US 12,333,613 B2
Computer network back-end system for transmitting graphical user interface data and control signals to a disparate device
Barath Jayaraman, Fort Mill, SC (US)
Assigned to TRUIST BANK, Charlotte, NC (US)
Filed by Truist Bank, Charlotte, NC (US)
Filed on Feb. 3, 2023, as Appl. No. 18/163,911.
Prior Publication US 2024/0265468 A1, Aug. 8, 2024
Int. Cl. G06Q 40/12 (2023.01)
CPC G06Q 40/12 (2013.12) 10 Claims
OG exemplary drawing
 
1. A system for providing recommendations to a user for reducing spending in response to the user identifying how much he/she wants to save, said system comprising:
a back-end server operating an online or mobile banking application and including:
at least one processor for processing data and information;
a communications interface communicatively coupled to the at least one processor; and
a memory device storing data and executable code that, when executed, causes the at least one processor to:
allow the user to enter how much the user wants to save;
allow the user to enter information concerning areas that the user does not want to reduce spending;
determine what the user is spending on, where the user is spending, how much the user is spending and how often the user is spending; and
provide recommendations to the user of areas that the user can reduce spending considering the amount the user wants to save and the areas that the user does not want to reduce spending, wherein the at least one processor prioritizes the recommendations for reducing spending by first recommending areas for reducing spending on things that the user has not identified as being areas that the user does not want to reduce spending and then recommending areas for reducing spending on areas that the user does not want to reduce spending, and wherein the at least one processor prioritizes the recommendations for reducing spending using machine learning and at least one neural network, and wherein the at least one neural network includes nodes that have been trained to prioritizes the recommendations for reducing spending, and wherein the nodes in the at least one neural network have been trained by training nodes in a neural network simulation model that employs unsupervised learning and training data to prioritizes the recommendations for reducing spending and a target variable, wherein the unsupervised learning is used to configure the neural network to generate a self-organizing map, reduce the dimensionally of an input data set, and to perform outlier/anomaly determinations to identify data points in the data set that falls outside a normal pattern of the data, and wherein training the nodes in the simulation model employs an iterative training and testing loop that incorporates weights associated with the nodes in the simulation model and iterative calculations that are tested, compared to the target variable and updated in subsequent iterative calculations to improve predictability of the target variable.