Looking for a Patent Attorney? Check Out IP Australia’s ‘Engaging an Attorney Toolkit’

IP Australia – the government authority responsible for (among other things) examining and granting Australian patents – has just published its Engaging an Attorney Toolkit, an online ‘guide on how, why, what and when to engage your patent attorney.’  The toolkit is the result of a project undertaken in the second half of 2017, with the assistance of external branding and communications consultants, having the aim of dispelling myths and closing knowledge gaps around patent protection.  It is intended primarily to assist people and businesses with minimal knowledge and experience of the patent system in preparing to engage with an attorney.

In the course of the project, online surveys and interviews were conducted with various stakeholders, including patent attorneys (full disclosure: I was one of the patent attorneys interviewed), to identify what new prospective patent applicants know, do not know, and should know, about the process of obtaining a patent.  Naturally, IP Australia is primarily concerned with assisting the public, and is hoping that the toolkit will help to reduce costs and make it easier and more attractive for innovators with limited understanding of the patent system to engage with an attorney.  If it is successful, however, the toolkit will also benefit attorneys by creating better-prepared and more knowledgeable clients, reducing the time and effort often required to educate them about what to expect from their attorney, and from the patent system.

It is no secret that I have been a critic of some of IP Australia’s educational resources in the past.  Almost exactly one year ago I wrote about its information on ‘what to include in your application’, calling its downplaying of the importance of obtaining professional assistance ‘rubbish’, and suggesting that:

…for the overwhelming majority of prospective applicants, ‘seeking assistance from a patent attorney’ is not something that they ‘may consider’.  It is an essential step without which they might as well flush their application fees, and whatever their own time and effort is worth, straight down the toilet!

This might seem harsh, but it was backed by an analysis, based on IP Australia’s own data, of extremely poor outcomes for self-represented applicants when compared with those employing the services of patent attorneys.  IP Australia, to its credit, engaged positively with this criticism, and within two months had updated a number of pages on its website with stronger recommendations on the value of obtaining professional assistance and advice.

Compared to this short-term fix, however, the Engaging an Attorney Toolkit is a huge leap forward.  Not only does it contain valuable information, in an accessible format, but the very fact that it now exists sends a clear message to prospective applicants about the importance of seeking professional assistance.  For the first time, ‘Engaging an attorney’ is now a headline topic on the main page on applying for a patent, along with a link to the toolkit.  With just one small caveat (of which, more later) I would have no hesitation, if I were still working as a private-practice attorney, in referring prospective clients to the toolkit in the expectation that it would make the early stages of our engagement run more smoothly.

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Tags: Australia, Innovation policy, Patent attorney profession, Patent Office

The Impact of Machine Learning on Patent Law, Part 3: Who is the Inventor of a Machine-Assisted Invention?

In the first part of this series of articles, I argued that invention is inherently a creative act, and that since machine learning systems – however impressive or surprising their achievements – are incapable of human-like intelligence, reasoning, agency, or creativity, they therefore cannot ‘invent’.  I acknowledged, however, that machines are certainly increasingly involved as ‘assistants’ in the process of invention.  In the second part of the series I argued that where the result is a patentable invention there must always be at least one human inventor.  In this final part, I want to look at how we should go about identifying the inventor(s) in any given case of machine-assisted invention. 

There are, in fact, three main aspects of machine learning technology in relation to which inventions may arise.  The most rarefied of these is in the underlying machine learning algorithms and architectures themselves.  Comparatively speaking, very few people work in this area, and for the most part they are to be found in universities and research centres. 

Secondly, inventions may arise through the application of machine learning technology to solving problems and/or producing new results, products, and services.  I expect that this is currently the most common type of machine-learning-related invention, particularly given the wide range of software tools now available to assist programmers in implementing the underlying algorithms. 

Thirdly, there are ‘machine-assisted’ inventions, which are generated wholly or in-part by machine learning systems.  Currently, such inventions are relatively rare, considering that few applications of machine learning are actually directed to the generation of new technologies.  However, as machine learning increasingly finds its way into computer-aided design and engineering applications, this may change.

In all cases, however, I would argue that it is possible – and, indeed, necessary – to identify one or more human inventors (and no machine inventors).  To suggest otherwise is, in my view, to misunderstand the true nature, and limitations, of machine learning systems.  In a letter to shareholders, published in 12 April 2017, Amazon CEO Jeff Bezos wrote what is possibly the most succinct and jargon-free summary of what distinguishes machine learning systems from ‘traditional’ programming:

Over the past decades computers have broadly automated tasks that programmers could describe with clear rules and algorithms. Modern machine learning techniques now allow us to do the same for tasks where describing the precise rules is much harder.

The evolution from ‘traditional’ programming to machine learning is not as dramatic as some of the hype might lead us to believe.  Instead of coding the ‘rules’, machine learning developers now build systems that are able to capture and generalise from patterns that exist in their input data.  These systems still operate using rules and algorithms – but now these algorithms determine how they go about doing the capturing and the generalisation, rather than how they produce the final result.  Viewed at an appropriate level of abstraction, then, little has changed, except for the power and scale of our machines.

In view of this, I argue that when it comes to machine-assisted inventions, inventorship will generally arise from successfully designing and applying a machine learning system on the path to achieving an inventive result, even in cases where the underlying software and/or hardware employed may have been developed or supplied by someone else.

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Tags: Artificial intelligence, Innovation, Inventorship, Machine learning, Patent law

Loss of CRISPR Priority in Europe is a Warning to All Patent Applicants

On 17 January 2018, a panel of the European Patent Office (EPO) opposition division wholly revoked a patent co-owned by the Broad Institute (‘Broad’) relating to CRISPR/Cas9 ‘gene editing’ technology.  The European patent in question, number EP2771468, is entitled ‘Engineering of systems, methods and optimized guide compositions for sequence manipulation’, and is a European equivalent to US patent no. 8,906,616, which is one of the key Broad patents involved in the US patent interference dispute with the University of California (UC) – which is currently on appeal to the US Court of Appeals for the Federal Circuit.  As is usual with European opposition proceedings, the ruling was issued immediately at the hearing (which had originally been scheduled to continue for two further days).  A full written decision providing detailed reasons is likely to be a few weeks away.

Technically, the basis for revocation in the final decision is likely to be lack of novelty and/or inventive step of all claims of the Broad patent.  However, the underlying reason for Broad’s failure to defend its patent is a loss of priority.  In particular, the EPO panel determined that Broad was not entitled to claim priority from four of its earlier US provisional applications, including the earliest filing, US provisional application no. 61/736,527, which was filed on 12 December 2012.  This loss of priority was fatal to the patent, as a result of a number of publications – including Broad’s own – that occurred subsequently, but prior to the full application’s filing date of 12 December 2013.

While this decision is obviously pivotal in the ongoing disputes between Broad and UC over ownership of foundational patent rights relating to CRISPR/Cas9 technology, it also provides an object lesson and a timely reminder of essential requirements for valid priority claims for patent applicants around the world, in all fields of technology.  While the circumstances of Broad’s case are somewhat more complex than most patent filings, what has happened to it here is not at all specific to the particular invention at issue.  In a nutshell, the problem was that, at the time of filing international application no. PCT/US2013/074819 (from which the European patent is derived), the named applicants – Broad, MIT and Harvard – did not collectively own all of the rights necessary to claim priority from the earlier provisional applications.  More particularly, the provisional applications named a researcher from Rockefeller University as a co-inventor/applicant, yet neither the researcher nor Rockefeller was named as an applicant on the international application.  Nor had the named applicants received any assignment from Rockefeller of the right to claim priority in the international application.

In fairness to Broad, the US national law relating to ‘internal’ priority (i.e. claiming the benefit of a US provisional application in a subsequent non-provisional application) is less strict, and focusses on the substance of the invention actually claimed in the later application rather than on a distinct right of priority.  But this can hardly be an excuse for ignorance of the international position, especially when the stakes are so high.  Broad has issued a statement, arguing that the EPO decision is based on a technicality, and asserting that it is ‘inconsistent with treaties designed to harmonize the international patent process, including that of the United States and Europe’.  I disagree.  Not only is this not the first time that the EPO has applied these rules in relation to priority claims, but it is not the only adjudicating body to have determined that they are, in fact, required by the international treaties in question.

Broad has vowed to appeal.  In the meantime – and in anticipation of the likelihood that any appeal will fail – all international applicants would be well-advised to ensure that they are clear on the ownership of the right to priority at the time of filing.  At the end of this article, I set out some guidelines for avoiding the troubles that Broad has encountered, not only at the EPO but at any international patent office.

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Tags: CRISPR, Europe, International treaties, Paris Convention, Patent Cooperation Treaty, Priority claim

The Impact of Machine Learning on Patent Law, Part 2: ‘Machine-Assisted Inventing’

In my previous article, I argued that existing (and foreseeable) artificial intelligence (AI) or machine learning (ML) systems do not exhibit creativity or inventiveness, and are incapable of anything that could reasonably be described as ‘invention’.  I acknowledged, however, that some such systems have generated results that may qualify as patentable inventions.  I therefore concluded with a question: if computers cannot invent, and yet the outcome of running a computer program can be an invention, then who – if anyone – is the inventor?

In addressing this question, it is important to understand that ML systems do not autonomously or independently generate novel outputs.  In my view, this is a fundamental error of understanding in Professor Ryan Abbott’s paper ‘I Think, Therefore I Invent: Creative Computers and the Future of Patent Law’, Boston College Law Review, Vol. 57, No. 4, 2016 (also available at SSRN), which I discussed in the first article of this series.  Abbott contends, in particular, that ‘machines have been autonomously generating patentable results for at least twenty years and that the pace of such invention is likely increasing.’ 

This, I have argued, is simply wrong.  The difference between a computer that is programmed to play the board game Go, and one that is programmed to learn to play Go is, of course, significant.  The former can only make moves that are determined in accordance with its explicit programming, whereas the latter may appear to ‘invent’ new strategies in response to patterns occurring in its training data that have not previously been recognised by human players.  But the appearance of invention is not the same thing as actual invention.  The ML player is still doing nothing more than following the instructions devised by its programmers.  The Google DeepMind AlphaGo system has become the world’s best Go player as a result of years of development, trial, experiment, and experience on the part of its designers.  AlphaGo plays as well as it does simply, and only, because that is what it was designed to do.  In this sense it is no more ‘autonomous’ than any other computer program.

In this second article I will explain why I believe that in the case of all existing (and currently foreseeable) ML systems which may generate inventions as output, there is always a human inventor.  This is consistent with the history and current state of patent law, as well as with the practical and technical reality of ML systems.

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Tags: Artificial intelligence, Innovation, Inventorship, Machine learning, Patent law

The Impact of Machine Learning on Patent Law, Part 1: Can a Computer ‘Invent’?

As a product of millions of years of evolution, the human brain is a remarkable organ.  Recent research indicates that a typical brain comprises somewhere in the vicinity of 80 to 100 billion neurons, and a roughly equal number of non-neuronal cells.  This mass of biological matter is capable of astonishing feats – many of them simultaneously – from enabling us, consciously and unconsciously, to control the behaviour and movement of our bodies, to sensing, comprehending and interacting with the environment around us, to communicating with one another using a variety of languages and symbols, to creating, composing and inventing brand new works of science, technology, and art.  In performing all of these tasks, the brain consumes just 20 watts of power.  By way of comparison, microprocessors at the high end of Intel’s latest Core i7 range consume up to 140 watts.

One relatively recent product of the amazing human brain is the range of technologies often collectively called ‘artificial intelligence’ (AI).  That is the last time I will use this particular phrase without irony in this series of articles – in my view, it is too vague a term, and tends to create an impression that computers are somehow approaching the capacity to operate on-par with human intelligence, which is simply not true.  Nonetheless, such luminaries as Stephen Hawking and Elon Musk have piped up over the past year or so with their concerns that our machines may soon rise up and render us obsolete or, worse still, destroy us!

In a similar vein, there are some people in the field of intellectual property who are starting to ask questions about whether computers can be ‘creative’ or ‘inventive’ and, if so, whether it should be possible for a computer to be named as an inventor on a patent application – or, conversely, whether some humans should be disentitled from inventorship on the basis that their computers, rather than themselves, were the true inventors.  One academic who has been making a name for himself in this emerging field of study is Professor of Law and Health Sciences at the University of Surrey, Ryan Abbott.  Professor Abbott is the author of, among other works on the topic, ‘I Think, Therefore I Invent: Creative Computers and the Future of Patent Law’, Boston College Law Review, Vol. 57, No. 4, 2016 (also available at SSRN), in which he argues that the law should embrace treating non-humans as inventors because this ‘would incentivize the creation of intellectual property by encouraging the development of creative computers.’

As I shall explain, however, I do not agree with Professor Abbott that computers can, or should, be regarded as inventors for the purpose of granting patents.  Furthermore, while Abbott accepts claims that patents have already been granted on what he calls ‘computational inventions’, I firmly believe that a computer is yet to ‘invent’ anything.  In my view, the researchers and technologists who claim otherwise have an interest in promoting a particular perspective, and in doing so they are subtly extending the definitions of ‘creation’ and ‘invention’ to encompass the contribution of their machines, to the detriment of the human operators who are responsible for providing the true creative input in the process.

I am further concerned that, should this view of ‘machine as (co)inventor’ prevail, it will in fact be to the detriment of the patent system.  I think it highly unlikely that lawmakers – whether they be legislators or common-law judges – will embrace the idea of granting patents on machine inventions.  On the contrary, it seems far more probable that if the notion takes hold that computers are actually doing the ‘inventing’ in many cases, it will simply become even more difficult for humans to secure patent protection for computer-implemented, or computer-assisted, inventions.

This is a complex topic that I intend to cover in a series of three articles.  In this first part, I will introduce the field of machine learning, give some examples, and then attempt to dispel some of the hype that has developed around this technology – including in Abbott’s work.  My aim here is primarily to refute the argument that existing machines are capable of engaging in ‘creative’ or ‘inventive’ activity.  In part 2, I will delve into the role of machine learning in assisting with the generation of new inventions.  Finally, I will look at how to go about identifying the (human) inventors in such cases.

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Tags: Artificial intelligence, DABUS, Innovation, Inventorship, Machine learning, Patent law