• Recent
  • Class
  • Inventors
  • Assignees
  • Top Lists
  • Pricing
  • Account
Patents-Review.com
  1. Home
  2. Inventor
  3. Z
  4. Zha…
  5. Zhang Xiaochen
🔗 Permalink
Inventor profile of:

Xiaochen Zhang

City:

Thornhill

Country:

Canada

Published Applications:

8

Last publication date:

2020-10-08

Top Assignees for applications by Xiaochen Zhang

The entities that hold a legal rights for patent applications filed by inventor Zhang Xiaochen:

  • Geotab Inc. 8 Oakville, Canada

Recent patent applications by Zhang Xiaochen

Xiaochen Zhang from Thornhill, CA has applied for patents for these inventions. The list has both pending applications and granted patents:

#1 | 2020-10-08
US20200320868A1
Physics

Intelligent telematics system for defining road networks

#2 | 2020-10-08
US20200320867A1
Physics

Traffic analytics system for defining road networks

#3 | 2020-10-08
US20200320866A1
Physics

Method for defining road networks

#4 | 2020-10-08
US20200320865A1
Physics

Method for defining intersections using machine learning

#5 | 2020-10-08
US20200320863A1
Physics

Intelligent telematics system for defining vehicle ways

#6 | 2020-10-08
US20200320862A1
Physics

Method for defining vehicle ways using machine learning

#7 | 2020-10-08
US20200320861A1
Physics

Traffic analytics system for defining vehicle ways

#8 | 2020-06-30
US16444228
Physics

Method for defining intersections using machine learning

InventorID:

2874922 ⎘

  1. Home
  2. Inventor
  3. Z
  4. Zha…
  5. Zhang Xiaochen

Some parts © 2022-2026 Patents-Review.com

Browse & Discovery
  • Home
  • Patent Classification
  • Inventor Index
  • Assignee Index
  • Interesting Applications
Search & Tools
  • Basic Search
  • Advanced Search
  • Patent Alerts
  • Top Lists
  • Statistics
Information & Resources
  • About US Patent System
  • Terms & Privacy
  • Pricing
  • Contact
Quick Access
  • Latest Applications
  • Top Inventors 2026
  • Top Assignees 2026
  • Featured Patents
Disclaimer: This website is intended for informational purposes only, and its content is based on public patent records. Please note that some sections of this website have been created or processed using machine learning models. We provide content in good faith but make no guarantees regarding accuracy or completeness. By using this site, you accept any risks and waive claims against us for errors or omissions.