This inventor holds 2 USPTO granted patents. Top assignee: Blackshark.ai Gmbh. Active years: 2021-2022.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
Company Filing History:
Years Active: 2021-2022
Title: Arno Hollosi: Innovator in Geo-Spatial Workload Scheduling and Machine Learning
Introduction
Arno Hollosi is a notable inventor based in Graz, Austria. He has made significant contributions to the fields of geo-spatial workload scheduling and machine learning. With a total of 2 patents, his work focuses on enhancing the efficiency of distributed systems and improving interactive learning cycles.
Latest Patents
Hollosi's latest patents include "Geo-spatial workload scheduling in distributed systems" and "Visual image annotation utilizing machine learning for in-time feedback." The first patent addresses the complexities of workload scheduling by allowing geospatial dependencies between workload items. This innovation enables the processing of dependent workload items before all items in a previous step have been completed, thus optimizing workflow efficiency. The second patent introduces an interactive learning cycle that provides immediate feedback to operators annotating images. This system allows for a sparsely-labeled data set to be transformed into a densely-labeled training set, enhancing the speed and accuracy of image annotation.
Career Highlights
Arno Hollosi is currently employed at Blackshark.ai GmbH, where he applies his expertise in developing innovative solutions. His work at the company has positioned him as a key player in advancing technologies related to spatial data processing and machine learning applications.
Collaborations
Hollosi collaborates with talented individuals such as Stefan Habenschuss and Pavel Kuksa. These partnerships contribute to the innovative environment at Blackshark.ai GmbH, fostering creativity and technological advancement.
Conclusion
Arno Hollosi's contributions to the fields of geo-spatial workload scheduling and machine learning demonstrate his commitment to innovation. His patents reflect a deep understanding of complex systems and a drive to improve efficiency in technology.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile