Gyeongsangbuk-do, South Korea

Hak-Kyoung Kim

This inventor holds 133 USPTO granted patents and 1 published patent application and 110 EPO patents, primarily in Autonomous Driving Safety (CPC class G06K9-6262). Top assignees: Stradvision, Inc., Stadvision, Inc., Stradvision Korea, Inc.. Active years: 2016-2022.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Hak-Kyoung Kim. Retrieved from https://idiyas.com/inventor/hak-kyoung-kim

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

USPTO Granted Patents = 133 

% Patents Active = 100.0

 

Average Co-Inventor Count = 12.6

ph-index = 11

Forward Citations = 701(Granted Patents)

Forward Citations (Not Self Cited) = 576(Dec 10, 2025)


Inventors with similar research interests:

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Location History:

  • Seoul, KR (2018)
  • Pohang-si, KR (2019 - 2021)
  • Gyeongsangbuk-do, KR (2016 - 2022)

Company Filing History:


Years Active: 2016-2022

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Areas of Expertise:
Neural Networks
Sensor Fusion
V2X Communication
Reinforcement Learning
Trajectory Analysis
Emergency Vehicle Detection
Blind Spot Monitoring
3D Space Reconstruction
Lane Departure Warning
Adaptive Deep Learning
Object Detection
Autonomous Vehicle Localization
133 patents (USPTO):Explore Patents

Title: Hak-Kyoung Kim: Innovating for Optimized Learning and On-Device Continual Learning

Introduction:

In the realm of innovations and patents, Hak-Kyoung Kim is a remarkable figure known for his contributions to the fields of hardware optimization, neural networks, and continual learning. With numerous patents to his name, Kim's work has fueled advancements in various industries, including smartphones, drones, vessels, and military applications. Let's delve into his latest patents, career highlights, collaborations, and the importance of his work.

Latest Patents:

1. Learning method and learning device for CNN using 1xK or Kx1 convolution to be used for hardware optimization: This patent focuses on a method for optimizing hardware performance in the training of convolutional neural networks (CNNs). By reshaping and adjusting feature maps, this innovation enhances the learning process while meeting key performance indicators (KPIs).

2. Testing method and testing device using the same: This patent introduces a technique for testing neural networks used in devices like smartphones, drones, vessels, or military equipment. The method ensures on-device continual learning by generating new data samples and leveraging generative adversarial networks (GANs) or online learning methodologies.

Career Highlights:

Hak-Kyoung Kim's career encompasses significant contributions to pioneering companies in the technology sector. Notably, he has worked with:

1. StradVision, Inc. (formerly known as STADVision, Inc.): As part of this cutting-edge organization, Kim has undoubtedly played a vital role in developing advanced vision perception technologies, particularly for autonomous vehicles. These technologies are poised to revolutionize transportation systems and enhance road safety.

Collaborations:

Collaboration is often key to achieving groundbreaking advancements. Hak-Kyoung Kim has successfully worked alongside exceptional professionals in his field, including:

1. Hongmo Je: Collaborating with Kim, Je has brought invaluable expertise to their shared projects. Their combined efforts have contributed to several groundbreaking innovations in CNNs and hardware optimization.

2. Kye-Hyeon Kim: Kye-Hyeon Kim's collaboration with Hak-Kyoung Kim has led to significant breakthroughs in continual learning techniques for neural networks.

Conclusion:

Hak-Kyoung Kim's exceptional work in hardware optimization, on-device continual learning, and neural networks has propelled him to the forefront of innovation. With numerous patents in his name and collaborations with top-notch professionals and companies, he has undeniably made significant contributions to various industries. Through his ingenuity, Kim is helping shape a future where optimized learning and continual improvement are central to technological advancements.

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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