Need help with understanding a term in a patent

Question

Let us suppose there is a patent with the claim: "a method for performing machine learning technique on a device." How to interpret this term machine learning technique? There are many machine learning operations and some of them are using matrix multiplication internally. Does it mean that performing matrix multiplication on the device independendlty from machine learning process is also patented or it has to be in context of a machine learning process to be patented?

Specifically, I am actually referring with the question to the first claim of the US7219085B2 patent.

Answer 1

You’re quoted words "a method for performing machine learning technique on a device" might be a paraphrase from the abstract but do not actually appear in the patent. In understanding what a patent covers, words matter. The words that matter the most are the claims.

1. A computer-implemented method comprising: processing a machine learning technique using a graphics processing unit to obtain results, wherein processing a machine learning technique further comprises using a pixel shader to compute an inner product that is at least one of: (a) a vector inner product; (b) a matrix inner product; decomposing the inner product into sub-problems and performing multiple passes over the sub-problems using pixel shaders; using the results to provide solutions for use by a computer application.

If you are asking if to infringe the claim you need to be doing the steps (a) and (b) in a certain context, the context would need to be processing a machine learning technique with a pixel shader.

The scope of "machine learning technique" would be interpreted as one skilled in the art would understand it further boxed in by the specification and the prosecution history. That means that if, to overcome a rejection, the applicant stated a limited meaning of machine learning technique then they are stuck with that limitation when it comes to infringement.

The other independent claim 14 is much more specific about the machine learning context in that it specifies "training learnable parameters."

Answer 2

I see from your comment you are most interested in what a machine learning technique consitutes. You could do worse than simply reading the Wikipedia article. I'm not an expert, but have dabbled in the implementation of machine learning in a field which I am an expert in.

In a nutshell, machine learning is a class of computer algorithms which use large amounts of example data to train a model which can then be used to predict a result from unknown data. For example you might want your model to be able to tell you if image contains a balloon. You train your model on perhaps 10,000 images which have been prejudged as to whether they have a balloon. Then test the model on another set of images where you know the answer. If all goes well, the model can then be used to detect balloons in images with high accuracy.

The training process for machine learning algorithms such as convolutional neural networks is very numerically intensive. They can be parallelized however which means that you can split the computation to run an many processors at once. Graphical Processing Units (GPRs) are comprised of large numbers of processors so can be used to speed up training dramatically. This particular patent is an example of one such method.