Jeff O’Neill Image

Jeff O’Neill

Counsel

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 Amazon.com, 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.

Errors in Issued Patents as a Measure of Patent Quality

Companies spend considerable sums of money to develop patent portfolios that protect their valuable innovations. Given the large stakes, it behooves companies to obtain high quality patents. I’ll start this article with an example of a patent mistake that resulted in a bad outcome for the patent owner. iRobot lost a patent infringement claim against a competitor that perhaps could have been avoided. The issue was that important concepts of the claims were not described in the patent, and the meaning of the claims was not clear. The independent claims included the phrase “instructions configured to cause a processor” but the only use of “instructions” in the patent related to operational instructions for a user. Because the patent did not sufficiently describe the “instructions” in the claims, iRobot did not obtain its desired claim construction, and the Federal Circuit found no infringement. It seems plausible that better claim drafting might have avoided these errors and achieved a better outcome for iRobot.

Winning Strategies for Getting Past the Five Types of Patent Examiner

Any patent attorney knows that each patent examiner can vary greatly in approach to examination. In this article, five different types of patent examiners and suggest prosecution strategies for each to help you get better outcomes for your clients.

Visualizing Outcome Inconsistency at the USPTO

In an ideal world, your chance of getting a patent allowed is based on the merits of your patent application and independent of the largely random assignment of the patent examiner.  As any patent attorney knows, however, this is not the case.  Some examiners allow patents too easily and others seem predisposed against allowing any patents at all… The patent application grant rate across the USPTO is 66%.  One would expect that a distribution of examiner grant rates would follow a bell-like curve with a reasonably small standard deviation, but that is not what the data shows.

Predicting Future Patent Outcomes

In this article, I compute a “three-year grant rate” that shows the probability of obtaining a granted patent within three years of the first office action. This three-year grant rate tells you how difficult an examiner is and when you can expect to be granted a patent. The greatest benefit of the three-year grant rate is that it incorporates information about both the difficulty of the examiner and the length of time to obtain a patent into a single, easy to understand number. If your examiner has a three-year grant rate of 18%, it is easy to explain to your client that they have an 18% chance of getting a patent issued in three years.