Adaptive Driving Models: Non-technical

The decision is based on an examination appeal and relates to an adaptive deep learning models calibrated and personalized for ("users" of) autonomous vehicles. The Applicant argued that continuous learning of a deep learning model and it allowed for efficient fine-tuning through fast re-training with minimal data.

The Board disagreed as continuous learning aspect is not part of the claimed invention, and person skilled in the art implements a method for providing a deep learning model to an autonomous vehicle which uses a static library of deep learning models for different vehicles, from which a managing device selects a model corresponding to the vehicle in question. Thus, the application was refused.