US-12627759-B1 - AI agent network for emergency management applications

Abstract

An emergency response data system (ERDS) integrates an AI agent network into an emergency management application to generate emergency response analytics to facilitate operational efficiency of one or more ECCs. The ERDS provides an emergency management application operable by an emergency communications center (ECC) computing system to display an emergency management user interface (UI) at one or more ECC. The ERDS stores, in one or more data structures, historical emergency response analytics. The ERDS receives, with the emergency management UI, user instructions related to the historical emergency response analytics. The ERDS provides the user instructions to the AI agent network. The ERDS generates, with the AI agent network, emergency response suggestions. The AI agent network includes a number of AI agents communicatively coupled together to analyze emergency response data and generate suggestions to facilitate operational efficiency of an ECC.

Inventors

• Martin Vejmelka
• Michael Martin
• James Patrick Olejar, Jr.
• Zachery LaValley

Assignees

• RAPIDSOS, INC.

Dates

Publication Date

20260512

Application Date

20250530

Claims (20)

1. 1 . An emergency response data system having an artificial intelligence (AI) agent network operable to generate emergency management suggestions, comprising: memory having instructions; and one or more processors coupled to the memory and operable to execute the instructions to perform one or more operations, comprising: provide an emergency management application operable by an emergency communications center (ECC) computing system to display an emergency management user interface (UI) at one of a plurality of ECCs; receive user input data with the emergency management UI, wherein the user input data includes at least one of text input, a cursor action within the emergency management UI, or speech input; receive external data from one or more external data sources, wherein the external data is time-sensitive data related to a current emergency incident, the time-sensitive data including at least one of sensor data, telematics data, public records data, traffic data, or weather data; provide the user input data and the external data to the AI agent network, wherein the AI agent network includes a plurality of distinct AI agents communicatively coupled together, the plurality of AI agents including a first AI agent that is operable to communicate with a user and a second AI agent that is operable to analyze and retrieve the historical user input data; generate, with the AI agent network, emergency management suggestions based on the user input data and the external data; and display the emergency management suggestions with the emergency management UI to facilitate operational efficiency at the one of the plurality of ECCs.
2. 2 . The emergency response data system of claim 1 , wherein the one or more operations further comprise: store historical user input data in one or more data structures; and provide the historical user input data to the AI agent network, wherein the emergency management suggestions are at least partially based on the historical user input data.
3. 3 . The emergency response data system of claim 2 , wherein the historical user input data includes 911 call data, wherein the 911 call data includes audio data from one or more 911 calls or includes transcripts of the one or more 911 calls.
4. 4 . The emergency response data system of claim 1 , wherein the plurality of AI agents includes a third AI agent operable to perform Internet-based research to support generation of the emergency management suggestions.
5. 5 . The emergency response data system of claim 1 , wherein the plurality of AI agents includes a third AI agent operable to request information from one or more services.
6. 6 . The emergency response data system of claim 1 , wherein each of the plurality of AI agents includes a perception module operable to acquire data through one or more input channels.
7. 7 . The emergency response data system of claim 1 , wherein each of the plurality of AI agents includes a reasoning module operable to perform at least one of deductive reasoning, inductive reasoning, abductive reasoning, or analogical reasoning to make decisions.
8. 8 . The emergency response data system of claim 1 , wherein each of the plurality of AI agents includes a tools module operable to delegate tasks to one or more of the plurality of AI agents or to delegate tasks to one or more services.
9. 9 . The emergency response data system of claim 1 , wherein each of the plurality of AI agents includes a learning module operable to improve AI agent performance over time.
10. 10 . The emergency response data system of claim 1 , wherein the one or more external data sources include at least one of live call audio of a 911 call, medical data, floor plan data, building data, or ambient conditions data.
11. 11 . The emergency response data system of claim 1 , wherein the plurality of AI agents includes a first subset of the plurality of AI agents, wherein the first subset of the plurality of AI agents are operable to ingest the external data from the one or more external data sources, wherein each of the first subset of the plurality of AI agents are configured to ingest one of the one or more external data sources.
12. 12 . The emergency response data system of claim 1 , wherein the user input data includes 911 call audio data, wherein the one or more operations further comprise: analyze the 911 call audio data; compare the emergency management suggestions to an analysis of the 911 call audio data; and perform a quality assurance analysis based on a comparison of the emergency management suggestions and the analysis of the 911 call audio data.
13. 13 . A computer-implemented method that when executed by data processing hardware causes the data processing hardware to perform operations comprising: providing an emergency management application operable by an emergency communications center (ECC) computing system to display an emergency management user interface (UI) at one of a plurality of ECCs; receiving user input data with the emergency management UI, wherein the user input data includes at least one of text input, a cursor action within the emergency management UI, or speech input; storing historical user input data in one or more data structures; providing the user input data, external data, and the historical user input data to an artificial intelligence (AI) agent network, wherein the AI agent network includes a plurality of distinct AI agents communicatively coupled together, the plurality of AI agents including a first AI agent that is operable to communicate with a user and a second AI agent that is operable to analyze and retrieve the historical user input data, wherein the external data is time-sensitive data related to a current emergency incident, the time-sensitive data including at least one of sensor data, telematics data, public records data, traffic data, or weather data; generate, with the AI agent network, emergency management suggestions based on the user input data and the historical user input data; and display the emergency management suggestions with the emergency management UI to facilitate operational efficiency at the one of the plurality of ECCs.
14. 14 . The computer-implemented method of claim 13 , further comprising: receiving the external data from one or more external data sources; and providing the external data to the AI agent network, wherein the emergency management suggestions are at least partially based on the external data.
15. 15 . The computer-implemented method of claim 13 , wherein the historical user input data includes 911 call data, wherein the 911 call data includes audio data from one or more 911 calls or includes transcripts of the one or more 911 calls.
16. 16 . The computer-implemented method of claim 13 , wherein the plurality of AI agents includes a third AI agent operable to perform Internet-based research to support generation of the emergency management suggestions.
17. 17 . The computer-implemented method of claim 13 , wherein the plurality of AI agents includes a third AI agent operable to request information from one or more services.
18. 18 . The computer-implemented method of claim 13 , wherein each of the plurality of AI agents includes a perception module operable to acquire data through one or more input channels.
19. 19 . The computer-implemented method of claim 13 , wherein each of the plurality of AI agents includes a reasoning module operable to perform at least one of deductive reasoning, inductive reasoning, abductive reasoning, or analogical reasoning to make decisions.
20. 20 . The computer-implemented method of claim 13 , wherein each of the plurality of AI agents includes a tools module operable to delegate tasks to one or more of the plurality of AI agents or to delegate tasks to one or more services.

Description

TECHNICAL FIELD This disclosure relates generally to emergency management systems, and in particular to providing real-time suggestions to emergency management and response personnel. BACKGROUND In the critical moments following an emergency, rapid and accurate information dissemination is paramount for effective response. Emergency communication centers (ECCs), such as 911 call centers, serve as vital hubs for receiving initial reports and coordinating the dispatch of first responders. Similarly, operations centers (OCs), like global security operations centers (GSOCs) or railway network operations centers (NOCs), manage incidents within their specific domains. First responders, including firefighters, police officers, and emergency medical technicians, rely on timely and relevant information to navigate to the scene, assess the situation, and implement appropriate actions. The ability of emergency personnel to quickly understand the nature of the emergency, the location, and any potential hazards significantly impacts response times and the safety of both the public and the responders. Traditional emergency response workflows often involve manual information gathering and dissemination, which can be time-consuming and prone to human error, particularly under the high-pressure conditions inherent in emergency situations. Furthermore, the increasing volume and diversity of data sources related to emergencies, such as sensor data from smart devices, telematics information from vehicles, and real-time environmental conditions, present both opportunities and challenges for efficient analysis and utilization. The effective integration and intelligent processing of this disparate information hold the key to enhancing situational awareness and optimizing emergency response strategies. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures is represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. In the drawings: FIGS. 1A and 1B illustrate example diagrams of an emergency response digital assistant system, in accordance with embodiments of the disclosure. FIGS. 2A, 2B, 2C, and 2D illustrate example diagrams of user interfaces and processes for providing emergency response artificial intelligence (AI) agent insights in an emergency management application, in accordance with aspects of the disclosure. FIGS. 3A, 3B, 3C, 3D, 3E, and 3F illustrate example diagrams of user interfaces and processes for providing agentic AI emergency response analytics in an emergency management application, in accordance with embodiments of the disclosure. FIGS. 4A, 4B, and 4C illustrate example diagrams of agentic AI architectures for emergency response, in accordance with embodiments of the disclosure. FIG. 5 illustrates a diagram of an emergency response retrieval, augmentation, and generation (RAG) system, in accordance with embodiments of the disclosure. FIG. 6 illustrates an example diagram of an agentic AI emergency response system, in accordance with embodiments of the disclosure. FIG. 7 illustrates an example diagram of a machine, in accordance with embodiments of the disclosure. FIG. 8 illustrates an example diagram of an emergency response digital assistant system, in accordance with embodiments of the disclosure. FIG. 9 illustrates an example flow diagram of a process for providing emergency response analytics using AI queries in emergency management applications, in accordance with embodiments of the disclosure. FIG. 10 illustrates an example diagram of instructions provided to aspects of an emergency response AI agent, in accordance with embodiments of the disclosure. FIGS. 11A and 11B illustrate example diagrams of instructions provided to aspects of an emergency response AI agent, in accordance with embodiments of the disclosure. DETAILED DESCRIPTION Various aspects of the disclosure include methods and systems for providing agentic artificial intelligence (AI) emergency response analytics. In the following description, numerous specific details are set forth to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the techniques described herein can be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring certain aspects. Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification a