24 July 2016

Be a USPTO Patent ‘Examiner Ninja’

NinjaIf you have any involvement or interest in the prosecution of US patent applications – particularly in the contested fields of computer-implemented inventions or biotechnology – you will probably be aware that disturbing things are afoot at the US Patent and Trademark Office (USPTO).  Specifically, in the wake of the US Supreme Court’s decisions in the Alice, Mayo and Myriad cases, patent allowance rates have plummeted in examination sections devoted to subject matter that could be regarded as relating to computer-implemented ‘business’ processes, diagnostic methods, and genetic technologies.

In this hostile environment, applicants and their advisers need every tool they can get their hands on to understand what they are up against, and to create the best possible strategies for staying out of – or, if necessary, getting out of – trouble.  And, as in many other fields of endeavour, so-called ‘big data’ is being touted as a possible solution. 

In particular, the contents of the USPTO’s Patent Application Information Retrieval (PAIR) database, which records all information about every patent application making its way through the office procedures, are increasingly accessible to anyone wants to use that data, whether for commercial or non-commercial purposes.  PAIR Bulk Data is now available directly from the USPTO, and includes the information included in the bibliographic (‘application data’), published document and patent term extension data tabs in Public PAIR dating back to 1981.  Additional examination information for many recent applications is available in the USPTO’s Patent Examination Research Dataset (PatEx).  Up until 2015, Google maintained bulk-downloadable USPTO PAIR data sets.

All of this information can be used to obtain and analyse a variety of statistics.  For example, if you are dealing with a ‘difficult’ examiner it is possible to find out whether it is just your case that is problematic, or if the examiner has a history of rejecting a majority of applications he or she has reviewed.  The data might also tell you whether the examiner is more likely to allow an application following an interview and/or whether filing a Notice of Appeal may lead to a more favourable outcome.

Of course, all of the sources I have listed above supply ‘raw’ data, in such exciting formats as CSV, XML and JSON, which is of limited use unless you have the time, and the technical skills, to convert it into a form that is more suitable for analysis.  Fortunately, there are people out there who are already doing this for us all.  Commercial offerings include LexisNexis PatentAdvisor and Juristat, while free services are provided by Examiner Watchdog and Examiner Ninja.  So I am going to provide a quick review of these services, and what they can do for you.

Commercial Services

The down-side of commercial providers is, of course, that they expect to be paid for their services!  In return, however, you might reasonably expect that they keep their databases up-to-date, that they provide a wider range of information and analysis, and that they offer efficient and responsive customer service and assistance.

I have not personally tried LexisNexis PatentAdvisor, but I am happy to give it some publicity based on the work IP Watchdog’s Gene Quinn has being doing with it.  Over the past few weeks, Gene has been looking at the rates of allowance and abandonment of patent applications assigned for examination in the USPTO’s Technical Centre (TC) 3600.  For those who may not be aware, the USPTO organises its examiners into TCs, each of which comprises a number of Art Units.  Each Art Unit is responsible for examination of applications relating to a specific technology niche, based upon the patent classification system.  The USPTO maintains a list of patent classes arranged by Art Unit on its web site.  TC 3600 can be regarded broadly as including all of the classes associated with ‘e-commerce’ technologies.

The most alarming of the recent IP Watchdog articles is entitled ‘E-Commerce Art Units: Where Patent Applications Go to Die’.  In that article, Gene employs LexisNexis PatentAdvisor to look specifically at allowance rates (defined as the number of patents issued relative to the total number of disposals, i.e. applications allowed and abandoned) in TC 3600 over the recent period from the beginning of 2015.  The data show that allowance rates in TC 3600 for the first half of 2016 vary between 1.3% in Art Unit 3689 (just three patents issued out of 235 disposals) and 32.1% in Art Unit 3627.  This is, obviously, not good news for anyone with an application languishing in any of these Art Units.

A free two-day trial of LexisNexis PatentAdvisor is available, if you want to give it a try.

Another commercial service provider is Juristat.  I signed up for their free trial a few months ago, and am pleased to say that I found Juristat’s Examiner Reports service to be a powerful and useful tool for analysing examiner behaviour.  By comparison to the free offerings discussed below, Juristat’s data is more complete in terms of recent updates, and far more comprehensive in the information that it extracts from PAIR records.  For example, Juristat extracts additional information from Office Actions, which enables it to show details of the grounds for rejections issued by each examiner in individual applications.  Juristat also now has a tool called Etro, which makes predictions as to which TC an application is likely to be assigned based upon an analysis of proposed claim language.

Juristat’s subscription-based pricing presents a barrier to prospective users, such as myself, whose primary business is not US patent prosecution and who may therefore have need only of casual access to its reports.  However, for businesses such as US patent attorney firms, which could presumably gain value from a number of Juristat reports each month, the current pricing looks pretty competitive.

Free Tools

For those of us who cannot justify the cost of commercial services, there are a couple of free online tools which can provide a lot of useful information, particularly when used in combination.

My personal favourite is Examiner Ninja, which is an amazingly professional and polished web application developed by Los Angeles based IT professional and recently-qualified patent attorney Justin Roettger.  The reports generated by Examiner Ninja are comparable in clarity and content to the basic information provided in Juristat’s Examiner Reports, although they lack Juristat’s ability to dig deeper into specific applications and grounds of rejection.

For each examiner in its dataset, Examiner Ninja can generate a report showing overall statistics (number of applications assigned to the examiner, number issued, number abandoned and number pending), along with allowance rates for the examiner as compared with overall rates for their Art Unit, and for the USPTO as a whole.  These rates are also broken down into allowance rates before and after a fist final rejection.

Examiner Ninja also provides an analysis of the relative benefits of examiner interviews, requests for continued examination (RCEs) and appeals.  A chart showing the examiner’s history of pending applications, allowances and abandonments is also available, as is a summary of average timings.  These are all features that are also present in Juristat.

The other service worth knowing about is Examiner Watchdog, which is a side project for patent attorney Jonathon Gillen.  While this service is completely free to use, there is a mechanism to contribute towards its upkeep via a PayPal donation.

While not as slick or sophisticated as Examiner Ninja, Examiner Watchdog allows you to quickly pull up a summary of examiner statistics, as well as a complete listing of all associated applications in the system’s dataset.  These are colour-coded to show whether they are pending, allowed or abandoned, so it is easy to pick an example if you want to dive into the Public PAIR system yourself and see the details of what happened in a particular case.

The other feature of Examiner Watchdog that I really like is its ability to search by Art Unit as well as by examiner name.  This means you can pull up a list of all examiners in a selected Art Unit (there are typically no more than about 40), and get a quick overview of representative allowance rates and other statistics.  From this listing, you can click through to look at individual examiners.

The big downside with the free services is lack of currency.  They are dependent upon freely available datasets, which are either more limited in their content, or less frequently updated, than the data gathered by commercial operators.  Both Examiner Ninja and Examiner Watchdog have limited data from 2015 onwards.  However, I notice that the USPTO seems to have recently updated its 2015 PatEx dataset, so we will hopefully soon see new information flow into these services.

Conclusion – Use the Tools at Your Disposal

Big data is obviously not a total panacea for all rejections of US patent applications.  If the claimed invention is not new, or is obvious in view of the prior art, or genuinely falls into a category of ineligible subject matter, no amount of examiner information will – or should – save you from rejection!

However, human nature being what it is – particularly in the face of legal change and uncertainty – it is pretty clear that not all rejections are justified, that there are Art Units where applications more often than not go to die, and that there are some examiners whose job it is to perform the terminations.  In these cases, knowledge may be power, even if it is only to enable you to gain a realistic picture of the likely costs and prospects of success in dealing with a particular examiner.

The earlier you make use of the available information, the better.  For example, an invention relating to improved processing of survey data might be presented as either an advance in market research (i.e. a field of application for which it was originally developed), or as an improved machine-learning system for analysing survey responses and predicting future behaviour.  The way in which such an invention is described and claimed in a patent application could be the difference between ending up in TC 3600, where it will probably be presumptively  invalid unless you can show otherwise, or in the far more subject-matter friendly TC 2100.

Whether or not someone ends up getting a patent should not come down to how well they deploy big data analytics to play the system.  But for applicants in fields in which the system is currently stacked against them, this does seem to be what it has come to.  I hope things improve, but in the meantime I will be taking advantage of whatever tools I have at my disposal.


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