IP Library Granted Patent US 12,456,375
Granted Patent B2
US 12,456,375 · App. 18/485,887 · Granted Oct 28, 2025

Risk management system with internet of things

Inventors: Sohail Farooqui (Round Lake Beach, IL); Enriqueta Zurlo (Park Ridge, IL); Jeraldine Dahlman (Evanston, IL)
Assignee: Allstate Insurance Company
G08G1/163G06Q40/08G08G1/166H04L67/12H04W4/46
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,456,375
App. No.
18/485,887
Granted
Oct 28, 2025
Kind
B2
Abstract

A risk management system that includes an internet of things (IoT) integrated logic engine connected to IoT-capable sensors and devices and autonomous entity sensors and devices. The logic engine processes and analyzes in real-time the data from the plurality of IoT-capable sensors and the autonomous entity sensors. The logic engine further identifies novel patterns and pre-defined data patterns in the data from the plurality of IoT-capable sensors and the autonomous entity sensors to determine that a risk is occurring or imminent. The logic engine further sends real-time notifications to a set of subscribers of the risk management system about the risk that is occurring or imminent and provide inter-device communications to provide real-time warnings between one or more of the plurality of sensor-enabled devices and the autonomous entity devices.

Claims (54)

1 . A risk management system, the system comprising:

a plurality of internet of things (IoT)-capable sensors, the plurality of IoT-capable sensors configured to record data from a plurality of sensor-enabled devices;

a plurality of autonomous entity sensors connected to the risk management system, the plurality of autonomous entity sensors configured to record data associated with a plurality of autonomous entity devices;

an IoT integrated logic engine that includes a protocol-agnostic data aggregator of the data from the plurality of IoT-capable sensors; and

the IoT integrated logic engine configured to:

receive and aggregate data from the plurality of IoT-capable sensors;

receive and aggregate data from the plurality of autonomous entity sensors;

continuously process and analyze the data from the plurality of IoT-capable sensors and the autonomous entity sensors;

identify novel patterns and pre-defined data patterns in the data from the plurality of IoT-capable sensors and the autonomous entity sensors; and

evaluate risk based on the novel patterns and pre-defined data patterns from the plurality of IoT-capable sensors and the autonomous entity sensors.

2 . The risk management system of claim 1 , wherein the plurality of sensor-enabled devices include one or more sensors and infrastructure or one or more smart homes and smart buildings.

3 . The risk management system of claim 1 , wherein the IoT integrated logic engine uses artificial intelligence algorithms to process and analyze the data from the plurality of IoT-capable sensors.

4 . The risk management system of claim 1 , wherein the IoT integrated logic engine is further configured to use a scoring algorithm for valuing the novel data patterns from the plurality of IoT-capable sensors.

5 . The risk management system of claim 4 , wherein the IoT integrated logic engine is further configured to determine a potential impact to the risk based on the novel data patterns and the scoring algorithm.

6 . The risk management system of claim 5 , wherein the IoT integrated logic engine is further configured to add the novel data pattern to a library of known data patterns.

7 . The risk management system of claim 1 , wherein the IoT integrated logic engine is further configured to:

filter each risk that is occurring or imminent per interests of a given subscriber according to a risk assessment profile for the given subscriber; and

send notifications to a set of subscribers of the risk management system about the risk that is occurring or imminent based on the filtering.

8 . The risk management system of claim 1 , wherein the IoT integrated logic engine is further configured to send notifications to a set of subscribers of the risk management system about the risk that is occurring or imminent, wherein the set of subscribers includes one or more of the following: government agencies, retail entities, home owners, and insurance companies.

9 . The risk management system of claim 1 , wherein the IoT integrated logic engine is further configured to provide inter-device communications to provide warnings between one or more of the plurality of sensor-enabled devices and the autonomous entity devices, wherein the warnings include information from nearby sensor-enabled devices based upon location, speed, direction, and mapping.

10 . The risk management system of claim 1 , wherein the IoT integrated logic engine is further configured to provide inter-device communications to provide warnings between one or more of the plurality of sensor-enabled devices and the autonomous entity devices, wherein the warnings include one or more of the following: inter-device proximity alerts, inter-device collision course alerts, and inter-device dangerous conditions alerts.

11 . One or more non-transitory computer readable media storing computer readable instructions that, when executed, cause an apparatus to:

(a) receive and aggregate data, by an internet of things (IoT) integrated logic engine, from a plurality of IoT-capable sensors from a plurality of sensor-enabled devices, the plurality of IoT-capable sensors and configured to record data from the plurality of sensor-enabled devices, wherein aggregation of the data from the plurality of IoT-capable sensors is protocol-agnostic;

(b) receive and aggregate data, by the IoT integrated logic engine, from a plurality of autonomous entity sensors from one or more autonomous entity devices, the plurality of autonomous entity sensors connected to the IoT integrated logic engine and configured to record data from the one or more autonomous entity devices;

(c) process and analyze the data, by the IoT integrated logic engine, from the plurality of IoT-capable sensors and the autonomous entity sensors;

(d) identify novel patterns and pre-defined data patterns in the data, by the IoT integrated logic engine, from the plurality of IoT-capable sensors and the autonomous entity sensors; and

(e) determine, by the IoT integrated logic engine, that a risk is occurring or imminent based on the identified novel patterns and pre-defined data patterns.

12 . The one or more non-transitory computer readable media storing computer readable instructions of claim 11 , wherein the plurality of sensor-enabled devices include one or more sensors and infrastructure or one or more smart homes and smart buildings.

13 . The one or more non-transitory computer readable media storing computer readable instructions of claim 11 , further including an instruction that, when executed, cause the apparatus to:

(f) send notifications, by the IoT integrated logic engine, to a set of subscribers about the risk that is occurring or imminent; and

(g) develop a risk assessment profile of risks as to what a given subscriber would be most interested in.

14 . The one or more non-transitory computer readable media storing computer readable instructions of claim 11 , further including an instruction that, when executed, cause the apparatus to send notifications, by the IoT integrated logic engine, to a set of subscribers about the risk that is occurring or imminent; wherein the set of subscribers includes one or more of the following: government agencies, retail entities, home owners, and insurance companies.

15 . The one or more non-transitory computer readable media storing computer readable instructions of claim 11 , further including an instruction that, when executed, cause the apparatus to:

(f) send notifications, by the IoT integrated logic engine, to a set of subscribers about the risk that is occurring or imminent; and

(g) provide inter-device communications, by the IoT integrated logic engine, to provide warnings between the plurality of IoT-capable sensors and the autonomous entity sensors, wherein the warnings include information from nearby sensor-enabled devices based upon location, speed, direction, and mapping.

16 . The one or more non-transitory computer readable media storing computer readable instructions of claim 11 , further including an instruction that, when executed, cause the apparatus to:

(f) send notifications, by the IoT integrated logic engine, to a set of subscribers about the risk that is occurring or imminent; and

(g) provide inter-device communications, by the IoT integrated logic engine, to provide warnings between the plurality of IoT-capable sensors and the autonomous entity sensors, wherein the warnings include one or more of the following: inter-device proximity alerts, inter-device collision course alerts, and inter-device dangerous conditions alerts.

17 . A risk management system, the system comprising:

a plurality of internet of things (IoT)-capable sensors, the plurality of IoT-capable sensors configured to record data from a plurality of sensor-enabled devices;

a plurality of autonomous entity sensors connected to the risk management system, the plurality of autonomous entity sensors configured to record data from a plurality of autonomous entity devices;

an IoT integrated logic engine that includes a processor that provides a protocol-agnostic data aggregator of the data from the plurality of IoT-capable sensors; and

the processor and a non-transitory memory unit storing computer-executable instructions, which when executed by the processor, cause the processor to:

receive and aggregate data from the plurality of IoT-capable sensors from the plurality of sensor-enabled devices;

receive and aggregate data from the plurality of autonomous entity sensors from the autonomous entity devices;

process and analyze the data from the plurality of IoT-capable sensors and the autonomous entity sensors;

identify novel patterns and pre-defined data patterns in the data from the plurality of IoT-capable sensors and the autonomous entity sensors;

determine that a risk is occurring or imminent based on the novel patterns and pre-defined data patterns from the plurality of IoT-capable sensors and the autonomous entity sensors;

utilize a scoring algorithm for valuing new data from the plurality of IoT-capable sensors and the plurality of autonomous entity sensors;

determine a potential impact to the risk based on the new data and the scoring algorithm; and

send notifications to a set of subscribers of the risk management system about the risk that is occurring or imminent.

18 . The risk management system of claim 17 , wherein the plurality of sensor-enabled devices include one or more sensors and infrastructure or one or more smart homes and smart buildings.

19 . The risk management system of claim 17 , further including a computer-executable instruction to develop a risk assessment profile of risks as to what a given subscriber would be most interested in.

20 . The risk management system of claim 17 , wherein the set of subscribers includes one or more of the following: government agencies, retail entities, home owners, and insurance companies.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 13, 2023
From: FAROOQUI, SOHAIL; ZURLO, ENRIQUETA; DAHLMAN, JERALDINE
To: ALLSTATE INSURANCE COMPANY
Reel/Frame 065209/0447 →
Continuity (3)
Continuation 17862549 · Jul 12, 2022
Continuation 16397747 · Apr 29, 2019
Related Publication 20240119839A1 · Apr 11, 2024
References Cited (29)
US 7085637B2 · Breed et al. · 2006 [cited by applicant]
US 7421321B2 · Breed et al. · 2008 [cited by applicant]
US 10037668B1 · DesGarennes et al. · 2018 [cited by applicant]
US 10043376B1 · Poomachandran et al. · 2018 [cited by applicant]
US 20150045063A1 · Mishra et al. · 2015 [cited by applicant]
US 20160171564A1 · Ciaramelletti et al. · 2016 [cited by applicant]
US 20170177798A1 · Samuel et al. · 2017 [cited by applicant]
US 20170227965A1 · Decenzo et al. · 2017 [cited by applicant]
US 20180060159A1 · Justin et al. · 2018 [cited by applicant]
US 20180168464A1 · Barnett, Jr. et al. · 2018 [cited by applicant]
US 20180176727A1 · Williams · 2018 [cited by applicant]
US 20180181094A1 · Funk et al. · 2018 [cited by applicant]
US 20180182187A1 · Tong et al. · 2018 [cited by applicant]
US 20180225605A1 · Fabara et al. · 2018 [cited by applicant]
US 20180241764A1 · Nadolski et al. · 2018 [cited by applicant]
US 20180246780A1 · Acharya et al. · 2018 [cited by applicant]
US 20190339688A1 · Cella · 2019 [cited by examiner]
US 20220126864A1 · Moustafa et al. · 2022 [cited by applicant]
Enon Chaczko, Frank Jiang and Benazir Ahmed, “Road Vehicle Alert System Using IOT,” visited Sep. 21, 2018, <htlps://ieeexplore.ieee.org/documenl/8121714> (006591.01921). [cited by applicant]
Trinity IOT Applied Building, “Digital Platforms. Transform the Way we Live and Work,” visited Sep. 21, 2018, <http://www.trinityiol.in/> (006591.01921). [cited by applicant]
A. Jesudoss, Muthuram .B.O. and Lourdson Emmanuel .A, “Safe Driving Using lot Sensor,” visited Sep. 31, 2018, <htlps://acadpubl.eu/hub/2018-118-21/articles/21e/3.pdf> (006591.01921). [cited by applicant]
Striim, “Detecting Patterns and Anomalies—Uncover Time-Sensitive Insights Fast,” visited Sep. 21, 2018, <https://www.striim.com/solutions/detecting-patterns-anomalies/> (006591.01921). [cited by applicant]
Antonio M. Lopes, Paulo Abreu and Maria Teresa Restivo, “Analysis and Pattern Identification on Smart Censors data,” visited Sep. 21, 2018, <https://ieeexplore.ieee.org/documenl/7984409> (006591.01921). [cited by applicant]
Duck Jin Chai, Long Jin, Kyoung Ho Bae, Buhyun Hwang and Keun Ho Ryu, “Continuous Sensor Data Mining Model and System Design,” visited Sep. 21, 2018, <htlps://ieeexplore.ieee.org/documenl/4568554> (006591.01921). [cited by applicant]
John Treadway, “Using an IoT Gateway to Connect the “Things” to the Cloud,” visited Sep. 21, 2018, <https://www.researchgate.net/publication/220283962_Sensor_data_analysis_for_equipment_monitoring> (006591.01921). [cited by applicant]
A. C. B. Garcia, Cristiana Bentes, Rafael Heitor C. De Melo and Thadeu Penna, “Sensor Data Analysis for Equipment Monitoring,” visited Sep. 21, 2018, <hllps://www.researchgate.net/publication/220283962_Sensor_data_analy… [cited by applicant]
Data From Sky, “A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications,” visited Sep. 21, 2018, <http://datafromsky.com/> (006591.01921). [cited by applicant]
Nida Saddaf Khan, Sayeed Ghani and Sajjad Haider, “Real-Time Analysis of a Senso(s Data for Automated Decision Making in an IoT-Based Smart,” visited Sep. 21, 2018, <hllps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022067/… [cited by applicant]
Jean-Aime Maxa, Mohamed-Slim Ben Mahmoud, Nicolas Larrieu, “Extended Verification of Secure UAANET Routing Protocol,” dated Sep. 13, 2016 (006591.01921). [cited by applicant]