Patent · US Active

Table item information extraction with continuous machine learning through local and global models

US10241992B1 · kind B1 · utility

16Cited by

31References

20Claims

0Family size

Assignee

OPEN TEXT SA ULC · CA

Inventors

Mattias Theodor Middendorf · Konstanz, DE
Gisela Barbara Cäcilie Hammann · Vaterstetten, DE
Carsten Peust · Konstanz, DE

Key dates

Filing date	Apr 27, 2018
Grant date	Mar 26, 2019
Priority date	—
Expiry date	Apr 27, 2038

Classification

Technology area (CPC G)Physics
CPC primaryG06V30/414
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as a negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.