Inference service deployment method, device, and storage medium

ChangeBridge: Patent Grants - AI & Computing (G06N)

Published March 31st, 2026

Detected March 31st, 2026

Summary

The USPTO granted Patent US12591462B2 to Baidu for an inference service deployment method. The patent covers selecting an optimal inference service version based on runtime environment performance and deploying it to the target system. The patent names 9 inventors and includes 17 claims.

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What changed

The USPTO granted Patent US12591462B2 to Beijing Baidu Netcom Science Technology Co., Ltd. covering a method for deploying AI inference services by obtaining runtime environment performance information, selecting a target version from multiple candidate versions based on that performance data, and deploying the selected version. The patent application was filed on November 3, 2022, under application number 17980204, and includes 17 claims covering the technical methodology.

This is a patent grant notice establishing intellectual property rights rather than imposing regulatory compliance obligations. No action is required from regulated entities. Technology companies developing AI inference services should be aware of this existing patent when designing deployment systems to avoid potential infringement issues. The patent's existence may inform competitive positioning in AI service deployment technology.

Source document (simplified)

← USPTO Patent Grants

Inference service deployment method, device, and storage medium

Grant US12591462B2 Kind: B2 Mar 31, 2026

Assignee

Beijing Baidu Netcom Science Technology Co., Ltd.

Inventors

Zhengxiong Yuan, Zhenfang Chu, Jinqi Li, Mingren Hu, Guobin Wang, Yang Luo, Yue Huang, Zhengyu Qian, En Shi

Abstract

Provided are an inference service deployment method, a device and a storage medium, relating to the field of artificial intelligence technology, and in particular to the field of machine learning and inference service technology. The inference service deployment method includes: obtaining performance information of a runtime environment of a deployment end; selecting a target version of an inference service from a plurality of candidate versions of the inference service of a model according to the performance information of the runtime environment of the deployment end; and deploying the target version of the inference service to the deployment end.