Nano Dimension Ltd. announced that a patent was granted for technology developed by its industrial artificial intelligence (AI) group, DeepCube, that enables better training and optimization of decentralized deep learning-based AI models. The U.S. patent, formally titled System and method for mimicking a neural network without access to the original training dataset or the target model, (the Neural Network Mimicking Patent) addresses one of the core challenges of deploying AI models in the real-world, specifically continuously training models on new data when that data belongs to a customer. In the industry at-large, dealing with new customer data has often been a limitation due to sensitivity and confidentiality concerns that limit data shareability.

The new patent addresses this challenge by ultimately training and improving the AI models on customers? premises, without Nano Dimension having direct access to the new data or model. This patent is another key component in Nano Dimension?s efforts to transform DeepCube from a solely in-house AI group to a leading industrial AI solution provider.

Nano Dimension has already made progress in this initiative, having announced agreements and memorandums of understanding (MOUs) with several parties. DeepCube is currently developing an end-to-end AI platform for industrial usage that is not only limited to additive manufacturing. The software platform is intended to run autonomously on customers?

premises, and continuously improve itself, such that the more it is used, the higher the accuracy will become. Nano Dimension?s DeepCube alone has 50 patent applications filed, of which 27 have already been granted. These patent applications are filed in 8 different jurisdictions, providing a truly global IP protection.