May Mobility. has been granted a patent for a system that enables autonomous agents to make decisions based on data collected from both onboard sensors and remote infrastructure devices. The system calculates probabilities for various behavioral policies to autonomously control the agent’s operations. GlobalData’s report on May Mobility gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on May Mobility, AI assisted drone control was a key innovation area identified from patents. May Mobility's grant share as of June 2024 was 50%. Grant share is based on the ratio of number of grants to total number of patents.
Autonomous decision-making system for agents using sensor data
The patent US12032375B2 outlines a system and method for the operation of an autonomous agent, integrating a sensor system onboard the agent and a remote infrastructure device. The system is designed to generate two data streams: a first data stream from the onboard sensor system and a second from the remote device. A distributed computing system processes these data streams to calculate probability values for various predetermined behavioral policies. The system autonomously selects the policy with the highest probability value and controls the agent's actions accordingly. Additionally, the computing system can buffer data streams and track other agents in the environment, estimating their intents to inform policy selection.
The method described in the patent emphasizes the continuous determination of observation data streams and the selection of behavioral policies based on these inputs. It highlights the role of a distributed computing system, which may include nodes both onboard the autonomous agent and at the remote device. The method also allows for the buffering of data streams and the reception of the second data stream when the agent is within communication range of the remote device. The overlapping fields of sensing from both data streams enable the tracking of agents and the estimation of their intents, further refining the decision-making process for the autonomous agent's behavior.
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