Granted Patent B2

US 12,620,249 · App. 17/941,547 · Granted May 5, 2026

Inventors: Jose Javier Yebes Torres (Valladolid, ES); Sricharan Amarnath (West Chennai, IN); Roberto Arroyo (Madrid, ES)

Assignee: Nielsen Consumer LLC

G06V30/413G06F9/30003G06V30/42

Abstract

Claims (32)

1 . At least one non-transitory machine readable medium comprising instructions to cause at least one programmable circuit to at least:

perform Optical Character Recognition (OCR) on an image of a document to generate OCR data;

search the OCR data to identify a first row that includes product description information;

search the OCR data to identify at least one of a product code of a product identified by the product description information, a product quantity of the product, or a price of the product that is missing from the first row; and

identifying the at least one of the missing product code, the missing product quantity or the missing price by at least one of:

identifying a maximum integer value in the OCR data as the missing product code product code;

identifying a minimum integer value in the OCR data as the product quantity; or

identifying a decimal value in the OCR data as the price; and

retrain a machine learning model based on at last one of the maximum integer value, the minimum integer value, or the decimal value.

2 . The at least one non-transitory machine readable medium of claim 1 , wherein the product code is a first product code, the product quantity is a first product quantity, the price is a first price, and the document is a first document, wherein the instructions cause the at least one programmable circuit to retrain the model to automatically detect a second product code, a second product quantity, and a second price in a second document.

3 . The at least one non-transitory machine readable medium of claim 1 , wherein the model includes processing key information extraction from documents using improved graph learning convolutional networks.

4 . The at least one non-transitory machine readable medium of claim 1 , wherein the model is to transform key information extraction into a sequence tagging based on layout of the document.

5 . The at least one non-transitory machine readable medium of claim 1 , wherein the document is physical and the OCR data is digital.

6 . An apparatus comprising:

at least one memory;

machine readable instructions; and

at least one programmable circuit to execute the machine readable instructions to at least:

perform Optical Character Recognition (OCR) on an image of a document to generate OCR data;

search the OCR data to identify a first row that includes product description information;

search the OCR data to identify at least one of a product code, a product quantity, or a price that is missing from the first row;

search the OCR data to identify integer values;

identify the maximum integer value of the integer values;

identify the minimum integer value of the integer values;

associate, when the maximum integer value is different than the minimum integer value, the maximum integer value with the product code;

associate, when the minimum integer value is different than the maximum integer value, the minimum integer value with the product quantity;

search the OCR data for a decimal value;

associate the decimal value with the price; and

retrain a machine learning model based on at least one of the product code, the product quantity, or the price.

7 . The apparatus of claim 6 , wherein the product code is a first product code, the product quantity is a first product quantity, the price is a first price, and the document is a first document, and wherein at least one of the at least one programmable circuit is to retrain the model to automatically detect a second product code, a second product quantity, and a second price in a second document.

8 . The apparatus of claim 6 , wherein the model includes processing key information extraction from documents using improved graph learning convolutional networks.

9 . The apparatus of claim 6 , wherein the model is to transform key information extraction into a sequence tagging based on layout of the document.

10 . The apparatus of claim 6 , wherein the document is physical and the OCR data is digital.

Assignments (3)

Priority Claims (1)

Continuity (1)

References Cited (397)