AI System for Active Shooter Detection via Sensor Signals

ChangeBridge: Patent Apps - AI & Computing (G06N)

Published March 26th, 2026

Detected April 1st, 2026

Summary

USPTO published patent application US20260087415A1 disclosing an AI system for detecting active shooters using sensor signals from multiple devices. The system correlates sensor data exceeding threshold levels, applies machine learning to classify emergency types and severity, and generates response information for dispatch.

View original document View source feed page

What changed

USPTO published patent application US20260087415A1 filed by inventors William Delmonico, Joseph Schmitt, Michael Kramer, and Randy Kurtz. The application discloses a system and methods for classifying emergency events using artificial intelligence. The system receives sensor signals from multiple devices at a location, determines when signals exceed expected thresholds, correlates the signals, and applies an AI model trained to classify correlated signals into emergency types, determine spread and severity, and generate emergency response information.

This publication is informational and does not impose compliance obligations. Technology companies developing AI-based safety systems, government agencies deploying emergency response technologies, and law enforcement agencies should review this patent to assess prior art in the field and competitive positioning. Patent applications typically take 2-3 years to mature to grant, and granted patents would provide patent holder with exclusion rights in the disclosed technology.

Source document (simplified)

← USPTO Patent Applications

ACTIVE SHOOTER DETECTION AND RESPONSE SYSTEM

Application US20260087415A1 Kind: A1 Mar 26, 2026

Inventors

William Delmonico, Joseph Schmitt, Michael Kramer, Randy Kurtz

Abstract

Disclosed are system and techniques for classifying events such as emergencies. A system can include a computer system to perform operations including: receiving sensor signals from a group of devices at a location, determining whether one or more of the sensor signals exceed expected threshold levels, in response to determining that the one or more of the sensor signals exceed the expected threshold levels, correlating the sensor signals, classifying the correlated sensor signals into an emergency event based on applying an artificial intelligence (AI) model to the correlated sensor signals, the AI model having been trained to classify the correlated sensor signals into a type of emergency, determine a spread of the emergency event, and determine a severity level of the emergency event, generating, based on information associated with the classified emergency event as output from the AI model, emergency response information, and returning the emergency response information.