AI and Patents: AI Lets Inventors Make Their Wishes Come True

By Robert Plotkin and Cynthia Gilbert

This is a special series of articles about the impact of AI on patent protection from Blueshift IP: Software Patent Experts. This series is intended to be easy to understand and to focus on how to tailor AI patents to your business’ goals. This installment focuses on how artificial intelligence is enabling inventors to make their wishes come true, and the impact of that on patent law.

To understand what we mean when we say that AI makes inventors’ wishes come true, let’s take a simple example in which an inventor wants to invent a more powerful spring for a mousetrap. Traditionally, before AI, the inventor would need training in physics and mechanical engineering in order to understand how springs work and would need to understand the properties of different types of metals that can be used to make a spring. The inventor might start by analyzing springs in existing mousetraps to identify what they do well and what they don’t do well. The inventor might also look at springs in other types of devices, such as scales and mattresses and door handles, to get ideas for features that might work well in a mousetrap.

Then the inventor would come up with an idea for a new spring, often by some combination of drawing it on paper or using CAD software, doing calculations to make predictions about how the spring would perform in the real world, and even by tinkering with existing physical springs in the real world. Then the inventor would build a physical prototype of the spring, integrate it within a mousetrap, and then test that mousetrap under real-world conditions to see how well it works. Then the inventor would use what he or she learned from that experiment to modify the design for the spring to make it better, or possibly throw out that design and go back to the drawing board if the spring was a total failure.

This process of research, evaluation, design, construction, and testing could go on for a long time. Thomas Edison reportedly tested fibers from over 6,000 plants, including baywood, boxwood, hickory, cedar, and bamboo, for use in the incandescent light bulb. This is why he famously said that “Genius is one percent inspiration and ninety-nine percent perspiration.”

Fast forward to today, and AI is automating significant parts of this process. As a result, inventors can focus much more of their time being inspired and much less of their time perspiring. AI is flipping Edison’s equation on its head.

In particular, if Edison were alive today, he could create a database of the electrical and thermal properties of various types of plant fibers and feed that information into AI software, and he could then program the AI to simulate pumping electricity into each of the plant fibers in a circuit, and simulate the amount and type of light that would be produced as a result. The AI could even tell Edison which fibers performed better than others. As a result, Edison could provide the inspiration–and some hard data–into the AI, and leave it to the AI to perform the gruntwork of performing detailed experiments on all 6,000 plant fibers.

Of course we are simplifying things and today’s inventors still need knowledge of physics and also in computer programming. But the key point is that AI can automatically generate prototypes for new inventions, simulate those prototypes, test the simulations without having to build them, evaluate the results of those tests, and then modify the prototypes and repeat the process. And AI can do this millions of times faster than a human inventor.

This not only makes the inventive process faster and less expensive, it also eliminates a lot of work for the human inventor. It also shifts some of the skills that the human inventor needs in order to be successful. In the age of AI it’s less important for the inventor to know how to physically build and test prototypes. And it’s more important for the inventor to know how to describe the requirements for a successful invention. In the case of a spring, this might involve writing a description, in a language that AI can understand, that the AI should maximize the speed with which the spring contracts and minimize the weight and cost of the spring’s materials. Again, this is an oversimplification, but the key point is that the job of the inventor in the age of AI focuses more on describing the result that the invention should achieve and less on how the invention should achieve that result. Figuring out how is the job of the AI.

It’s a lot like when a manager or business owner instructs a team of inventors to design a new version of the company’s product that is smaller, faster, and less expensive than the current version, and leaves it up to the team to figure out how to make that happen.

That’s why we said that today’s AI is enabling inventors to make their wishes come true. You can think of what today’s inventors are doing with AI is writing a wish for the invention that they want the AI to create, and then handing that wish over to the AI to make the wish come true by figuring out the details by designing, testing, and evaluating prototypes.

All of this has significant implications for patent law, and I won’t be able to go into all of them today. One of them is that AI often can generate not just one, but a very large number of inventions based on a single description provided to the AI by the human inventor. Although some of these inventions may be better than others, the mere fact that AI can generate such a large number of inventions creates the possibility that a single inventor or company can obtain a much larger number of patents for a particular type of product more quickly than in the past. Historically, when a new field of technology opened up, it took some time for many different inventors to come up with different ways of solving problems in that field. All of them could obtain patents on different approaches. This encouraged competition and imposed some limit on the extent to which a single person or company could obtain all of the key patents in a particular field.

AI has the potential to change this, by enabling someone to use AI to generate a wide variety of inventions for solving the same problem very quickly, and to obtain patent protection for all of them before competitors have a chance to enter the fray. In recent decades we’ve seen product cycles go from years to quarters to months. AI has the potential to shorten the length of the development cycle even more significantly, so much so that there isn’t sufficient time for competitors to innovate and obtain their own patents.

For those companies seeking to obtain patents to protect their innovations and defend themselves against competitors, this situation magnifies the incentive to leverage AI to innovate rapidly and to file patent applications for AI-generated inventions as quickly as possible, in order to beat competitors to the punch. And the resulting patents have the potential to be extremely broad, covering a wide range of products and services, and in this way block competitors from competing to an extent that was rarely possible before AI. In short, this creates a situation in which the first company to innovate in a new field has the potential to take home a huge amount of patent protection and to leave its competitors out in the cold.

But obtaining that kind of broad patent protection requires more than just the technical knowledge to use AI for inventing. The reason is that obtaining a patent that will successfully cover a wide variety of inventions generated by AI requires special expertise in software patents and in AI patents. Attempting to write and obtain a patent that covers this type of invention without that kind of expertise will likely result in the patent application being rejected, or in being much narrower than it could be, which could leave the door open to competitors obtaining patents that you could have blocked them from obtaining.

The ways in which AI enables inventors to make their wishes come true is just one of many impacts that AI is having on patent law and strategy. Stay tuned for the next installment of this special series on AI and patents. If you are using AI to invent in your business and want to talk about obtaining strong, broad, and defensible patents for those inventions, please contact us directly at




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