Whatever your sector, you’ve undoubtedly heard that “AI is ubiquitous, and it is going to revolutionise everything” from a marketing department or someone on the street. While much of what we see and use in the digital world is AI-based, the long-promised industry upheaval has yet to materialise. As a result, what Artificial Intelligence AI can do for you right now might differ from what you expect.

Imagine you recently read about the publication of Beethoven’s 10th symphony, composed by an AI using various of the composer’s rough sketches while strictly supervised by a group of professionals. You may wonder what AI is capable of achieving. Is AI a mere tool or the “genius” behind the composition?

Can Artificial Intelligence AI  be an inventor? It is a topic that is hotly disputed right now in intellectual property (IP) circles. Is it a complex subject with no clear solution that is undoubtedly outside the purview of this article? Returning to the more certain topic of “composer or instrument,” we can show how AI can, at the very least, be an effective tool in the right hands.

Technology’s capabilities

The speed at which machine-based Artificial intelligence can digest large amounts of data gives it its genuine power. You have a powerful tool in your arsenal if you can teach it to recognise patterns in the data without user input. The data must, on the one hand, be to enable effective processing and analysis. But, the training and fine-tuning procedures used by the system’s creator define the quality or utility of any artificial intelligence AI product for the user. The best results will probably not be secure by querying a patent database using ordinary text-analysis tools in an IP setting. Even many humans find the terminology used in patents to be complex, let alone computers. Thus, any system designed to search patents must conform to the nuances of patent language and, maybe, those of specific technology disciplines.

Machine learning algorithms can also analyse patent datasets to retrieve patent datasets effectively. They can achieve this, for example, by compiling statistics that are solely count-based, such as the size of the patent family and the number of citing papers. These well-known indexes enable a quick study of your data. In light of this, contemporary patent analytics can provide you with reliable metrics to get deeper insights into a specific dataset, depending on the goal of your research. Many service providers now provide patent value estimators to offer you an idea of the monetary value of a patent. You must use these metrics cautiously to give value to your IP analysis efforts, much like when searching for patents.

What you offer to the table

How can you test the output quality of AI-based functions if their impact on patent analysis is so significant? We suggest a straightforward strategy that you can use. Take a patent search you completed before, act as if it were your first time doing it, but this time with the help of artificial intelligence (AI), and compare the outcomes.

The success of this test depends on a few factors:

• Take your time to comprehend how to provide the software with the data it needs to complete its mission. For the algorithm to better learn your question, it will require some background. You will only get inclusive results if you start with a small number of Boolean-style terms.
• Look for an iterative method to improve your outcomes. If there are more options to enter the data you want to pay attention to, use them. The results will be more accurate the more carefully you can specify your search criteria to the artificial Intelligence AI.
• Be ready for any unexpected consequences that the programme may bring.

They need to be correct from your vantage point. Still, if you examine these ostensibly incorrect discoveries and figure out how they ran them to earth, you will better understand how the system functions. The machine cannot read your mind and does not think in the same way as you.

keeping track of the competition online

However, AI also makes a promise to automate professional IP investigations. Where can we see this put into practice? Let’s examine a real-world illustration: technology monitoring. From a business standpoint, we need to keep up with what the competition is doing and be aware of potential competitors. But you must finish this assignment, or you will think about the “Friday afternoon optional” work. Some businesses set up their monitoring profiles once, allowing them to continue operating well into their prime. By doing this, you risk becoming sucked into the adaptive technology ecosystems that are challenging to understand with static Boolean queries.

The arguments for delaying the creation of a specific monitoring mechanism are strong: Finding the correct query is difficult and time-consuming. They always refer to a compromise between catching all pertinent papers and making too much noise in Boolean words. Later in the process, this noise and the absence of a ranking that allows users to “sort by relevance” deters users from analysing new publications.

In this case, advanced methods like deep learning can significantly outperform the Boolean approach. This computational method creates incredibly complex models that deeply comprehend the patterns of your training data, enabling even more accurate information retrieval. Your deep neural network will learn to grasp your preferences in patents and technology, frequently scanning new publications to recover the ones most pertinent to you.

The genius of this strategy resides in how simple it is to set up a system like this. You should provide it with a list of patents that are technologically connected and pertinent to you. Most likely, you’ve already compiled one during earlier investigations. After setting your choices, you can prioritise the outputs, and the neural network will automatically show the findings according to relevancy. Such algorithms can improve their comprehension of your tendencies over time by learning from your input.

Unquestionable merits the excitement surrounding AI: The potential uses of the newest computing technologies appear only constrained by our imagination, from reconstructing lost symphonies to lightening your daily workload. As we’ve seen, AI has already saved IP Docketer’s specialists time looking for relevant patents. And this shouldn’t be a surprise since technology will only advance and change as individuals like you provide more information. Optimise is one AI-powered solution aiming to deliver the most relevant patents swiftly. Find out what its cutting-edge approaches can achieve for your business and unleash the full power of your IP team!