• Recent
  • Class
  • Inventors
  • Assignees
  • Top Lists
  • Pricing
  • Account
Patents-Review.com
  1. Home
  2. Inventor
  3. H
  4. Hes…
  5. HESCH Wayne Edward Jason
🔗 Permalink
Inventor profile of:

Wayne Edward Jason HESCH

City:

Waterloo

Country:

Canada

Published Applications:

6

Last publication date:

2024-02-15

Recent patent applications by HESCH Wayne Edward Jason

Wayne Edward Jason HESCH from Waterloo, CA has applied for patents for these inventions. The list has both pending applications and granted patents:

#1 | 2024-02-15
US20240054592A1
Physics

SYSTEM AND METHOD FOR MULTI-INSTITUTIONAL OPTIMIZATION FOR A CANDIDATE APPLICATION SYSTEM

#2 | 2024-02-15
US20240054591A1
Physics

System and method for achieving candidate diversity in a candidate application system

#3 | 2023-08-17
US20230260066A1
Physics

SYSTEM AND METHOD FOR RAPID STUDENT VERIFICATION

#4 | 2022-07-28
US20220237725A1
Physics

SYSTEMS AND METHODS FOR OPTIMIZING DATA SHARING IN RELATION TO A PLURALITY OF ADMISSION APPLICATIONS

#5 | 2020-08-27
US20200273127A1
Physics

System and method for multi-institutional optimization for a candidate application system

#6 | 2020-08-27
US20200273126A1
Physics

System and method for achieving candidate diversity in a candidate application system

InventorID:

2834517 ⎘

  1. Home
  2. Inventor
  3. H
  4. Hes…
  5. HESCH Wayne Edward Jason

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.