• Recent
  • Class
  • Inventors
  • Assignees
  • Top Lists
  • Pricing
  • Account
Patents-Review.com
  1. Home
  2. Inventor
  3. H
  4. Haa…
  5. Haag Norman
🔗 Permalink
Inventor profile of:

Norman Haag

City:

Stuttgart

Country:

Germany

Published Applications:

7

Last publication date:

2024-11-07

Top Assignees for applications by Norman Haag

The entities that hold a legal rights for patent applications filed by inventor Haag Norman:

  • Robert Bosch GMBH 3 Stuttgart, Germany

Recent patent applications by Haag Norman

Norman Haag from Stuttgart, DE has applied for patents for these inventions. The list has both pending applications and granted patents:

#1 | 2024-11-07
US20240369688A1
Physics

LIDAR SENSOR AND ENVIRONMENT RECOGNITION SYSTEM

#2 | 2024-07-18
US20240242432A1
Physics

METHOD AND DEVICE FOR DETERMINING CONCEALED OBJECTS IN A 3D POINT CLOUD REPRESENTING AN ENVIRONMENT

#3 | 2024-03-21
US20240094348A1
Physics

OPERATING METHOD AND CONTROL UNIT FOR A LIDAR SYSTEM, LIDAR SYSTEM, AND DEVICE

#4 | 2022-12-29
US20220413149A1
Physics

OPERATING METHOD AND CONTROL UNIT FOR A LIDAR SYSTEM, LIDAR SYSTEM, AND DEVICE

#5 | 2022-06-23
US20220196803A1
Physics

LIDAR SENSOR

#6 | 2021-07-01
US20210199776A1
Physics

LIDAR device including an accelerated runtime analysis

#7 | 2020-06-25
US20200200909A1
Physics

OPTOELECTRONIC SENSOR AND METHOD FOR OPERATING AN OPTOELECTRONIC SENSOR

InventorID:

2774003 ⎘

  1. Home
  2. Inventor
  3. H
  4. Haa…
  5. Haag Norman

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.