US 11,948,190 B2
Smart learning systems for integrating and linking multiple data accounts across a network
Andrew Kamron Rostami, Weston, MA (US); Robert J. Sherman, Norwood, MA (US); Christina Elizabeth Sweeny, Chadds Ford, PA (US); Gaurav Sethi, Garnet Valley, PA (US); Scott Joseph Slagsvol, Beverly, MA (US); Rebecca Marie Hornbuckle, Boston, MA (US); Shaun Kevin Fisher, Natick, MA (US); Najmeh Mirzaie, East Boston, MA (US); and Grace Elisabeth Nicklin, Somerville, MA (US)
Assigned to Citizens Financial Group, Inc., Providence, RI (US)
Filed by Citizens Financial Group, Inc., Providence, RI (US)
Filed on Nov. 3, 2022, as Appl. No. 17/980,241.
Application 17/980,241 is a continuation of application No. 17/160,664, filed on Jan. 28, 2021, granted, now 11,526,934.
Claims priority of provisional application 62/967,788, filed on Jan. 30, 2020.
Prior Publication US 2023/0070588 A1, Mar. 9, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/02 (2023.01); G06Q 20/20 (2012.01); G06Q 20/24 (2012.01); G06Q 20/36 (2012.01); G06V 40/20 (2022.01)
CPC G06Q 40/02 (2013.01) [G06Q 20/20 (2013.01); G06Q 20/24 (2013.01); G06Q 20/36 (2013.01); G06V 40/20 (2022.01)] 30 Claims
OG exemplary drawing
 
1. A system comprising:
one or more information source systems;
a user device associated with a user;
one or more electronic data accounts associated with the user; and
a server in communication with the one or more information source systems via at least one network, the user device and the one or more electronic data accounts, the server comprising an interactive graphical user interface (GUI), the server configured to:
link the one or more electronic data accounts,
monitor transactional data among the one or more information source systems and the linked accounts for any changes in spending activity associated with the user,
generate a user specific prediction model for the user by:
identifying historical activity of the user from the one or more linked electronic data accounts,
identifying further historical activity of other users with similar demographics to the user,
learning, by the user specific prediction model, financial activity patterns and behavior of the user based at least on the historical activity of the user, and
learning, by the user specific prediction model, further financial activity patterns and further behavior of the other users based on the further historical activity of the other users;
predict, via the user specific prediction model, a behavioral activity of the user based on at least one of the monitored transactional data and historical transactional data associated with the user, the predicted behavioral activity including one or more predicted problems and deficiencies in at least one among the linked accounts associated with the user,
analyze, via the user specific prediction model, the activity patterns of both the user and the other users,
generate at least one notification instruction specific to the user based on the predicted behavioral activity and the analyzed activity patterns, the at least one notification instruction including at least one of an alert and a suggestion, the suggestion identifying one or more system features and functions configured for avoiding at least one among the one or more predicted problems and deficiencies,
unlock at least one among the one or more system features and functions,
generate the interactive GUI for display on the user device, the interactive GUI presenting one or more of the at least one notification instruction and a user profile of the user among the linked accounts, and
dynamically update the interactive GUI to reflect real-time changes in at least one of the at least one notification instruction, the monitored transactional data, the user profile and the predicted behavioral activity.