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What word comes next: Happy Birthday to ____?
For most readers, the answer that springs to mind will be
“You”. But why did you think that was the answer?
You may have been fantastically lucky and just picked the word
“you” at random from the 170,000+ words currently in use
in the English language (in which case today might be a good day to
buy a lottery ticket!). But it is much more likely you picked the
word “you” because you’ve heard the phrase
“Happy Birthday to You” many times before. You’ve
probably sung it (thrice repeated each time!) to many others, and
in return had it sung to you. You’ll have seen it written down
on birthday cards and cakes, banners and balloons, and all manner
of other sources. Statistically speaking, you know that the phrase
“Happy Birthday to” has a much higher probability of
being followed by “you” than by any other word, even if
you limit only to phrases that make grammatical sense.
In more technical terminology, in answering “you” to
the question above, you are in essence applying a large
language model. In simple terms, a language model assesses a
corpus (or group) of text, and then calculates a probability
distribution which assesses the likelihood of a given sequence of
words existing in that corpus. Applying this method to the question
above, one could consider assessing the corpus of every sentence
you’ve ever heard, read or spoken; producing a subset of every
phrase beginning “Happy Birthday to”; and then ranking
all such phrases by their occurrence. Statistically, “Happy
Birthday to You” will (most likely) vastly outrank all other
potential candidates.
This may all seem rather esoteric, but it is the essence of the
technology behind ChatGPT-3, which has garnered much attention
since being launched by OpenAI in November 2022. And should anyone
think this is just the latest internet fad to be quickly forgotten,
OpenAI has been recently valued by some analysts at almost USD 30
billion, due at least in part to the potential offered by
tools such as ChatGPT-3.
ChatGPT (the GPT stands for Generative Pre-Trained Transformer)
applies large language models to allow it to respond to user input
in a highly articulate manner. So if you ask ChatGPT-3 a question
such as “What is the tallest mountain in the world”,
ChatGPT-3 will confidently state “The tallest mountain in
the world is Mount Everest. It is located in the Himalayas on the
border of Nepal and Tibet, and its summit stands at 29,029 feet
(8,848 meters) above sea level….”, before providing
further context.
ChatGPT-3’s ability to produce detailed responses to complex
queries has led many in academia to foresee the decline of the
essay as a meaningful means of testing knowledge. After all, why
bother researching a topic if you can just type in questions such
as “Describe the key features of Cicero’s rhetorical
style” or “Compare and contrast the chemistry of the
elements sodium and potassium” and immediately obtain a
discursive essay on the subject?
Outside academic considerations, uses for technology such as
ChatGPT-3 are becoming apparent in many fields. Copy writing and
advertising, medical writing, journalism, law, and many other
“white collar” jobs will all need to take account of such
technologies, and inevitably some professions will fall away while
new roles emerge.
So what about the impacts of technologies like ChatGPT-3 in the
field of patent law? Well, perhaps the best way to answer this is
to ask ChatGPT-3 itself. Here’s what it answered when posed
that question:
Chat GPT-3, a state-of-the-art language model, has the
potential to revolutionize the field of patent law. This AI system
is able to generate text that is similar to human-written text, and
it has the ability to assist in various aspects of the patent
process.
One potential use of chat GPT-3 in patent law is in the area
of patent drafting and prosecution. This process can be
time-consuming and complex, but chat GPT-3 could be trained on a
large dataset of existing patent applications and used to generate
draft applications. This could save time and resources for patent
practitioners and applicants, as they would not need to spend as
much time drafting and reviewing the application.
Another potential use of chat GPT-3 in patent law is in the
area of patent litigation. Reviewing large volumes of technical
documents is necessary in patent litigation, but it can be a
tedious process. Chat GPT-3 has the potential to assist in this
process by generating summaries or analyses of technical documents,
which could save time and resources for attorneys and technical
experts.
Additionally, chat GPT-3 may have potential applications in
the area of patent licensing and transactions. It could be used to
generate draft agreements or to assist in the negotiation process
by generating alternative language or provisions.
Overall, chat GPT-3 has the potential to greatly impact the
field of patent law by streamlining and automating various aspects
of the patent process. It could help reduce the time and resources
needed to draft, prosecute, and review patents, as well as assist
in patent litigation and licensing transactions.
So are the days of the patent attorney numbered?
Well, perhaps not just yet. ChatGPT-3 can certainly produce a
sensible, if unimaginative, claims set for a simple
“invention” (a request for it to produce a claims set for
“an invention which is the use of aspirin to treat
migraine” led to a technically sensible claims set with method
of treatment claims, pharmaceutical composition claims and kit
claims). However, a patentable invention is necessarily
new and non-obvious in view of the state of the art, defined as
“everything made available to the public by means of a
written or oral description, by use, or in any other way, before
the date of filing…“. But it is this very same
“state of the art” on which tools like ChatGPT-3 are
trained.
This limitation means it is at least arguable that tools trained
on existing corpuses of work, like ChatGPT-3, are
inherently incapable of producing anything
inventive – after all, if the means of generating
the text is based on prediction from the prior art, how can an
“inventive step” occur? At least at present, it seems
that the built-in reliance on the existing works means that
ChatGPT-3 can only play the role of the notional “skilled
person” – a skilled practitioner in the relevant field
who has average knowledge and ability; who is aware of what is
common general knowledge in the art and is also presumed to have
had access to everything in the “state of the art”, but
who is devoid of inventive capacity.
This can be readily seen when ChatGPT-3 is asked if it is
inventive to use aspirin to treat migraine. The entirely reasonable
answer is “The use of aspirin to treat migraine is not
considered to be an inventive concept because it has been known and
used for this purpose for a long time…“. But when
ChatGPT-3 is asked about the use of aspirin in treating conditions
such as erectile dysfunction or colour-blindness (which could be
inventive if proven), ChatGPT-3 states, in essence, that such use
is not inventive because that effect is not scientifically known
– which would be the entire essence of the invention!
For now, tools such as ChatGPT-3 and its successors will provide
ways to increasingly efficiently analyse text and generate boiler
plate. But when it comes to defining a patentable invention, such
tools are inherently limited by their reliance on the art. In the
words of ChatGPT-3 itself:
It is possible for chat-gpt3 to generate text that describes
an invention, but it is important to note that the model is not
capable of creating new ideas or concepts on its own. It can only
generate text based on the information and examples it has been
trained on.
Given this, the role of the inventor and the patent attorney
seems assured – for now at least.
The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.
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