Patenting of algorithms

[sontag@control.rutgers.edu]

There was recently a message to this bboard regarding the patenting of neural
net algorithms (as opposed to copyrighting of software).  With permission, I
am reprinting here a report prepared by the Mathematical Programming Society
regarding the issue of patenting algorithms.  (The report appears in the
forthcoming issue of SIAM News.)

I DO NOT want to generate a discussion of this general topic in connectionists;
the purpose of reprinting this here is just to make people aware of the report
and its strong recommendations, which are especially relevant for an area such
as neural nets.  (I suggest the use of comp.ai.neural-nets for discussion.)

-eduardo
PS: I have not included the Appendices that are referred to, as I did not
obtain permission to reprint them. ---Copyright issue, NOT patent...!  :-)
PS_2: Note the irony: the first signatory of the report is George Dantzig, who
in essence designed the most useful algorithm for (batch) perceptron learning.

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\centerline{\bf Report of the Committee on Algorithms and the Law}
\medskip
\centerline{Mathematical Programming Society}

\beginsection Background and charge

The Committee was appointed in the spring of 1990 by George Nemhauser,
Chairman of the Mathematical Programming Society (MPS). 
Its charge follows:

{\narrower \noindent
``The purpose of the committee should be to devise a position for 
MPS to adopt and publicize regarding the effects of patents on the 
advancement of research
and education in our field. The committee may also wish to comment on the
recent past history.''
\smallskip}

This is the report of the Committee. It comprises a main body with our
assumptions, findings of fact, conclusions, and recommendations.
There are two appendices, prepared
by others, containing a great deal of specific factual information and
some additional analysis.

\beginsection Assumptions

MPS is a professional, scientific society whose
members engage in research and teaching of the theory, implementation and 
practical use of optimization methods.  

It is within the purview of MPS
to promote its activities (via publications, symposia, prizes, newsletter),
to set standards by which that research can be measured (such as criteria for 
publication and prizes, guidelines for computational testing, etc.), 
and to take positions on issues which directly affect our profession.

It is not within the purview of MPS to market software products, and MPS 
should not become involved in 
issues related to the commercial aspects of our profession 
except where it directly affects research and education.

The Committee is unable to make expert legal analyses or to provide 
legal counsel. The main body of this report is therefore written from 
the perspective
of practitioners of mathematical programming rather than from that
of attorneys skilled in the law.
                               
MPS is an international society.
However, the Committee has interpreted its charge as applying
apecifically to U.S. patent law and its application to algorithms.
All comments and conclusions of this report should be read
with this fact in mind.

\beginsection Facts about patents and copyrights

The three principal forms of legal protection for
intellectual property are the copyright, the patent,
and the trade secret.  Copyrights and patents are  
governed by federal law, trade secrets by state law.
Setting aside the issue of trade secrets, some of 
the distinctions between copyrights and patents
can be summarized as follows.
 
{\it Type of property protected:\/}  Patents protect ideas,
principally ``nonobvious'' inventions and designs.  It
is well estabished that ``processes'' are patentable. 
The Patent Office currently grants patents on algorithms
and software, on the basis of the ambiguous 1981 U.S.
Supreme Court decision in {\it Diamond v. Diehr.} 
 
Copyrights do not protect ideas.  Instead, they
protect the {\it expression} of ideas, in ``original works of
authorship in any tangible medium of expression.''  The
principle that software is copyrightable appears to have
been well established by the 1983 decision of the U.S.
Court of Appeals in {\it Apple v. Franklin.}
 
{\it How protection is obtained:\/}  Federal law is now in
essential conformity with the Bern Copyright Convention.
As a consequence, international copyrights are created
virtually automatically for most works of authorship.
Government registration of copyrights is simple and
inexpensive to obtain.
 
By contrast, patents are issued by the U.S. Patent
Office only after an examination procedure that is both
lengthy (three years or more) and costly (\$10,000 and
up in fees and legal expenses).  An inventor must avoid
public disclosure of his invention, at least until patent
application is made, else the invention  will be deemed
to be in the public domain.  Patent application proceedings
are confidential, so that trade secret protection can
be obtained if a patent is not granted.
 
{\it Length of protection:\/}  U.S. patents are for 17 years.
Copyrights are for the lifetime of the individual plus 50
years or, in the case of corporations, 75-100 years.
 
\beginsection Facts about algorithms
 
Algorithms are typically designed and developed in a 
highly decentralized manner 
by single individuals or small groups working together.  This requires
no special equipment, few resources, and little cost.
The number of people involved is also quite large compared to the needs of
the marketplace.
Independent rediscovery is a commonly occurring phenomenon.

There is a long and distinguished history of public
disclosure by developers of mathematical algorithms via the usual
and widely-accepted channels of publication in scientific journals and
talks at professional meetings.  These disclosures
include the theoretical underpinnings of the method, implementation
details, computational results, and case studies of results on
applied problems.
Indeed, algorithm development is based on the tradition 
of building upon previous work by generalizing and improving solution principles
from one situation to another.

The commercial end product of an algorithm (if there is any) is
generally a software package, where the algorithm is again generally 
implemented by a very small number of individuals.
Of course, a larger group of people may be involved in building the package
around the optimization software to handle the user interface, data processing,
etc.  Also, others may be involved to handle functions like
marketing, distribution, and maintenance. 

Competition in the marketplace has been traditionally based on the 
performance of particular implementations and features provided by 
particular software products.  The product is often treated like a 
``black box'' with the specific algorithm used playing a rather minor role.
 
The cost of producing, manufacturing, distributing and advertising 
optimization software is often quite small. Even when this is
not the case, it is generally the
implementation of algorithms that is costly, rather than their 
development.  Software manufacturers have a need to
protect their investment in implementation, but have little need
to protect an investment in algorithmic development.  In the absence
of patents, algorithms--like all of mathematics and basic science--
are freely available for all to use.
 
Traditionally, developers of optimization software have protected
their investments by keeping the details of their implementation 
secret while allowing the general principles to become public.
Software copyrights
are also an appropriate form of protection, and are now widely used.
Moreover, despite unresolved legal questions concerning the ``look
and feel'' of software, the legal issues of copyright protection
seem to be relatively well settled.

Often an optimization package is a small 
(but important) part of an overall planning process.  That process is often
quite complex; it may require many resources and great cost
to complete, and the potential benefits may be uncertain and distributed
over a long time period.
In such situations it is usually quite difficult to quantify the 
net financial impact made by the embedded optimization package.

\beginsection Public policy issues

{\it Will algorithm patents promote invention?} 
Article I, Section 8 of the U.S. Constitution empowers
Congress  ``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.''  Inasmuch as patents are intended
to provide an incentive for invention, it seems appropriate
to inquire whether patenting of algorithms will, in fact,
create an incentive for the invention of algorithms.
                           
Given the existing intensity of research and the
rapid pace of algorithmic invention, it seems hard to argue
that additional incentives are needed.  In fact, there is
good reason to believe that algorithm patents will inhibit
research, in that free exchange of ideas will be curtailed,
new developments will be held secret, and researchers will
be subjected to undesired legal constraints.
 
{\it Will algorithm patents provide needed protection for
software manufacturers?}  Copyright and trade secret protection
appear to provide the sort of protection most needed by
software manufacturers.  By their nature, patents seem to
offer a greater potential for legal confrontation than
copyrights.  Instead of providing protection, algorithm
patents actually pose a threat to smaller software houses
lacking the resources to defend themselves in costly patent
litigation.  It can be argued that patents encourage an
oligarchical industrial structure and discourage competition.
 
{\it Is the Patent Office able to deal with algorithm patents?}
There is abundant evidence that the Patent Office is not
up to the job.  Many algorithmic ``inventions'' have been
granted undeserved patents, greatly increasing the potential
for legal entanglement and litigation.  Moreover, it seems
unlikely that there will be any substantial improvement in
the quality of patent examinations.  

\beginsection Conclusions 
     
It seems clear from the previous discussion that the nature of work 
on algorithms is quite different from that in other fields 
where the principles of 
patents apply more readily. This in itself is a strong argument
against patenting algorithms.

In addition, we believe that the patenting of algorithms would have
an extremely damaging effect on our research and on our teaching,
particularly at the graduate level, far outweighing any imaginable
commercial benefit.
Here is a partial list of reasons  for this view:
\item{$\bullet$} Patents provide a protection which is not warranted given the
nature of our work.
\item{$\bullet$} 
Patents are filed secretly and would likely slow down the flow
of information and the development of results in the field.
\item{$\bullet$} 
Patents necessarily impose a long-term monopoly over inventions.
This would likely restrict rather than enhance the availability of
algorithms and software for optimization.
\item{$\bullet$} 
Patents introduce tremendous uncertainty and add a large cost and risk factor
to our work.  This is unwarranted since our work does not generate large 
amounts of capital.
\item{$\bullet$} 
Patents would not provide any additional source of public information 
about algorithms.
\item{$\bullet$} 
Patents would largely be concentrated within large institutions
as universities and industrial labs would likely become the owners of patents
on algorithms produced by their researchers.
\item{$\bullet$} 
Once granted, even a patent with obviously invalid claims would
be difficult to overturn by persons in our profession due to high legal costs.
\item{$\bullet$} 
If patents on algorithms were to become commonplace, 
it is likely that nearly all algorithms,
new or old, would be patented to provide a defense against
future lawsuits and as a potential revenue stream for future royalties.
Such a situation would have a very negative effect on our profession.

\beginsection Recommendations

The practice of patenting algorithms is harmful to the progress of research
and teaching 
in optimization, and therefore harmful to the vital interests of 
MPS. MPS should therefore take such actions as it
can to help stop this practice, or to limit it if it cannot be stopped.

In particular: 
\item{$\bullet$}
The MPS Council should adopt a resolution opposing the patenting of 
algorithms on the grounds that it harms research and teaching.
\item{$\bullet$}
MPS should urge its 
sister societies ({\it e.g.,} SIAM, ACM, IEEE Computer Society, AMS) to take
a similar forthright position against algorithm patents. 
\item{$\bullet$}
MPS should publish information in one or more of its publications as
to why patenting of algorithms is undesirable.
\item{$\bullet$}
The Chairman of MPS should write in his official capacity to
urge members of Congress to pass a law declaring algorithms non-patentable
(and, if possible, nullifying the effects of patents already granted 
on algorithms).
\item{$\bullet$}
MPS should support the efforts of other organizations to intervene 
in opposition to the patenting of algorithms 
(for example, as friends of the court or with Congress).
It should do so by means such as
providing factual information on mathematical programming issues and/or 
history, and commenting on the impact of the patent issue to our research
and teaching in mathematical programming.  
MPS should urge its members to do likewise.

\vskip .6 in
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\centerline{The Committee on Algorithms and the Law}
\smallskip
\+&&&George B. Dantzig\cr
\+&&&Donald Goldfarb\cr
\+&&&Eugene Lawler\cr
\+&&&Clyde Monma\cr
\+&&&Stephen M. Robinson (Chair)\cr

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\+&&&26 September 1990\cr


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