Nathan Choe assists the Post-Grant Proceedings Practice with inter partes review (IPR) and patent reexaminations before the USPTO’s Patent Trial and Appeal Board (PTAB). Nathan’s experience includes assisting with drafting responses to non-final and final office actions, conducting clearance and infringement searches, drafting design applications, and working closely with clients with diverse technical interests.

Prior to joining Wolf Greenfield, Nathan completed his undergraduate degree in Electrical and Computer Engineering with a minor in Chemistry. As an undergraduate at Duke, Nathan was a part of the Duke’s Nanomaterials Laboratory, where he developed carbon nanotube thin film transistor based chemical sensors and analyzed their operating points in active and static fluid environments. He was also a part of the Urodynamics Laboratory at the Duke Hospital, where he developed a machine learning model interpreting time series-based detrusor pressure data to diagnose uro-overflow and UTI conditions in pediatric spina bifida patients. Nathan’s technical background includes computer architecture, software development, robotics, organic and biochemistry, organic/inorganic biosensors, and machine learning.
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Nathan Choe assists the Post-Grant Proceedings Practice with inter partes review (IPR) and patent reexaminations before the USPTO’s Patent Trial and Appeal Board (PTAB). Nathan’s experience includes assisting with drafting responses to non-final and final office actions, conducting clearance and infringement searches, drafting design applications, and working closely with clients with diverse technical interests.

Prior to joining Wolf Greenfield, Nathan completed his undergraduate degree in Electrical and Computer Engineering with a minor in Chemistry. As an undergraduate at Duke, Nathan was a part of the Duke’s Nanomaterials Laboratory, where he developed carbon nanotube thin film transistor based chemical sensors and analyzed their operating points in active and static fluid environments. He was also a part of the Urodynamics Laboratory at the Duke Hospital, where he developed a machine learning model interpreting time series-based detrusor pressure data to diagnose uro-overflow and UTI conditions in pediatric spina bifida patients. Nathan’s technical background includes computer architecture, software development, robotics, organic and biochemistry, organic/inorganic biosensors, and machine learning.