USPTO Patent Grant for ML Hyperparameter Tuning

ChangeBridge: Patent Grants - AI & Computing (G06N)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted a patent (US12585960B2) to International Business Machines Corporation for a method of dynamically tuning hyperparameters during machine learning model training. The patent describes a system that automatically adjusts hyperparameter tuning strategies based on computing resource quotas and usage limits.

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What changed

The United States Patent and Trademark Office (USPTO) has issued patent US12585960B2 to International Business Machines Corporation. This patent covers a method for dynamically tuning hyperparameters during machine learning model training. The described technology automatically rejects or terminates training based on predefined computing resource quotas and usage limits, aiming to optimize resource allocation and training efficiency.

While this is a patent grant and not a regulatory rule, it signifies a new intellectual property development in the field of AI and machine learning. Companies developing or utilizing ML models, particularly those with significant computational resource requirements, should be aware of this patented technology. No immediate compliance actions are required, but it may influence future technology development and licensing strategies within the AI sector.

Source document (simplified)

← USPTO Patent Grants

Dynamically tuning hyperparameters during ML model training

Grant US12585960B2 Kind: B2 Mar 24, 2026

Assignee

International Business Machines Corporation

Inventors

Yuan-Chi Chang, Venkata Nagaraju Pavuluri, Dharmashankar Subramanian, Timothy Rea Dinger

Abstract

A method of automatically tuning hyperparameters includes receiving a hyperparameter tuning strategy. Upon determining that one or more computing resources exceed their corresponding predetermined quota, the hyperparameter tuning strategy is rejected. Upon determining that the one or more computing resources do not exceed their corresponding predetermined quota, a machine learning model training is run with a hyperparameter point. Upon determining that one or more predetermined computing resource usage limits are exceeded for the hyperparameter point, the running of the machine learning model training is terminated for the hyperparameter point and the process returns to running the machine learning model training with a new hyperparameter point. Upon determining that training the machine learning model is complete, training results are collected and computing resource utilization metrics are determined. A correlation of the hyperparameters to the computing resource utilization is determined from the completed training of the machine learning model.