24 September 2020

Has COVID-19 Affected Patent, Trade Mark or Design Filing Activity – an Australian Perspective

Patent-Insights LogoThis is a guest contribution from Mike Lloyd of Patent-InsightsFurther details about the author can be found at the end of the article.

COVID-19 has had many devastating impacts all around the world, both in terms of fatalities and other health impacts, and also in economic terms.  While the economic impact is yet to be fully understood, it has led to significant drops in GDP activity in many countries. In Australia, the drop in GDP due to COVID-19 has been estimated to be 7%.

But how has this affected IP activity – particularly in relation to intellectual property rights (IPR) filed in Australia, and filed globally by Australian IP owners?

Mark Summerfield has considered this in relation to patents being filed in Australia in recent blogs, and found a 5% decline, which is in line with the drop in GDP, and the largest fall over a comparable time period since the GFC .  This study is intended to consider more international aspects, if from an Australian perspective, and also to consider registered designs and trade marks.

16 September 2020

COVID Update–Australian Patent Filings Down by Five Per Cent Since March

Masked VirusMost of the developed world has been in the grip of the medical, social, and economic effects of the COVID-19 pandemic since around March this year.  I published my first monthly review of the impact on patent filings in early April, at which time it was really far too soon to discern any trends.  But with data now available for the month of August it is becoming clear that – a surge in innovation patent applications by Chinese applicants aside – 2020 is likely to be a low year for patent filings in Australia.  I reported last month that the number of standard patent applications filed in July had been virtually identical to the same month in 2019, following on from a similar result in June.  However, August has marked a return to the negative trends of April and May

Overall, for the six months from the beginning of March to the end of August, the number of standard patent applications filed is down by just over 5% compared to the same period in 2019.  Hardest hit are new ‘original’ filings, i.e. applications filed directly in Australia that are not derived from an existing international application under the Patent Cooperation Treaty (PCT) or divided from an existing Australian standard patent application.  PCT national phase entries are down by around 2%, while divisional applications have declined by just under 3%.  However, new direct national filings between March and August fell by 28% compared with the same period last year, following two years of growth in this category of applications.

The number of provisional applications filed in Australia between March and August is also down, by 1.5% over 2019.  While this may not seem like much, patent attorneys are bearing the brunt of this decline – attorney-assisted filings fell by almost 4%, while the number of applications filed by applicants not represented by an attorney actually increased.

Chinese applicants continued to drive growth in innovation patent applications in August, with filings up by an astonishing 230% compared with 2019.  Over the period from March to August, innovation patent filings increased by nearly 130%.

Meanwhile, New Zealand continues to shrug its shoulders in the face of COVID-19.  Despite an overall decline in filings in August, PCT national phase entries have actually increased compared with 2019 during the six months since the start of March.  And while direct filings were down, this may simply continue a longer-term trend that seems to have been occurring anyway.

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.


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