US-20260127522-A1 - SYSTEMS AND METHODS FOR A HOME SAVINGS PLATFORM

US20260127522A1US 20260127522 A1US20260127522 A1US 20260127522A1US-20260127522-A1

Abstract

A provider computing system includes one or more processing circuits including one or more processors coupled to one or more memory devices having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to determine a user track for a user using one or more machine learning models. The instructions further cause the one or more processors to identify one or more features to provide to the user based on the user track. The instructions further cause the one or more processors to generate a user interface including the one or more features. The instructions further cause the one or more processors to cause the user interface to be displayed to the user.

Inventors

Moses Harris
Ramsay Huntley
Aishah Miller
Placide Muhizi
Poonam Rao
Timothy Craig Seagren
Jud Murchie

Assignees

WELLS FARGO BANK, N.A.

Dates

Publication Date: 20260507
Application Date: 20251229

Claims (20)

1 . A provider computing system associated with a provider, the provider computing system comprising: one or more processing circuits including one or more processors coupled to one or more memory devices, the one or more memory devices having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to: receive feedback pertaining to features identified by one or more machine learning models for inclusion in at least one user interface based on historical data associated with a plurality of users; update the one or more machine learning models using the received feedback; determine, using the one or more machine learning models, a user track for a user; identify, using the one or more machine learning models, one or more features to include in a user interface based on the user track; generate the user interface including the one or more features; and cause the user interface to be displayed to the user.
2 . The provider computing system of claim 1 , wherein the user track is determined from a plurality of user tracks, the plurality of user tracks comprising at least a first track for users planning to rent a home long-term, a second track for users aspiring to purchase a home, and a third track for users that are homeowners.
3 . The provider computing system of claim 2 , wherein the one or more features included in the user interface comprise a first set of features when the user track is the first track, a second set of features when the user track is the second track, and a third set of features when the user track is the third track, each of the first set of features, the second set of features, and the third set of features being different.
4 . The provider computing system of claim 1 , wherein the instructions further cause the one or more processors to: collect user information of the user, the user information including one or more of a purchase history of the user, a goal of the user, or a vision board of the user; determine a recommended track for the user based on the user information; transmit a track recommendation including the recommended track to the user device; and receive the user track selection from the user.
5 . The provider computing system of claim 4 , wherein determining the recommended track is performed using one or more machine learning models and the instructions further cause the one or more processors to: train the one or more machine learning models to determine the recommended track using training data comprising prior user track selections corresponding to prior user information; and update the one or more machine learning models based on the user information and the user track selection.
6 . The provider computing system of claim 1 , wherein the user track corresponds to a user goal and the one or more features include a progress tracking feature that provides a visual indication of an amount of progress achieved by the user toward the user goal.
7 . The provider computing system of claim 6 , wherein the instructions further cause the one or more processors to: train one or more machine learning models to determine one or more goal achievement suggestions using training data comprising prior goal achievements corresponding to prior user information; determine the one or more goal achievement suggestions using the one or more machine learning models based on the user information; receive feedback from the user pertaining to an effect of the one or more goal achievement suggestions; and update the one or more machine learning models based on the user information and the feedback.
8 . The provider computing system of claim 6 , wherein the one or more features further include one or more selectable options to perform one or more actions associated with the user goal.
9 . The provider computing system of claim 1 , wherein the one or more features include a first selectable option to open a tax savings account associated with the user track, and the instructions further cause the one or more processors to: receive a selection of the first selectable option from the user; in response to receiving the selection of the first selectable option, open the tax savings account; and in response to opening the tax savings account, updating the user interface to include a second selectable option to take out a loan against funds within the tax savings account.
10 . The provider computing system of claim 1 , wherein the instructions further cause the one or more processors to train the one or more machine learning models to identify features for inclusion in the user interface corresponding to user tracks by training the one or more machine learning models to identify an estimated relevance for the features, and wherein generating the user interface includes arranging the one or more features within the user interface in a descending order of the estimated relevance from left to right.
11 . A computer-implemented method comprising: receiving, by the one or more processors, feedback pertaining to features identified by one or more machine learning models for inclusion in at least one user interface based on historical data associated with a plurality of users; updating, by the one or more processors, the one or more machine learning models using the received feedback; determining, by the one or more processors and using the one or more machine learning models, a user track for a user; identifying, by the one or more processors using the one or more machine learning models, one or more features to include in a user interface based on the user track; generating, by the one or more processors, the user interface including the one or more features; and causing, by the one or more processors, the user interface to be displayed to the user.
12 . The computer-implemented method of claim 11 , wherein the user track is at least one of a long-term renter track, an aspiring homeowner track, or a current homeowner track.
13 . The computer-implemented method of claim 12 , wherein the one or more features included in the user interface comprise a first set of features associated with long-term renting when the user track is the long-term renter track, a second set of features associated with purchasing a home when the user track is the aspiring homeowner track, and a third set of features associated with owning a home when the user track is the current homeowner track.
14 . The computer-implemented method of claim 11 , wherein the user track corresponds to a user goal and the one or more features include a progress tracking feature that provides an indication of progress by the user toward the user goal.
15 . The computer-implemented method of claim 11 , wherein the one or more features include a selectable option to open a tax savings account associated with the user track.
16 . A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one processing circuit of a provider computing system associated with a provider, cause operations comprising: receiving feedback pertaining to features identified by one or more machine learning models for inclusion in at least one user interface based on historical data associated with a plurality of users; updating the one or more machine learning models using the received feedback; determining, using the one or more machine learning models, a user track for a user; identifying, using the one or more machine learning models, one or more features to include in a user interface based on the user track; generating the user interface including the one or more features; and causing the user interface to be displayed to the user.
17 . The non-transitory computer-readable medium of claim 16 , wherein the user track corresponds to a user goal, is one of a long-term renter track, an aspiring homeowner track, or a current homeowner track, and includes a progress tracking feature that provides an indication of progress by the user toward the user goal.
18 . The non-transitory computer-readable medium of claim 17 , wherein the operations further comprise: training one or more machine learning models to determine one or more goal achievement suggestions using training data comprising prior goal achievements corresponding to prior user information; determining the one or more goal achievement suggestions using the one or more machine learning models based on user information; receiving feedback from the user; and updating the one or more machine learning models based on the user information and the feedback.
19 . The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise: collecting user information of the user, the user information including one or more of a purchase history of the user, a goal of the user, or a vision board of the user; determining a recommended track for the user based on the user information; transmitting a track recommendation including the recommended track to the user device; and receiving the user track selection from the user.
20 . The non-transitory computer-readable medium of claim 19 , wherein determining the recommended track is performed using one or more machine learning models and the operations further comprise: training the one or more machine learning models to determine the recommended track using training data comprising prior user track selections corresponding to prior user information; and updating the one or more machine learning models based on the user information and the user track selection.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. patent application Ser. No. 18/235,754, filed Aug. 18, 2023, which is incorporated herein by reference in its entirety and for all purposes. TECHNICAL FIELD Aspects and embodiments of the present disclosure relate to systems and methods for providing a home savings platform. BACKGROUND Customers have a variety of differing housing-related goals. For example, some customers may wish to be long-term renters because they wish to rent in a certain part of a city or in a certain building that is rent-only, or they may wish to have the flexibility to move more easily than someone who owns a home. Some customers qualifying for rental assistance may financially benefit from remaining as renters and receiving rental assistance from the government. These customers may therefore not wish to own their own home, as doing so would cause them to lose their rental assistance payments. Other customers may desire to purchase a home or may currently own a home, but wish to save for one or more home-related expenses (e.g., saving for appliances, maintenance, and/or renovation projects). Each of these customer types may find differing information useful and/or relevant. SUMMARY One embodiment relates to a provider computing system associated with a provider. The provider computing system comprises one or more processing circuits including one or more processors coupled to one or more memory devices, the one or more memory devices having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to determine a user track for a user based on one or more of a user track selection received from a user device associated with the user or user information of the user. The instructions further cause the one or more processors to identify one or more features to provide to the user based on the user track. The instructions further cause the one or more processors to generate a user interface corresponding to the user track, the user interface including the one or more features. The instructions further cause the one or more processors to cause the user interface to be displayed to the user. Another embodiment relates to a computer-implemented method. The computer-implemented method includes determining, by one or more processors of a computing system, a user track for a user based on one or more of a user track selection received from a user device associated with the user or user information of the user. The computer-implemented method further includes identifying, by the one or more processors, one or more features to provide to the user based on the user track. The computer-implemented method further includes generating, by the one or more processors, a user interface corresponding to the user track, the user interface including the one or more features. The computer-implemented method further includes causing, by the one or more processors, the user interface to be displayed to the user. Still another embodiment relates to a non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one processing circuit of a provider computing system associated with a provider, cause operations including determining a user track for a user based on one or more of a user track selection received from a user device associated with the user or user information of the user. The operations further include identifying one or more features to provide to the user based on the user track. The operations further include generating a user interface corresponding to the user track, the user interface including the one or more features. The operations further include causing the user interface to be displayed to the user. This summary is illustrative only and is not intended to be in any way limiting. Other aspects, inventive features, and advantages of the devices or processes described herein will become apparent in the detailed description set forth herein, taken in conjunction with the accompanying figures, wherein like reference numerals refer to like elements. Numerous specific details are provided to impart a thorough understanding of embodiments of the subject matter of the present disclosure. The described features of the subject matter of the present disclosure may be combined in any suitable manner in one or more embodiments and/or implementations. In this regard, one or more features of an aspect of the invention may be combined with one or more features of a different aspect of the invention. Moreover, additional features may be recognized in certain embodiments and/or implementations that may not be present in all embodiments or implementations. BRIEF DESCRIPTION OF THE FIGURES FIG. 1 is a block diagram of a computing environment that provides users pursuing different living situations with relevant information and resources, according to an example embodiment. FIG.