IP Library Granted Patent US 12,026,967
Granted Patent B2
US 12,026,967 · App. 17/567,731 · Granted Jul 2, 2024

Travel document validation using artificial intelligence and unsupervised learning

Inventors: Marcelo Motta (Washington, DC); Nathan Thomas Carpenter (Washington, DC); Enrique Segura (Washington, DC)
Assignee: Securiport LLC
G06V30/42B42D25/24G06V30/413G06V30/416
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Quick Facts
Patent No.
US 12,026,967
App. No.
17/567,731
Granted
Jul 2, 2024
Kind
B2
Abstract

Systems, devices, methods, and instructions for travel document validation, including receiving data for one or more travel documents, generating a set of features for artificial intelligence model training, validating, and testing, upon receiving data for a plurality of travel documents, data from a first subset of travel documents is used to train an artificial intelligence model, a second subset of travel documents is used to validate the artificial intelligence model, and a third subset of travel documents is used to test the artificial intelligence model.

Claims (30)

1. A travel document validation device, the travel document validation device comprising:

a processor; and

a non-transitory memory storing one or more programs for execution by the processor, the one or more programs including instructions for:

receiving data for one or more travel documents;

generating a set of features for artificial intelligence model training, validating, and testing;

upon receiving data for a plurality of travel documents, data from a first subset of travel documents is used to train an artificial intelligence model, a second subset of travel documents is used to validate the artificial intelligence model, and a third subset of travel documents is used to test the artificial intelligence model.

2. The travel document validation device according to claim 1 , wherein the received data is scanned.

3. The travel document validation device according to claim 1 , wherein the received data includes color, black-and-white, ultraviolet, and infrared spectra images.

4. The travel document validation device according to claim 1 , wherein the received data includes document type, issuer, issue date, expiration date, and/or biometrics for validation.

5. The travel document validation device according to claim 1 , wherein the received data includes an RFID.

6. The travel document validation device according to claim 1 , wherein the generated set of features is extracted from images of the one or more travel documents, extracted sub-images, text, and/or security markings.

7. The travel document validation device according to claim 1 , wherein the generated set of features can be extracted from biometric templates, encoded images, and/or checksums.

8. A non-transitory computer readable storage medium storing one or more programs configured to be executed by a processor, the one or more programs comprising instructions for:

receiving data for one or more travel documents;

generating a set of features for artificial intelligence model training, validating, and testing;

upon receiving data for a plurality of travel documents, data from a first subset of travel documents is used to train an artificial intelligence model, a second subset of travel documents is used to validate the artificial intelligence model, and a third subset of travel documents is used to test the artificial intelligence model.

9. The non-transitory computer readable storage medium according to claim 8 , wherein the received data is scanned.

10. The non-transitory computer readable storage medium according to claim 8 , wherein the received data includes color, black-and-white, ultraviolet, and infrared spectra images.

11. The non-transitory computer readable storage medium according to claim 8 , wherein the received data includes document type, issuer, issue date, expiration date, and/or biometrics for validation.

12. The non-transitory computer readable storage medium according to claim 8 , wherein the received data includes an RFID.

13. The non-transitory computer readable storage medium according to claim 8 , wherein the generated set of features is extracted from images of the one or more travel documents, extracted sub-images, text, and/or security markings.

14. A method for operating a travel document validation device, the method comprising:

receiving data for one or more travel documents;

generating a set of features for artificial intelligence model training, validating, and testing;

upon receiving data for a plurality of travel documents, data from a first subset of travel documents is used to train an artificial intelligence model, a second subset of travel documents is used to validate the artificial intelligence model, and a third subset of travel documents is used to test the artificial intelligence model.

15. The method according to claim 14 , wherein the received data is scanned.

16. The method according to claim 14 , wherein the received data includes color, black-and-white, ultraviolet, and infrared spectra images.

17. The method according to claim 14 , wherein the received data includes document type, issuer, issue date, expiration date, and/or biometrics for validation.

18. The method according to claim 14 , wherein the received data includes an RFID.

19. The method according to claim 14 , wherein the generated set of features is extracted from images of the one or more travel documents, extracted sub-images, text, and/or security markings.

Assignments (3)
SECURITY INTEREST Recorded Apr 16, 2026
From: SECURIPORT LIMITED LIABILITY COMPANY
To: KHR SERVICING, LLC, AS COLLATERAL AGENT
Reel/Frame 074388/0008 →
SECURITY INTEREST Recorded Dec 12, 2022
From: SECURIPORT LIMITED LIABILITY COMPANY
To: ALTER DOMUS (US) LLC, AS COLLATERAL AGENT
Reel/Frame 062060/0686 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 13, 2022
From: MOTTA, MARCELO; CARPENTER, NATHAN THOMAS; SEGURA, ENRIQUE
To: SECURIPORT LLC
Reel/Frame 058639/0849 →
Continuity (2)
Provisional Application 63133179 · Dec 31, 2020
Related Publication 20220207901A1 · Jun 30, 2022