Erlangen, Germany

Cornelius Jacob

This inventor holds 1 USPTO granted patent. Top assignee: Siemens Healthineers Ag. Active years: 2026.


Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Cornelius Jacob - Innovator in Medical Imaging Technology

Introduction

Cornelius Jacob is a notable inventor based in Erlangen, Germany. He has made significant contributions to the field of medical imaging, particularly in the classification of dynamically contrast-enhanced medical image data of the liver. His innovative approach has the potential to enhance diagnostic accuracy and improve patient outcomes.

Latest Patents

Cornelius Jacob holds a patent for a method that classifies medical image data using a classification algorithm. This algorithm is designed to create an image-based correlation between an image data set and a phase of a plurality of defined phases relative to the time of administration of a contrast agent. The method includes additional plausibility checking, ensuring the reliability of the classification. The image data may consist of at least three image data sets, each capturing the examination region within a period of less than two hours. This patent showcases his expertise in integrating technology with healthcare.

Career Highlights

Cornelius Jacob is currently employed at Siemens Healthineers AG, a leading company in the healthcare technology sector. His work at Siemens Healthineers AG allows him to collaborate with other experts in the field and contribute to groundbreaking advancements in medical imaging.

Collaborations

One of his notable coworkers is Robert Grimm, with whom he collaborates on various projects related to medical imaging technology. Their combined expertise enhances the innovative capabilities of their team.

Conclusion

Cornelius Jacob's contributions to medical imaging through his patent and work at Siemens Healthineers AG highlight his role as a key innovator in the field. His advancements in the classification of medical image data are paving the way for improved diagnostic methods in healthcare.

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