Granted to my IBM colleagues and myself in 2005, it was for a topcoat waterproof material for a photoresist — a light-sensitive substance used to make circuit patterns for semiconductor chips. It was a proud moment for me — especially as I knew that this patent contained novel capabilities that were critical for a brand-new technology called immersion lithography. This technology soon became the basis for how all advanced chips are manufactured, even to this date.
I also knew it had contributed to IBM’s patent leadership that year. Just like during the 13 years before and 15 years after, IBM has been getting more patents granted than any other company in the US.
For me, this patent leadership symbolizes much more than just the mere fact of being at the top. A patent is evidence of an invention, protecting it through legal documentation, and importantly, published for all to read. The number of patents we produce each year — and in 2020, it was more than 9,130 US patents — demonstrates our continuous, never-ending commitment to research and innovation. We are actively planting the research seeds of the bleeding edge technological world of tomorrow. Our most recent patents span artificial intelligence (AI), hybrid cloud, cyber-security and quantum computing. It doesn’t get more future-looking than this.
The US patent system goes back to the very dawn of our nation. It is detailed in the Constitution, enabling the Congress to grant inventors the exclusive right to their discoveries for a specific period of time. It is an assurance designed to motivate inventors to keep innovating.
One might argue against having patents that don’t get immediately turned into commercial products. But I disagree. Inventing something new is similar to putting forward a well thought out theory that may, one day, be verified experimentally. Perhaps not straight away, but it’s still vital to have theories to enhance our overall understanding of a field and to keep progress going. Having future-looking patents is just as important as those aimed at products of today, and a broad portfolio of scientific advances always ends up contributing to waves of innovation.
Patents drive innovation and a nation’s economic performance. Over the years, they have given us breakthrough technologies such as the laser, self-driving cars, graphene and solar panels. We at IBM have developed and patented such widely used products as the automated teller machine (ATM), speech recognition technology, B2B e-commerce software with consumer-like shopping features for processing business orders, the hard disk drive, DRAM (the ubiquitous memory that powers our phones and computers), and even the famous floppy disk that’s now history, to name just a few.
Tackling the world’s problems
A patent’s assurance of the protection of inventions is a key reason why companies invest billions of dollars in research and development. This results in scientists and engineers in different companies trying to find the best, original solutions to the world’s problems, paving the way for new and better products. And we haven’t run out of global problems to solve, far from it. Innovation is what helps us deal with pandemics, tackle global warming, address energy and food shortages, and much more.
Last year, just in the field of AI, our researchers received more than 2,300 patents. To take two examples among many: a novel way to search multilingual documents using natural language processing, and an ultra-efficient system for transferring image data taken by an on-vehicle camera. These both speak to the innovation and original thinking from our inventors in AI.
In cloud, we received about 3,000 patents, many focusing on data processing categorizations that can help bring services to the edge. In cyber-security, I’d like to single out patents in fully homomorphic encryption — an area of cryptography where computations are made on data that stays encrypted at all times. With so many data leaks jeopardizing the privacy of our medical, genomic, financial and other sensitive records, secure encryption is more important than ever.
Finally, there is quantum computing. This next-generation technology is getting ever better. I am convinced that in the near future, products relying on quantum computation will be an integral part of our daily lives. By inventing and patenting those products today, we are ensuring our quantum future.
One of our quantum computing patents deals with running molecular simulations on a quantum computer. Performing such simulations faster and across a much wider molecular space than a classical computer can ever do could help us design new molecules for novel drugs or catalysts. Another patent addresses the use of quantum computing in finance, to run risk analysis more precisely and efficiently than ever before.
That’s far from all. Quantum computers of today are ‘noisy’ — meaning that the quantum bits, or qubits, they rely on get easily affected by any external disturbances. Many of our patents detail ways to make qubits much more stable and even suggest approaches to correct the remaining errors in future stable qubits, offering a path to realize quantum error-correction and unleash the power of quantum computers to solve the currently unsolvable.
I’ll end with a reflection. A vibrant culture of innovation combines patenting, publishing, contributing to open-source, and active in-market experimentation and discovery. All are needed, fueled by the joy that innovators experience with the spark of novel ideas, and the desire to bring them to life.
Inventing What’s Next.
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We have unveiled in the laboratory new details on how the famous Titan haze may have formed and what its chemical make-up looks like. Our findings in the latest issue of the Astrophysical Journal detail how we've resolved molecules of different sizes, giving snapshots of the different stages through which molecules grow to build up the haze.
In our latest paper published in the Microbiome Journal, we propose a way to improve the speed, sensitivity and accuracy of what’s known as microbial functional profiling – determining what microbes in a specific environment are capable of.
Our team has developed Physics-informed Neural Networks (PINN) models where physics is integrated into the neural network’s learning process – dramatically boosting the AI’s ability to produce accurate results. Described in our recent paper, PINN models are made to respect physics laws that force boundaries on the results and generate a realistic output.