18 June 2017

IP Australia’s Chief Economist on the Role of Economic Modelling, the Innovation Patent... and Perpetual Motion!

Benjamin Mitra-Kahn[Note: This is the third, and final, part of the transcript of my conversation last year with IP Australia’s Chef Economist, Benjamin Mitra-Kahn.  The first two parts were A Conversation with IP Australia’s Chief Economist and Talking ‘Data’ with IP Australia’s Chief Economist.]

The Australian innovation patent system has been under a cloud for some time, with first the (now defunct) Advisory Council on Intellectual Property (ACIP), and then the Australian Government’s Productivity Commission, calling for the system to be abolished.  The original source of evidence behind these calls was a report produced by IP Australia’s Office of the Chief Economist entitled The economic impact of innovation patents.  The report used Australian patent filing data, linked to company-level business information, to make the case that the innovation patent is not achieving the objective of stimulating innovation among Australian small and medium enterprises (SMEs).

Many patent professionals have a different perspective on this issue, albeit one that is strongly influenced by the particular cross-section of users of the innovation patent system that they encounter in their daily practice.  I recently read a ‘defence’ of the system by a New Zealand-based practitioner arguing, in essence, that innovation patents had been useful to a number of his clients, and he would be sorry if they were to be abolished.  While I believe that there is an argument to be made, based upon the data, that more weight should be given to attorney-represented SME applicants in assessing the worth of the innovation patent system, the kinds of ‘feelpinions’ expressed in that article do not, in my view, constitute a very useful contribution to the debate.

But if those at the coal face might have difficulty in seeing the forest for the trees (if you will pardon my carbon-based mixed metaphor), might it not also be the case that economists, with their penchant for aggregating and analysing data, could sometimes fail to see the trees for the forest?  Either way, it seems that a clash of cultures has arisen between those who view the individual trees as important and those who believe that the system can only be properly evaluated via an aerial view of the entire forest.  My own inclination, absent evidence to the contrary, is to presume that each of these perspectives lies at the extreme of a continuum, and that they are therefore equally likely to provide an incomplete view.

In any event, this disparity in the perspectives of IP practitioners and economists was a topic I was very keen to discuss with IP Australia’s Chief Economist, Ben Mitra-Kahn, when we spoke last year.  But before we got to that, we first covered the future of IP Government Open Data, a.k.a. the IPGOD, and the fate of patents on perpetual motion under the new ‘utility’ requirements introduced in 2013 by the Raising the Bar patent law reforms.

12 June 2017

Reforms Cut Average Patent Opposition Delays by Two Years, But They Still Take Too Long

Oppositions are downAustralia has a pre-grant patent opposition system, meaning that an opportunity is provided after a patent application has been approved by an examiner, but before a patent is actually granted, for third parties to raise objections.  The usual policy justification for pre-grant oppositions is that they allow grounds of invalidity that may have been unavailable to the examiner to be raised by parties who may have greater familiarity with, and access to, knowledge and prior art information relating to the claimed invention.  Most often, an opponent is a competitor of the patent applicant, and thus has a particularly strong incentive to ensure that an invalid patent that may hinder its own activity is not issued.  Accordingly, the public benefits from this additional level of scrutiny which can reduce the incidence of invalid patents being granted.

The disadvantage of pre-grant opposition proceedings is that they extend the period during which a patent application remains pending – sometimes by many years.  In the event that (as is overwhelmingly the case) a patent is ultimately granted, the patentee may have been deprived of a significant portion of the time during which it might have been able to immediately enforce its rights.  The wider public is also disadvantaged by lengthy delays, during which time the ultimate scope of any patent granted – or, indeed, whether or not a patent will be granted at all – remains uncertain.

Prior to the Raising the Bar (‘RTB’) reforms to the Australian patent laws and regulations, which came into effect on 15 April 2013, the largest contribution to opposition delays was the time taken by opponents and patent applicants to prepare evidence supporting their respective positions.  Although the rules nominally allocated three months to each of the three usual evidentiary periods, applications for extensions of time, and for opportunities to file additional evidence had become, in practice, almost impossible for the Patent Office to refuse.  However, the RTB reforms included significant amendments to the Patents Regulations 1991 that have substantially limited the ability of each party in a patent opposition to obtain an extension of time to prepare evidence.  With four years now having passed since the amendments commenced operation, it is timely to take a closer look at their impact on opposition delays.

Using data available in the 2017 edition of IP Australia’s IP Government Open Data (IPGOD) set, I have analysed 211 patent oppositions that have commenced since 2004, and which proceeded through to a Patent Office hearing and final decision.  Of these, 102 were conduced entirely under the pre-RTB regime, while 57 were conducted wholly according to post-RTB extension-of-time regulations.  The remaining 52 oppositions were ‘transitional’, in the sense that they commenced pre-RTB, but included at least one evidentiary period governed by the post-RTB rules relating to extensions of time.

The main findings from my analysis are that:
  1. the RTB reforms have cut the average total duration of an Australian patent opposition by nearly 50%, from just over four years (1512 days) to just over two years (773 days);
  2. variability in opposition duration has dropped even more dramatically, by more than a factor of three, but the standard deviation remains high at over 200 days;
  3. the average total time required for parties to prepare and file evidence has been slashed from 803 days to just 250 days;
  4. however, the period between finalisation of evidence and a decision following a hearing remains lengthy, averaging 332 days, and also highly variable, with a standard deviation of 130 days;
  5. furthermore, where an application is not refused as a result of opposition (i.e. the overwhelming majority of cases), the period between the decision and grant of the patent is hugely variable, having a standard deviation of 162 days, in relation to an average of just 121 days.
A common contributor to non-evidence-related delays, both before and after a hearing on the substantive merits of an opposition, is the procedure that must be followed if the applicant wishes to amend the application to address grounds of opposition.  Additionally, although appeals of opposition decisions to the Federal Court of Australia are relatively rare, when they do occur they contribute significantly to the average duration and variability of post-decision delays.  Finally, it appears that it is not uncommon for delays to be incurred after finalisation of evidence primarily due to a failure of the Patent Office to progress the opposition expeditiously to a hearing.  These delays may be on the order of many months, as a result of which the average time between completion of evidence and a decision in post-RTB oppositions is now greater than the average time required for preparation and filing of all evidence by both opponent and applicant.

It is therefore clear that while the RTB reforms to the requirements for obtaining an extension of time to prepare evidence have been highly effective, many patent oppositions are still taking months longer to resolve than should be required.  What has changed, however, is that opponents and applicants are no longer the primary source of delays.  The next frontier for further improvement in the speed of resolution of patent oppositions lies within the Patent Office.

04 June 2017

Private R&D Expenditure Positively Impacted by Clustering and Academic Research Spending, New Study Finds

Network effectsThe Australian Government’s Office of the Chief Economist recently published a new research paper entitled The role of spillovers in research and development expenditure in Australian industries (which I will refer to as ‘the Spillover paper’).  The paper describes an econometric model that uses data from Australian companies that conduct research and development (R&D), and looks at how R&D activity of other firms and public institutions affect a firm’s own R&D expenditure, i.e. the effects of ‘spillover’ of R&D being conducted elsewhere.  The paper also examines the impact of geographical proximity and clustering on these R&D spillovers.

Overall, the model indicates that there are positive effects on R&D expenditure due to spillovers from peers and clients to companies that are located nearby (within 25 or 50km).  Furthermore, R&D expenditure by academia also has a positive influence on a company’s R&D expenditure within state boundaries.  However, R&D spending by government bodies appears to have the opposite effect, seemingly ‘crowding out’ private R&D spending.

The study has important policy implications, because it suggests that public support for R&D, whether to private firms through grants and/or tax incentives, or through funding of research in universities and other public institutions, results in benefits not only to the organisation receiving the direct support, but also to other firms and institutions more broadly.

Significantly, the modelling provides further evidence that Australia’s reputation for having a woefully low level of industry/research collaboration (which is based on one rather dubious OECD data point) is largely undeserved.  I have previously observed that Australian companies clustered geographically close to major academic institutions tend to file more patent applications, while research by IP Australia has shown a healthy rate of patent applications naming industry and research partners as co-applicants from Australia when compared with other OECD nations.  The Spillover paper supports this by showing a positive correlation between academic and industry R&D spending, particularly for companies and institutions located within the same state (and, in practice, probably more closely than this, although the paper does not break down academic R&D expenditure below state level).

I discuss further details of the model, and the paper’s key findings, later in this article.  If this is all you are interested in, feel free to skip ahead.  But first I would like to take the opportunity to explain the general process of econometric modelling for readers who may be interested in better understanding how economists think about the kinds of questions addressed by this paper, and how to interpret their results.

28 May 2017

Talking ‘Data’ With IP Australia’s Chief Economist

Benjamin Mitra-KahnIt has been an unduly long time – just over a year, in fact – since I published the first part of a conversation I had with IP Australia’s Chief Economist, Benjamin Mitra-Kahn.  But the two of us (mainly me, if I am honest) have finally got our act together to edit most of the transcript into a readable form.  I am therefore very pleased to be able to start publishing the remainder of our discussion.  Despite the passage of time, the content is still highly relevant, indeed in some ways even more so, considering the increasing importance of economic analysis and the role of data science in driving government policy in relation to intellectual property.

Late last year, for example, the Australian Productivity Commission (PC) released its final report on its Inquiry Into Australia’s Intellectual Property Arrangements.  The report reviewed the Australian IP system in its entirety, and made a number of significant recommendations to the Government, which are currently under consideration.  While prior reviews and inquiries into components of the IP system had been conducted by panels that included economists, the PC’s review was the widest, and the first to be conducted entirely by economists.  Naturally, the PC sought to draw conclusions and make recommendations based on evidence, and a key theme of the final report is the need for accountability in the IP system, including by developing and maintaining a sound evidence base to inform policy decisions.

It is in this context that economic research, such as the 2012 study by Boston University academics James Bessen and Michael Meurer which concluded that ‘patent trolls’ cost the US economy $29 billion in 2011, can have a huge impact.  Some people (including me) questioned the reliability of the source data and methods used in that study, but a far larger number – including some widely-read media outlets – simply took the Bessen and Meurer results at face value.

I was therefore very interested to get the views of IP Australia’s Chief Economist on that particular study, and to talk about the work that is being done in Australia to make better-quality data available to researchers, and other interested stakeholders, through the IP Government Open Data (IPGOD) initiative.

21 May 2017

Identifying Patent-Eligible Software Claims... Using Software

MatrixAs I have previously reported, the major and immediate impact of the US Supreme Court’s Alice decision in June 2014 was to reduce the rate of ‘business method’ patents issued by the US Patent and Trademarks Office (USPTO) by three-quarters, while having a negligible effect on ‘technical’ software-implemented inventions.  While the data in my earlier article ended in December 2015, I have now been able to update my results to the end of March 2017, as shown in the chart below.  There has been no change in the overall trend during the intervening 15 months, with ‘business method’ patent grants still running at around 50% of 2007 numbers, while technical software patents, and patents across all areas of technology, are issuing at rates nearly twice those of 2007.
Patent Grants (March 2017)
In a recent post on the Bilski Blog, US patent agent Mark Nowotarski has made similar observations about the impact of Alice on ‘business method’ patent grants, going on to analyse the characteristics of those patent claims are are still being allowed in the USPTO’s business method Art Units.  He has noted that it is commonly by including ‘physical limitations’ (e.g. reciting hardware such as ‘mobile devices/sales kiosks’ or ‘physical sensors’) and/or ‘software limitations’ (e.g. reciting technical functionality such as ‘graphics/ image processing’ or ‘cryptography/security’) that applicants have been able to overcome Alice-based subject-matter rejections.

This got me thinking.  If there are common forms of technical language that arise in patent-eligible claims, then might it be possible to train a machine-learning system to predict whether a particular claim is, or is not, likely to be patentable?

It turns out that this does indeed appear to be possible.  I built a machine-learning model using data published by the USPTO, including the claims of 24,462 recently-abandoned 4967 recently-allowed applications, all examined within ‘software’ and ‘business methods’ Art Units.  In cross-validation tests (i.e. using a portion of the known data for training, and the remainder to test model performance) I was able to achieve around 75% prediction accuracy.  In trials of a hand-picked ‘random’ sample of more recent patents and published applications, not in the training/test set, the model correctly classified all actually allowed claims (of four examples) as patentable.  It also classified the claims of four abandoned applications as unpatentable, and two published-but-rejected-and/or-amended claims as unpatentable.  In only one case did the model classify a claim that had been rejected on subject-matter grounds as likely-patentable.

The model may thus be capable, with a probability of success of over 70%, of determining whether or not a proposed claim to a computer-implemented invention includes sufficient technical content to overcome a subject-matter-based rejection, at least under the Alice test as it is applied by the USPTO.

Copyright © 2014
Creative Commons License
The Patentology Blog by Dr Mark A Summerfield is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia License.