Drafting Claims for Supervised Machine-Learning Systems

Supervised machine-learning models are at the heart of some of the biggest advances in artificial intelligence (AI) that have emerged in the past decade. This post revisits the nuts and bolts of drafting claims in this area, and includes two different industry examples of implementing supervised machine learning: medical imaging and speech detection.

In order for a supervised machine-learning model (such as an artificial neural network, a decision tree, etc.) to “learn” a mapping from given inputs to desired outputs, a large training data set of known, correctly mapped inputs and outputs is necessary. Once properly trained on the training data set, the model will hopefully produce correct outputs for new inputs that were not part of the training data set.

Read the full article at the link below.