Machines of Ordinary Skill in the Art: How Inventive Machines Will Change Obviousness
| Author | Ryan Abbott |
| Position | Ryan Abbott is a professor of law and health sciences at theUniversity of Surrey School of Law in the United Kingdom, and anadjunct assistant professor at the David Geffen School of Medicineat the University of California, Los Angeles. He can be reached atr.abbott@surrey.ac.uk. This article is adapted from the author'sarticle 'Everything Is ... |
| Pages | 32-65 |
30 ©2019. Published in Landslide®, Vol. 11, No. 5, May/June 2019, by the American Bar Association. Reproduced with permission. All rights reserved. This information or any portion thereof may not be copied or
disseminated in any form or by any means or stored in an electronic database or retrieval system without the express written consent of the American Bar Association or the copyright holder.
Machines of Ordinary
Skill in the Art
How Inventive Machines
Will Change Obviousness
By Ryan Abbott
Machines are widely facilitating innovation and have been autonomously generating patentable
inventions for decades.1 “Autonomously” here refers to the machine, rather than to a person,
meeting traditional inventorship criteria. In other words, if the “inventive machine” were a nat-
ural person, it would qualify as a patent inventor. In fact, the U.S. Patent and Trademark Ofce
(USPTO) may have granted patents for machine inventions as early as 1998. In earlier works, I
examined instances of autonomous machine invention in detail and argued that such machines
ought to be legally recognized as patent inventors to incentivize innovation and promote fair-
ness. The owners of these machines would be the owners of their inventions. Terms such as
“computers” and “machines” are used interchangeably here to refer to algorithms or software
rather than to physical devices or hardware.
Image: iStockPhoto
©2019. Published in Landslide®, Vol. 11, No. 5, May/June 2019, by the American Bar Association. Reproduced with permission. All rights reserved. This information or any portion thereof may not be copied or
disseminated in any form or by any means or stored in an electronic database or retrieval system without the express written consent of the American Bar Association or the copyright holder. 31
Inventive Machine Standard
What happens when inventive machines become a standard
part of research and development? The impact will be tre-
mendous, not just on innovation, but also on patent law. Right
now, patentability is determined based on what a hypotheti-
cal, noninventive, skilled person would nd obvious. The
skilled person represents the average worker in the scientic
eld of an invention. Once the average worker uses inventive
machines, or inventive machines replace the average worker,
then inventive activity will be normal instead of exceptional.
If the skilled person standard fails to evolve accordingly,
this will result in too lenient a standard for patentability. Pat-
ents have signicant anticompetitive costs, and allowing the
average worker to routinely patent his or her outputs would
cause social harm. As the U.S. Supreme Court has articulated,
“[g]ranting patent protection to advances that would occur in
the ordinary course without real innovation retards progress
and may . . . deprive prior inventions of their value or utility.”2
The skilled standard must keep pace with real-world condi-
tions. In fact, the standard needs updating even before inventive
machines are commonplace. Already, computers are widely
facilitating research and assisting with invention. For instance,
computers may perform literature searches, data analysis, and
pattern recognition. This makes current workers more knowl-
edgeable and creative than they would be without the use of
such technologies. The Federal Circuit has provided a list of
nonexhaustive factors to consider in determining the level of
ordinary skill: (1) “type[s] of problems encountered in the art,”
(2) “prior art solutions to those problems,” (3) “rapidity with
which innovations are made,” (4) “sophistication of the tech-
nology,” and (5) “educational level of active workers in the
eld.”3 This test should be modied to include a sixth factor:
(6) “technologies used by active workers.”
This change will more explicitly take into account the fact
that machines are already augmenting the capabilities of work-
ers, in essence making more obvious and expanding the scope
of prior art. Once inventive machines become the standard
means of research in a eld, the test would also encompass the
routine use of inventive machines by skilled persons. Taken
a step further, once inventive machines become the standard
means of research in a eld, the skilled person should be an
inventive machine. Specically, the skilled person should be an
inventive machine when the standard approach to research in a
eld or with respect to a particular problem is to use an inven-
tive machine (the “inventive machine standard”).
To obtain the necessary information to implement this test,
the USPTO should establish a new requirement for applicants
to disclose when a machine contributes to the conception of an
invention, which is the standard for qualifying as an inventor.
Applicants are already required to disclose all human inventors,
and failure to do so can render a patent invalid or unenforce-
able. Similarly, applicants should need to disclose whether a
machine has done the work of a human inventor. This informa-
tion could be aggregated to determine whether most invention
in a eld is performed by people or machines. This information
would also be useful for determining appropriate inventorship,
and more broadly for formulating innovation policies.
Whether the inventive machine standard is that of a skilled
person using an inventive machine or just an inventive machine,
the result will be the same: the average worker will be capable
of inventive activity. Conceptualizing the skilled person as using
an inventive machine might be administratively simpler, but
replacing the skilled person with the inventive machine would be
preferable because it emphasizes that the machine is engaging in
inventive activity, rather than the human worker.
Yet simply substituting an inventive machine for a skilled
person might exacerbate existing problems with the nonob-
viousness inquiry. With the current skilled person standard,
decision makers, in hindsight, need to reason about what
another person would have found obvious. This results in
inconsistent and unpredictable nonobviousness determinations.
In practice, the skilled person standard bears unfortunate simi-
larities to Justice Stewart’s famously unworkable denition of
obscene material: “I know it when I see it.”4 This may be even
more problematic in the case of inventive machines, as it is
likely to be difcult for human decision makers to theoretically
reason about what a machine would nd obvious.
An existing vein of critical scholarship has already advo-
cated for nonobviousness inquiries to focus more on economic
factors or objective “secondary” criteria, such as long-felt but
unsolved needs, the failure of others, and real-world evidence
of how an invention was received in the marketplace. Inventive
machines may provide the impetus for such a shift.
Nonobviousness inquiries utilizing the inventive machine
standard might also focus on reproducibility, specically
whether standard machines could reproduce the subject mat-
ter of a patent application with sufcient ease. This could
be a more objective and determinate test that would allow
the USPTO to apply a single standard consistently, and it
would result in fewer judicially invalidated patents. A non-
obviousness inquiry focused on either secondary factors or
reproducibility may avoid some of the difculties inherent in
applying a “cognitive” inventive machine standard.
Regardless of how the test is applied, the inventive machine
standard will dynamically raise the current benchmark for
patentability. Inventive machines will be signicantly more
intelligent than skilled persons and also capable of considering
more prior art. An inventive machine standard would not pro-
hibit patents, but it would make obtaining them substantially
more difcult: A person or computer might need to have an
unusual insight that other inventive machines could not easily
recreate; developers might need to create increasingly intelli-
gent computers that could outperform standard machines; or,
most likely, invention will be dependent on specialized, non-
public sources of data. The nonobviousness bar will continue
to rise as machines inevitably become increasingly sophisti-
cated. Taken to its logical extreme, and given that there may be
no limit to how intelligent computers will become, it may be
that every invention will one day be obvious to commonly used
computers. That would mean no more patents should be issued
without some radical change to current patentability criteria.
Machines Will Become Increasingly Inventive
Machine intelligence or articial intelligence (AI), which is
to say an algorithm able to perform tasks normally requiring
human intelligence, is becoming increasingly sophisticated.
©2019. Published in Landslide®, Vol. 11, No. 5, May/June 2019, by the American Bar Association. Reproduced with permission. All rights reserved. This information or any portion thereof may not be copied or
disseminated in any form or by any means or stored in an electronic database or retrieval system without the express written consent of the American Bar Association or the copyright holder.
32
In 2017, DeepMind’s Go-playing program AlphaGo beat the
game’s world champion. That feat was widely lauded in the AI
community because of the sheer complexity of Go—there are
more board congurations in the game than there are atoms in
the universe. Go was the last traditional board game at which
people had been able to outcompete machines. Later that
year, an improved AI by DeepMind, AlphaGo Zero, defeated
AlphaGo 100 games to zero. AlphaGo Zero did this after train-
ing for only three days by playing against itself. Unlike its
predecessor, it did not train from prior human games.
AI like DeepMind’s may soon outperform people at
more practical tasks relevant to R&D. Indeed, in December
2018, DeepMind’s AlphaFold AI took top honors in the 13th
Critical Assessment of Structure Prediction (CASP), a com-
petition for predicting protein structure. Predicting protein
structure can be an important component of drug discovery,
for example. Similarly, IBM’s agship AI system Watson is
being used to conduct research in drug discovery.
Ultimately, the developers of DeepMind hope to create arti-
cial general intelligence (AGI). Existing “narrow” or specic AI
systems focus on discrete problems or work in specic domains.
AGI could even be set to the task of self-improvement, result-
ing in a continuously improving system that surpasses human
intelligence. Such an outcome has been referred to as the intel-
ligence explosion or the technological singularity. AI could then
innovate in all areas of technology, resulting in progress at an
incomprehensible rate. As the mathematician Irving John Good
wrote in 1965, “the rst ultraintelligent machine is the last inven-
tion that man need ever make.”5
Inventive Is the New Skilled
In the future, having inventive machines replace the skilled
person may better correspond with real-world conditions.
Right now, there are inherent limits to the number and capa-
bilities of human workers. The cost to train and recruit new
researchers is signicant, and there are a limited number of
people with the ability to perform this work. By contrast,
inventive machines are software programs which may be non-
rivalrous. Once Watson outperforms the average industry
researcher, IBM may be able to simply copy Watson and have
it replace most of an existing workforce. Copies of Watson
could replace individual workers, or a single Watson could do
the work of a large team of researchers.
Thus, one way in which inventive machines will change the
skilled paradigm is that they will make an average worker inven-
tive compared to a static skilled person standard. Yet as the use
of inventive machines becomes standard, their outputs should no
longer be inventive because their widespread use should instead
raise the bar for obviousness. To generate patentable output
in world of inventive machines, it may be necessary to use an
advanced machine that can outperform standard machines, or a
person or machine will need to have an unusual insight that stan-
dard machines cannot easily recreate. Inventiveness also may
depend on the data supplied to a machine, such that only certain
data would result in inventive output.
Skilled People Use Machines
In some instances, using an inventive machine may require
signicant skill, for example, if the machine is only able to
generate a certain output by virtue of being supplied with cer-
tain data. Determining which data to provide a machine, and
obtaining that data, may be a technical challenge. Also, it
may be the case that signicant skill is required to formulate
the precise problem to put to a machine. In such instances, a
person might have a claim to inventorship independent of the
machine, or a claim to joint inventorship. This is analogous
to collaborative human invention where one person directs
another to solve a problem. Depending on the details of their
interaction, and who “conceived” of the invention, one person
or the other may qualify as an inventor, or they may qualify
as joint inventors. Generally, however, directing another party
to solve a problem does not qualify for inventorship. Particu-
larly after the development of AGI, there may not be a person
instructing a computer to solve a specic problem. AGI
should be able to solve not only known problems but also
unknown problems.
The changing use of machines also suggests a change to
the scope of prior art. Currently, for purposes of obviousness,
prior art must be in the eld of an applicant’s endeavor, or rea-
sonably pertinent to the problem with which the applicant was
concerned. This analogous art test was implemented because
it is unrealistic to expect inventors to be familiar with anything
more than the prior art in their eld, and the prior art relevant
to the problem they are trying to solve. However, a machine
is capable of accessing a virtually unlimited amount of prior
art. Advances in medicine, physics, or even culinary science
may be relevant to solving a problem in electrical engineer-
ing. Machine augmentation suggests that the analogous art test
should be modied or abolished once inventive machines are
common, and that there should be no difference in prior art for
Ryan Abbott is a professor of law and health sciences at the
University of Surrey School of Law in the United Kingdom, and an
adjunct assistant professor at the David Geffen School of Medicine
at the University of California, Los Angeles. He can be reached at
r.abbott@surrey.ac.uk. This article is adapted from the author’s
article “Everything Is Obvious,” 66 UCLA L. Rev. 2 (2019).
Machines have
been autonomously
generating patentable
inventions for decades.
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