US 12,033,376 B2
Method and system for training neural network for entity detection
Reghu Hariharan, Marlborough, MA (US); Jeevan Prakash, Mumbai (IN); Harsh Kothari, Mumbai (IN); Kavitha S, Mumbai (IN); and Rajat Jaiswal, Mumbai (IN)
Filed by Quantiphi Inc., Marlborough, MA (US)
Filed on Dec. 16, 2021, as Appl. No. 17/552,762.
Prior Publication US 2023/0196748 A1, Jun. 22, 2023
Int. Cl. G06V 10/00 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 30/10 (2022.01); G06V 30/18 (2022.01)
CPC G06V 10/82 (2022.01) [G06V 10/764 (2022.01); G06V 10/7747 (2022.01); G06V 30/10 (2022.01); G06V 30/18143 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method for training a neural network to be implemented for detecting at least one entity in a document to derive relevant inferences therefrom, comprising:
obtaining at least one document;
processing, the at least one document via a detection module to detect a widget entity, wherein the detected widget entity is classified as active or inactive based on a detected state of the widget entity;
modifying, the classified widget entity into a corresponding machine-readable widget entity based on the detected state;
processing, the at least one document via an extraction module to detect a text entity in near vicinity of the classified widget entity;
generating a training pair comprising the machine-readable widget entity and the corresponding text entity; and
training the neural network using the generated training pair,
wherein the detected at least one entity comprises adding a tag to each of the detected at least one entity for labelling the detected at least one entity for enabling named entity recognition (NER), and wherein the tag comprises a likelihood of the detected at least one entity.