Computing & Software

Artificial Intelligence is a technical field that has exploded in importance over the past few years, with many applications already in widespread use. Patentability of AI is, however, an area where careful consideration of where the invention lies is crucial. Patentable inventions may lie for example in the creation of Artificial Neural Networks (ANNs) and their applications, although care has to be taken regarding how such inventions are defined to reduce the risks of objections relating to patentability of computer programs as such. Recent case law in the UK has confirmed that ANNs are considered to be computer programs and therefore need to be assessed in the same way as for other computer-implemented inventions, in particular by determining what the invention provides in terms of a technical effect. The same principle will apply to other areas relating to AI in general.

Applications of AI can encompass many different technical fields. Areas where AI can be useful include data analysis, in particular pattern recognition, which can be applied widely across many fields. Healthcare, and medical diagnostics in particular, has benefited from the use of AI in, for example, image analysis, biomarker diagnostics, medical data analysis and data management. There are also more general problems in physics that can be assisted or solved by machine learning methods as an alternative to classical numerical methods, such as light scattering analysis for particle measurements, computer-aided tomography and coded aperture hyperspectral imaging among many others.

Our team of patent attorneys have wide-ranging experience in dealing with AI-related inventions, ranging from hardware implementations for ANNs through to applications of AI across different technical fields.

Amy Bishton has worked with clients applying AI in healthcare, agriculture and computer security fields, and has experience with inventions relating to detecting adversarial attacks, crop monitoring, wound analysis, and use of behavioural features for identification.

Oliver Pooley works with clients developing hardware and software for machine learning, and various applications of AI. Oliver’s experience covers fields including automated vehicles, the healthcare sector, imaging techniques and speech recognition. He has extensive experience in developing strategies combining different types of IP to protect AI systems.

David Combes has extensive experience in drafting and prosecuting patent applications relating to applied AI, covering applications including machine learning architectures for medical image segmentation, cancer detection by machine learning analysis of biomarkers, machine learning applied to spectroscopy, machine learning applied to particle sizing, electrocardiogram analysis, surgical planning, adaptive optics and a range of others. David works with a major manufacturer of particle analysis instruments, and with several top UK universities and spin-outs.

Francesco Di Lallo has specialist knowledge and a master’s degree in AI and machine learning. Francesco has experience in drafting and prosecuting patent applications including: improvements in automatic driver-assistance systems, medical imaging including improvement in image acquisition and analysis, machine learning applied to spectroscopy, large language model architecture, point-cloud clustering and segmentation, and improvements on stability and security of neural networks.

Scott King has drafted and prosecuted applications in the area of AI and machine learning, covering inventions including fraud detection, medical imaging and livestock monitoring, among others.

John Lawrence has experience with AI inventions relating to materials processing in microwave systems.

Malin Keijser Bergöö has drafted, prosecuted and been granted AI patents for several different Swedish companies, including the use of machine learning to enable and improve functionality such as a fully convolutional network to convert MR images into CT images using an adversarial training strategy and an image gradient difference loss function.

Carrie Duckworth has worked on AI-related applications for companies working to develop nanometre-scale imaging and machine learning models, including applications relating to generation of training data sets, dynamic training, AI process control, deep learning models, and machine learning models in general.

Matthew Philpotts works with university and industry-based clients seeking to leverage AI for a wide variety of real-world applications. Examples include the use of AI in electrocardiogram analysis, semi-supervised medical image processing and particle detection/analysis.