I was looking at an American machine learning patent application the other day, and it detailed many specific parts. It goes through how they label the data to solve their problem, and how they train the model. Here they specifically mention that they used the method SVM to train their model.
However, a later section, starting on page 24, of the patent is called "claims". Here they claim "A method for training a machine learning algorithm or statistical inference model" to solve the problem they tried to solve using the way of labelling data they used. This sounds like they now trying to patent that way of labelling the data for said problem regardless of which ML model is used and not just SVM like they used. However, this way of labelling data predates their patent.
Since I may be reading the patent application wrong, I wonder how specific that patent would be if it is granted. Would that entail that nobody else can split their datasets into positive and negative datasets for identifying peptides in a commercial setting? Or, does it only apply if that team also uses SVMs?
Also, how should I read the claims section of the patent? Is it one claim with several parts (meaning it's one very specific claim)? Or, are they trying to be granted several separate claims? I see later claims are referring to earlier claims, but I am still having a difficult time completely understanding the those.