US 12,271,746 B1
Model-based personalization architecture
Mohsen Sardari, Burlingame, CA (US); Anna Bloom, Park City, UT (US); Jonathan Lamberts, Denver, CO (US); Ran Lin, Foster City, CA (US); Khilesh Mistry, Oakland, CA (US); and Sagnik Mazumder, Belmont, CA (US)
Assigned to Block, Inc., Oakland, CA (US)
Filed by Block, Inc., Oakland, CA (US)
Filed on Oct. 20, 2022, as Appl. No. 17/970,392.
Int. Cl. G06F 9/451 (2018.01); G06Q 20/32 (2012.01)
CPC G06F 9/453 (2018.02) [G06Q 20/3276 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method implemented by at least one computing device of a payment service, the method comprising:
receiving, by the at least one computing device, contextual information regarding an interaction between the payment service and a user device associated with a user;
determining, by the at least one computing device and based at least in part on inputting the contextual information into a trained machine learning (ML) model, at least one of a propensity metric or a value metric for the user, wherein the trained ML model is trained based on one or more propensity signals;
dynamically configuring, by the at least one computing device and based at least in part on the propensity metric, a user interface to enable the user to access data associated with a user account, wherein the configuring comprises arranging user interface elements on the user interface in a layout personalized for the user and based at least in part on the propensity metric, wherein each user interface element represents content particular to a service offered by the payment service, the payment service offering multiple services including the service;
based at least in part on receiving an indication of an interaction with a user interface element, associating an applet corresponding to a selected service with which the user interface element is associated with the user account, the applet configured to initialize an onboarding flow associated with enabling the user to access or add the selected service to the user account; and
in response to the applet initializing the onboarding flow, launching the onboarding flow associated with the selected service, wherein content included in the onboarding flow is customized based at least in part on the contextual information associated with the user or the selected service.