Youngstown, OH, United States of America

Renee L Vitullo

This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: Ge Precision Healthcare LLC. Active years: 2024.

USPTO Granted Patents = 1 

% Patents Active = 100.0

Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:


Years Active: 2024

Loading Chart...
1 patent (USPTO):Explore Patents

Title: Innovations in Fetal Heart Rate Analytics by Renee L Vitullo

Introduction

Renee L Vitullo is an accomplished inventor based in Youngstown, OH (US). She has made significant contributions to the field of healthcare technology, particularly in the area of fetal heart rate analytics. Her innovative work utilizes machine learning techniques to enhance the monitoring of fetal health during labor.

Latest Patents

Renee holds a patent for her invention titled "Deep learning based fetal heart rate analytics." This patent describes techniques for performing fetal heart rate (FHR) analytics using machine learning. The computer-implemented method involves training a machine learning model through a supervised process to identify patterns in cardiotocograph data that correspond to physiological events associated with fetuses and their mothers. The method also includes real-time analysis of new cardiotocograph data during labor to identify these patterns.

Career Highlights

Renee is currently employed at GE Precision Healthcare LLC, where she continues to develop innovative solutions in healthcare technology. Her work focuses on improving fetal monitoring systems, which can lead to better outcomes for mothers and their babies.

Collaborations

Renee collaborates with talented professionals in her field, including Rohit Pardasani and Siddharth Ajith. Together, they work on advancing healthcare technologies that can significantly impact patient care.

Conclusion

Renee L Vitullo's contributions to fetal heart rate analytics exemplify the intersection of technology and healthcare. Her innovative patent demonstrates the potential of machine learning to improve maternal and fetal health monitoring.

Profile summary based on public USPTO records.
Please report any incorrect information to [email protected]
Loading…