Whether an A.I. ought to be granted patent rights is a timely question given the increasing proliferation of A.I. in the workplace. Examples: Daimler-Benz has tested self-driving trucks on public roads^[1], A.I. technology has been applied effectively in medical advancements, psycholinguistics, tourism and food preparation,^[2] a film written by an A.I. recently debuted online^[3] and A.I. has even found its way into the legal profession,^[4] and current interest in the question of whether an A.I. can enjoy copyright rights with several articles having already being published on the subject of A.I. and copyright rights.^[5]

In 2014 the U.S. Copyright Office updated its Compendium of U.S. Copyright Office Practices with, inter alia, a declaration that  the Office will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author.”^[6]

To grant or not to grant: A human prerequisite?

One might argue that Intellectual Property (IP) laws and IP Rights were designed to exclusively benefit human creators and inventors^[7] and thus would exclude non-humans from holding IP rights. The U.S. Copyright Office’s December 2014 update to the Compendium of U.S. Copyright Office Practices that added requirements for human authorship^[8] certainly adds weight to this view.

However, many IP laws were drafted well before the emergence of A.I. and in any case, do not explicitly require that a creator or inventor be ‘human.’ The World Intellectual Property Organization’s (WIPOs) definition of Intellectual Property talks about creations of the mind^[9] but does not specify whether it must be a human mind. Similarly, provisions in laws promoting innovation and IP rights, such as the so-called Intellectual Property Clause of the U.S. Constitution^[10], also do not explicitly mention a ‘human’ requirement. Finally, it ought to be noted that while the U.S. Copyright Office declared it would not register works produced by a machine or mere mechanical process without human creative input, it did not explicitly state that an A.I. could not have copyright rights.^[11]

Legal personhood

One might also argue that an A.I. is not human, and is therefore not a legal person and, thus, is not entitled to apply for much less be granted a patent. New Zealand’s Patents Act, for example, refers to a patent ‘applicant’ as a ‘person’.^[12]

Yet this line of argument could be countered by an assertion that a legal ‘person’ need not be ‘human’ as is the case of a corporation and there are many examples of patents assigned to corporations.^[13]

The underlying science

To answer the question of patent rights for an A.I. we need to examine how modern A.I. systems work and, as an example, consider how machine translation applications such as Google Translate function.

While such systems are marketed as if they’re “magic brains that just understand language”,^[14]the problem is that there is currently no definitive scientific description for language^[15] or language processing^[16]. Thus, such language translation systems cannot function by mimicking the processes of the brain.

Rather, they employ a scheme known as Statistical Machine Translation (SMT) whereby online systems search the Internet identifying documents that have already been translated by human translators– for example books, and organizations like the United Nations, or websites. The system scans these texts for statistically significant patterns and once the computer finds a pattern it uses the pattern to translate similar text in the future.^[17] This, as Jaron Lanier and others note, means that the people who created the translations and make translation systems possible are not paid to for their contributions^[18].

Many modern A.I. systems are largely big data models that operate by defining a real world problem that needs to be solved, then conceiving a conceptual model to solve this problem which is typically a statistical analysis that falls into one of three categories: regression, classification or missing data. Data is then fed into the model and used to refine and calibrate the model. As the model is increasingly refined it is used to guide the location of data and, after a number of rounds of refinement finally results in a model capable of some predictive functionality.^[19]

Big data models can be used to discover patterns in large data sets^[20] but also can, as in the case of translation systems, exploit statistically significant correlations in data.

None of this, however, suggests that current A.I. systems are capable of inventive or creative capacity.

Patentability?

So to get a patent, an invention must:

• Be novel in that it does not form part of the prior art^[21]
• Have an inventive step in that it not obvious to a person skilled in the art^[22]
• Be useful^[23]
• it must not fall into an excluded category that can include discoveries, presentations of information and mental processes or rules or methods for performing a mental act.^[24]

Why discoveries are not inventions is tied with the issue of obviousness and as noted by Buckley J. in Reynolds v. Herbert Smith & Co., Ltd^[25] who stated:

“Discovery adds to the amount of human knowledge, but it does so only by lifting the veil and disclosing something which before had been unseen or dimly seen. Invention also adds to human knowledge, but not merely by disclosing something. Invention necessarily involves also the suggestion of an act to be done, and it must be an act which results in a new product, or a new result, or a new process, or a new combination for producing an old product or an old result.”

Therefore in order to get a patent, an A.I. must first be capable of producing a patentable invention but, given current technology, is this even possible?

A thought exercise

Consider the following:

You believe that as a person exercises more, he/she consumes more oxygen and have tasked your A.I. with analyzing the relationship between oxygen consumption and exercise.

You provide the A.I. with a model suggesting that oxygen consumption increases with physical exertion and data that shows oxygen consumption among people performing little, moderate (e.g. walking briskly) and heavy exercise (e.g. running).

The A.I. reviews the data, refines the model, collects more data and comes up with a predictive model (e.g. when a person exercises X amount, he/she consumes Y amount of oxygen and when the person doubles his/her exertion, his oxygen consumption rate triples).

As this is essentially a statistical regression, the model will not always completely accurate in its predictions due to differences between individuals (i.e. for some persons the model will predict oxygen consumption fairly accurately, for others its results will be far off).

However, this particular model has another, more fundamental limitation – it fails to consider that a human cannot exercise beyond a certain point because his/her heart would be incapable of sustaining such levels of exertion^[26] or because over-exercise may trigger an unexpected reaction (e.g. death).^[27]

If one were to feed this model data of persons who have collapsed or died during exercise (and thus, in the latter case, not consume any oxygen), would the A.I. be able to ‘think outside its box’ and:

• Question the cause of these data discrepancies and have the initiative to conduct further investigation?
• Note and correct the limitation in the original model (which would require a significant amendment)?

Or would it simply alter the existing model by changing the slope of the regression line?

SMT and other A.I. have similar limitations, in the case of SMT, once the system is built, linguistic knowledge becomes necessary to achieve perfect translation at all grammatical levels^[28] and SMT systems presently cannot translate cultural components of the source text into the target language, provide very literal, word for word translations that do not recognize idioms, slang, and terms that are not in the machine’s memory and lack human creativity^[29] To do so would require a change to the underlying machine translation model, and the question arises whether this would have to be done by the human creators of the SMT or whether the SMT itself would be able to make the necessary corrections and adjustments to the model.

Should the SMT or, in the earlier example the A.I., be unable to improve and, in this case, innovate on the existing model does it have the creative or inventive capacity to conceive an invention is truly inventive? And if either the SMT or the AI can produce something that appears novel and inventive, given the nature of how A.I. presently operates (i.e. as big data models), would such a product be the result of an analysis of existing data to uncover hitherto unseen relationships – in other words, a discovery?

Returning to the original question about patent rights for an A.I., perhaps the question we should ask is not whether an A.I. should be able to get a patent, but whether an A.I., given current technology, can create a patentable invention in the first place and if the answer to that question is ‘no’, then the question of granting patent rights to an A.I. is moot.

References:

[1]  Jon Fingas, ‘Daimler tests a self-driving, mass-produced truck on real roads’, Engadget, Oct. 4, 2016.

[2] Sophie Curtis, ‘Cognitive Cooking: How Is A.I. Changing Foodtech?’ RE•WORK, April 19 2016.

[3] Analee Newitz, ‘Movie written by algorithm turns out to be hilarious and intense’ ArsTechnica, June 9, 2016.

[4]  Susan Beck, ‘AI Pioneer ROSS Intelligence Lands Its First Big Law Clients, The American Lawyer, May 6, 2016.

[5] See for example:

• Madeleine de Cock Buning ‘Is the EU exposed on the copyright of robot creations?‘ www.roboticslawjournal.com,Nov. 2015.
• Daniela Hernandez, ‘Could you take a robot to court if it steals your work? Fusion, Nov. 11, 2015.

[6] Copyright Office, Compendium of U.S. Copyright Office Practices (3d ed. 2014). § 313.2

[7] Hettinger argued that ‘the most powerful intuition supporting property rights is that people are entitled to the fruits of their labor’

• See: Edwin Hettinger, “Justifying Intellectual Property’, Philosophy & Public Affairs, Vol. 18, No. 1, Winder 1989, p. 31-52

[8] Copyright Office, Compendium of U.S. Copyright Office Practices (3d ed. 2014). § 306.

[9] According to WIPO, ‘Intellectual property (IP) refers to creations of the mind, such as inventions; literary and artistic works; designs; and symbols, names and images used in commerce.’

[10] Article I, Section 8, Clause 8 of the United States Constitution states: The Congress shall have Power To…. promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries.

[11] According to the U.S. Copyright Office, ‘Copyright exists from the moment the work is created. You will have to register, however, if you wish to bring a lawsuit for infringement of a U.S. work’

See ‘Copyright in General’,

[12] s.5(1) Patents Act 2013

[13] See for example, U.S. Patent 5,953,441 ‘Fingerprint sensor having spoof reduction features and related methods’ which was assigned to Harris Corporation.

[14] Nilagia McCoy  ‘Jaron Lanier: The Digital Economy Since Who Owns The Future?’ October 8, 2015.

[15] This was noted by Jaron Lanier who delivered the keynote address at the opening of the Conference on the Global Digital Content Market taking place from April 20-22, 2016 at WIPO Headquarters in Geneva, Switzerland.

• Catherine Saez, ‘IP Should Serve More Actors In New Ways, Keynote Speaker At WIPO Says’, Intellectual Property Watch, April 21, 2016.

[16] See for example:

• Erin Harte, ‘Language Processing In The Human Brain’, Brainworld, July 22, 2011.
• AD Friederici, The brain basis of language processing: from structure to function, Physiol Rev. 2011 Oct;91(4):1357-92.
• Jerry Ball, Mary Freiman, Stuart Rodgers & Christopher Myers, ‘Toward a Functional Model of Human Language Processing.’

[17] Inside Google Translate, July 9, 2010.

[18] Catherine Jewell, ‘Digital pioneer, Jaron Lanier, on the dangers of “free” online culture,’ WIPO Magazine, April 2016.

[19] Noah Silverman.

[20] i.e. ‘data mining’.

[21] s 6, New Zealand Patents Act 2013

[22] s 7, New Zealand Patents Act 2013

[23] s 10, New Zealand Patents Act 2013

[24] See for example:. 93(2) Hong Kong Patents Ordinance, Cap 514 or the Canadian Patent Act R.S.C., 1985, c. P-4

[25] (1903), 20 R.P.C. 123

[26] Wikipedia, ‘Heart Rate’.

[27] This may be caused by a congenital condition but some also believe that prolonged excessive exercise may exacerbate matters. See:

• Lou Schuler, ‘Death by Exercise’ Men’s Health June 8, 2003.
• Timothy D Noakes, ‘Sudden Death and Exercise’.

[28] Mireia Farrus, Marta R. Costa-Jussa, Jose B. Marino, Marc Poch, Adolfo Hernandez, Carlos Henrıque,  Jose A. R. Fonollosa, ‘Overcoming statistical machine translation limitations: error analysis and proposed solutions for the Catalan–Spanish language pair’ Language Resources & Evaluation, DOI 10.1007/s10579-011-9137-0.

[29] ‘Machine Translation vs. Human Translators’ 

See also:

• Wikipedia, ‘Statistical Machine Translation’.

Enter artificial intelligence (AI)

There has been much hoopla about AI-based legal systems that, some might have you believe, may soon replace lawyers (no doubt causing a degree of anxiety among some legal professionals). There is some misunderstanding among many lawyers, and much of the public, about what AI systems are presently capable of. Can a legal AI, based on current technology, actually “think” like a lawyer? No. At best, today’s AI is an incomplete substitute for a human lawyer, although it could reduce the need for some lawyers (I’ll get to all that later).

However, something we should think seriously about right now is the long-term implication of the introduction of AI into the legal environment—notably the potential loss of legal wisdom.

Why doesn’t AI think like a human?

Let’s explore why AI doesn’t actually mimic the human brain. As an example, let’s look at automated translation systems such as those available from Google, Facebook or Microsoft. Such systems might appear to work the way human translators do, but what they actually do is match patterns derived from analyses of thousands, if not millions, of pages of text found on the web, employing a technology known as statistical machine translation. For instance, if such a system wants to know how to translate the English greeting “hello” into French, it scans English and French translations on the web, statistically analyses the correlations between “hello” and various French greetings, then comes to the conclusion that the French equivalent of “hello” is “bonjour”.

Current AI is good at this kind of pattern matching, but less so at cognition and deductive reasoning. Consider the human brain: not only does it store a large number of associations, and accesses useful memories (sometimes quickly, sometimes not), it also transforms sensory and other information into generalisable representations invariant to unimportant changes, stores episodic memories and generalises learned examples into understanding. These are key cognitive capabilities yet to be matched by current AI technology.

Thus, while present AI-based legal systems might analyse judicial decisions—for example, to help litigators gain insights to a judge’s behaviour or a barrister’s track record—they do so by scrutinising existing data to reveal patterns, and not by extrapolating from the content of those decisions the way an experienced human legal professional might.

The temptation to make redundant

As AI systems become more capable, the temptation grows to use such systems not only to supplement but also to eliminate the need for some personnel. An AI system weak in cognition but strong in pattern matching probably could not replace an experienced professional in terms of drawing inferences, deductive reasoning or combining different practice areas to arrive at more comprehensive solutions. However, it could perform certain tasks such as searching for patterns of words in documents for evidence gathering that have hitherto been delegated to lower level staff—such as paralegals, trainees, and junior associates—and do so better than any human could.

While one might argue that the introduction of AI systems will lighten the workload of legal professionals and thereby improve their quality of life, it also potentially diminishes the need for junior legal staff, which would only exacerbate the oversupply problem in the legal profession.

Shrink now, suffer later?

If fewer junior legal professionals are hired, this implies a smaller population of lower level staff, thus a smaller feeder pool for more senior positions. And, as more tasks are automated, this could deprive junior legal professionals of opportunities to gain important experience—ie, get their 10,000 hours. Will this result in fewer quality, experienced legal professionals in the future?

And the future of legal AI?

There are yet two more (albeit related) things to think about.

First: the development and maintenance of a good AI system requires both technical and legal competency. Put another way, a legal AI system programmed by systems experts ignorant in the law will be seriously, if not fatally, flawed. Thus, if we want to continue to develop more capable legal AI systems, good content providers—ie, good lawyers—will be needed.

Second: as laws, the legal business and social environments in their respective jurisdictions evolve, developments that might not have been anticipated just a few years earlier will emerge. Only the very best legal and other minds will be able to cope with some of these developments—and update the relevant legal AI systems accordingly. For example, when the US passed the Leahy-Smith America Invents Act (AIA) in 2011, it introduced new review procedures for existing patents with the intent of improving patent quality. It also had the effect of introducing several unintended consequences, including the use of such procedures by hedge funds to invalidate patents for the purpose of affecting the stock price of the companies holding the patents and the negative impact the AIA has had on inventors. Updating an AI system to properly incorporate these developments requires not only a deep understanding of US patent law but also a perspective on patents, finance and the impact of patent policy and procedures on innovation—something that can only really be appreciated after years of experience. Moreover, this is something that could not have been programmed into an AI system half a decade ago, and such content could probably not have been provided by a less capable, less experienced legal professional to an AI developer.

So what, if anything, can be done?

Sadly, there are no easy answers. Graduating fewer lawyers might alleviate the problem of oversupply, but would also result in unemployment at educational institutions.

While the government (or government-backed NGO) could establish some sort of training centre for under-employed junior lawyers, where these professionals could offer services pro bono to build their experience, this also smacks of government interference in the private practice market. But we need to start thinking of solutions now.  The introduction of AI into the legal profession and the potential prospect of putting more lawyers out of work could have profound implications for legal AI systems and the profession as a whole.