on the health status metrics.

20250173730. ARTIFICIAL INTELLIGENCE MODELING ASSESSING FUTURE RECURRING TRANSACTIONS (Stripe, .)

Abstract: disclosed herein are methods and systems for using machine learning to improve the likelihood of success of recurring transactions. in one example, a suite of different machine learning models can be used together, such that a first machine learning model predicts a likelihood of success for a recurring transaction associated with a user account and the second machine learning model predicts whether a pre-authorization would help with the predicted likelihood of success. as a result, a server may pre-authorize the recurring transactions at a time earlier than the scheduled transaction time and place a hold on the user account using an amount predicted by the second machine learning model where the hold amount can be adjusted in accordance with the user account's activities. data associated with the recurring transaction itself can be ingested by the second machine learning model for re-calibration purposes.

20250173781. CONFIGURABLE TRIGGER-BASED EVENT PROCESSING (Stripe, .)

Abstract: aspects of the subject technology include receiving a selection of a trigger event, a set of workflows associated with the trigger event, wherein each workflow is assigned a respective priority attribute, and a set of custom expressions, wherein each custom expression corresponds to a respective workflow and specifies one or more conditions associated with the trigger event. aspects also include receiving an occurrence of the trigger event and, in response to a determination that the trigger event matches a custom expression for at least one workflow, performing a workflow of the set of workflows having a greatest priority and for which the associated respective custom expression is satisfied, wherein performing a workflow includes performing a set of recovery actions. aspects also include, in response to a determination that the trigger event does not match any of the respective sets of custom expressions, performing a default workflow.

20250175471. DETECTION MITIGATION AUTOMATED ACCOUNT GENERATION USING ARTIFICIAL INTELLIGENCE (Stripe, .)

Abstract: disclosed herein are systems and methods for detecting automated account generation requests. an example method includes receiving an application programming interface (api) request to generate a new user account. the method then includes executing a machine learning model to predict a likelihood of the api request having been generated automatically using one or more programming protocols. the machine learning model may be trained using historic requests known to have been generated using a machine or a programming/algorithm. when the machine learning model determines that the api request is likely to have been machine-made, the method includes executing an additional security protocol associated with the new user account.