Dr. Ron van de Sand
Founder & Developer of markencheck.ai
Dr. Ron van de Sand is the founder and developer of markencheck.ai. His focus is data-driven trademark research, EUIPO register data, and AI-assisted methods for structured preliminary screening of brand names.
Ron holds a PhD (Università degli Studi di Roma Tor Vergata) in artificial intelligence and machine learning and has worked for over a decade at the intersection of automation, AI, machine learning, and software. He researched at the Institute for Cyber-Physical Production Systems at TH Wildau, among others; his work on data-driven fault diagnosis and cyber-physical production systems appeared in journals such as Control Engineering Practice. As an AI consultant at adesso and Sixt, he put data-driven systems into practice. He has founded several startups (including notivo and markencheck.ai) and today is Co-Founder and CTO at Boomerent, where he builds the AI that analyzes complex contracts, costs, and deadlines. He brings the same data-driven approach to the analysis of the EU trademark register.
markencheck.ai grew out of a problem he ran into himself: between manual research, complex register data, and hard-to-interpret results, a fast, understandable first step was missing. Ron builds the search pipeline, the similarity model, and the analysis he writes about in these articles.
markencheck.ai is a technical research tool and no substitute for legal advice. Ron is not an attorney; the articles explain data, procedures, and methodology — they do not provide a legal assessment of an individual case.
Topics
Research & Publications
- Data-driven fault diagnosis for heterogeneous chillers using domain adaptation techniques
Control Engineering Practice · 2021
- Overall Prompting Effectiveness for Optimising Human-Machine Interaction in Cyber-Physical Systems
Journal of Integrated Design and Process Science · 2023
- A Data-Driven Approach Towards the Application of Reinforcement Learning Based HVAC Control
Journal of the Nigerian Society of Physical Sciences · 2023
- A data-driven fault diagnosis approach towards oil retention in vapour compression refrigeration systems
IEEE International Conference on Electrical and Power Engineering (CANDO-EPE) · 2019




