Big data solutions to determining IP risk and value The significant increase in the volume and availability of data about intellectual property, combined with major advances in data science, make this the perfect time to apply big data solutions in order to produce powerful analytics and IP visualisations By Nigel Swycher Over the last decade, intellectual property has made significant progress towards its development into an asset class. Achieving asset class status is shorthand for describing assets that have recognised and ascertainable value, are capable of being traded in established markets and are accepted as collateral for debt. Measured against these criteria, intellectual property has a long way to go. A number of recent reports have considered some of the specific challenges to IP valuation and why banks struggle to lend against intellectual property. All argue that the catalyst for change is a combination of education, information and informed debate. This article considers whether big data can act as a catalyst to speed up the evolution of intellectual property into a fully functioning asset class. In this context, big data is shorthand for describing a three-stage process. First, aggregation of all data relating to intellectual property and related events (eg, litigation and licensing). Second, the application of data science and machine learning to analyse the data. Third, using analytics to visualise the drivers of risk and value. This review begins by looking at the data available today. There is an impressive amount some available from public sources (eg, patent registries), some from commercial providers that have taken on the task of combining a number of these datasets into a single source (eg, DWPI from Thomson Reuters). However, our review concludes that there is no one dataset that gets close to N = all, being the big data requirement that encourages the collection of all relevant data before applying analytics to discover hidden meanings and trends. We then set out views on how, if all IP data were aggregated, data science can be applied to assess risk and value. We have specifically focused on patents, because of the coincidence of perceived risk and value, and the area where there is the most public data relating to the asset (patents are always registered rights) and (albeit to a lesser extent) related litigation and licensing. We have included our views on how visualisation will accelerate the acceptance of the big data solution by communities that have to this point left intellectual property to the specialists. We advocate strongly that without intellectual property becoming mainstream, it will be much harder for this evolution to take place. We have identified the key constituents in this community as including banks, accountants, lawyers, insurers and consultants, as well as corporates (both large and small). In this area we build on feedback from a pilot study we have been conducting over the last year. Our conclusion is that big data can make a valuable contribution and those who are prepared to take on the Herculean challenge of aggregating the world s IP data will be able to create a common language which will improve communication between IP specialists and non-specialists (ie, everyone Intellectual Asset Management July/August 2014 41
else). This is an essential prerequisite for the next stage of IP evolution. Asset classes and evolution There was a day when the idea of trading against the anticipated future value of orange juice or pork bellies would have been unthinkable. Before that, people struggled with creating systems to handle the complexities surrounding the valuation and transfer of land. While the systems and rules surrounding those and many other asset classes are more advanced, many external and measurable indicators suggest that intellectual property is evolving as an asset class faster than before. See Figure 1 for a brief overview of the last decade. It would have been hard to imagine 10 years ago that Nortel would sell its patents for $4.5 billion, that patent aggregators such as Intellectual Ventures would be able to deploy billions of dollars to accumulate over 70,000 patents or that the US administration would be considering fundamental changes to patent law in response to the (then unknown) phenomenon of non-practising entities (NPEs). However, before embarking on the quest for evolution, there is merit in considering why change is important. The starting position is a familiar one. Intangible assets and intellectual property now account for over 70% of the value of many organisations. However, this value is in many cases not recognised or not capable of being realised. In this context, we simply refer to two recent European reports that focus on lending against intellectual property (UK Intellectual Property Office, 2013/34) and IP valuation (European Commission Report, November 2013). There are common themes across all studies of this type: namely, that intellectual property is a complex asset and largely misunderstood by the markets and participants that make the relevant valuation or lending decisions. The same reasoning has been used in recent times to explain why there has been such limited growth in the IP insurance market. Evolution will enable intellectual property to join the ranks of other functional asset classes. IP data today There is a relative abundance of IP data today. This includes data made available by IP registries around the world relating to patents, trademarks and designs and by the courts relating to litigation. However, there is a difference between information Figure 1. Times are changing Annual patent infringement lawsuits 6000 5000 4000 3000 2000 1000 2004* 2005 Intellectual Ventures founded 2000-2003 Acacia IPO IPNav founded Source: Deltasight Growth of worldwide patent grants Growth of worldwide licence payments and business intelligence. A brief study of patent data highlights the problems. Patent registries across the globe function independently. There is therefore no one view of patent families and no single view of ownership. Domestic registries in some of the world s most active patenting markets operate in their own language (eg, Japanese or Chinese), meaning that much of the available information is not readily accessible. The state of the patent landscape changes daily, with new applications, renewals and grants. This means that keeping up to date with the development is practically impossible. These challenges have led to the creation of global patent databases, such as DWPI from Thomson Reuters. Such databases aggregate and structure the patent data from all major registries, thereby presenting a view of families (ie, multiple patent rights relating to the same invention) and creating searchable English language abstracts. Products of this sort are an essential prerequisite for progress, but are not sufficient by themselves. The reason for Patents, licensing and lawsuits 2004-2013 2006 2007 2008 2009 2010 2011 2012 2013* *2004 and 2013 figures are extrapolated for licensing revenue and 2013 patent grants Key events 2000-2013 Non-practising entity (NPE) lawsuits Non-NPE lawsuits Offensive plays Defensive plays Transaction-related 1st Ocean Tomo auction Open Invention network founded RPX founded AST founded RoundRock founded 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Google Motorola acquisition Nortel auction 200% 175% 150% 125% 100% 75% 50% 25% AOL - Microsoft transaction Unified Patents founded Kodak - RPX transaction AST - MIPS transaction Growth (v 2004) patent grants and licences paid 42 Intellectual Asset Management July/August 2014
this is that patent analysis is a minority sport. The patent attorneys who prepare and prosecute patents, and who opine on whether a product infringes a valid claim of a specific patent, are specialists and in relatively short supply. While they need access to global patent data to do their job, this does nothing to help with the evolution of the asset class. It is also important to recognise that identifying patent assets is only the starting point. To build up a complete picture requires the addition of ownership, licensing and litigation data in fact, all data that could potentially impact on an asset s value or risk. This is a theme to which we will return and will continue to analogise to the development of other asset classes. The read-across here is obvious who would buy or lend against land unless they could establish undisputed ownership, be confident that there were no unwanted tenants and had checked that there were no environmental hazards within the boundaries or nearby? It is generally accepted that it is not possible to establish who owns patent rights and which rights have been or are being licensed or litigated against whom from publicly available sources. This does not diminish the relevance and importance of the data that is available for example, from: Patent Freedom, which for many years has been aggregating and curating information about NPE litigation and specifically grouping together the many litigation vehicles used by the likes of Intellectual Ventures and Acacia Technologies; or ktmine, which has assembled the world s largest database of IP licences, primarily through a review of US and European corporate filings. There are those who are critical of these initiatives, largely because the data is incomplete. Patent Freedom does not have information about claims and demands that stop short of litigation, and has no information relating to confidential settlements (which are the majority of NPE outcomes). ktmine data is limited by public filings and there are many licences which are never disclosed in this way. Similarly, the majority of licences that are filed have had certain pieces of commercially sensitive information removed. Ownership is a topic on its own and is well illustrated by the following example: John (J) an inventor works for a company (C) and files for a patent (P). C changes its name to D, which the local Patent Office enters as D+. E buys the shares in D and sells P to an NPE (N). No (human) reader of this conundrum has any difficulty in answering the question who owns P? The chain of title abstracts as follows: J to C Inventor assignment. C to D Change of name. D to D+ Typo, no change (normalise to D). E buys D No change, but P is now owned by E group. E to N N is the owner. However, if you were to conduct a search of the patent databases of each country where P is registered (there may be between five and 10 patent rights for the same invention, each claiming priority from the original document), you will receive back a range of answers. Assuming that the NPE has chosen not to record the last transaction, none of them will be correct. So, approaching patent data with a simple question such as what patents are owned by the E group? is not trivial. Having conducted an extensive review of all data relating to patents and related events, we have concluded that no one source of IP data can be used to establish risk and value, and that a better view can be achieved through aggregation and analytics. Enter big data. Crafting a big data solution There is no one definition of big data. We are comfortable with the following: Big Data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organisations and more (from Big Data, A Revolution That Will Transform How We Work And Think by Viktor Mayer- Schönberger and Kenneth Cukier). So, a big data solution to IP risk and value requires an aggregation of all available data relevant to risk and value, and the application of machine learning and data science to find correlations which might otherwise remain undetected. The approach we advocate is built on two fundamentals. First, the broad consensus as to what contributes to patent risk and value, such that strong patents add value and litigation creates risk. Second, that risk and value should not be regarded as absolutes, but should be assessed relative to comparable companies (eg, A has generated more licensing revenue than B; X is being sued more than Y). Over the last 12 months we have Intellectual Asset Management July/August 2014 43
taken on the challenge of aggregating the world s most complete patent datasets. In some cases they are freely available (eg, US reassignment data) or available for a nominal cost (eg, INPADOC relating to patent status). In other cases, we work with the owners of proprietary or curated data. We strongly adhere to the recipe set out in big data: gather as much as possible, and if feasible, get everything: N = all (Big Data, referred to above). The practical challenge to data aggregation is that the data is messy (ie, the structures and systems within all of these datasets are different). While this is a common feature of all big data projects, the author has been reliably informed that IP data is messier than most. However, this can be overcome. The real challenge is to apply data science to extract new insights or create new forms of value. Our preferred visualisation is set out in Figure 2. In Figure 2, T represents the company under consideration and C1 and C2 are two other companies which own similar technology. The visualisation provides an instantaneous view of which company has the lowest IP value (T) and which has the greatest risk (C1). This form of grid is powerful, not least because it resonates with approaches taken in many other fields. However, it is essential that the analytics that accompany a grid of this sort support the key messages. inventors, key words and abstracts (as well as codes); and proximity the essence of the grid is its ability to compare the target with its comparables. The comparables represented on the grid are those corporate groups that own patent portfolios most similar to the target s portfolio. Once again, this is not something that can be attempted just by looking at IPC codes or semantic searching it is a big data challenge, well suited to modern-day algorithmic assessment. What needs to be stressed is that this is a comparable analysis, not a competitor analysis. The analytics identify and plot similar portfolios, without any information about products, services or market positioning. Plotting risk and value The grid has plotted the target relative to comparable 1 and comparable 2, based on all available data. The risk axis The primary driver of risk is litigation, and it is important to assess intra-competitor/ operating company litigation separately from NPE litigation. The United States is generally regarded to be the epicentre of IP Figure 2. Risk and value grid Identification of targets and comparables The grid focuses on owners, not technologies. The primary use case for analytics of this sort is identification of what patents are owned by a corporate group (the target). The analytics must therefore have the capability to present: ownership this, as we have already described, is non-trivial, but working with organisations such as 1790 Analytics it is possible to refine the rather haphazard ownership data from public sources; clustering while data about overall patent holdings is a starting point, it will never be sufficient on its own. What is required is clustering (ie, sorting patents into groupings of inventions all related to similar technologies). With advances in data science, this no longer needs to be a crude sort by International Patent Classification (IPC) codes, but can be a sophisticated algorithmic analysis based on numerous data points and indicators including citations, Higher risk C1 T Higher value Lower value C2 Lower risk 44 Intellectual Asset Management July/August 2014
Table 1. Potential benefits of big data for different IP stakeholder groups Corporates Lawyers Accountants Investment banks Venture capital/private equity Asset managers Patent agents Consultants Insurers The question of whether the company is maximising the value associated with its intellectual property (eg, monetisation) and minimising IP risk (eg, litigation) is now a main board issue. While the gap between the main board and the IP teams is closing, it is essential for there to be ways of representing and communicating complex trends, not only within the organisation itself, but also when compared to peers and competitors. Due diligence and risk management (eg, warranties and disclosures) remain an important aspect of mergers and acquisitions, joint ventures and initial public offering engagements. For too long, the exercise has been driven by information delivered by the target, with little or no reference to external sources of data. As the role of fixed assets has become established and predictable, the move is towards intangibles. In some cases, there are new laws (eg, the Patent Box in the United Kingdom); in other cases it is the need to value intellectual property in a range of commercial, corporate and intra-group (eg, transfer pricing) transactions. Banks are major consumers of data. Their research analysts support both the buy side and sell side, both of which need up-to-date data on all aspects of their corporate clients and targets. Corporate financiers are always on the hunt for mispriced assets and any edge that business intelligence can provide. For venture capital/private equity, it is all about identifying, valuing and executing against an opportunity. Investment activities are commonly focused on areas of high innovation technology, media and telecommunications, and pharma/biotech in particular. Tools that can help to identify intangible assets, monitor their development and benchmark their performance can make a valuable contribution. Asset managers are assessed by their absolute and relative performance. If there is accessible and actionable intelligence that would help to make more informed decisions, it is essential for this to be introduced into the mix. Patent agents still rely on the preparation, filing and prosecution of patents for their core revenue. Increasingly, however, they are the first point of call for IP advice and strategy. While patent agents have the necessary specialism to assess any amount of technical detail, there are many instances where an immediate and high-level overview is the appropriate starting point. Fortunes have been won and lost on the innovation battleground, and in many areas the weapon of choice is patents. In some cases, the IP issues will be the main drivers of risk and value, and having no entry point for this assessment can add cost and delay to a broad range of assignments. There has been no significant development in the IP insurance market for over a decade. IP risk assessment and rating is largely a manual and imprecise art (with limited recourse to science). Development of this market depends on access to data and the means to interpret it objectively, reliably and swiftly. risk. This is also the most comprehensive source of public and curated litigation data (eg, Patent Freedom for NPE litigation and Lex Machina for all IP litigation). While the underlying data can be presented to the user, the grid presents a rolled-up view. This does require an element of subjectivity (eg, settled actions present less risk than active litigation); but provided that no one takes too literal a view of the distance between the centre of T and C1/C2, we do not regard this to be a significant design limitation. The value axis The most obvious driver of value is where patents have been licensed or sold (or where there are comparables). While available datasets in both areas are incomplete, they are being actively used by IP specialists the world over (eg, in licensing negotiations and litigation). On that basis, it is reasonable to use this data to position the target and its comparables using this data. However, the advantage of adopting a big data solution is that it can take into account other drivers of value, such as: standards-essential patents, being patents that have been disclosed or proven to be standards essential; disputes where an owner has successfully enforced or defended the validity of its rights; and quality, where objective metrics establish or suggest patent quality (sometimes referred to a strength). In the sections that follow, we first consider whether analytics and visualisations of this sort are helpful to the communities that need to engage with this asset class, and then consider the level of detail that needs to be surfaced to enable powerful visualisations to be transformed into actionable insight. Engagement with the wider community It is essential that the wider business community understands more about the drivers of IP risk and value for further progress to be made it was not the orange growers or the pig breeders that established the orange juice and pork belly futures market. We have engaged with a range of market participants and have tabulated our findings in Table 1. Our review suggests that there is significant demand for IP data, presented in a form that could establish a common language. Data aggregation and analytics We have suggested earlier that it is possible to aggregate the world s patent data and use this to visualise the relative risk and value of comparable companies. However, this will not move the evolution dial on its own, unless the business community can comprehend and validate the underlying drivers. At a minimum, this requires presentation of: patents owned by a corporate group; those patents to be sorted into clusters in a fair approximation of how corporate Intellectual Asset Management July/August 2014 45
owners assess their holdings; key characteristics of the portfolio, such as profile by strength, age or territory; similar patents owned by others; litigation by or against a group, distinguishing between NPE or operating company litigation; and actual or comparable licensing or similar transactions. It is also known from the development of data services in many other areas that these resources must be available on demand. This means that all of the output referred to above needs to be algorithmically generated and delivered instantaneously. For some, this might feel like the day that Jeff Bezos laid off all his book reviewers in favour of his data-powered recommendation engine. He did this because the results were faster and better. Machine versus man. In the patent world, there are already a number of tools that focus on clustering and proximity. Some of these have abandoned a rigid analysis of IPC codes or citations, and all work at speeds and in ways that could not be replicated manually. It is our view that the time is right for the best of the data and analytics to be combined and presented to the business community. Enabling actionable insight Business intelligence is an enabler, not a solution provider. The fact that the price of gold falls does not mean that the trader should sell, any more than a rising stock price determines when to buy. However, all this information provides insight, which can be actioned as appropriate. What is appropriate will typically depend on facts, circumstances, needs and requirements specific to the trader. At present, business intelligence relating to intellectual property has not been made available to the wider business community and this is stifling development of and innovation around the asset class. A big data solution will provide an immediate view of the what, leaving it for users to discover the why. In our work in this area, we refer to this as giving cause to wonder, as in Table 2. We would disagree with those who would suggest that intellectual property is such a unique and complex asset that its analysis must be reserved only to IP specialists. The future is now This is not a thought experiment. There are products which are being developed for and trialled by a broad range of interested parties (eg, finance, accounting and legal) who to this point have had virtually no comprehensible access to IP data. These initiatives come Table 2. Cause to wonder What Company A has stopped filing in its largest patent cluster. Company B rarely files for US patents. Company C has more patents in a specific area than its comparables, Company A and Company B. Company A has never been sued by an NPE, while Company C has five active litigations. Company B has entered into a licence with Company A, and Company A is in litigation with Company C. at exactly the right time, as the United States, the European Union and the United Kingdom are all looking for a more transparent and rational way to measure and manage risk, to value and account for intangibles, and to encourage banks to recognise, assess and lend against assets which make such an important contribution to enterprise value. Nigel Swycher is CEO of Aistemos Limited, London aistemos.com Action plan The business community needs access to more information relating to intellectual property before it can evolve into an asset class which functions in ways that are even vaguely comparable to traditional assets such as land, equity or debt: With the significant improvement in the quality and quantity of IP data available today, the time is right to aggregate, analyse and visualise this data for this community. Recent advances and improvements in data science, and the fall in technology costs associated with projects of this sort (notably cloud computing), will help to achieve this goal more quickly and cheaply. Our research suggests that a big data solution to the challenge will work well. This requires aggregation of all available datasets that may have Why Has there been a divestment or a strategic change of direction? Is there a legal, technical or strategic reason for this? Does this impact on the relevance of Company B as an acquisition, joint venture or collaboration partner? Does Company C own more or better patents? Does it pose a competitive threat or could it be a potential seller? Has Company A entered into a series of licences or is its technology different from Company C s? Is Company A the next in line? What are the economics between Company A and Company B? Will the Company A v Company C litigation create more value for A or impact on its revenue from Company B? A a bearing on the risk and value of patent portfolios. Some of this data is already publicly available, but in many cases it will require aggregation and collaboration between owners of proprietary datasets. The solution will not be perfect, because the available data is not complete. Big data solutions thrive in these environments. We will continue to encourage the business community including banks, accountants and insurers to engage with IP data. We are confident that with their insight, new products and services will develop to help maximise the value and minimise the risk associated with intellectual property. In short, big data has the potential to be the catalyst for the evolution of intellectual property as an asset class. 46 Intellectual Asset Management July/August 2014