The question of how to patent an algorithm has a broad scope. The law covers inventions in the form of machines, composition of matter, and new processes. While an algorithm’s abstract flavor sets off alarms, its practical application is not so abstract that it would be ineligible for patenting. As long as you have first-hand knowledge of the project, POSITA, or practical application, you should be able to practice your patent.

Table of contents

  1. Practical applications of an algorithm may be patentable.
  2. POSITA must be able to practice your patent.
  3. Non-obviousness of the method.
  4. Consideration for first-hand knowledge of the project or algorithm.

Practical applications of an algorithm may be patentable

Algorithms can be patented, but only when their practical applications are technical. Computer programs, mathematical methods, and business methods are often excluded from patentability. However, there are a few practical applications of algorithms that may qualify as patentable. Read on to learn about them. Here are some examples:

One example is an algorithm used in navigation. It can identify spoken words around it and respond accordingly. Unlike traditional voice assistants, Alexa does not require a wakeword to respond. However, it can recognize spoken words even when you are not near it. In order to qualify for patent protection, an algorithm must improve the functioning of a computer or provide a technical advantage in a related field. It must also be able to articulate tangible results and technical benefits.

POSITA must be able to practice your patent

Whether a POSITA “person of ordinary skills in the art” is required to practice your patent depends on your invention and case. In general, however, POSITAs must have an advanced degree and experience in a particular disease state. The US Supreme Court has noted that POSITAs must be innovative and fit multiple patents together. Furthermore, in some cases, courts have found that a POSITA must have a background in the field of drug discovery.

All about POSITAs

Patent practitioners and patent examiners often ignore the importance of a “person of ordinary skills in the art” (“POSITA”) in discussing important patent law issues like obviousness rejections, claim terms in litigation, and other crucial issues. The “person of ordinary skills in the art” is a hypothetical individual around which most of the patent law revolves. Despite the importance and power of the POSITA argument, they are often weak and conclusory. It is difficult to imagine yourself as this hypothetical person (as he or she doesn’t exist), but it is a crucial first step in patent prosecution.

In practice, this means that a POSITA can know everything (and all) written in any prior art document. This is the foundational definition that allows a POSITA to begin using objectively its knowledge to show the POSITA’s knowledge. The POSITA becomes more than a hypothetical result of a careful review of the prior art. A patent attorney can then craft persuasive arguments based on the POSITA.

Non-obviousness of the method

A question that often arises when applying for patents is “Is the algorithm obvious?” The standard for obviousness is the distance between a specific invention and the prior art. The invention must be an “adequate distance” above the prior art. This requires judgment. As a software engineer, you may wonder if a new algorithm is truly “obvious.”

The non-obviousness of an algorithm criterion is tricky to define. While computer scientists understand the term, software engineers do not. In some cases, an algorithm may be patented if it is based on algorithms that are known to other scientists in the field. In these cases, the algorithm may be easier to license or assign to other parties. Nevertheless, there is no universally accepted non-obviousness criterion for software and algorithms.

Another example of an algorithm’s non-obviousness is a coffee cup holder. This coffee holder is a patentable item. Its holder uses corrugated paper, which is more environmentally friendly than other paper. This method is also based on a combination of references. It is a useful example of “combining references.”

As mentioned, the objective of evaluating a patent’s non-obviousness is to determine the extent of an invention’s relation to the prior art. The court will evaluate the claims based on the extent of the prior art and whether a person of ordinary skill would have anticipated the invention. This approach may not be based on novelty or utility, but rather on its ability to prevent infringement.

A patent application that contains an algorithm will almost certainly be rejected unless it is sufficiently inventive. While it may seem logical, the invention should be so novel that the existing industry practice cannot be reverse-engineered. The AI must be able to identify the PSITA involved in the invention. In addition, it should be capable of identifying the POSITA in the application, ensuring it is used only in an official capacity. Likewise, the Human owner will not be able to reverse-engineer the AI process.

Consideration for first-hand knowledge of the project or algorithm

A Canadian team recently trained a machine-learning algorithm to identify the early stages of Alzheimer’s disease. The early stages of this condition are subtle enough for most specialists to miss, but trained algorithms are capable of identifying brain losses in seconds. Such AI developments are unlikely to be patented, but they could revolutionize research into the disease.

But before you start filing for a patent, consider the following:

  • Novelty- This means your invention cannot have been made public, not even by you, before the filing date.
  • Step ingenious- Your product or process should be innovative. It should not be obvious.
  • Industrial applicability-This means that the invention must be feasible to manufacture. You can also apply for a patent to a new type of playing card that is more comfortable than the existing ones. However, a patent is not possible for an idea for a card game.
  • Be a patentable subject matter.