Andrew Kotz

Title: Andrew Kotz: Inventor Advancing Machine Learning for Energy and Mobility

Introduction

Andrew Kotz is an inventor based in Denver, Colorado, whose patent work explores how machine learning can support energy prediction and the analysis of mobile activity. His inventions address moving people and machines, including vehicles, aircraft, watercraft, and robots. Kotz is associated with Exergi Predictive LLC and holds 2 patents.

Latest Patents

Kotz’s latest patents focus on applying machine learning to energy use and mobility patterns.

“Machine learning based energy use prediction” describes techniques for predicting the energy use of mobile actors, such as a person or machine traveling from one place to another. The approach is designed to account for routes of varying complexity and other relevant variables, with potential applications across different types of mobile machines and industries.

“Machine learning based generation and use of synthetic drive cycles” concerns using machine learning to generate and apply synthetic drive cycles for people and mobile machines, as well as synthetic duty cycles for machines. These techniques can represent movement across routes of varying complexity and may apply to vehicles, robots, aircraft, and watercraft.

Career Highlights

Kotz’s patent portfolio brings together machine learning, energy-use prediction, and the modeling of mobile activity. The inventions describe methods that could help analyze or anticipate energy demands across a broad range of routes, machines, and operating conditions. His work is associated with Exergi Predictive LLC.

Collaborations

Kotz’s listed coworkers include Harish Panneer Selvam and William Northrop. Their shared professional context connects them with work in machine learning and mobile energy applications.

Conclusion

Andrew Kotz’s two patents highlight machine learning approaches to predicting energy use and generating synthetic drive or duty cycles for people and mobile machines. Together, these inventions reflect a focus on understanding mobility and energy demands across diverse applications.