united-states patents
How can we attribute an invention to a natural person if an invention was fully invented by an AI system?
<p>Can Computer Systems Using AI Patent Their Own Inventions</p>
<blockquote>
<p>Increasingly, companies are using AI to invent new methods and
products. But can such inventions be patented given the requirement
that each patent application must identify at least one “inventor”
which is defined as an “individual,” not a machine? (In re Application
of Application No. 16/524,350).</p>
<p><strong>The USPTO rejected a recent attempt to name an AI system as the
inventor in two patent applications, holding that an “inventor” is
limited to a natural person. Other major patent offices have also
found that an AI cannot be named as a patent “inventor,” including the
European, Japanese, Canadian, and Australian patent offices.</strong></p>
<p>These decisions leave open the question of what, if any, legal
protections are available for inventions and other works created
solely by AI systems.</p>
</blockquote>
<p>If an invention is invented by an AI system, does the law allow a company to put the name of a random person as the inventor even if that person didn't do anything to contribute to the invention? Or is choosing a random person considered a crime? How do we then determine who is eligible to be chosen as the inventor?</p>

<p>The company wouldn't have to put the name of a random person. &quot;AI&quot; is a tool created with the expertise of a data scientist to provide output to a specific class of problems or operate in a specific environment, and further human actions are often required to place the output in context. &quot;AI&quot; is just advanced machine learning methods that humans can use as a tool (with the humans building and using the AI as a tool being the real inventors).</p>
<p>More (hopefully helpful) factual background following: This is not to say that machine learning algs are not impressive, or that the future might not hold something we cannot imagine. But machine learning algorithms are not created and do not provide output in a vaccuum.  To create a machine learning algorithm, a data scientists has to a) frame the question or goal, b) decide what sort of algorithm to use, c) gather machine-parsable data, d) &quot;train&quot; the model by feeding it data so the algorithm adjusts to match the real world. Then, someone generally has to interpret the results and apply them.</p>
<p>Consider, as a mock example, that I take have access to 1 thousand different shapes of the outsides of an airplane wing from my competitors. I represent these designs as 3d vectors, and then I put each one into a machine learning algorithm called a neural network. The neural network updates and adjusts to fit the patterns with every design I put in, as I have programmed it to do. Then, I use my algorithm to output a new suggestion for the design. I have to look at this suggestion and determine if its trash, or how to actually create and test it. This is a silly example, but in it I have invented the new plane wing, not the algorithm I used to help me.</p>
<p>The results of machine learning algs can be very good at specific tasks, like playing chess (like Google's AlphaGo), but they lack expertise in broader scope. Even though you couldn't beat AlphaGo in chess, AlphaGo could never accomplish the countless tasks you do every day or recognize as many patterns in the broader world as you. (And maybe never will.) Machine learning algorithms are distinct from some other forms of computational assistance because a) they use statistics instead of intuition, so an explanation of why an alg decides on something is not always meaningful to a human, b) they can be really, really good, and c) some models learn and adjust to the world in a way more similar to humans and animals than a more vanilla problem solving algorithm. There is a lot of interesting research in things like unsupervised learning (where algorithms find patterns without being told exactly what to look for) and reinforment learning. But it is still inappropriate to think of AI as something that could &quot;invent&quot; something. (And I know that you framed the question as AI &quot;inventing&quot; something just to match the language used in the quote; its just a misleading way of describing the process.)</p>
<p>If you're really invested in this question, maybe try watching some YouTube vids about machine learning techniques, if you have not previously done so.</p>
