Unique device identification system

Patent metadata

Field	Value
Publication number	US-2025348888-A1
Application number	US-202519215795-A
Country	US
Kind code	A1
Filing date	May 22, 2025
Priority date	Jul 27, 2021
Publication date	Nov 13, 2025
Grant date	—

Abstract

Systems, methods, devices, instructions, and other aspects are provided for unique device identification. In one aspect, a method is providing including receiving, at a server computer, transaction data associated with a client device, wherein the transaction data includes device configuration data, aggregating the device configuration data for the client device received within a threshold time period to generate a unique set of device configuration characteristics, receiving, at the server computer, transaction request data associated with the client device, verifying the client device using the unique set of device configuration characteristics, and facilitating a transaction associated with the transaction request data based on verification of the client device using the unique set of device configuration characteristics.

First claim

1 . (canceled)

2 . A computer-implemented method comprising:
generating, via a neural network and based on device configuration data associated with a plurality of devices including for a client device, a unique set of device configuration characteristics that create a non-unique overlap between device configuration characteristics associated with one or more other devices of the plurality of devices; reducing memory and reducing opportunities for spoofing by removing one or more device configuration characteristics from the unique set of device configuration characteristics; associating, with a machine learning model, historical return transaction data with the unique set of device configuration characteristics; receiving, at a server system, transaction request data associated with the client device, wherein the transaction request data is associated with a return transaction; verifying the client device using the unique set of device configuration characteristics; initiating a transaction associated with the transaction request data based on verification of the client device using the unique set of device configuration characteristics; and identifying the transaction as fraudulent based on the historical return transaction data.

3 . The computer-implemented method of claim 2 , wherein generating the unique set of device configuration characteristics includes matching device configuration data of two or more devices, wherein the device configuration data includes data for the client device, and wherein matching includes using a time period to generate the unique set of device configuration characteristics associated with the time period.

4 . The computer-implemented method of claim 2 , further comprising training the machine learning model to create one or more datasets that result in uniqueness for devices.

5 . The computer-implemented method of claim 2 , further comprising:
receiving additional transaction request data associated with an additional client device, wherein the transaction request data is associated with an additional return transaction; verifying the additional client device using the unique set of device configuration characteristics; initiating a transaction associated with the transaction request data based on verification of the additional client device using the unique set of device configuration characteristics; and facilitating an additional transaction associated with the additional transaction request data.

6 . The computer-implemented method of claim 2 , wherein the unique set of device configuration characteristics includes partner identifiers received from one or more third party servers associated with the client device.

7 . The computer-implemented method of claim 2 , wherein the unique set of device configuration characteristics includes data from a validated uniform resource locator history tracked via cookies or via local storage at the client device.

8 . The computer-implemented method of claim 2 , wherein the unique set of device configuration characteristics are accessed using hypertext transform protocol-based post and get commands with the client device.

9 . The computer-implemented method of claim 2 , wherein removing the one or more device configuration characteristics includes associating the device configuration characteristics with a deletion timer and generating an additional unique set of device configuration characteristics that are deleted when the deletion timer expires without additional device configuration characteristics being received by the client device.

10 . The computer-implemented method of claim 2 , further comprising storing the unique set of device configuration characteristics in a cross-domain identification database.

11 . A device comprising:
memory; and one or more processors coupled to the memory and configured to: generate, via a neural network and based on device configuration data associated with a plurality of devices including for a client device, a unique set of device configuration characteristics that create a non-unique overlap between device configuration characteristics associated with one or more other devices of the plurality of devices; reduce memory and reducing opportunities for spoofing by removing one or more device configuration characteristics from the unique set of device configuration characteristics;
associating, with a machine learning model, historical return transaction data with the unique set of device configuration characteristics;
receive, at a server system, transaction request data associated with the client device, wherein the transaction request data is associated with a return transaction;
verify the client device using the unique set of device configuration characteristics;

initiating a transaction associated with the transaction request data based on verification of the client device using the unique set of device configuration characteristics; and
identify the transaction as fraudulent based on the historical return transaction data.

12 . The device of claim 11 , wherein generating the unique set of device configuration characteristics includes matching device configuration data of two or more devices, wherein the device configuration data includes data for the client device, and wherein matching includes using a time period to generate the unique set of device configuration characteristics associated with the time period.

13 . The device of claim 11 , wherein the one or more processors are further configured to train the machine learning model to create one or more datasets that result in uniqueness for devices.

14 . The device of claim 11 , wherein the one or more processors are further configured to:
receive additional transaction request data associated with an additional client device, wherein the transaction request data is associated with an additional return transaction; verify the additional client device using the unique set of device configuration characteristics; initiate a transaction associated with the transaction request data based on verification of the additional client device using the unique set of device configuration characteristics; and facilitate an additional transaction associated with the additional transaction request data.

15 . The device of claim 11 , wherein removing the one or more device configuration characteristics includes associating the device configuration characteristics with a deletion timer and generating an additional unique set of device configuration characteristics that are deleted when the deletion timer expires without additional device configuration characteristics being received by the client device.

16 . A non-transitory computer readable storage medium comprising instructions that, when executed by one or more processors of a device, cause the device to perform operations including:
generating, via a neural network and based on device configuration data associated with a plurality of devices including for a client device, a unique set of device configuration characteristics that create a non-unique overlap between device configuration characteristics associated with one or more other devices of the plurality of devices; reducing memory and reducing opportunities for spoofing by removing one or more device configuration characteristics from the unique set of device configuration characteristics; associating, with a machine learning model, historical return transaction data with the unique set of device configuration characteristics; receiving, at a server system, transaction request data as

Assignees

• Synchrony Bank

Inventors

• Mukherjee Joydeep
• Yagnesak Karen
• Ravi Viveka Vardhan
• Vadakapurapu Sarath
• Kumar Ravish
• Dadireddy Vish

Classifications

• G06Q20/409 Primary

  Device specific authentication in transaction processing · CPC title

• G06Q20/204

  comprising interface for record bearing medium or carrier for electronic funds transfer or payment credit · CPC title

• G06Q20/24 Primary

  Credit schemes, i.e. "pay after" · CPC title