US12608585B2: GDM Holdings Neural Network Patent
Summary
USPTO granted patent US12608585B2 to GDM HOLDING LLC on April 21, 2026. The patent covers a neural network system for robotic agents, combining simulation-trained deep neural networks with robot-trained networks to process environmental observations and generate policy outputs defining robotic actions. Six inventors are named: Razvan Pascanu, Raia Thais Hadsell, Mel Vecerik, Thomas Rothoerl, Andrei-Alexandru Rusu, and Nicolas Manfred Otto Heess. The patent contains 22 claims across CPC classifications G06N 3/008, G06N 3/092, G06N 3/10, G06N 3/045, G06N 3/08, G06N 3/082, and G06N 20/00.
“The neural network system is configured to receive an observation characterizing a current state of a real-world environment being interacted with by a robotic agent to perform a robotic task and to process the observation to generate a policy output that defines an action to be performed by the robotic agent in response to the observation.”
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GovPing monitors USPTO Patent Grants - AI & Computing (G06N) for new telecom & technology regulatory changes. Every update since tracking began is archived, classified, and available as free RSS or email alerts — 32 changes logged to date.
What changed
USPTO issued patent US12608585B2 to GDM HOLDING LLC, covering a neural network system that processes observations of a robotic agent's real-world environment and generates policy outputs to define actions. The system combines a simulation-trained deep neural network (trained in a simulated environment) with a robot-trained DNN that maps observations directly to policy outputs. The patent names Razvan Pascanu, Raia Thais Hadsell, Mel Vecerik, Thomas Rothoerl, Andrei-Alexandru Rusu, and Nicolas Manfred Otto Heess as inventors. Twenty-two claims span neural network architectures (G06N 3/008, G06N 3/092, G06N 3/10, G06N 3/045, G06N 3/08, G06N 3/082) and machine learning applications (G06N 20/00). Competitors developing reinforcement learning systems for robotic control should review this intellectual property for freedom-to-operate implications.
Archived snapshot
Apr 23, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Neural networks for selecting actions to be performed by a robotic agent
Grant US12608585B2 Kind: B2 Apr 21, 2026
Assignee
GDM HOLDING LLC
Inventors
Razvan Pascanu, Raia Thais Hadsell, Mel Vecerik, Thomas Rothoerl, Andrei-Alexandru Rusu, Nicolas Manfred Otto Heess
Abstract
A system includes a neural network system implemented by one or more computers. The neural network system is configured to receive an observation characterizing a current state of a real-world environment being interacted with by a robotic agent to perform a robotic task and to process the observation to generate a policy output that defines an action to be performed by the robotic agent in response to the observation. The neural network system includes: (i) a sequence of deep neural networks (DNNs), in which the sequence of DNNs includes a simulation-trained DNN that has been trained on interactions of a simulated version of the robotic agent with a simulated version of the real-world environment to perform a simulated version of the robotic task, and (ii) a first robot-trained DNN that is configured to receive the observation and to process the observation to generate the policy output.
CPC Classifications
G06N 3/008 G06N 3/092 G06N 3/10 G06N 3/045 G06N 3/08 G06N 3/082 G06N 20/00
Filing Date
2022-07-25
Application No.
17872528
Claims
22
Mentioned entities
Parties
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