31 August 2020

Beware Unregistered Patent Practitioners!

Smooth CriminalThe title of this article is intentionally ambiguous.  Does it mean that prospective clients should beware of unregistered practitioners?  Or does it mean that unregistered practitioners should beware of getting caught out?  Ideally, it would mean both.  In practice, as things currently stand in Australia, it mostly means neither.  Unregistered practitioners are not a problem that most innovators genuinely seeking professional representation are presently at great risk of encountering.  By the same token, the number of people carrying on business, practising, or acting as patent attorneys while unregistered is so small, and the circumstances in which they are doing so are so limited, that the prospects of any enforcement action being taken against them are essentially nil.

But unregistered practise does happen, and the extent of it is worth keeping an eye on, if only to ensure that it does not become a larger problem in the future.

In this article I will discuss the legal framework around the provision if patent attorney services in Australia, including the information that clients are entitled to receive from registered attorneys.  I will provide some numbers demonstrating the relatively small scale of the problem of unregistered practitioners.  And I will explain the enforcement regime, and potential penalties that can be imposed upon people found to be acting as patent attorneys without being appropriately qualified and registered.  Additionally, I will report on some feedback I obtained from IP Australia on their current approach to unregistered practitioners, and the prospects of any change in the foreseeable future.

30 August 2020

A Recurrent Neural Network for Classifying Patent Application Technology based on Titles

Sorry DaveIn a companion article, I presented the results of using a machine learning model to classify Australian provisional applications into 35 fields of technology based upon nothing but their titles.  In this article, I provide additional technical detail of the model, along with results of its performance in testing and validation.  I also make some observations on the costs of machine learning, in terms of hardware, computation, and energy consumption.  Even for a relatively modest model, these costs may become non-negligible, while recent reports indicate that large-scale state-of-the-art machine learning systems are most likely costing millions of dollars in compute resources and energy to develop.

It is not obvious that a neural network model could be trained to predict the technical field of a patent application given nothing but the title as input.  Human specialists (i.e. patent searchers and examiners) classify applications into very specific technical categories defined by various patent classification systems, such as the International Patent Classification (IPC), or the Cooperative Patent Classification (CPC) which has been jointly developed by the US and European patent offices.  In doing so, the specialists have access to the full patent specification and claims to enable them to determine the subject matter of the invention.

However, while accurate classification at the specificity of systems such as the IPC and CPC based only upon a title would doubtless be impossible – even for a human expert – a less challenging task, such as predicting a field of technology selected from a relatively small number of choices, may be feasible.

Here, I report results of training a neural network model on the task of classifying patent applications according to 35 technical fields grouped into five technology sectors.  The model achieves 67% accuracy, averaged across all technical fields, and nearly 80% accuracy in the best case (‘organic fine chemistry’), if forced to classify each title into a single field of technology.  However, not all misclassifications are necessarily ‘wrong’, given that the subject matter of a single patent application may cross multiple fields of technology.  At the higher level of ‘technology sector’, the model’s accuracy varies between 73% and 91%.  Furthermore, when the model output is used to identify multiple potential fields, the ‘correct’ classification appears in the top four predictions in over 89% of cases.

Australian Provisional Filings Have Declined in 2020 in Almost Every Field of Technology… Except ‘Pharmaceuticals’

ChemistIn my previous article, I observed that while the numbers of Australian provisional applications filed in 2020 up until May had been down on 2019, filings in June and July were higher than during the same period last year, and that overall numbers have thus far shown greater resilience than in the last major economic downturn, i.e. the global financial crisis (GFC) of 2007-2009.  I also noted back in May that many self-represented applicants, of both provisional and innovation patent applications, appeared to be directing their innovative attentions to problems arising out of the COVID-19 pandemic.  I have been wondering, therefore, whether there may be a similar trend in provisional filing activity more generally that is helping to prop up the numbers, despite economic pressures associated with the pandemic.

I have now conducted some analysis, and it appears that there may be evidence to support this hypothesis.  For the months of January to July, it appears that provisional filings associated with every industrial sector except chemistry are down compared with 2019 numbers, and that most of the growth within the chemistry sector can be attributed to pharmaceuticals.  This is certainly consistent with an enhanced focus on healthcare, quite likely prompted by the global pandemic.

If you are familiar with the Australian patent system, you may already be wondering how I managed to analyse the sectors and fields of technology associated with provisional filings.  Provisional applications are not published in full, and only limited bibliographic information is available, including the identity of the applicant, and the title of the application.  Only a small fraction of these applications are filed by applicants whose industry sector and/or technology interests may be readily identified, most being filed by small private companies and individuals.  That really leaves only the title as a means for ‘guessing’ the technology to which an application relates.  Fortunately, thanks to machine learning technology, and the availability of a large amount of data relating to the classification of prior patent applications, we can do quite a bit better than just guessing!

On the assumption that many readers will not be as interested in the technical details of the machine learning approach, this article includes:

  1. a brief introduction to the machine learning model, sufficient to explain the classification system used, and generally how the model ‘learned’ to classify applications by title; and
  2. some results of analysing provisional filings over the past few years, which show a general decline in application numbers in most fields of technology, with the exceptions of those relating to ‘instruments’, which have been fairly flat, and ‘chemistry’, which has experienced a boost so far this year.

For those interested, in a separate article I provide additional technical detail of the machine learning model, and how it performed in testing and validation, as well as discussing how accurate we might expect it to be on the provisional application data.

19 August 2020

COVID Update – July Filings Surprisingly Resilient, but Self-Filers & Chinese Applicants Remain Major Contributors to Small Gains

MaskI live in the Victorian capital city of Melbourne, where we have been in ‘Stage 4’ lockdown since 2 August 2020.  With just a few permitted exceptions, the wearing of masks is mandatory, we must stay home except for essential activities – which must be carried out within a 5km radius, and may include no more than one hour of exercise per day – and we are subject to a curfew between 8pm and 5am each day.  The reason we are allowing ourselves to be subjected to such draconian restrictions is simple.  COVID-19 is a highly contagious disease with (as yet) no known cure or vaccine (unless you believe the Russians, which nobody reputable does).  The virus kills a significant number of people who contract it, particularly those who are older and/or have existing medical conditions, and there is increasing evidence that it may cause a range of long-term health problems even in those who are relatively young and physically fit.  Given this, I have little patience for the opinions of ‘rationalists’ (most of whom, oddly, seem to be privileged middle-aged white men) who argue that the damage caused to the economy by restrictions is too high a price to pay in order to save the lives of a few old folk.  Quite aside from the fact that those ‘old folk’ have a lifetime of contributions to society behind them, and are other people’s beloved parents, grandparents, partners, friends, and carers, without a crystal ball we just don’t know what the counterfactual looks like.  What we do know, for an absolute fact, is that we can save people from COVID-19.  And, while we are making sacrifices to (hopefully) keep deaths in our country down to a few hundred, the world’s (supposedly) most advanced economy is providing us with an object lesson in the consequences of failing to make those sacrifices.  If we had the same per capita mortality rate as the US, there would be over 13,000 Australians dead today who are, instead, still alive.

Of course we are paying, and will continue to pay, a high economic price for those lives.  Within this cost, it is to be expected that an economic downturn, and the uncertainty created by the COVID pandemic, will have an impact on levels of research, development, and commercialisation, which will, in turn, affect the numbers of patent applications filed.  By way of comparison with another recent downturn, the charts below show the numbers of standard and provisional applications filed in Australia over periods encompassing the global financial crisis (GFC), which largely played out between mid 2007 and early 2009.  The data indicates that the effect of the GFC on provisional filings – predominantly made by domestic applicants as a first step into the patenting process – was almost immediate.  A notable decline in standard application filings lagged the GFC by a couple of years, due to the delays built-in to the patent system through international agreements such as the Paris Convention and the Patent Cooperation Treaty (PCT).

Patent filings spanning the GFC era

One aspect of the above numbers that may be concerning to those patent attorneys reliant on a domestic client base is that the 20% of new provisional filings that ‘disappeared’ in the wake of the GFC have never returned, whereas standard application filings (of which 90% originate with foreign applicants) recovered to pre-GFC levels (though not, it must be said, to pre-GFC growth rates) within about five years.  We would hope not to see a similar permanent reduction in new domestic filings as a result of the COVID-19 pandemic, for the sake of Australian innovation and the economy more generally, if not for the livelihoods of a few patent attorneys.

This is why I have been following patent filing numbers since March (see reports also on filings through April, May and June).  I now have numbers for July, which show filings to have been surprisingly resilient, despite the economic challenges created by restrictions and uncertainty.  Standard patent applications during the month were almost identical to the same period last year, while provisional application filings were significantly up on 2019.  While self-represented applicants once again made a substantial contribution to the strong showing of provisional filings, applications filed with professional advice and assistance were also higher in July.  Meanwhile, innovation patent filings continue to boom, up by an astonishing 189% compared to July 2019, once again almost entirely driven by Chinese applicants.

Over in New Zealand, monthly filings continued to fluctuate around the same levels as in 2019.  The relatively low application numbers, and associated volatility resulting from normal month-to-month variations, makes it difficult to discern whether there is, at this stage, any underlying trend in filing activity.

Read on for this month’s updated charts.

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