Here's what actually issued. On January 7, 2020, Google LLC was granted US10528866B1, "Training a document classification neural network," with Andrew M. Dai and Quoc V. Le named as inventors — Le being a familiar name on Google's foundational machine-learning work. The CPC footprint is squarely in the neural-network class: G06N 3/08 (learning methods) and G06N 3/0445 (recurrent architectures). That classification is the first signal of scope: this is a training-method patent, not a product claim.
Read plainly, the contribution is a procedure for teaching a network to put documents into categories. Document classification is the workhorse task behind spam filtering, topic tagging, and content routing — the kind of capability that sits underneath dozens of shipped Google features without ever being named in marketing. The recurrent-architecture CPC suggests the claimed method processes text as a sequence, which is how language was handled before the transformer era fully took over.
Why patent something this ordinary? Because the value in machine learning often lives in the training recipe, not the headline application. A method that trains a classifier more efficiently, or with less labeled data, is reusable across many products. Google, which operates at a scale where a small training improvement compounds across billions of documents, has an obvious reason to stake claims on the procedure itself.
On scope, the house discipline applies. This is a granted B1 patent and therefore enforceable, but the independent claim covers a particular training method with specific steps. It does not lock up the idea of classifying documents, nor every recurrent text classifier. Read the claim language for the boundary; the broad title is a label, not the monopoly.
The takeaway for the IP reader: US10528866B1 is a clean example of the foundational layer of AI patenting that predates the generative-AI gold rush. The architecture-and-product patents get attention now, but grants like this one — issued at the start of 2020 — show how the major labs had already been quietly staking claims on the training procedures that everything else rests on.