Google patents neural network layer that compresses data using codes
Google describes a specific type of neural network layer called an N-grammer layer that helps computers process sequences of data. It works by converting input data into short, discrete codes to create a compressed version before the main network processes it.
In the filing’s own words
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network comprising an N-grammer layer and an output neural network, the N-grammer layer configured to: at each of one or more heads: receive a sequence of input embeddings; generate a discrete latent representation of the sequence of input embeddings by using a learned product quantization codebook; generate a plurality of n-gram indices from the discrete latent representation; and generate a latent n-gram representation of the sequence of input embeddings; and generate a sequence of output embeddings, and the output neural network configured to: receive the sequence of output embeddings; and process the sequence of output embeddings to generate the network output.
What exactly they claim (claim 1)
1. (canceled)
Facts
- Company
- Google (Google LLC)
- Official title
- ATTENTION NEURAL NETWORKS WITH N-GRAMMER LAYERS
- Publication number
- US20260320291A1
- Application number
- 19313643
- Filed
- Aug 28, 2025
- Published
- Oct 15, 2026
- Inventors
- 2
- Claims
- 22
Classified under
G06F 40/20 Physics & computingG06N 3/04 Physics & computingMore from Google in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.
