USPTO Patent Grant: Digital Rights Management for Machine Learning Models

ChangeBridge: Patent Grants - AI & Computing (G06N)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted patent US12585991B2 to Deere & Company for techniques related to the digital rights management of machine learning models used in agriculture. The patent covers methods for training models to generate valid or invalid agricultural inferences based on sensor data ranges.

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What changed

The United States Patent and Trademark Office (USPTO) has granted patent US12585991B2, assigned to Deere & Company, for "Digital rights management of machine learning models." This patent details techniques for training agricultural inference machine learning models to distinguish between valid and invalid agricultural inferences based on sensor data falling within or outside specified value ranges. The patent specifically addresses the generation of agricultural inferences related to agricultural conditions using ground truth sensor data.

This patent grant is primarily an intellectual property matter and does not impose new regulatory obligations on companies. However, it may impact companies developing or utilizing AI and machine learning models in the agricultural sector, particularly concerning the protection and management of intellectual property related to these technologies. Companies operating in this space should be aware of this patent and its potential implications for their own innovations and licensing strategies.

Source document (simplified)

← USPTO Patent Grants

Digital rights management of machine learning models

Grant US12585991B2 Kind: B2 Mar 24, 2026

Assignee

Deere & Company

Inventors

Yueqi Li

Abstract

Techniques for training an agricultural inference machine learning model to generate valid agricultural inferences of agricultural conditions based on ground truth sensor data that falls within a plurality of ground truth sensor value ranges associated with a particular agricultural area, and to generate invalid or ambiguous agricultural inferences of agricultural conditions based on ground truth sensor data that falls outside of the plurality of ground truth sensor value ranges associated with a particular agricultural area. The agricultural inference machine learning model is trained, based on ground truth sensor data for the particular agricultural area, to determine if the subsequently received ground truth sensor data falls within or outside of that plurality of ground truth sensor value ranges that correspond to the particular agricultural area.