Company Filing History:
Years Active: 2024-2025
Title: Innovations of Donghan Lee
Introduction
Donghan Lee is an accomplished inventor based in Anaheim, California. He has made significant contributions to the field of automated document processing, holding a total of four patents. His work focuses on enhancing the understanding and transformation of structured and semi-structured information from images into textual representations.
Latest Patents
One of Donghan Lee's latest patents is titled "Automated document processing for detecting, extracting, and analyzing tables and tabular data." This invention involves a computer-implemented method that detects and classifies columns of tables within image data. It includes extracting tables and classifying columns to improve data analysis. Another notable patent is "Automated transformation of information from images to textual representations, and applications therefor." This patent addresses the challenges faced by current generative models in understanding structured information in document images. It proposes a method to transform both structured and semi-structured information into a textual format that enhances comprehension by generative models.
Career Highlights
Throughout his career, Donghan Lee has worked with notable companies such as Kofax, Inc. and Tungsten Automation Corporation. His experience in these organizations has contributed to his expertise in automated document processing and machine learning applications.
Collaborations
Donghan has collaborated with talented individuals in his field, including Iurii Vymenets and Stephen Michael Thompson. These collaborations have further enriched his innovative work and contributed to the development of his patents.
Conclusion
Donghan Lee's contributions to automated document processing and machine learning have positioned him as a significant figure in the field of innovation. His patents reflect a deep understanding of the challenges in processing document images and offer solutions that enhance the capabilities of generative models.