This is a guest blogpost by Julian Nolan, CEO, Iprova

A technology revolution is taking place in the research and development (R&D) departments of businesses around the world. Driven by data, machine learningand algorithms, artificial intelligence (AI) is helping scientists to invent new and better products faster and more effectively. But how is this possible and why is it necessary?

Invention has long been thought of as the product of great minds: the result of well-rounded scholars and thinkers like Leonardo Da Vinci and Thomas Edisonmaking synaptic links between ideas that most people would never consider. And for hundreds of years, this has indeed been the case.

However, the times are changing and we’re currently in a position where information experiences exponential growth, yet innovation and invention has slowed. Great minds are still behind new products and services, but the vast quantity of information now available to mankind exceeds the grasp of a single researcher or R&D team — particularly as many researchers specialise in narrow fields of expertise rather than in multiple disciplines. Developments outside of those fields are often unknown, even though they may be relevant.

As such, we find that many new patented inventions are not the result of making truly novel links between concepts, but rather a linear step forward in the evolution of a product line.

This is now changing by putting artificial intelligence at the core of the invention process itself. At Iprova we have developed a technology that uses advanced algorithms and machine learning to find inventive triggers in a vast array of sources of information, from new scientific papers to market trend research across a broad spectrum of industries.

This technology allows us to review the data in real-time and make inventive connections. That’s why we are able to spot advancements in medical diagnostics and sensor technology and relate them to autonomous vehicles for example.

According to the European Patent Office (EPO), the typical patenting process is 3–4 years. When you consider that the typical research process from conception to invention takes place over a similar amount of time, most companies are looking at a minimum of six years to bring products to market.

This is where machine learning makes a big difference. Our own technology reviews huge amounts of data and identifies new inventive signals at high speed, which means that our invention developers can take an idea and turn it into a patentable invention in only a couple of weeks — significantly reducing the overall lead time and research costs of inventions.

Thinking back to Da Vinci or Edison, the only reason why we still remember their names today is because their inventions were ground breaking at the time. Others may have been working on similar creations, but their names didn’t make history because of one simple fact. They weren’t first. Fast forward to today, being first is all businesses care about when it comes to taking new products to market. Yet, in the age of data explosion this can only be achieved in one way – using artificial intelligence at the invention stage itself.

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