Prof. Shine Tu (WVU Law) has been doing interesting work studying patent prosecution and how differences between patent examiners impact the process. I asked him to provide a guest post to help readers get started on his work. – DC
by Shine Tu
Although we know that individual patent examiners can greatly affect an inventor’s chance to (1) get a patent at all and (2) get it in a timely way, there has been very little work determining how examiners are able to either delay or compact prosecution while still maintaining their quotas via the count system. Understanding how examiners work the quota system with very different outcomes can be critical for practitioners trying to understand what sort of responses or claim narrowing they should make. It also has significance for those looking to understand and improve the very process intended to spur invention.
In a previous study, I have shown that there are extreme variations on allowance rates between examiners. For example, in analyzing 10 years of patents from Technology Center 3700 I found that there were approximately 200 examiners from 3700 who had issued over 120,000 patents (approximately 51% of the patents from this Technology Center). In contrast, there was a group of approximately 300 examiners who issued less than 800 patents (less than 1% of the patents from this Technology Center). [See https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1939508]. In this current dataset, I find that not only is there a difference in allowance rates, but there is a significant difference in prosecution times. Fast examiners allow applications in approximately 1.64 years, average examiners in 3.07 years, and slow examiners on average will allow a case in 5.85 years. This delay of over four years (fast versus slow examiners) increases direct costs to applicants in the form of PTO and attorney fees, as well as indirect costs such as reduced growth, sales, and follow-on innovation.
In a set of two articles, I explored how examiners can either: (1) slow down the patent prosecution process by using a strategy of constant rejections [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3539731] or (2) speed up the patent prosecution process by using a strategy of fast allowances [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3546944]. To create a sufficiently large sample to be statistically significant, I coded the patent prosecution histories of 300 patents and reviewed 100 patents from slow, average, and fast examiners from workgroup 1610. Every rejection issued by the examiner and every response and traversal argument by the applicant was recorded.
As an initial matter, these data show that examiners in each group have similar amounts of experience at the PTO and similar average current docket sizes. However, the allowance rates of these examiner groups vary dramatically, with 79.55%, 61.65%, and 27.7% allowance rates corresponding with fast, average and slow examiners, respectively. The Office Action to Grant (OGR) score shows that these fast examiners grant a patent for every 1.5 Office Actions written, while it takes average examiners roughly 4 Office Actions and slow examiners a stunning 10.5 Office Actions before they grant one patent.
Fast examiners seem to be using a count maximization strategy based on allowances. A typical applicant who gets a fast examiner will usually have one or two Office Actions before an allowance. Fast examiners do not use many prior art rejections. Additionally, the rejections employed by fast examiners rely heavily on Obviousness-type Double Patenting (ODP) and/or 35 USC 112 rejections. Fast examiners have four times as many ODP rejections compared to slow examiners. Most applicants can (and do) traverse these ODP rejections by simply filing a terminal disclaimer. Interestingly, use of the ODP rejection is a super-efficient way to employ a count maximization strategy. This is because little work is needed to find an ODP rejection, due to the closed universe of patents, and an ODP rejection is relatively easy for the applicant to traverse. Thus, an ODP rejection followed by a terminal disclaimer gets the examiner to maximum counts with minimal effort.
In contrast, slow examiners seem to be using a strategy based on rejections. First, slow examiners have a much higher restriction rate (almost twice) and encounter three times as many traversals to these restriction requirements. These data are consistent with a rejection strategy because examiners can create a large patent family and cycle through rejections with less work, especially since they should already be familiar with the specification from the other restricted family members. Furthermore, slow examiners may not be able to avail themselves of the ODP rejection strategy employed by fast examiners because of the safe harbor created by 35 USC 121.
Not only do slow examiners use more prior art, the sources of prior art differ for slow examiners versus fast and average examiners. Slow examiners employ a rejection strategy based on prior art, with five times as many 102(a/e) rejections and six times as many 103 rejections compared to fast examiners. For 102(a/e) rejections, slow examiners rely on both US patents as well as printed publications, while fast and average examiners rely on US patent applications. Interestingly, for 102(b) rejections all examiners rely more on printed publications and secondarily on US patents. With 103 rejections, examiners also all mainly rely on US patents and, secondarily, on printed publications. Thus, all examiners search and employ prior art from different databases, however, they use the prior art that they find in different ways.
Unsurprisingly, applicants traverse prior rejections from slow examiners at a much higher rate than fast examiners. Specifically, with 102 and 103 rejections, applicants will push back against slow examiners most commonly with a missing elements argument. In contrast, most applicants respond to fast examiner 102 and 103 prior art rejections by simply filing claim amendments. Interestingly, applicants will also push back against 103 rejections from slow examiners by making a “no motivation to combine” argument. This may be because slow examiners use seven times as many references as fast examiners.
Slow examiners also put the brakes on prosecution by filing multiple 112 rejections. Specifically, slow examiners utilize three times as many 112 second rejections, four times as many enablement rejections and seven times as many written description rejections. With slow examiners, applicants use arguments to traverse enablement and written description rejections. In contrast, applicants with fast examiners usually only make claim amendments to traverse enablement or written description rejections.
Practitioners need to understand what type of examiner they have. Understanding and using this data is paramount to help manage client expectations as well as to help create a rational prosecution strategy. I note that all of these data can be accessed through services such as LexisNexis PatentAdvisor® to help determine which examiner you may encounter. This may also be important for patent prosecution strategy since slow examiners may require a strategy that involves an appeal. While fast examiners may require a strategy that involves fewer amendments and more arguments.
Although I do not make any definitive judgements about the quality of the claims passed by different examiners nor even if there is an “optimal” or “ideal” allowance rate, these varying trends indicate a wide discrepancy in examiners’ methodology that may be affecting the overall quality and number of patents created. By analyzing the differences, my studies suggest how the counts system might be modified to ensure a more efficient and balanced process where all examiners apply the rules of patentability fairly and consistently. One possible solution, for example, would be to review applications from both fast and slow examiners at a higher rate. Another solution may be to deduct counts from examiners who make too many erroneous rejections. Conversely, adding counts for examiners who dealt with difficult applicants could also be in order. Alternatively, we could completely reform the count system and create an examiner incentive structure that focuses more on quality and less on quantity. Only by looking in-depth at examiner behaviors will we be able to (1) better understand and navigate the current system and (2) make reforms to the current process that will truly encourage innovation.