Jeff O’Neill Image

Jeff O’Neill


GTC Law Group

Jeff O’Neill, Counsel at GTC Law Group, specializes in preparation and prosecution of patent applications in the areas of machine learning, deep neural networks, artificial intelligence, natural language processing, speech recognition, and signal processing. Jeff has extensive industry experience, including the development of models for state-of-the-art speech recognition systems and training neural networks for text classification.

Jeff is also the founder of two companies, Patent Bots and OpaVote.  Patent Bots provides tools that help patent attorneys produce higher quality patents and work more efficiently.  OpaVote helps organizations run elections online and specializes in ranked-choice voting for more representative outcomes.

Jeff’s previous legal experience includes working as patent counsel for, practicing in IP litigation for a Boston law firm, and clerking for the First Circuit Court of Appeals. Prior to becoming an attorney, Jeff completed a Ph.D. in signal processing from the University of Michigan, did postdoctoral studies in signal processing at the Ecole Normale Supérieure in Lyon, France and completed his postdoctoral studies in image processing at Boston University.

Jeff received his J.D. from Cornell Law School. He is admitted to practice in Massachusetts and before the United States Patent and Trademark Office.

Recent Articles by Jeff O’Neill

Since 2020, Patent Errors Have Decreased by 11.24%

In an ideal world, issued patents would not contain errors. In reality, patent drafting is tedious and time-consuming work and perfection is not an attainable goal. The patent industry seems to be steadily getting better, though. In a recent study, we uncovered an 11.24% decrease in errors per patent over the past four years. We observed this decrease by reviewing every patent issued by the U.S. Patent and Trademark Office (USPTO) since 2020 – nearly 1.4 million patents.

The USPTO’s Increased Automation of Patent Assignments is Good for the Patent System

After a patent application is filed with the USPTO, it gets assigned to an art unit and a patent examiner in that art unit who is responsible for reviewing the application, doing a prior art search, and determining whether to grant a patent…. In the past, this process was manual. People would review patent applications to assign classification codes, and then other people would determine the art unit and examiner to be assigned using the classification codes. More recently, the USPTO is automating the assignment process. The assignment process is a great candidate for automation using machine learning, because large amounts of training data are available to train a machine learning model. Automating the assignment process has several advantages: lower costs, faster processing, and more consistent and likely better assignments of applications to art units and examiners.