Recently, there have been rapid developments in Artificial Intelligence (AI) affecting our daily life (e.g. ChatGPT) and profoundly changing most industry and technology sectors, including automotive.
AI, including machine learning and deep learning, helps improve many aspects related to vehicle technology. These include, for example, autonomous driving, advanced driver assistance systems and safety features, intelligent route planning, cloud and telecom services and user to car interaction (e.g., speech recognition). Other aspects are predictive maintenance of vehicle parts, development and testing of software and components for vehicles as well as manufacturing using AI driven industrial processes or robots. In addition, AI allows analysing driver behaviour for, e.g., better risk analysis also for insurance companies, or improving fleet management in shared mobility. In electrically driven vehicles, for example, AI based models allow an improved prediction of battery life.
While AI already plays a role in many of these aspects, the influence of AI is expected to increase steadily. In autonomous driving, for example, there is still a way to go from the now implemented partially self-driving vehicles (so-called levels 2 or 3) to the planned fully self-driving vehicles (so-called level 5). Self-driving vehicles rely on a variety of sensors like Lidar and cameras to detect and then classify objects in the environment, including other vehicles, obstacles and pedestrians. Here, AI can help analysing and interpreting vast amounts of data received from sensors (e.g., predicting movements of other vehicles or pedestrians) and deciding how to react. AI can be considered a critical component because it allows the vehicle to receive and evaluate information and to act, similar to a human driver.
As for any technology, intellectual property (IP), in particular patent protection, plays an important role also in AI-related innovations in the automotive field. According to research reports, the global automotive AI market is expected to grow from 2 billion USD in 2018 to 16 billion USD in 2028.
Patenting AI, however, is not a trivial task. AI and machine learning are typically based on computational models and algorithms. In most European jurisdictions, computational models and algorithms per se are considered to be of abstract mathematical nature lacking technicity and are thus excluded from patentability. Applying AI for a technical purpose, e.g., in vehicle control, however, can overcome this exclusion – and this holds true for many of the AI related aspects in automotive technology. Nevertheless, care should be taken in drafting patent applications in order to highlight the technical effects achieved by the AI thus avoiding accusations of a non-technical subject-matter. Furthermore, the patent application will have to detail the ways of training and implementing the AI for the respective technical application. Another hurdle during examination proceedings is to prove an inventive step of a particular AI or machine learning application over the prior art. Considering this, companies often have to deliberate about whether to apply for a patent or to keep the knowledge as a trade secret.
To summarize, AI will accelerate the shift from mechanics and electronics in vehicles and related components towards software. This shift from inventions in the field of mechanics and electronics to inventions in the field of software engineering and computer science will also have to be faced by the patent systems worldwide. Awareness should be raised in the industry, in particular among software engineers and computer scientists, that software – including AI – applied to a technical solution could well be patentable.
If you would like to discuss any IP or AI-related issues, please contact firstname.lastname@example.org, or meet us at eMove 360 Europe 2023 trade fair – Hall A6, stand number 130C.