IP TRANSLATOR, or why MY BRANE HERTZ

If the words IP TRANSLATOR don’t immediately ring a bell, don’t worry. Probably it just means that you are not a EUropean trademark lawyer. (Neither am I, but I play one on TV.) IP TRANSLATOR, or more formally Chartered Institute of Patent Attorneys v Registrar of Trade Marks (C-307/10) is an important trademark case decided by the EU Court of Justice earlier this week. To help you understand what I’m ranting about, let’s start with some background. (Oh, and it’s IP as in Intellectual Property, not Internet Protocol.)

1. The two essential components of the registration of a trade mark are (a) the sign and (b) the goods and services which that sign is to designate. Each of those components makes it possible to define the precise subject-matter of the protection conferred by the registered trade mark on its proprietor.
(from Advocate General Bot's Opinion or draft judgement)

What this means in practice is that the same word(s) can be a registered trademark for several different owners at the same time, as long as the products are different enough. High-reputation marks are a bit different in that they receive a broader scope of protection because anyone else trying to register, say, ROLLS ROYCE for anything whatsoever has certainly chosen the mark to try to gain unfair advantage of the earlier brand’s reputation. Even if many of the trademarks you can recall might be high-reputation marks (and certainly all the most valuable brands are, which is kind of the whole point), the vast majority of trademarks in the registries are not high-reputation marks, and thus must coexist with similar marks of others’ if necessary. In the EU trademark registry at OHIM (Office for Harmonization on the Internal Market (Trade Marks and Designs)), at the top of the list (apart from single-character renditions of picture marks) there are right now 90 different entries (granted/pending/rejected applications) for FUSION, also 90 for ECLIPSE, and 89 for EVOLUTION.

In order for the registrars (and everyone else) to keep track of the different products for which marks are registered, they are classified into 45 different classes according to the Nice Classification, 34 classes for tangible goods and 11 (up by three quite recently) for services. The classes are quite uneven in their scope, ranging from

Class 9. Scientific, nautical, surveying, photographic, cinematographic, optical, weighing, measuring, signalling, checking (supervision), life-saving and teaching apparatus and instruments; apparatus and instruments for conducting, switching, transforming, accumulating, regulating or controlling electricity; apparatus for recording, transmission or reproduction of sound or images; magnetic data carriers, recording discs; compact discs, DVDs and other digital recording media; mechanisms for coin-operated apparatus; cash registers, calculating machines, data processing equipment, computers; computer software; fire-extinguishing apparatus.

to

Class 23. Yarns and threads, for textile use.

The classification also contains an Alphabetical list of over 8000 more specific product descriptions divided into the individual classes (637 in class 9; 22 in class 23). Overall, in my opinion, the Nice Classification stands out as a relic of a bygone era designed to fully leverage the state-of-the-art computing power of index card technology. (Using words like ‘gutta-percha’ in the class headings doesn’t exactly help.) Of course, fifty-odd years ago the registries were also much more manageable in size.

In EU (at OHIM and in most of the Member States), it has been possible to register a trademark not only for a detailed list of specific goods and services (obviously covering only those) but also for all goods and services in an entire class by using the class heading as a product description. This is different from eg the US, where only specific products must be used, and trademark protection normally only extends to those products for which the mark is actually used in trade, even if the original registration also covers additional products.

Another thing to remember is that a trademark must be able to distinguish the owner’s product from those of others’. Because of this, a trademark cannot just be descriptive and quite simply name the product by its common name. Trademark degeneration, or a trademark becoming a generic name for a certain type of product, can mean that the trademark is no longer protected. (This has happened for example for ASPIRIN in the US, whereas in Europe Bayer’s registration is still valid.)

In the IP TRANSLATOR case, the CIPA applied for registration of IP TRANSLATOR as a UK trademark for ‘Education; providing of training; entertainment; sporting and cultural activities’ in class 41, which also happens to be the class heading for that class. The UK Registrar refused the application based on OHIM’s classification guidelines (Communication 4/03) on the grounds that IP TRANSLATOR is descriptive for translation services, which are also a part of class 41, even though they certainly do not naturally fall under any of the categories listed in the class heading in normal parlance. Of course there was an appeal, and this is where the ECJ was asked for a prejudicial opinion. And thus the ECJ opined:

[...]trade marks must be interpreted as meaning that it requires the goods and services for which the protection of the trade mark is sought to be identified by the applicant with sufficient clarity and precision to enable the competent authorities and economic operators, on that basis alone, to determine the extent of the protection conferred by the trade mark.

Directive 2008/95 [...] does not preclude the use of the general indications of the class headings [...] to identify the goods and services for which the protection of the trade mark is sought, provided that such identification is sufficiently clear and precise.

An applicant for a national trade mark who uses all the general indications of a particular class heading [...] to identify the goods or services for which the protection of the trade mark is sought must specify whether its application for registration is intended to cover all the goods or services included in the alphabetical list of that class or only some of those goods or services. If the application concerns only some of those goods or services, the applicant is required to specify which of the goods or services in that class are intended to be covered.

In response to this OHIM issued a new Communication 2/12 to supersede the earlier 4/03, requiring for future and pending applications one extra bit of information for each class of goods and services using complete class headings, namely whether it is intended to cover the entire class or not. This alone would only address the issue raised in last paragraph of the ruling, and ignore the first two altogether. Furthermore, the second paragraph in fine read together with first one quite simply means that not all class headings are created equal. The ones that are sufficiently clear and precise standing by themselves (like class 23) are fine, but others (like class 9) are not. This would of course bring registration practices more in sync between the EU and the US, which some might argue is a Good Thing. Actually this is even spelled out quite clearly earlier in the judgement:

54 In that connection, it must be observed that some of the general indications in the class headings of the Nice Classification are, in themselves, sufficiently clear and precise to allow the competent authorities to determine the scope of the protection conferred by the trade mark, while others are not such as to meet that requirement where they are too general and cover goods or services which are too variable to be compatible with the trade mark’s function as an indication of origin.

55 It is therefore for the competent authorities to make an assessment on a case-by-case basis, according to the goods or services for which the applicant seeks the protection conferred by a trade mark, in order to determine whether those indications meet the requirements of clarity and precision.

In response to this, OHIM is, for the time being, reviewing all incoming and pending applications using class headings on a case-by-case basis, while also trying to make up its mind as to which class headings are still okay, together with the local registrars. Even if registrations such as the class heading for class 9 are no longer allowed, the ones registered up to this point will of course still remain in force unchanged.

Of course my interpretation is far from being the only one around. From a trademark attorney’s job security perspective, this one by Niamh Hall as seen on IPKat would probably be the best one I have come across so far. Not only would class heading registrations be allowed for all classes, but they would only cover all products listed for that class in the Alphabetical list of the Nice Classification (and perhaps not even the latest one if the mark is older), and not just all the products that would eventually end up in that particular class, thus potentially excluding over 85% of the individual products listed in EuroClass, the product classification system used by OHIM and an increasing number of local registrars. From the perspective of legal certainty and predictability, this wouldn’t exactly be optimal.

(provide 'rant)

Lies, damned lies, and performance metrics

In the DESI IV e-discovery workshop at ICAIL last June, in one of the presentations there was a single slide that really caught my attention. It showed the performance of a large number of e-discovery systems on two axes representing the most common metrics used in information retrieval, computational linguistics, and what have you: precision and recall. (The slide was based an earlier publication which I unfortunately did not register at the time and have not been able to find since, pointers extremely welcome.)

What was so intriguing about that slide was that apart from a small number of outliers, almost all of the systems evaluated were at the bottom left quadrant, with both precision and recall (typically well) below 50%. Based on my expectations from my previous line of work, this was quite shocking, and had I known this earlier, I would certainly have worded a paper or two a bit differently as far as using e-discovery as an example is concerned. All the same, I suppose even at these figures e-discovery already outperforms all the alternatives, but there is certainly still considerable room for improvement.

What I was accustomed to was that a marketable product should deliver well above 90% in both columns or else the users would just stop using it. Whether they are 93% or 98% is not all that important, because that kind of variation is mostly just noise and depends on how well suited the test materials happen to be for that system. In particular, if you use a manually annotated test corpus as a gold standard, in a commercial scenario, you can really only use it once, because whatever is left between the actual performance and 100% are what we in the business call bugs, and they should be dealt with unless there is a good reason not to do so. And so our gold standard becomes tainted once these bugs are fixed. Which is of course no reason not to use the new figures for marketing purposes. (‘Press statements aren’t delivered under oath.’ - Jim Hacker, PM)

Performance is not just an e-discovery issue. It is raised in many other legal technology contexts as well. For example, the Swedish trade mark law start-up Markify prides itself with the ‘99% accuracy’ of its system, for example in this recent Arctic Startup profile. The actual study on which this claim is based is also available. The results are based on querying a set of 1000 actual cases of successful US trade mark oppositions and the question was whether the different services would return the correct mark (that of the opponent) when queried for the mark that is being opposed. Here are the results, and for added entertainment easier overall comparison I have also computed the F-scores for all of them:

System	Recall	Precision	F-score
Markify	99.7%	0.02%	0.04%
Thomson Reuters Compumark	45.5%	0.21%	0.4%
CT Corsearch	34.6%	0.43%	8.5%
USPTO	34.2%	0.31%	0.6%
Trademarkia	32.5%	0.75%	1.5%
CSC	31.8%	0.55%	1.1%

If recall is all you are after, improving on that 99% is really easy. Simply by returning the entire database for each query you can reach 100% just like that. At the same time precision naturally drops down to epsilon but so what. Of course this is not quite fair (there was no proper indication of the placing of the correct answer on the list of results), but still, just returning the desired answer is definitely not enough, at least when it is returned in a needle-in-a-haystack mode, where, even if the result is there, it is increasingly likely to be missed by the person reading the results the longer the list is. For what it’s worth, I tried to search for ‘äpyli’ (that’s Helsinki slang for ‘apple’) on Markify's system and quit after 10 pages of results at which time the trade mark of a well-known Cupertino-based fruit company had not yet shown up, and the results being shown at that point were already much further away. I suppose ‘the other [sic!] high quality paid trademark search services that can run $500 a word’ can still breathe easy.

What is Legal Technology?

In my opinion, AI & law desperately needs a makeover. One simple but effective way for the field to reinvent itself is rebranding, and the best label for this I can think of is Legal Technology (oikeusteknologia, rättsteknologi, ret(t)steknologi, õigustehnoloogia, R/rechtstechnologie, technologie juridique, tecnologia giuridica, юридическая технология &c).

As someone who has followed and worked in language technology for about two decades now, I see AI & law as being now in the same state as language technology was in the early 1990s. I have presented some lessons learned on how to approach real-world problems at the detail level in my robo-judge paper, so I won't go into them here. Instead, my proposal here looks into language technology as a field that has succesfully reinvented itself a couple of times already. Early on, it was only known as natural language processing (NLP) as a subfield of AI and a form of basic research rarely with any concrete application in mind. (A notable exception to this is machine translation, which also happens to be older than the term AI itself. More on that in a separate post as well as an article written jointly by me and Anniina Real Soon Now.) Then came computational linguistics, which was centered on using computational models and techniques as a tool for linguistic research. (This is where I think AI & law is now.) Of these, in particular corpus linguistics has become mainstream in virtually all subfields of linguistics, but other computational methods are now widely used outside computational linguistics proper as well. Through the 1990s computational linguistics also started to find its way into commercial applications in domains such as language checking, information retrieval, text-to-speech and vice versa, dialogue systems, and machine translation. As these real-world applications started to generate increasingly important research questions in their own right, language technology was born.

"Legal technology" as a term is not my invention. For example, in the US there has been a bicoastal biannual conference called LegalTech® since 2006. As far as I know, most of the technologies presented there are are not all that interesting from an AI & law perspective, with topics such as case management and billing platforms, synchronizing your BlackBerry with your Outlook and stuff like that, and whatever new cruft Westlaw and LexisNexis have come up with each year.

More to the point are for example the LawTechCamps arranged by Daniel Martin Katz (of Computational Legal Studies) and others in June 2011 and next week in Toronto and in the end of June in London. There is also a growing number of start-up companies in the field at least in the US, as listed just the other day on the eLawyering blog. Most of the start-ups listed seem to be working on applications having to do with contracts (possibly a sign of flock mentality from the venture capital side?). Contracts are also the target of the only out legal tech start-up I know of here in Finland, Sopima. With a large number of companies on the same domain fighting over the same market from somewhat different perspectives, it is clear that only some of these companies will be able to succeed (at least as far as the US companies are concerned, Europe is a different kettle of fish because of the different legal culture(s) and the prevalence of non-English languages). The best products have to address a real-world problem and solve it well and efficiently. Usability is another key success factor, and it still seems to be generally neglected in legal IT. Just because a certain design is a possible way to do something does not mean it is the best way (indeed it rarely is, though at least it usually is not quite this bad; required reading: Donald Norman's The Design of Everyday Things, MIT Press 1989). In particular, just replicating ancient practices from the age of pen and paper (and secretaries) and possibly adding some bells and whistles is a true and tried pattern, unfortunately. And the result is an application that takes a week-long course just to get started with it. All the same, the technically best solution does not necessarily win the game. In the end, it all boils down to the viability of the business model and the ability to make it into a reality. (Here's a convenient rule of thumb: marketing costs money, selling makes money. Close early, close often.)

So how can the AI & law community contribute to the impending legal technology boom? One approach is to take an existing, reasonably well-developed Good Old-Fashioned AI & law technology, and to find a real-life legal problem which it could possibly solve. (I'm afraid I can't come up with an example.) The other approach is to take an existing problem (= market need) in the legal community, a problem of the kind that should be solvable by computing, and looking around all over the place in computer science in search of that solution. (Here e-discovery is a prime example, though it does not travel well, and performance-wise it is quite disappointing by the language technology metrics I'm used to but at least it is still equally reliable yet faster and cheaper than people doing the same job.) Since language is in a key role in law, language technology is one obvious place to look at but it should definitely not be the only one for any legal tech company. I'm sure the next 20 years will be a lot more interesting (and profitable) for the field than the past 20.

Von IBM lernen heißt siegen lernen

(Paul Rand 1981)

IBM have a unique and highly visible record in AI winnage. While Herbert Simon and Allen Newell predicted already back in 1957 that a computer would beat the best human player in chess in only ten years' time, it took actually four times that long until IBM's Deep Blue finally won in a tournament against Garry Kasparov in 1997. Since then, computers have basically been unbeatable in chess. Over the years the work needed to reach this point has led to a number of reassessments regarding human cognition, how complex many tasks are and how good at least some of us actually are at them.

Another IBM Research landmark took place last year, when Watson, an AI program built to excel in Jeopardy!, beat two all-time champions in a specially televised tournament. Allegedly you can find the actual shows on Youtube (but of course I can't link to them because I'm a too much of a copyright prude). And of course there are bound to be more such landmark achievements to come. One of those could be delivered by the Blue Brain project, which is an attempt to build a molecular-level computer simulation of an entire human brain.

The game show Jeopardy! itself is perhaps not that well known in Finland. As far as I can remember, a local version of it ran on one of the channels I never watch for a season or two after which it was probably cancelled. It is a quiz format with 2 sets of 6 categories of questions with 5 questions each worth a different amount of money plus various added features. The questions or clues rely heavily on wordplay and all kinds of creative elements so it is definitely not just a matter of parsing the clue and looking up the answer, as the variability in the clues makes them impossible to interpret them correctly just by parsing. The most easily distinguishing characteristic of the game is that answers (at least pragmatically speaking) in turn have to be phrased syntactically in the form of a question. For a novice competitor of the human persuasion this of course adds to the cognitive load and occasionally makes them forget this, thus making even a factually correct answer technically incorrect. For a computer program equipped with a good lexical database, on the other hand, complying with this rule is fairly trivial: just check whether the answer is a person or not and whether it is singular or plural and prefix who/what and is/are accordingly. If you want to get really fancy, also see whether you should prefix the answer with an article (definite or indefinite).

Chess as a game is computationally closed. The numbers of pieces, their positions and possible moves and complete games are all finite. The number of possible games is enormous, and except for the very end of the game, it is quite simply impossible to even list all of them, never mind also evaluating all of them to see which move is the best one to take next. So while simpler games such as tic-tac-toe can be solved by simple brute force in an instant (see xkcd), Deep Blue had to rely heavily on heuristics and libraries of sequences of moves to accomplish its task. (By the way, Deep Blue used a great deal of dedicated custom hardware to do this, while Watson ran just on general-purpose computers.) Jeopardy!, on the other hand, is not computationally closed. The list of potential topics for the categories is unbounded, and typically a category combines at least two of them, described initially only through a clever and opaque title for the category.

As an occasional legal theorist and thus someone who is also interested in rules just for their own sake, at least for me it is interesting to note that for chess, the rules of the game itself are only about ten pages long. What is even more relevant is that Deep Blue does not even implement all of them properly. For example, the program was not able to recognize a tie, but rather relied on people to recognize such events. I have not been able to find a mention about what the program would have done in the event of an invalid move by its opponent that goes unnoticed by the judge, either. (Not that that is likely to be a real issue at that level.) And of course a computer is completely unaffected by such possible rule violations as Tal's devious and distracting smile, which was also used as an example by Ronald Dworkin (Taking Rights Seriously p. 102). Chess and in particular Chess AI is therefore not a very good model for the legal field. As an application, it is not easily generalizable, and the market for chess processors must be quite limited.

Watson, on the other hand, was not built just to kick some human butt in Jeopardy! and to get a ton of publicity while doing it. IBM have announced plans to develop the underlying DeepQA technology further for deployment in a variety of fields of expertise, including law. For the time being it seems to me that they are strongly concentrating on the medical and financial sectors. (It certainly wouldn't surprise me if their market research had shown that lawyers are too conservative and not tech-savvy enough for it to be a potential commercial success, at least for now.) But there is still a live connection, as CMU, the academic home of both Deep Blue and Watson, is just down the street from Pitt Law School, one of the centres of AI & law research in the US. Watson principal investigator Dr. David Ferrucci also gave a keynote address at ICAIL last June in Pittsburgh. We'll have to wait and see.

The idea of Watson, J., has even reached the top of the legal establishment at our poor frontiers (or whatever 'raukoilla rajoilla' is in English). At the Finnish Bar Association's annual conference this January, the Parliamentary Ombudsman of Finland, Petri Jääskeläinen, LL.D., opened his address (only in Finnish, sorry) with Watson (and Chicken Run, nice touch there) and the horror scenario of computers deciding cases of law. My own position on the issue is of course already on the record: I'm afraid neither my rank nor my GI tract are strong enough for me to trust my gut instincts enough to even consider the question whether without having at least some clues about the how, where and why as well. And right now we're really only getting to the point where we are shaping the right questions while at the same time trying to figure out at least tentative answers to them to see whether indeed even the questions make sense. It is not like decision-support systems or the evil robot judges will just appear out of nowhere (and for the time being the utter bogosity of public sector software procurement procedures is certainly the first and the best line of defence against them...), but rather they should preferably appear in contexts where they actually make all kinds of sense. Meanwhile, there are plenty of much less scary types of intelligent software technologies not used enough in the legal (especially information retrieval) field to get things started, but more about them later on.

More on Deep Blue and chess:
Feng-Hsiung Hsu: Behind Deep Blue: Building the Computer that Defeated the World Chess Champion (Princeton UP 2002)
Diego Rasskin-Gutman: Chess Metaphors: Artificial Intelligence and the Human Mind (MIT Press 2009)
Pertti Saariluoma: Chess Players' Thinking: A Cognitive Psychological Approach (Routledge 1995)

More on Watson: IBM Research (lots of videos as well)