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

After a patent application is filed with the U.S. Patent and Trademark Office (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.

Examiners at the USPTO have expertise in specific technology areas, and to ensure a high-quality examination of a patent, it is important to match the technology of the patent to the expertise of an examiner.

The details of the assignment process are not important here, but the idea is that a patent application is “classified,” which means that codes are assigned to indicate the technological subject matter of the patent, and then an art unit and examiner are selected with expertise in that technology.

Recent Changes

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.

Several companies provide tools that predict the art unit that will be assigned to a patent application after it is filed. Examples include Juristat, Patent Bots, PatSnap, and Serco. A patent practitioner can pay to use these tools to influence the art unit that will be assigned to a patent application after it is filed. The analysis here uses the Patent Bots tool.

The USPTO has stated publicly that it is transitioning to using more automation in the patent assignment process, and the improved accuracy of the art unit predictor tool provides independent confirmation.

The improved accuracy of the Patent Bots art unit predictor tool over the last two years shows that the USPTO is increasing the use of automation in assigning patent applications to art units. This conclusion is based on differences between manual processing by people and automated processing by machines.

People are much less predictable than machines. Asking different people to classify the same patent might result in different outcomes. Even asking the same person to classify the same patent on two different days might result in different outcomes. By contrast, when a machine does the classification, the outcome will always be the same.

Accordingly, when you try to predict what a person will do in assigning a patent to an art unit, the accuracy of the prediction will be lower because there is more randomness in what the outcome will be. But when you try to predict what a machine will do, the accuracy will be higher because the machine produces the same result each time. If the process is fully automated, then it should be possible to obtain near perfect accuracy since a machine learning model has the capability to learn even a complex decision-making process, as long as that process is consistent.

The Data

As the USPTO transitions the patent assignment process to replace decisions by people with decisions by machines, it should thus be easier for a third-party tool to predict the outcome. The following graph shows the accuracy of the Patent Bots art unit predictor over the last two years:

The art unit predictor tool predicts five possible art units and the accuracy here corresponds to one of the five predicted art units being the correct art unit. The accuracy numbers in the above chart are computed at six-month intervals over two years.

The implementation of the art unit predictor tool has not changed over this time period. The only change is the data used to train and test the art unit predictor tool. The accuracy of the predictor is clearly increasing over time.

The increased accuracy of the art unit predictor tool means that the USPTO is becoming more predictable in its process of assigning applications to art units. This is better for the patent system because similar applications will be assigned to the same art units and those art units will have (or learn) more expertise in those technology areas.

The Fully Automated Future

As the USPTO continues to automate the patent assignment process, the ability of third parties to predict the art unit assigned to an application will continue to increase. Where the USPTO process is fully automated, it may even be possible to have near perfect accuracy, including predicting the individual examiner that will be assigned to a patent application.

If and when the USPTO assignment process becomes fully automated, it is plausible that the USPTO might provide a public facing tool to allow all patent applicants to see the likely assignment of their application to an art unit or examiner. This would obviate the need for third-party art unit prediction tools.

Consistency in the process for examining patent applications benefits the entire patent community because it promotes more consistent and thus fairer outcomes. Automating the patent assignment process is one step in promoting consistency and I look forward to further advances in USPTO technology to increase consistency and fairness in the patent examination process.

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