IP Library › Granted Patent US 12,236,700
Granted Patent B1
US 12,236,700 · App. 18/785,077 · Granted Feb 25, 2025

System for automatically processing documents

Inventors: Thomas Oliver Cantrell (Maidens, VA); Joseph James Ford, III (Manakin Sabot, VA)
Assignee: Capital One Services, LLC
G06V30/414G06V10/7715G06V10/82G06V30/19127
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Quick Facts
Patent No.
US 12,236,700
App. No.
18/785,077
Granted
Feb 25, 2025
Kind
B1
Abstract

A computer implemented method, system, and non-transitory computer-readable device that may be used in a remote deposit environment. A plurality of differing neural networks, such as customizable neural networks, are selected, based on an architecture and features of the data field, to extract data from specific data fields on a document. The selected customizable neural networks are trained by historical or synthetic data corresponding to the data fields. Upon receiving, from a neural network Optical Character Recognition (OCR) system, a selected first trained customizable neural network model and at least a second selected trained customizable neural network model, the data fields are extracted, based on a series or parallel configuration of the customizable neural networks, and the extracted data communicated to a remote deposit process.

Claims (57)

1. A computer-implemented method, the method comprising:

receiving, from a neural network Optical Character Recognition (OCR) system, a first trained customizable neural network model, wherein the first trained customizable neural network model comprises a first architecture and a corresponding first data training set based on data field parameters of a first data field from a plurality of data fields from imagery of a physical document;

receiving, from the neural network OCR system, a second trained customizable neural network model, wherein the second trained customizable neural network model comprises a second architecture and a corresponding second data training set based on data field parameters of a second data field from the plurality of data fields from the imagery of the physical document;

extracting the first data field and the second data field from the imagery of the physical document, based on the first trained customizable neural network model and the second trained customizable neural network model, respectively;

accumulating, in computer storage, the extracted first data field and the second data field, wherein the first data field and the second data field comprise at least a portion of the plurality of data fields of the physical document usable in a remote deposit transaction; and

communicating the accumulated first data field and the second data field to a remote deposit process.

2. The computer-implemented method of claim 1 , further comprising classifying the extracted first data field and the second data field.

3. The computer-implemented method of claim 2 , further comprising generating a confidence score for the classification of the extracted first data field and the second data field.

4. The computer-implemented method of claim 1 , further comprising:

receiving, from the neural network OCR system, a third trained customizable neural network model, wherein the third trained customizable neural network model comprises a third architecture and a corresponding third data training set based on data field parameters of a third data field from the plurality of data fields from the imagery of the physical document;

extracting the first data field, the second data field and the third data field from the imagery of the physical document by the first trained customizable neural network model, the second trained customizable neural network model, and the third trained customizable neural network model, respectively;

accumulating, in computer storage, the extracted first data field, the second data field, and the third data field, wherein the first data field, the second data field, and the third data field comprise at least a portion of the plurality of data fields of the physical document usable in a remote deposit transaction; and

communicating the accumulated first data field, the second data field, and the third data field to a remote deposit process.

5. The computer-implemented method of claim 1 , further comprising:

receiving, from the neural network OCR system, a third trained customizable neural network model, wherein the third trained customizable neural network model implements any of the first architecture or the second architecture, while using a third data training set, based on a similarity of data field parameters of a third data field as compared to one or more of the data field parameters of the first data field or the second data field;

extracting the first data field, the second data field, and the third data field, from the imagery of the physical document, based on the first trained customizable neural network model, the second trained customizable neural network model, and the third trained customizable neural network model, respectively;

accumulating, in computer storage, the extracted first data field, the second data field, and the third data field, wherein the first data field, the second data field, and the third data field comprise at least a portion of the plurality of data fields of the physical document usable in a remote deposit transaction; and

communicating the accumulated first data field, the second data field, and the third data field to a remote deposit process.

6. The computer-implemented method of claim 1 , further comprising:

receiving, from the neural network OCR system, a third trained customizable neural network model, wherein the third trained customizable neural network model implements a modified version of any of the first architecture, or the second architecture, while using a third data training set, based on a similarity of data field parameters of a third data field as compared to one or more of the data field parameters of the first data field or the second data field;

extracting the first data field, the second data field, and the third data field, from the imagery of the physical document, based on the first trained customizable neural network model, the second trained customizable neural network model, and the third trained customizable neural network model, respectively;

accumulating, in computer storage, the extracted first data field, the second data field, and the third data field, wherein the first data field, the second data field, and the third data field comprise at least a portion of the plurality of data fields of the physical document usable in a remote deposit transaction; and

communicating the accumulated first data field, the second data field, and the third data field to a remote deposit process.

7. The computer-implemented method of claim 1 , further comprising:

receiving, from the neural network OCR system, a third trained customizable neural network model, wherein the third trained customizable neural network model implements a combination of one or more portions of the first architecture and the second architecture, while using a third data training set, based on a similarity of data field parameters of a third data field as compared to one or more of the data field parameters of the first data field or the second data field;

extracting the first data field, the second data field, and the third data field, from the imagery of the physical document, based on the first trained customizable neural network model, the second trained customizable neural network model, and the third trained customizable neural network model, respectively;

accumulating, in computer storage, the extracted first data field, the second data field, and the third data field, wherein the first data field, the second data field, and the third data field comprise at least a portion of the plurality of data fields of the physical document usable in a remote deposit transaction; and

communicating the accumulated first data field, the second data field, and the third data field to a remote deposit process.

8. The computer-implemented method of claim 1 , wherein the first architecture comprises a Residential Network (ResNet) architecture and the second architecture comprises a Transformer Architecture OCR (TrOCR).

9. The computer-implemented method of claim 1 , wherein the first trained customizable neural network model comprises a categorical convolutional neural network (CNN) model and the second trained customizable neural network model comprises a region-based CNN model.

10. The computer-implemented method of claim 1 , wherein the first architecture comprises any of: a Residential Network (ResNet) architecture, a Transformer Architecture OCR (TrOCR), a LeNet architecture, an AlexNet architecture, a VGG architecture, a GoogLeNet architecture, or a GoogleNet architecture.

11. The computer-implemented method of claim 1 , wherein the first trained customizable neural network model and the second trained customizable neural network model are arranged in series during the extracting, and the accumulating comprises aggregating in series.

12. The computer-implemented method of claim 1 , wherein the first trained customizable neural network model and the second trained customizable neural network model receive a replicated copy of the imagery of the physical document, are arranged in parallel during the extracting, and the accumulating comprises aggregating in parallel.

13. A system, comprising:

a memory; and

at least one processor coupled to the memory and configured to:

receive, from a neural network Optical Character Recognition (OCR) system, a first trained customizable neural network model, wherein the first trained customizable neural network model comprises a first architecture and a corresponding first data training set based on data field parameters of a first data field from a plurality of data fields from imagery of a physical document;

receive, from the neural network OCR system, a second trained customizable neural network model, wherein the second trained customizable neural network model comprises a second architecture and a corresponding second data training set based on data field parameters of a second data field from the plurality of data fields of the imagery of the physical document;

extract the first data field and the second data field, from the imagery of the physical document, based on the first trained customizable neural network model and the second trained customizable neural network model, respectively;

accumulate, in the memory, the extracted first data field and the second data field, wherein the first data field and the second data field comprise at least a portion of the plurality of data fields of the physical document usable in a remote deposit transaction; and

communicate the accumulated first data field and the second data field to a remote deposit process.

14. The system of claim 13 , further configured to classify the extracted first data field and the second data field and generate a confidence score for the classification of the extracted first data field and the second data field.

15. The system of claim 13 , further configured to:

receive, from the neural network OCR system, a third trained customizable neural network model, wherein the third trained customizable neural network model implements any of the first architecture, or the second architecture, while using a third data training set, based on a similarity of data field parameters of a third data field as compared to one or more of the data field parameters of the first data field or the second data field;

extract the first data field, the second data field, and the third data field, from the imagery of the physical document, based on the first trained customizable neural network model, the second trained customizable neural network model, and the third trained customizable neural network model, respectively;

accumulate, in the memory, the extracted first data field, the second data field, and the third data field, wherein the first data field, the second data field, and the third data field comprise at least a portion of the plurality of data fields of the physical document usable in a remote deposit transaction; and

communicate the accumulated first data field, the second data field, and the third data field to a remote deposit process.

16. The system of claim 13 , wherein the first trained customizable neural network model comprises a categorical convolutional neural network (CNN) model and the second trained customizable neural network model comprises a region-based CNN model.

17. The system of claim 13 , wherein the first architecture or the second architecture comprises any of: a Residential Network (ResNet) architecture, a Transformer Architecture OCR (TrOCR), a LeNet architecture, an AlexNet architecture, a VGG architecture, a GoogLeNet architecture, or a GoogleNet architecture.

18. The system of claim 13 , wherein the first trained customizable neural network model and the second trained customizable neural network model are arranged in series during the extracting, and the accumulating comprises aggregating in series.

19. The system of claim 13 , wherein the first trained customizable neural network model and the second trained customizable neural network model receive a replicated copy of the imagery of the physical document, are arranged in parallel during the extracting, and the accumulating comprises aggregating in parallel.

20. A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

receiving, from a neural network Optical Character Recognition (OCR) system, a first trained customizable neural network model, wherein the first trained customizable neural network model comprises a first architecture and a corresponding first data training set based on data field parameters of a first data field from a plurality of data fields from imagery of a physical document;

receiving, from the neural network OCR system, a second trained customizable neural network model, wherein the second trained customizable neural network model comprises a second architecture and a corresponding second data training set based on data field parameters of a second data field from the plurality of data fields from the imagery of the physical document;

extracting the first data field and the second data field, from the imagery of the physical document, based on the first trained customizable neural network model and the second trained customizable neural network model, respectively;

accumulating, in computer storage, the extracted first data field and the second data field, wherein the first data field and the second data field comprise at least a portion of the plurality of data fields of the physical document usable in a remote deposit transaction; and

communicating the accumulated first data field and the second data field to a remote deposit process.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 26, 2024
From: CANTRELL, THOMAS OLIVER; FORD, JOSEPH JAMES, III
To: CAPITAL ONE SERVICES, LLC
Reel/Frame 068091/0398 →
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