This inventor holds 1 USPTO granted patent and 9 published patent applications. Top assignee: International Business Machines Corporation. Active years: 2025.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
Company Filing History:
Years Active: 2025
Title: Mattia Rigotti - Innovator in Artificial Intelligence
Introduction
Mattia Rigotti is a notable inventor based in Basel, Switzerland. He has made significant contributions to the field of artificial intelligence, particularly in the area of generating strong labels from weak labels using neural networks. His innovative approach has the potential to enhance the accuracy of machine learning models in various applications.
Latest Patents
Mattia Rigotti holds a patent titled "Generating strong labels for examples labelled with weak labels." This method utilizes an artificial neural network (ANN) that has been trained on a dataset with weak labels, such as classes of structural defects in civil engineering images. The process involves executing the trained ANN on test examples to infer weak labels, extracting explanatory features, and generating strong labels based on these features. The method also includes prompting user feedback to refine the weak labels further.
Career Highlights
Mattia Rigotti is currently employed at International Business Machines Corporation (IBM), where he continues to develop innovative solutions in artificial intelligence. His work focuses on improving the interpretability and reliability of machine learning systems, which is crucial for their application in real-world scenarios.
Collaborations
Mattia collaborates with talented colleagues, including Klára Janousková and Ioana Giurgiu. Together, they contribute to advancing research and development in their respective fields, fostering a collaborative environment that encourages innovation.
Conclusion
Mattia Rigotti's work exemplifies the intersection of artificial intelligence and practical applications, showcasing how innovative methods can lead to significant advancements in technology. His contributions are paving the way for more reliable and interpretable machine learning systems.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
