Systems and methods for pre-checking data transfers

Patent metadata

Field	Value
Publication number	US-2025284827-A1
Application number	US-202418601309-A
Country	US
Kind code	A1
Filing date	Mar 11, 2024
Priority date	Mar 11, 2024
Publication date	Sep 11, 2025
Grant date	—

Abstract

The present disclosure relates to systems and methods for pre-checking transfer information for data transfers using trained machined learning models. There is provided a computer system, comprising a processor; a communications module coupled to the processor; and a memory coupled to the processor. The memory stores instructions that, when executed, configure the processor to monitor input of transfer input for a data transfer in an input field of an interface displayed on a device in real-time, determine a format protocol that applies to the input field, determine whether the transfer input complies with the format protocol using a trained machine learning model, generate and transmit a signal to the device receiving the transfer input in real time, the signal indicating whether the transfer input complies with the format protocol, and receive modification to the transfer input in the input field prior to execution of the data transfer.

First claim

What is claimed is:

1 . A computer system, comprising:
a processor; a communications module coupled to the processor; and a memory coupled to the processor, the memory storing instructions that, when executed, configure the processor to:
monitor input of transfer input for a data transfer in an input field of an interface displayed on a device in real-time;
determine a format protocol that applies to the input field;
determine whether the transfer input complies with the format protocol using a trained machine learning model;
generate and transmit a signal to the device receiving the transfer input in real time, the signal indicating whether the transfer input complies with the format protocol; and
receive modification to the transfer input in the input field prior to execution of the data transfer.

2 . The system of claim 1 , wherein the transfer input is a transfer text input or the instructions, when executed, further configure the processor to convert the transfer input into a transfer text input.

3 . The system of claim 2 , wherein the instructions, when executed, further configure the processor to:
identify one or more aspects of the transfer text input that do not comply with the format protocol using the trained machine learning model; wherein the signal incorporates the one or more aspects of the transfer text input that do not comply with the format protocol.

4 . The system of claim 3 , wherein the format protocol relates to aspects of the transfer text input including a required number of characters in the transfer text input, and whether each character is a letter, a number, or a non-alphanumeric symbol.

5 . The system of claim 4 , wherein the instructions, when executed, further configure the processor to determine the format protocol that applies to the input field by:
receiving another input from another input field of the interface displayed on the device, the other input indicating the format protocol.

6 . The system of claim 5 , wherein the format protocol is determined from a plurality of format protocols.

7 . The system of claim 6 , wherein each format protocol of the plurality of format protocols is associated with a country, and the instructions, when executed, further configure the processor to determine the format protocol by determining the country the other input is affiliated with.

8 . The system of claim 4 , wherein the trained machine learning model is a generative artificial intelligence (GenAI) model.

9 . The system of claim 8 , wherein the instructions, when executed, further configure the processor, upon execution of the GenAI model, to obtain an output explaining why the one or more aspects of the transfer text input do not comply with the format protocol; wherein the signal includes the output.

10 . The system of claim 9 , wherein the GenAI model is a large language model (LLM), and the output further comprises one or more suggestions of how the one or more aspects of the transfer text input may be modified to help comply with the format protocol.

11 . The system of claim 9 , wherein the instructions, when executed, further configure the processor to transmit the signal with the output to the device via an email.

12 . The system of claim 8 , wherein the instructions, when executed, further configure the processor to:
receive an indication for further information regarding the one or more aspects of the transfer text input that do not comply with the format protocol; obtain, upon execution of the GenAI model, an output explaining why the one or more aspects of the transfer text input do not comply with the format protocol; and transmit the output to the device in response to the indication.

13 . A method comprising:
monitoring input of transfer input for a data transfer in an input field of an interface displayed on a device in real-time; determining a format protocol that applies to the input field; determining whether the transfer input complies with the format protocol using a trained machine learning model; generating and transmitting a signal to a the device receiving the transfer input in real time, the signal indicating whether the transfer input complies with the format protocol; and receiving modification to the transfer input in the input field prior to execution of the data transfer.

14 . The method of claim 13 , further comprising:
identifying one or more aspects of the transfer input that do not comply with the format protocol using the trained machine learning model; wherein the signal incorporates the one or more aspects of the transfer input that do not comply with the format protocol.

15 . The method of claim 14 , wherein the transfer input is transfer text input and the format protocol relates to aspects of the transfer text input including a required number of characters in the transfer text input, and whether each character is a letter, a number, or a non-alphanumeric symbol.

16 . The method of claim 14 , wherein determining the format protocol that applies to the input field comprises:
receiving another input from another input field of the interface displayed on the device, the other input indicating the format protocol.

17 . The method of claim 14 , wherein the trained machine learning model is a generative artificial intelligence (GenAI) model.

18 . The method of claim 17 , further comprising:
upon execution of the GenAI model, obtaining an output explaining why the one or more aspects of the transfer input do not comply with the format protocol; wherein the signal includes the output.

19 . The method of claim 17 , further comprising:
upon execution of the GenAI model, obtaining an output comprising one or more suggestions of how the one or more aspects of the transfer input may be modified to help comply with the format protocol.

20 . A computer-readable medium comprising instructions stored therein which, when executed by a processor, cause a computer to:
monitor input of transfer input for a data transfer in an input field of an interface displayed on a device in real-time; determine a format protocol that applies to the input field; determine whether the transfer input complies with the format protocol using a trained machine learning model; generate and transmit a signal to a the device receiving the transfer input in real time, the signal indicating whether the transfer input complies with the format protocol; and receive modification to the transfer input in the input field prior to execution of the data transfer.

Assignees

• Toronto Dominion Bank

Inventors

• Bajaj Hitesh
• Dunjic Milos
• Tax David Samuel
• Prendergast Jonathan Joseph
• Sharma Mohit
• Singha Hazari Abhijit
• Gupta Pranay
• Rastogi Kushank
• Kelly Thomas

Classifications

• G06F21/606 Primary

  by securing the transmission between two devices or processes · CPC title