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AmazonGranted patentAI & softwareInterest 3/10

Amazon patents faster way to group huge amounts of data

Amazon has been granted a patent for a method that speeds up the process of grouping large datasets into similar categories. The system splits the data into smaller chunks, groups them separately, and then combines those groups to find broader patterns, repeating this process to improve accuracy.

Why it mattersThis technique could help Amazon and other companies analyze massive amounts of information more quickly, potentially improving how they organize products or recommend content.

In the filing’s own words

Systems and methods are disclosed for implementation of a scalable and efficient scaling algorithm for clustering data objects within a data set, as well as implementation of the algorithm to control content generation and selection. A data set can be initially divided randomly into subsets. A base clustering algorithm, such as k-means clustering, can be applied in parallel to each subset to identify a set of local centroids representative of local clusters within each subset. The local centroids can be aggregated from across subsets and the base clustering algorithm can be applied to the aggregated local centroids to result in global clusters. Data objects associated with each global cluster (such as by being clustered within a local cluster corresponding to a global cluster) represent a global clustering of the data set. These global clusters can form the basis for additional iterations of local and global clustering, further refining global clustering.

What exactly they claim (claim 1)

1. A system for clustering a data set including a plurality of vectors, each vector associated with a content item and representing quantitative attributes of the content item as a multidimensional value, the system comprising: a data store storing the plurality of vectors; and a processor in communication with the data store and configured with computer-executable instructions to: divide the plurality of vectors into multiple partitions, each partition including a subset of vectors from the plurality of vectors; for each iteration of multiple iterations: apply a clustering algorithm to each partition of the multiple partitions to identify a set of local centroids for each partition, each local centroid of the set of local centroids clustering a subset of vectors of a partition; apply the clustering algorithm to an aggregation of the sets of local centroids from the multiple partitions to identify a set of global centroids, each global centroid of the set of global centroids clustering a subset of local centroids from the aggregation; update the multiple partitions based on the set of global centroids, wherein each partition of the multiple partitions is updated to include vectors associated with a distinct global centroid of the set of global centroids, and wherein a vector is associated with a particular global centroid of the set of global centroids when the vector is clustered within a local centroid that is clustered within the particular global centroid; and subsequent to a final iteration of the multiple iterations, output labels for individual vectors of the plurality of vectors, the labels reflective of the partition of the multiple partitions that includes an individual vector.

Facts

Company
Amazon (Amazon Technologies, Inc.)
Official title
Computationally efficient data set clustering using partitioning and iterative refinement
Patent number
US12743460B1
Application number
19087292
Filed
Mar 21, 2025
Granted
Sep 22, 2026
Inventors
3
Claims
20

Classified under

G06F 16/355 Physics & computing
Read the full filing (official PDF) ↗ Open in the Patent Tracker

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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.