Building Risk Profiling Using ML for Faster Mitigation

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Solution Overview

Problem

Existing technologies struggle to efficiently organize and utilize vast amounts of data associated with cities, identify and utilize relationships between various data types, systematically identify potential risks, quantify and document risks, and implement risk mitigating actions.

Innovation Solution

A city risk mitigation (CRM) computer system that includes a computing device configured to receive data from multiple sources, analyze it using trained machine learning models, generate risk profiles, and provide risk mitigation outputs such as alerts, recommendations, and instructions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If conventional techniques of city management are used, then simplicity of operation is maintained, but ability to organize and utilize vast amounts of data efficiently is insufficient

Engineering Contradiction:

Improvedata organization and utilization efficiencyVSAvoidsystem complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent introduces a computer system with machine learning models as an intermediary between raw city data and risk mitigation decisions. This intermediary automatically processes and organizes vast amounts of data from multiple sources (sensors, databases, third-party systems), transforming unstructured information into actionable risk profiles without requiring complex human intervention in data management

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual data organization and risk assessment processes with automated computer-based machine learning systems. The machine learning model automatically ingests, processes, and analyzes data from multiple sources, generating risk profiles and mitigation recommendations without human intervention, thereby efficiently handling vast data volumes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If more complex technologies are implemented throughout cities, then risk mitigation capability is improved, but data management complexity increases

Engineering Contradiction:

Improverisk mitigation capabilityVSAvoiddata management complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the risk mitigation system into distinct functional modules: data collection from multiple sources, machine learning-based risk analysis, risk profile generation, and mitigation recommendation delivery. This segmentation allows complex technologies to be implemented in a structured manner, with each module handling specific tasks independently, thereby managing overall system complexity while maintaining high risk mitigation capability

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The computer system performs multiple functions within a single integrated platform: collecting data from diverse sources (sensors, databases, third-party systems), analyzing risks using machine learning, generating risk profiles, and providing mitigation recommendations. This multi-functionality consolidates complex technologies into a unified system, reducing data management complexity while enhancing risk mitigation capability

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Measurement precision

If machine learning models are used to analyze data, then risk identification accuracy is improved, but processing time and computational resources increase

Engineering Contradiction:

Improverisk identification accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary data processing and feature extraction before applying the machine learning model. By pre-processing data from multiple sources and organizing it into relevant features, the system reduces the computational burden during actual risk analysis, thereby maintaining high risk identification accuracy while minimizing processing time

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The machine learning model focuses on analyzing only the most relevant features and data points necessary for risk identification, rather than processing every piece of available data in equal detail. This selective analysis maintains high accuracy for critical risk factors while reducing overall computational time and resource requirements

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250348967A1Systems and methods for analyzing and mitigating community-associated risks

Publication Date: 2025.11.13 STATE FARM MUTAL AUTOMOBILE INSURANCE COMPANY

AI summary

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