18 February 2018

Latest Annual US Patent Litigation Data From Lex Machina Provides Insights Into the ‘Troll Narrative’

Data PresentationAlthough there are various accounts of the story – particularly as people’s memories start to fade – there is little doubt that the term ‘patent troll’, as currently employed, has its origins at Intel at around the turn of the 21st century.  As Brenda Sandburg reported at the time (in 2001), Intel’s Peter Detkin defined a ‘patent troll’ as ‘somebody who tries to make a lot of money off a patent that they are not practicing and have no intention of practicing and in most cases never practiced.’  Ironically, Detkin subsequently went on to become a founder of Intellectual Ventures (IV), a company that has made a great deal of money from patents that it is not practicing, has no intention of practicing, and has never practiced.

Sorry – it seems I have that wrong.  According to IV’s profile of co-founder Nathan Myhvold, the company is actually ‘building a market for invention’ to enable inventors to ‘realize the value of their ideas’. It ‘manages one of the largest and fastest growing intellectual property (IP) portfolios in the world, with more than 40,000 assets and more than $6 billion in total committed capital’ and its investors include ‘many of the world’s most innovative companies and renowned academic and research institutions.’  All of which actually sounds kind of like a good thing, and not at all troll-y.  It is also notable that Detkin’s original definition would encompass most universities and research institutes.

So, where does the truth lie?  Somewhere between the two extremes, no doubt, as technology analyst Roger Kay has eloquently explained in an article on Medium this week, Where did the Patent Troll Narrative Come From?  At one end of the spectrum, there are certainly genuinely predatory ‘trolls’ using the patent system to extort payments from companies that lack the resources to fight back.  At the other end, however, are big tech companies, like Intel, Google, Facebook, and Apple, which have adopted a strategy of ‘efficient infringement’ – the practice of using a technology that infringes on someone’s patent, ignoring the patent holder entirely, and when (or if) the patentee decides to sue, tying them up in court by challenging the patent’s validity.  As I have argued in the past, slapping the positive word ‘efficient’ on the front does not alter the reality that this is also an abuse of the system, and of the infringers’ superior economic power.

In other words, it’s complicated.  As Kay notes:

Among combatants in the patent wars, entities are divided along multidimensional lines: practicing vs. non-practicing, dominant vs. upstart, technological vs. financial, inventing vs. acquiring, licensing vs. using internally, R&D-oriented vs. manufacturing-oriented, invention-focused vs. product-focused. While some of these creatures are truly odious, using the word “patent troll” to describe any of them would be to allow Intel and its allies ownership of the narrative. There are no hard and fast rules to separate them into good and bad buckets.

The latest Patent Litigation Year in Review report, from legal analytics company Lex Machina, provides some insights pertinent to the troll narrative, and in particular the impact of changes in US patent law driven, in large part, by lobbying from the ‘efficient infringer’ constituency which, as noted in this IAM Media article, ‘invested large sums in spreading a narrative around the problems posed by “patent trolls” that has been used to justify the need for a re-engineering of the US system to make it less friendly to all rights owners.’

Lex Machina’s analysis shows that since the commencement of the US patent law reforms introduced by the America Invents Act (AIA), rates of patent litigation have been in steady decline in real terms.  Furthermore, while the list of top plaintiffs remains dominated by non-practising entities (NPEs), in 2017 two pharmaceutical companies entered the top ten, with two more filling out the top 15.  And while headlines tend to be captured by a small number of very high awards of damages against big infringers, the reality for most plaintiffs is sobering.  Just 11% of all cases terminated since 2000 reached a final judgment, with around three-quarters settling.  While patentees are victorious slightly more often than defendants (around 60/40), compensatory damages are awarded in less than half of the cases won by plaintiffs, and for those cases in which ‘reasonable royalty’ damages were awarded during the three years up until the end of 2017, the median amount was just US$4.4 million – perhaps barely enough to justify litigation in a jurisdiction where the usual rule is that each party must bear its own costs of the proceedings.

While this might be bad news for the genuine trolls in the system, it is at least as bad – if not worse – for individual inventors, research groups, universities, and other innovators who are better-placed to invent than they are to commercialise their inventions, and therefore rely on licensing to secure a return from their efforts.

11 February 2018

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

Writing ToolsIP 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.

04 February 2018

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

Machine-AssistedIn 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.

28 January 2018

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

Dark and stormyOn 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.

21 January 2018

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

Software AssistedIn 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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