Why Software Is Abstract
by PolR
[This article is licensed under a Creative Commons License.]
Following the ruling of the Supreme Court
in Bilski, the USPTO asked, in substance, how to tell an abstract idea
from an application of the idea. In this article I propose an answer
to the question of what makes software abstract. It is
a follow up to the previous article, Physical
Aspects of Mathematics.
The logic is to look at why a mathematical
calculation is abstract and then see if the same logic applies to
software. It happens that it does. It is possible to show that software is abstract with
references to the underlying mathematical aspects. This is not, however, the
topic for this article. The argument is presented without any assumption as to
whether or not software is mathematics. I work from the observation
that a mathematical calculation solving a mathematical problem is
abstract. Then I look at what makes it abstract. Then I observe that the
exact same logic is applicable to all software whether or not the law
sees it as an algorithm as defined by Benson. This is not
surprising. Software is mathematics and this makes it abstract, but I
don't use or rely on this fact in making the arguments in this
article.
Abstraction Is SelfContainment
Let's start with experts, who can define for us
what makes mathematics abstract, in some writings on the psychology
of learning. See Mitchelmore,
Michael and White,
Paul — Abstraction
in Mathematics and Mathematical Learning (where you can
download the article as PDF)
We claim that the essence of abstraction
in mathematics is that mathematics is selfcontained: An abstract
mathematical object takes its meaning only from the system within
which it is defined. Certainly abstraction in mathematics at all
levels includes ignoring certain features and highlighting others, as
Sierpinska emphasises. But it is crucial that the new objects be
related to each other in a consistent system which can be operated on
without reference to their previous meaning. Thus, selfcontainment
is paramount.
These authors are university researchers
specializing in the psychology of learning mathematics with a focus
on the role of abstraction. They are authorities on the issue of what
is an abstract idea when the psychology of learning mathematics is
concerned.
Consider a calculation written down on paper.
Let's say you have
128 boxes each containing 48 cans of peas.
How many cans do you have? It is 128 ×
48 = 6144 cans.
This is an arithmetical computation. What is the
difference between this and computing 128 ×
48 in the abstract without referring to boxes, cans, and peas? Does
the computation change if it is cans of mushrooms instead? The
difference is purely semantic. The computation is not changed. The
written numbers do not record such information. The arithmetic
process, the physical manipulation of the digits, is the same no
matter what the written symbols represent in the real world.
This is why arithmetic is abstract. Mathematical
calculations are selfcontained in the sense that they are defined
entirely within mathematics, independent of the realworld meaning of
the numbers. This independence is the abstraction that makes
mathematics an abstract idea.
This same idea of selfcontainment applies to
computers for all type of computations, not only the ones restricted
to computations about numbers. During the actual computation the real
world meaning of the bits is not stored with the bits. Some
mathematical meaning is hardwired in the computer. Boolean and
numerical interpretations of bits are understood by the computer
instruction sets. This is all the meaning computers can understand.
When the computer carries out the computation any information about
realworld meaning is unused and physically absent from the computer.
Only the raw bits are present.
Abstractions may come in layers, each one being
located at its own selfcontained level. For example a particular
program may be written in a programming language with an elaborate
type structure that retains some information about realworld usage.
For example, numbers may be tagged with the unit like inches and
kilograms so we know what the number is used for. This source
language layer of abstraction is selfcontained. The information the
program is able to understand is defined in terms of the capabilities
of the programming language.
Then there is a layer closer to the hardware where
the operations must be the machine implemented instructions. Unless
the hardware has been designed especially for this purpose the
elaborate types of the programming language are not recognized at
this level. This degree of abstraction is also selfcontained.
The activity of the computer is selfcontained,
then, in the sense given by Mitchelmore and White. The activity on
the bits is entirely defined within the architecture of the computer
with no reference to the realworld meaning. This means all
computations made by the computer are abstract in the same manner
that mathematical calculations are abstract.
To confirm that this is true, please examine the
list of instructions from reference material like
The
Art of Assembly Language Programming. All the instructions a CPU
is physically capable of are described in such reference material.
None of the instructions ascribe or require any realworld meaning.
The computer neither knows nor needs to know whether it adds oranges
or apples. A requirement to understand the realworld interpretation
of the bits will prevent computer execution because no computer has
such capability^{1}.
An anonymous user replying to the previous
article mentioned a related issue which is worth repeating here.
This leads to my second point, that of
severability and intent  which was triggered by your analysis of
Alappat. The four circuits in that case _were_ severed, as you
pointed out. As such, they were a general purpose computing device;
general in the sense that they could be used for any purpose where
the mathematical operation they embodied was useful. On the other
hand, a mechanical analogue fire control computer is very limited 
it's pretty hard to think of another use, other than firecontrol,
for such a device. When someone tries to assert claims under various
tests by tying a program to a GP computer, these parts are clearly
severable.
Selfcontainment is not the ability to think of
another use for the same abstraction or the same device. It is not
being general purpose as opposed to being specific purpose in the
sense of this comment. Imagine for example a complex mathematical
model for climatology. It will be very hard to think of another use
of these specialized mathematical formulas^{2}.
But they are still mathematical formulas and abstract in the
selfcontainment sense. If we build a special purpose computer to
carry this computation it would still be selfcontained, even though
this computer might have no other use than climatology.
The Irrelevance of RealWorld Meaning
I understand that case law pays attention to the
realworld meaning of symbols. I am not a lawyer, obviously, but from
what I've read, patent law seems to care when the patent covers a
realworld application, because this is one of the factors that help
determine when the claimed subject matter is patentable. But failure
to factor into the analysis the irrelevance of realworld meaning to
the execution of an algorithm will bring inconsistencies. For an
example, see In re Alappat where the meaning of the numbers was
used to determine that the contested patent passed the useful,
concrete and tangible result test.
As another example, this article
on Patently O discussed Ex Parte Gutta, at a test
to determine whether a machine that involves a mathematical algorithm
is patentable subject matter:
The BPAI's test for a claimed machine (or
article of manufacture) involving a mathematical algorithm asks two
questions. If the a claim fails either part of the twoprong inquiry,
then the claim is unpatentable as not directed to patent eligible
subject matter.
 Is the claim limited to a tangible
practical application, in which the mathematical algorithm is
applied, that results in a realworld use (e.g., “not a mere
fieldofuse label having no significance”)?
 Is the claim limited so as to not
encompass substantially all practical applications of the
mathematical algorithm either “in all fields” of use of the
algorithm or even in “only one field?”
I don't know whether this test is still in use in
the postBilski world. But still its analysis is instructive
and relevant to this discussion.
Both prongs of the inquiry look at the purpose of
the claim in terms of practical applications. In a scenario where the
claimed machine does nothing but execute a computer program, this
test depends exclusively on information that is both unused during
the actual execution of the program and absent from the machine as it
carries out the computation. There are scenarios, then, where the
same machine can simultaneously be nonpatentable subject matter and
subject to several independent patents because this test is based on
information that is irrelevant to its intended purpose. The outcome
of the test depends on the purpose of the user and not on the
technology he is patenting. This outcome may and will vary from user
to user.
This observation applies to special purpose
computers where the computation is carried out by application
specific circuits. It also applies to software to the extent the
courts believe that loading software in memory makes a new
potentially patentable machine. Finally, although the BPAI test is
applicable to machine patents, the observation would also apply to a
similar test for process patents where the process is defined by the
stepbystep execution of the computation on the bits.
If the law uses an inquiry that looks at the
purpose of the claim in terms of practical applications to determine
whether or not the claim is patentable or not because it's an
abstract idea, where the claimed machine does nothing but execute a
computer program, this inquiry would depend on information that is
both unused during the actual execution of the program and absent
from the machine as it carries out the computation. Depending on how
that actual test is defined, there could be scenarios where the same
machine can be simultaneously nonpatentable subject matter and
subject to several independent patents because the inquiry is based
on information that is irrelevant to the actual technology. In this
hypothetical scenario the outcome of the inquiry depends on the
purpose of the user and not on the technology he is patenting. This
outcome may and will vary from user to user.
This observation applies to special purpose
computers where the computation is carried out by
applicationspecific circuits. It also applies to software to the
extent the courts believe that loading software in memory makes a new
potentially patentable machine. Finally the observation would also
apply to a similar test for process patents where the process is
defined by the stepbystep execution of the computation on the bits.
Let me give an example. Suppose someone patents a
method to draw the shape of a parabolic antenna for radio
applications and drafts it as a machine patent on the computer
running the program.
This is a practical application. But the program
nowhere uses the information that it is the shape of an antenna that
is being drawn^{3}.
The program is drawing a tridimensional geometric shape that will be
displayed on screen or printed on paper. The fact that this shape is
the shape of an antenna is totally irrelevant to the execution of the
algorithm and has no effect on the structure of the computer that
executes it. It is only when the output is produced that the human
viewers will interpret the shape as an antenna.
Then along comes an engineer in the field of
acoustics that has a use for a parabolic object. He may patent a
method to draw his object and draft it as a machine patent. This
likewise is a practical application. But both objects have the same
shape and are computed by the same method. The very same computer can
be used for both purposes. The code will be identical if the
programmer so desires. In such a scenario, would a second patent on
the same software be granted because a different application is
contemplated? In such case when a user uses the software the patent
that will be applicable will depend on the user's purpose and not on
the software itself.
Then here comes a mathematician in the field of
solid geometry, looking for a program to draw parabolic 3D objects.
The very same code will work just fine but solid geometry is
theoretical mathematical work and not a tangible practical
application that leads to realworld use. For the purposes of this
mathematician, the same software is not patentable subject matter.
In all three scenarios the "invention" is exactly
the same. It is the same computer running the same software
implementing the same algorithm and executing the same machine
instructions. But this kind of inquiry gives different answers in
each scenario. This indefiniteness is what we get if we ignore the
abstraction inherent in software and give undue consideration to the
realworld meaning of the bits.
Consider Diamond v. Diehr. The patent in
that case involved an algorithm for computing the time required to
cure rubber. But the patented process isn't content to just print the
answer. It actually cures the rubber. It is the actual curing that
separates this industrial process from a mathematical process because
there is a functional relationship with the actual rubber. Such a
relationship wouldn't occur if the process were just printing an
answer.
In Parker v. Flook, the patent uses a
mathematical algorithm to determine conditions that require human
attention. An alarm is sounded where these conditions occur. An
audible alarm is output.^{4}
Its purpose is to convey information. It means a condition
occurred. It answers the question of whether or not something
is worthy of attention. It doesn't act in the real world
beyond communicating the information. The Flook patent covers
a mathematical algorithm because the functional relationship with the
required intervention is not being claimed.
In the antenna example, printing the shape of an
antenna is output. The line between expressing and making a physical
realworld use of the meaning is crossed when the antenna is built.
Printing or displaying the shape of an antenna is a mathematical
algorithm. Building an antenna is not.
Where Are the Boundaries of an Algorithm With
the Real World?
So far we have gathered sufficient facts to answer
two questions:
 Why is software abstract?
 In a process involving software, where are
the boundaries of its abstract parts?
We know the answer to the first question. It is
the independence of the bits from their realworld meaning that makes
software abstract. Let's answer the second, looking again at a
mathematical example.
Consider a practical example. Here is a twostep
process to compute the circumference of a circle with a precision of
four decimal places:
 Measure the diameter of the circle with a
measuring instrument precise to four decimal places,
 Multiply the diameter by 3.1416. The result
is the circumference.
This is abstract because there is no correlation
with the real world at all. The calculation is selfcontained. A
circle is a geometric figure and the arithmetic operations work on
numbers.
What if we alter the process to make it a process
to compute the circumference of a bottle? Would it make it more
concrete?
Step b) is certainly not more concrete. A
mathematical computation doesn't depend on the realworld meaning of
the numbers. The fact that the diameter is the diameter of a bottle
doesn't change anything about this step. The difference is in step a)
because the measurement is now limited to measuring a bottle.
When a computation is done, the answer may have
practical applications. For instance, the diameter may be used to
compute the quantity of paint needed to paint a line around the
bottle. This will involve some more abstract mathematical
calculations, but eventually you put the exact quantity of paint in
your bottle painting machine for the quantity of bottles you have.
When you reach this point your activity is no longer independent of
the realworld meaning. You are back into the concrete world.
From this example I infer the answers to the
second question. In a process involving mathematics, its abstract
mathematical part starts at the initial input, when the real world is
described in mathematical terms by measurement or otherwise. It stops
after the final output where the answer is provided, at the point
when its meaning is acted upon.
In the actual process there is a flow of
information. This flow starts at the data gathering step where
information about the real world is collected. This information is
represented with a mathematical object like a number or a collection
of bits. This is the input. Then some mathematical operations occur.
An answer in the form of another mathematical object is produced and
communicated. This is the output. The information flow stops when the
information is received by a party, human or device, who interprets
the information to act upon its meaning. Everything that happens
between the data gathering and the acting upon the meaning is
abstract.
This logic is stated in terms of mathematical
calculations. It applies also to abstract bits independently of
whether or not the problem being solved is a mathematical problem in
the sense of Benson. At the initial input when the real world
is represented with bits, the abstract part of software begins. When
the answer is acted upon after the final output the abstract software
part has ended.
Would this not tell us where to draw the line
between an abstract algorithm and an application of one? A court
would know where are the boundaries of the algorithm and why it is
abstract. The court should be able to work out how the law applies to
a specific patent from this knowledge.
This analysis correlates well with the printed
matter doctrine, or more exactly the notion of a functional
relationship between the information and the substrate. Here is how
it goes:
When the realworld data is initially
gathered it is input recorded on some media, be it a piece of paper,
a USB key, a CDROM, a hard disk etc. Then it is printed matter with
no functional relationship with the substrate.
When the realworld data isn't initially
gathered at the computer it must be brought to it somehow. This
could be for example by communication over a network, an input
connection or insertion of the media in a reader device. Again the
realworld data is printed matter with no relationship with the
substrate.
When the data is processed by the computer
the realworld meaning of the data has no functional relationship
with the computer, because the computer never uses this meaning.
This is why the computer program is abstract, and it is part of why
the program is a mathematical algorithm.
When the answer is produced, it is bits
recorded in memory. Again this is printed matter with no functional
relationship with the substrate.
The answer may undergo some more steps. It
may be communicated over a network or recorded on some storage
device. If the answer is text or an image, it could be printed or
displayed. If it is sound or video, it could be played back. In all
scenarios, this is output. Again this is printed matter with no
functional relationship with the substrate.
Once the answer is interpreted and the
realworld meaning is acted upon (as opposed to mere communication)
then a functional relationship with the real world is found.
This analysis focuses exclusively on the
realworld meaning of the data, ignoring its mathematical meaning.
The functional relationship between computers and the mathematical
meaning of the raw bits depends on the mathematically exact
definition of algorithm and how this definition relates to actual
computers.
In the entire chain of events there is no point
where the realworld meaning has a functional relationship with the
underlying computer processing until the end when a functional
relationship with the meaning occurs^{5}.
Therefore, we should ask: do we have in the claim a legally
significant element outside of this chain? If there is none, the
claim is abstract.
This view is putting the emphasis on the claimed
subject matter. Activity which is not explicitly claimed does not
change a mathematical algorithm into a nonmathematical process. The
mere presence of a realworld problem does not suffice to make
software patentable. We also need to ask how this problem is solved.
Is the realworld solution actually claimed? Or is it information
processing that will eventually lead to the solution?
Consider the antilock brake system in a car.
There is an embedded computer controlling the brakes. The software by
itself is abstract. The apparatus formed by loading the software on
the embedded computer is also abstract because it is entirely
enclosed within the boundaries that delimit the abstract portion of
the invention. But if we look at the entire system then the brakes
are outside these boundaries. The brake
system taken as a whole is not abstract.
This analysis implies that when the realworld
problem requires nothing but the production of information from
already known information the resulting patent claim is always
abstract. Computers work in such manner that software solutions to
these problems are always abstract. For example consider a database
of scientific research on the chemistry of rubber with a search
function to help an engineer locate a scientific article. We may say
this is a realworld problem in the field of chemical engineering.
The computer will never use the realworld meaning of the database
content because it can't. It is all pure database algorithms that
rely only on the raw bits. Like the formula to compute the
circumference of a bottle, the solution to this database problem is
abstract, because the processing of the bits is selfcontained.
An Observation On Business Processes
This analysis also brings a partial answer to a
question asked by the USPTO in their request for comments.
The decision in Bilski suggested that it
might be possible to "defin[e] a narrower category or class of
patent applications that claim to instruct how business should be
conducted," such that the category itself would be unpatentable
as "an attempt to patent abstract ideas." Bilski
slip op. at 12. Do any such "categories" exist? If so, how
does the category itself represent an "attempt to patent
abstract ideas?"
My answer would be this: Any process made entirely
of information processing steps with no activity that acts upon the
information is nonpatentable because it is abstract. Communication
of an answer is not enough to make a business process not abstract. A
significant physical use of the realworld meaning of the information
is required. This is true when all agents executing the process are
human beings because then all steps are mental steps. If such a
process is adapted to be implemented in whole or in part on computers
or computer networks, then it remains nonpatentable because no
nonabstract element is introduced in the adaptation.
The
Limitations of SelfContainment in Identifying Mathematical Algorithms
There
is an implicit assumption that is pervasive in the whole article.
It is that the meaning of the bits are external to the computer.
What if the patent claim doesn't meet this assumption?
Suppose
some computer engineer includes in the design of his CPU instructions
to perform binarytoBCD conversions using the Benson algorithm. Is
this abstract? If the instructions are implemented in microcode this
is software and I suppose the Benson Supreme Court precedent
should be applicable. But what if the patent claims it as the making
of a better CPU, calling it a method to set the bits to their right
value in the registers? Can we say that the setting of the bits is
a physical activity that acts on the bits instead of merely referring
to their interpretation?
This
question speaks directly to the issue that was the topic of the previous
answer to the USPTO, Physical
Aspects of Mathematics. Mathematical activity needs a physical
representation in order to be carried out. Therefore the question
as to whether a patent is on an abstract idea can be split into
(at least) two separate questions.
 Whether the patent is on abstract information about a realworld application or about the real thing.
 Whether the patent is on a physical representation of mathematics.
This
relates to the two directions of modeling that were discussed in Physical
Aspects of Mathematics.
 Mathematics is used to provide a model of the physical reality.
This is what happens, for example, when we use mathematics to describe
the laws of physics.
 A physical device of process is used to carry out an abstract
mathematical operation. This is what happens, for example, when we use
a calculator to carry out a calculation.
Selfcontainment
is good at identifying the first type of issues. It is effective at
telling whether the patent is on an abstract computation on abstract
bits used to represent the external world as opposed to the real thing.
This approach has the merit to work without having to get into the complexities
of defining and identifying the notion of mathematical algorithm.
Selfcontainment by itself does not suffice to answer the second type
of issues because they require recognizing when a mathematical calculation
is done by physical means. This is not an inquiry of whether the actual
processing is done at a level of abstraction different from the realworld
application.
References Cases
Bilski
v. Kappos The Supreme Court decision
Diamond
v. Diehr, 450 U.S. 175, 182 (1981)
Gottschalk
v. Benson, 409 U.S. 63, 7172 (1972)
In
re Alappat, U.S. Court of Appeals Federal Circuit, 33 F.3d 1526
July 29, 1994
In
re Bilski the Court of Appeals for the Federal Circuit decision
In
re Gulak, 703 F.2d 1381
Parker
v. Flook 437 U.S. 584 (1978)
Footnotes
1 This
bit level degree of abstraction may also be independent from actual
hardware references. It is frequent that the computer is emulated in
software. In such case the instructions are executed on foreign
hardware that has no native ability to execute them. The universe of
reference that defines the software is not the actual hardware. It
is its abstract specifications.
2 Perhaps
someone with more imagination or a better understanding of
climatology than I can find a way to reuse these mathematical
equations. This is a feature of defining “abstract” as the
inability to reuse. This is a definition that depends on the amount
of knowledge and ability to imagine of human beings. It is always
possible that some people will find creative reuse where others can't.
This information may be present in descriptions of the algorithm
like patent claims. But when writing the actual code this
information does not translate into computer instructions. It has
only documentation value. The instructions only depend on the
abstract geometric properties of the antenna and not on the fact
that it is an antenna.
3 This
information may be present in descriptions of the algorithm like
patent claims. But when writing the actual code this information
does not translate into computer instructions. It has only documentation
value. The instructions only depend on the abstract geometric
properties of the antenna and not on the fact that it is an antenna.
4
In engineering the definition of symbols is recognizable physical
states that can be used to represent information. A sound emitted vs
no sound emitted is a pair of recognizable physical states. This is
a pair of symbols able to convey a Yes/No type of information:
whether or not something needs human attention.
5 This
is applicable to analog information such as sound and video. When
the inputs and outputs of a computer do nothing but a conversion
between the analog and digital domains the boundaries of the
abstract part of the process may extend across the analog portion
until the points where the information is initially captured or
eventually used. This is because even in analog format the
information is selfcontained, independent from the substrate
carrier. I believe this too fits well with the printed matter
doctrine because it is my understanding that this doctrine is
applicable to analog information. For example the TV show that is
aired is not patentable subject matter while equipment using
radiofrequencies for television broadcasting is patentable.
