Joseph J. Probst

Joseph advises clients regarding intellectual property matters, with a particular emphasis on assisting clients in obtaining US and international patents to protect their inventions and products.
Joseph drafts and prosecutes patent applications primarily directed to electrical and computer-implemented subject matter such as inventions regarding artificial intelligence but also has experience in many different fields of technology.
Joseph also has experience with intellectual property licensing, trademark registration, and patent non-infringement and invalidity opinions.

Joseph’s technological focus is machine learning (e.g., deep neural networks or “deep learning”) and other forms of artificial intelligence. Joseph has prepared and prosecuted over two hundred patent applications in such fields, including both applications for core machine learning technologies (e.g., novel model architectures and learning algorithms) and applications for applied machine learning technologies (e.g., autonomous robotics such as self-driving cars, camera control operations, medical diagnosis via imagery, and the like).
Joseph also has patent experience in technical fields such as semiconductor devices, solar cell fabrication, MEMS devices, LED lighting devices, camera lens assemblies, machine-readable symbol readers, RF-enabled medical devices, electric motor control circuits, satellite positioning, advanced metering infrastructure, image stitching, image compression, computer vision, capacitor chips, induction cooktops, and semiconductor wafers.
During law school, Joseph was an intern for the Honorable Randall R. Rader, Former Chief Judge of the U.S. Court of Appeals for the Federal Circuit.

Education: Clemson University (B.S., Bioengineering with concentration in Bioelectrical Engineering, 2009, magna cum laude); University of Washington (J.D., 2012, high honors (top 5% of graduating class)); Certificate in Machine Learning from Stanford University, 2020.
Honors: Order of the Coif; Washington State Bar Association Intellectual Property Section Scholarship Award Winner; Executive Board Member, University of Washington Moot Court Honor Board.

Presentations and Publications:

Patent Drafting for Machine Learning: Claim Strategy, Patent Eligibility, and Disclosure Requirements for the U.S. and Europe, Strafford Publications (Mar. 2021);
USPTO Report Traces the Rapid Diffusion of Artificial Intelligence Technologies across U.S. Patent Filings, Dority & Manning (Dec. 17, 2020), available here;
Balancing Patent and Trade Secret Protection for AI-Based Inventions, The Knowledge Group (Dec. 2020);
USPTO Reports on Public Views of Artificial Intelligence and Intellectual Property Policy, Dority & Manning (Nov. 5, 2020), available here;
U.S. Patent Issues in Artificial Intelligence, Law Offices of Shiga International Patent Office (Dec. 2019);
Overview of the Approach to AI in the United States, Artificial Intelligence and Intellectual Property: A Global Perspective, Imperial Hotel Tokyo (Dec. 2019);
When an Inter Partes Review is Instituted the USPTO is Required to Decide the Patentability of All Claims Challenged By Petitioner, Dority & Manning (May 9, 2018), available here;
Loaded Question: Examining Loadable Kernel Modules Under the General Public License v2, Washington Journal of Law, Technology, and Arts, 7 Wash. J.L. Tech. & Arts 265 (2012).