US-20260127526-A1 - SYSTEMS AND METHODS FOR ANALYZING AND MITIGATING COMMUNITY-ASSOCIATED RISKS

US20260127526A1US 20260127526 A1US20260127526 A1US 20260127526A1US-20260127526-A1

Abstract

A computer system for analyzing and mitigating risks associated with an event is provided. The computer system is configured to: (i) receive at least one of a city risk profile and a building risk profile from a database; (ii) receive city systems data from the city services computer system; (iii) utilize a trained machine learning model to determine at least one potential risk associated with the event; (iv) generate an event risk profile that includes the at least one potential risk associated with the event; and/or (v) generate a risk mitigation output based upon at least one of the city risk profile and the at least one potential risk, wherein the risk mitigation output includes at least one of a risk alert, a risk mitigation recommendation, and risk mitigation instructions. Computer systems for analyzing and mitigation risks associated with a city, a building, and a user are also provided.

Inventors

Matthew Megyese
Sarah Ann Lockenvitz
Paul Bates
Nicholas Carmelo Marotta
Cathy Jo Roth
Austin Rowley
Jared Wheet

Assignees

STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

Dates

Publication Date: 20260507
Application Date: 20200911

Claims (20)

1 . A computer system for analyzing and mitigating risks associated with an event, the computer system comprising at least one processor and at least one memory device, the at least one processor programmed to: train a machine learning model by defining function coefficients for the machine learning model based upon training data comprising historical city systems data comprising a layout of city security systems in a city and historical scheduled event data comprising historical scheduled events in the city and one or more risks associated with the historical scheduled events, wherein the function coefficients are defined by at least one of supervised machine learning, unsupervised machine learning, or reinforcement machine learning, thereby generating a trained machine learning model configured to identify risks and risk locations within the city; receive new city systems data from at least one city services computer system, the new city systems data identifying a location of a scheduled event and a time that the scheduled event is scheduled to occur within the city; utilize the trained machine learning model to determine at least one potential risk associated with the scheduled event; identify at least one individual potentially impacted by the at least one potential risk based at least in part upon the location of the scheduled event and at least one individual profile associated with the at least one individual indicating that at least one scheduled or predicted commute of the at least one individual includes travel proximate to the location; update at least one user profile associated with the at least one individual to be associated with a first risk level based on the at least one individual being potentially impacted by the at least one potential risk; transmit content associated with the at least one potential risk to a computing system associated with the at least one individual, wherein the content is configured to cause initiation of at least one risk mitigating action proximate to the time that the scheduled event is scheduled to occur, and wherein the at least one risk mitigating action is associated with risk reduction for the at least one individual with respect to the scheduled event; determine that the at least one risk mitigating action was performed proximate to the time the scheduled event was scheduled to occur; and update the at least one user profile to be associated with a second risk level based on the at least one risk mitigating action being performed proximate to the time the scheduled event was scheduled to occur, wherein the second risk level is lower than the first risk level.
2 . The computer system of claim 1 , wherein the content includes a risk alert and the computing system is an external computer device.
3 . The computer system of claim 2 , wherein the external computer device is a user computer device, and wherein the risk alert causes the user computer device to display a notification.
4 . The computer system of claim 2 , wherein the external computer device is the at least one city services computer system.
5 . The computer system of claim 1 , wherein the content includes a risk mitigation recommendation and the at least one processor is further configured to transmit risk mitigation instructions to an external computer device.
6 . The computer system of claim 5 , wherein the external computer device is a user computer device and wherein the risk mitigation recommendation contains precautionary measures intended for individuals within a certain distance of an area affected by the event.
7 . The computer system of claim 5 , wherein the external computer device is associated with the at least one city services computer system and wherein the risk mitigation recommendation contains recommended actions for mitigating the at least one potential risk associated with the event.
8 . The computer system of claim 1 , wherein the content includes risk mitigation instructions and the at least one processor is further configured to transmit the risk mitigation instructions to the computing system, wherein the computing system comprises an external computer device.
9 . The computer system of claim 8 , wherein the external computer device is a user computer device, and wherein the risk mitigation instructions are configured to cause the user computer device to alter its operations.
10 . The computer system of claim 8 , wherein the external computer device is the at least one city services computer system, and wherein the risk mitigation instructions are configured to cause the at least one city services computer system to implement the at least one mitigating action.
11 . The computer system of claim 8 , wherein the external computer device is the at least one city services computer system, and wherein the risk mitigation instructions cause the at least one city services computer system to alter a physical system associated with the city.
12 . The computer system of claim 1 , wherein the at least one processor is further configured to: receive event data from a third party computer system, wherein the event data is associated with the event; utilize the trained machine learning model to determine at least one additional potential risk associated with the event based upon at least the event data; generate an updated event risk profile that includes the at least one additional potential risk associated with the event; and generate a second risk mitigation output based upon at least one of the updated event risk profile or the at least one additional potential risk, wherein the second risk mitigation output includes at least one of a second risk alert, a second risk mitigation recommendation, or second risk mitigation instructions.
13 . The computer system of claim 1 , wherein the at least one processor is further configured to: receive sensor data from at least one sensor; utilize the trained machine learning model to determine at least one additional potential risk associated with the event based upon at least the sensor data; generate an updated event risk profile that includes the at least one additional potential risk associated with the event; and generate a second risk mitigation output based upon at least one of the updated event risk profile or the at least one additional potential risk, wherein the second risk mitigation output includes at least one of a second risk alert, a second risk mitigation recommendation, or second risk mitigation instructions.
14 . (canceled)
15 . A computer-implemented method for analyzing and mitigating risks associated with an event, the method implemented by a computer system comprising at least one processor and at least one memory device, the computer-implemented method comprising: training a machine learning model by defining function coefficients for the machine learning model based upon training data comprising historical city systems data comprising a layout of city security systems in a city and historical scheduled event data comprising historical scheduled events in the city and one or more risks associated with the historical scheduled events, wherein the function coefficients are defined by at least one of supervised machine learning, unsupervised machine learning, or reinforcement machine learning, thereby generating a trained machine learning model configured to identify risks and risk locations within the city; receiving new city systems data from a city services computer system, the new city systems data identifying a location of a scheduled event and a time that the scheduled event is scheduled to occur within the city; utilizing the trained machine learning model to determine at least one potential risk associated with the scheduled event; identifying at least one individual potentially impacted by the at least one potential risk based at least in part upon the location of the scheduled event and at least one individual profile associated with the at least one individual indicating that at least one scheduled or predicted commute of the at least one individual includes travel proximate to the location; updating at least one user profile associated with the at least one individual to be associated with a first risk level based on the at least one individual being potentially impacted by the at least one potential risk; transmitting content associated with the at least one potential risk to a computing system associated with the at least one individual, wherein the content is configured to cause initiation of at least one risk mitigating action proximate to the time that the scheduled event is scheduled to occur, and wherein the at least one risk mitigating action is associated with risk reduction for the at least one individual with respect to the scheduled event; determining that the at least one risk mitigating action was performed proximate to the time the scheduled event was scheduled to occur; and updating the at least one user profile to be associated with a second risk level based on the at least one risk mitigating action being performed proximate to the time the scheduled event was scheduled to occur, wherein the second risk level is lower than the first risk level.
16 . A non-transitory computer-readable storage medium comprising computer-executable instructions embodied thereon for analyzing and mitigating risks associated with an event, wherein when executed by at least one processor, the computer-executable instructions cause the at least one processor to: train a machine learning model by defining function coefficients for the machine learning model based upon training data comprising historical city systems data comprising a layout of city security systems in a city and historical scheduled event data comprising historical scheduled events in the city and one or more risks associated with the historical scheduled events, wherein the function coefficients are defined by at least one of supervised machine learning, unsupervised machine learning, or reinforcement machine learning, thereby generating a trained machine learning model configured to identify risks and risk locations within the city; receive new city systems data from at least one city services computer system, the new city systems data identifying a location of a scheduled event and a time that the scheduled event is scheduled to occur within the city; utilize the trained machine learning model to determine at least one potential risk associated with the scheduled event; identify at least one individual potentially impacted by the at least one potential risk based at least in part upon the location of the scheduled event and at least one individual profile associated with the at least one individual indicating that at least one scheduled or predicted commute of the at least one individual includes travel proximate to the location; update at least one user profile associated with the at least one individual to be associated with a first risk level based on the at least one individual being potentially impacted by the at least one potential risk; transmit content associated with the at least one potential risk to a computing system associated with the at least one individual, wherein the content is configured to cause initiation of at least one risk mitigating action proximate to the time that the scheduled event is scheduled to occur, and wherein the at least one risk mitigating action is associated with risk reduction for the at least one individual with respect to the scheduled event; determine that the at least one risk mitigating action was performed proximate to the time the scheduled event was scheduled to occur; and update the at least one user profile to be associated with a second risk level based on the at least one risk mitigating action being performed proximate to the time the scheduled event was scheduled to occur, wherein the second risk level is lower than the first risk level.
17 . (canceled)
18 . (canceled)
19 . The computer system of claim 1 , wherein the computer system comprises a user computing device, and wherein the at least one risk mitigating action comprises causing the user computing device to display an alternate transportation route determined to be safer than a current transportation route.
20 . (canceled)

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application Ser. No. 62/945,630, filed Dec. 9, 2019, entitled “SYSTEMS AND METHODS FOR ANALYZING AND MITIGATING COMMUNITY-ASSOCIATED RISKS,” the entire contents and disclosure of which are hereby incorporated herein by reference in their entirety. FIELD OF THE INVENTION The present disclosure relates to “smart cities” and, more particularly, to analyzing data in order to determine and mitigate risks associated with communities or gatherings of people, including cities, municipalities, towns, and/or events. BACKGROUND More than ever, information and communications technologies are being applied to new industries in order to improve efficiencies, analyze impact of projects, and mitigate risks. “Smart cities” may utilize information and communications technologies at a city-wide level to achieve these outcomes. Mitigating risk may be of particular concern for modern cities as infrastructure becomes ever-more complex, expensive, and technologically advanced. Risk factors within a city may include risks associated with individual buildings, the layout of the city itself, criminal activity within the city, construction, traffic, man-made events, and natural disasters, among others. As governments, companies, and individuals become more aware of potential safety and economic risks, and in some cases become more risk averse, reducing risk becomes even more desirable. Further, as more complex technologies are implemented throughout cities and the amount of available data continues to grow, managing this data in an efficient, useful way to achieve particular outcomes is increasingly important. Conventional techniques of city management and organization may have other drawbacks as well. BRIEF SUMMARY The present embodiments may relate to systems and methods for analyzing and mitigating city-related risks. The system may include one or more user computing devices, one or more environmental sensors, one or more third party computer systems, one or more insurance provider servers, and/or one or more databases. The computer systems and computer-implemented methods may enable effective organization and utilization of collected data in order to mitigate city-related risks. In one aspect, a computer system for analyzing and mitigating risks associated with an event may be provided. The computer system may include at least one processor and/or associated transceiver in communication with at least one memory device, at least one sensor, at least one third party computer device, at least one city services computer system including a controller, and at least one database. The at least one processor may be programmed to: (i) receive at least one of a city risk profile and a building risk profile from the at least one database; (ii) receive city systems data from the city services computer device; (iii) utilize a trained machine learning model to determine at least one potential risk associated with the event based upon the city systems data and at least one of the city risk profile and the building risk profile; (iv) generate an event risk profile that includes the at least one potential risk associated with the event; and/or (v) generate a risk mitigation output based upon at least one of the event risk profile and the at least one potential risk, wherein the risk mitigation output includes at least one of a risk alert, a risk mitigation recommendation, and risk mitigation instructions. The computer system may include additional, less, or alternate functionality, including that discussed elsewhere herein. In another aspect, a computer-implemented method for analyzing and mitigating risks associated with an event may be provided. The method may be implemented by a computer system including at least one processor and/or associated transceiver in communication with at least one memory device, at least one city services computer system including a controller, and at least one database. The method may include, via one or more processors and/or associated transceivers: (i) receiving at least one of a city risk profile and a building risk profile from the at least one database; (ii) receiving city systems data from the city services computer device; (iii) utilizing a trained machine learning model to determine at least one potential risk associated with the event based upon the city systems data and at least one of the city risk profile and the building risk profile; (iv) generating an event risk profile that includes the at least one potential risk associated with the event; and/or (v) generating a risk mitigation output based upon at least one of the event risk profile and the at least one potential risk, wherein the risk mitigation output includes at least one of a risk alert, a risk mitigation recommendation, and risk mitigation instructions. The computer-implemented method may include additional, less, or alternate actions, includin