Artificial intelligence (AI) refers to a computer's ability to solve problems in a way that mimics human thinking. It involves systems that collect and analyze information, learn from it, and make decisions independently.
The technical applications of AI are diverse and range from driver assistance systems in the automotive sector, speech recognition and facial recognition in mobile telecommunications devices, computer vision, robotics, chatbots for communicating with customers, automation and predictions for optimizing machine maintenance, to diagnostic support in the medical field. AI enables users to make predictions with unprecedented precision across a wide range of variable input parameters.
The complexity and flexibility of the various AI tools vary considerably in some cases. A simple approach would be, for example, a rule machine, a knowledge base, an expert system, or a knowledge graph, whose query would lead to a corresponding result via logical links.
This is to be distinguished from machine learning, i.e., the ability of systems to develop themselves further on the basis of new data. Machine learning is dynamic and adaptable and is based on algorithms such as support vector machines, regression analyses, k-means clustering, genetic algorithms, and deep learning with artificial neural networks.
Advantages of artificial intelligence
AI from an economic, technical, and social perspective
- Process automation: Leads to significant cost savings and speeds up processes in production, administration, and customer service.
- Increased productivity through intelligent systems: AI helps to make work processes more efficient – e.g. through predictive maintenance, optimized planning, or decision support.
- Processing large and complex amounts of data: AI can recognize patterns and correlations that remain invisible to humans – e.g. in real-time data or sensor networks.
- Self-learning systems (machine learning): AI continuously adapts to new conditions and improves its performance without manual intervention.
- Improved medical diagnostics: AI accurately detects abnormalities in image data or laboratory values – earlier and more reliably than many manual procedures.
- Accessibility & inclusion: AI technologies such as speech recognition, automatic translation, or adaptive user guidance open up new opportunities for participation.
Patentability of artificial intelligence
Many AI inventions can be protected very well
AI models and algorithms are considered mathematical methods that are not patentable “as such.” However, in practice, it has been shown that many AI inventions can indeed be protected—provided they make a technical contribution.
AI is patentable in particular if it fulfills a specific technical purpose, such as controlling a machine, improving image processing, or analyzing videos, or if its implementation produces a technical effect, for example through more efficient computing processes or specific optimization for the internal functioning of a computer.
For example, the use of a machine learning process in a medical image processing system for detecting tumor structures makes a technical contribution. The analysis of sensor signals in industrial monitoring systems using AI to predict machine failures is also a typical technical application of AI algorithms. In contrast, the classification of texts according to their content or of abstract data sets without technical use is not considered a technical purpose.
AI inventions are often patentable because they are used in a specific, technical, and limited field of application and, for example, combine data in a novel way to achieve innovative technical results. Similarly, the architecture of an AI itself can be novel, for example through increased robustness or reliability, and thus make a technical contribution.
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Typical examples of patentable AI
Artificial intelligence has become a driver of innovation in many industries.
- Automotive: Driver assistance systems, object recognition, autonomous navigation
- Telecommunications: Speech and facial recognition, predictive user experience
- Robotics: Machine control, autonomous motion planning
- Industry: Predictive maintenance, process automation
- Medicine: AI-supported diagnostic systems, image processing
- Transportation: Traffic flow analysis and control with AI-based forecasts
