Imagine your client asks you to draft a patent application concerning software for accepting online payment. You cringe because, as any patent professional is aware, these types of software and/or business methods applications have become excruciatingly difficult to prosecute in the wake of the Supreme Court’s decision in Alice Corp. Pty. Ltd. v. CLS Bank Internat’l, et al., 573 U.S. (2014).
In particular, Technology Center (TC) 3600 is a hotbed of Alice rejections. In addition to reviewing applications related to national security, animal husbandry, and nuclear weapons, TC 3600 is home to three e-commerce art unit groups.
We used a dataset of over 6 million patent applications and their prosecution histories to measure Alice rejections by tech center (see Figure 1). It’s clear from this analysis that the vast majority of Alice rejections occur in TC 3600.
If knowing that TC 3600, with its three e-commerce art unit groups, is a perilous place for an application to land, it must be even better to be able to predict the individual art unit to which an application will be assigned… right?
Art Unit or Class?
When a patent application is submitted to the USPTO, it is then sorted into a class and subclass based on the subject matter claimed in the application. The USPTO then uses this class/subclass designation to route applications to the appropriate tech center and art unit group. We decided to examine which, then, is a better indicator of the likelihood of receiving an Alice rejection – art unit or class.
As shown in Figure 2, while there are clusters of art units that receive more Alice rejections than others (many of which are in TC 3600), there are others scattered across the USPTO that also receive significant numbers of Alice rejections. For example, while Art Unit 3622 has the highest incidence of Alice rejections (646), Art Unit 1631, in a different technology center, still has a significant amount (310).
However, when we looked at the distribution of Alice rejections by class, it became clear that class is actually a better predictor of an application’s propensity to receive an Alice rejection (see Figure 3).
As is clear in Figure 3, nearly half of all applications with an Alice rejection are assigned to Class 705.
Further, by examining distribution of Alice rejections between the five art units and classes with the most Alice rejections, it’s clear that class is the better indicator. As seen in Figures 4 and 5, the distribution of Alice rejections between the top five art units is relatively even while Class 705 has seen more than twice as many Alice rejections as the next four most common classes combined.
The disparity between the art units is confused even more so when we consider the total number of Alice rejections in each art unit, rather than just counting the total number of applications receiving an Alice rejection. Doing that, we can see that, while 3622 and 3623 have almost equal numbers of applications with Alice rejections, 3623 actually has more in total. This means that applications in that art unit are more likely to receive multiple Alice rejections and take longer to prosecute.
Within each art unit, there are discrepancies between individual examiners. Consider two examiners in Art Unit 3622. Each has examined roughly the same number of applications and has over five years of experience in Art Unit 3622, but have widely varying allowance rates. Examiner A has a 31% allowance rate, while Examiner B has an allowance rate of only 6.5%. Additionally, Examiner A has issued Alice rejections on 34 applications while Examiner B has issued them on only 13 applications. This may indicate that, while Examiner A is more likely to issue an Alice rejection, he is also more likely to eventually grant an allowance. Due to this type of variance, a prediction that your application is likely to land in Art Unit 3622 may not provide the best indication of the likelihood of an Alice rejection.
Figure 5 makes clear that Class 705 is vastly more likely to see an Alice rejection than any other class and is also more likely to see applications with multiple Alice rejections. Thus, anticipating when an application is likely to wind up in Class 705 (Data processing: financial, business practice, management, or cost/price determination) gives the prosecutor a better indication that the application is likely to receive an Alice rejection than predicting an art unit assignment alone.
Better Outcomes with Big Data
Armed with that information, the next question is how to go about predicting when an application is likely to be assigned to TC 3600 and/or Class 705. By using big data, we are able to help patent practitioners ascertain and alter the probability that their applications will be assigned to particular classes and technology centers.
Class 705, with an overall allowance rate of only 48.6%, is clearly an undesirable destination for an e-commerce patent application. By using big data analytics, we can narrow down the specific words that are steering an application toward a particular class. Altering these words allows a patent practitioner to change the odds of an application being assigned to a tech center or class with a higher allowance rate, or to one in which an attorney or firm has performed well in the past. For example, the words “loan” and “auction” are strongly associated with an application’s assignment to Class 705.
Ultimately, while we always talk about technology center and art unit assignments, class is a far better predictor of an application’s chances of success, particularly when it comes to the recent uptick in §101 rejections in the wake of Alice. With the ability to use big data to alter the likelihood that an application will be assigned to a particular class or tech center, attorneys can make more informed strategic decisions for their clients, obtain better results at the USPTO, and increase the value of their services.
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