is the founder of Patent Bots, a company that provides tools for patent attorneys, such as automated patent proofreading and patent examiner statistics. Jeff is also a practicing patent attorney at GTC Law Group, where he assists startups with technology relating to speech recognition and machine learning. Previously, Jeff was in-house patent counsel for Amazon, an associate for Boston law firm Wolf Greenfield, and a clerk for the First Circuit Court of Appeals.
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.
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.
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.
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.
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.