USPTO Patent Grant: Fujitsu Limited - Remote Graph Generation ML

ChangeBridge: Patent Grants - AI & Computing (G06N)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted Fujitsu Limited a patent (US12585987B2) for a method of remote statistical generation of graphs for graph machine learning. The patent details a process for analyzing graph data, determining statistical information, and using this to generate new graphs.

View original document View source feed page

What changed

The United States Patent and Trademark Office (USPTO) has issued patent US12585987B2 to Fujitsu Limited, covering a novel method for the remote statistical generation of graphs used in machine learning. The patent describes operations including retrieving an initial graph, identifying node and edge types, determining their counts and statistical distributions, and analyzing combinations of edge-types connecting node groups to generate a second graph based on the statistical information of the first.

This patent grant signifies the formal recognition of Fujitsu's innovation in AI and machine learning infrastructure. While this is a patent grant and not a regulatory rule imposing obligations on other entities, it highlights advancements in the field of graph machine learning. Companies operating in AI development, particularly those focused on data analysis and graph-based algorithms, may find the patented technology relevant to their research and development efforts.

Source document (simplified)

← USPTO Patent Grants

Remote statistical generation of graphs for graph machine learning

Grant US12585987B2 Kind: B2 Mar 24, 2026

Assignee

Fujitsu Limited

Inventors

Wing Yee Au, Kanji Uchino

Abstract

According to an aspect of an embodiment, operations may include retrieving a first graph. The operations may further include identifying a set of node-types, determining a first count of each of the identified set of node-types, and determining first statistical information. The operations may further include identifying a set of edge-types, determining a second count of each of the identified set of edge-types and determining a two-dimensional (2D) distribution of each of the identified set of edge-types. The operations may further include determining second statistical information, identifying a set of combinations of edge-types connecting three node-types and determining a third count of each of a set of three node-type groups. The operations may further include determining a three-dimensional (3D) distribution of each of the set of three node-type groups, determining third statistical information, and transmitting first graph statistics associated with the retrieved first graph for generation a second graph.