Company Filing History:
Years Active: 2025
Title: Innovations of Nicholas F Nett in Machine Learning Systems
Introduction
Nicholas F Nett is an accomplished inventor based in Vernon, CT (US). He has made significant contributions to the field of machine learning, particularly in the area of automated database element processing and prediction output generation. His innovative work has the potential to impact various sectors, especially in health management.
Latest Patents
Nicholas F Nett holds a patent for a computerized method of automatic distributed communication. This method involves training two machine learning models with historical feature vector inputs to generate likelihood and mean count outputs. Specifically, for each entity in a set, the method processes a likelihood feature vector input to predict the likelihood of avoidable negative health events within a specified time period. Additionally, it processes a mean count feature vector input to estimate the expected number of such events. The system automatically distributes structured campaign data to entities based on these outputs, showcasing a practical application of machine learning in health management.
Career Highlights
Nicholas is currently associated with Cigna Intellectual Property, Inc., where he continues to develop and refine his innovative ideas. His work focuses on leveraging machine learning to enhance predictive analytics in healthcare, aiming to improve patient outcomes and reduce avoidable health events.
Collaborations
Throughout his career, Nicholas has collaborated with notable colleagues, including David Fogarty and Yee Wah Eva Lee. These partnerships have fostered a collaborative environment that encourages the exchange of ideas and the advancement of technology in their field.
Conclusion
Nicholas F Nett's contributions to machine learning and healthcare innovation exemplify the impact of technology on improving health outcomes. His patent and ongoing work at Cigna Intellectual Property, Inc. highlight the importance of predictive analytics in modern healthcare.