San Jose, CA, United States of America

Hae-Jong Seo

This inventor holds 24 USPTO granted patents and 21 published patent applications and 4 EPO patents, primarily in Autonomous Machine Applications. Top assignees: Nvidia Corporation, Sharp Laboratories of America, Inc, Qualcomm Incorporated. Active years: 2014-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Hae-Jong Seo. Retrieved from https://idiyas.com/inventor/hae-jong-seo

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 24 

% Patents Active = 79.2

 

Average Co-Inventor Count = 4.2

ph-index = 6

Forward Citations = 78(Granted Patents)


Location History:

  • Vancouver, WA (US) (2014)
  • Campbell, CA (US) (2023)
  • San Jose, CA (US) (2015 - 2024)

Company Filing History:


Years Active: 2014-2026

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Areas of Expertise:
Deep Learning
Object Detection
Sensor Fusion
Landmark Detection
Path Perception
Image Contrast Analysis
Regression-Based Detection
Camera-Based Inputs
Machine Learning
Autonomous Driving
Pixel Value Isolation
Optical Fingerprint Capture
24 patents (USPTO):Explore Patents

Title: Hae-Jong Seo: A Pioneer in Machine Learning and Autonomous Technologies

Introduction

Hae-Jong Seo is a distinguished inventor currently based in San Jose, CA, known for his significant contributions to the fields of machine learning and autonomous technologies. With a prolific portfolio of 19 patents, Seo continues to push the boundaries of innovation, developing advanced solutions aimed at enhancing the capabilities of autonomous machines.

Latest Patents

Among Hae-Jong Seo's latest innovations is a patent focused on "Sensor Fusion for Autonomous Machine Applications Using Machine Learning." This invention utilizes a multi-sensor fusion machine learning model, such as a deep neural network (DNN), to integrate data from various machine learning models. The deployment of this technology ensures that the output is refined, mitigating issues related to duplicate, inaccurate, or noisy data while recognizing learned associations between different fields of view from the sensors.

Another notable patent is for "Regression-Based Line Detection for Autonomous Driving Machines." This technology emphasizes the preservation of intricate spatial information from the input resolution of the machine learning model to accurately regress lines in images. The model is trained to predict distances for each pixel to identify line pixels in the input image, providing essential information for understanding the surrounding environment, which is vital in various autonomous machine applications.

Career Highlights

Hae-Jong Seo has made notable contributions during his career, particularly while working at esteemed organizations such as Nvidia Corporation and Sharp Laboratories of America, Inc. His cumulative experience in these innovative environments has allowed him to develop groundbreaking technologies that significantly enhance the functionality of autonomous systems.

Collaborations

Throughout his career, Hae-Jong has collaborated with other talented professionals, including Minwoo Park and David Nister. These collaborations have been pivotal in driving forward the research and applications of machine learning within the realm of autonomous technology, exemplifying the power of teamwork in fostering innovation.

Conclusion

Hae-Jong Seo's work stands as a testament to the profound impact of innovation in the fields of machine learning and autonomous machines. Through his patents and collaborative efforts, he continues to inspire the progress of technology, solidifying his status as a key figure in this dynamic industry. As he forges ahead, the future looks promising for advancements arising from his research and inventions.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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