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This is a record of a document published elsewhere, first published by the United States Patent and Trademark Office, as US-12607973-B2, on 21 April 2026. OpenTechnical did not first disclose it. The identifier, hashes and timestamps below record what this library served and when, not when the material became public. View the original.

OT-2026-000000278

Stochasticity mitigation in deployed AI agents

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Classification
Sensors, Controls & Machine Vision › Industrial Control
Original source
US-12607973-B2, the United States Patent and Trademark Office, 21 April 2026
Document SHA-256
9df1b19b18c7334d89d01989d7fba2c9175d677054b4575fc19e7b36e45a0d79

Abstract

The techniques disclosed herein mitigate stochasticity when controlling a mechanical system with artificial intelligence (AI) agents. In some configurations, AI agents are created using data generated by a machine learning model. Stochasticity is segmented temporally into near term and long term, and different strategies are used to address stochasticity in the different timeframes. For example, long term stochasticity may be addressed with changes to the reward function used to train the model. Short term stochasticity may be addressed by applying a margin to the output of an AI agent. Example margins include window averaging, clamps, and statistical process control bounds. In one configuration, AI agents are regression brains that are generated from setpoints inferred by the model from environmental states. The limitations inherent to fitting a regression line to this data may result in some predicted setpoints being outside of an allowed range.

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OpenTechnical Publication OT-2026-000000278, “Stochasticity mitigation in deployed AI agents,” published September 15, 2026 at 19:02:41 UTC, https://opentechnical.org/p/OT-2026-000000278.

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