Home Automation Risk Mitigation via Machine Learning

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Solution Overview

Problem

Existing IoT-based security management systems lack the capability to effectively detect and mitigate critical situations such as natural disasters and man-made emergencies by dynamically adjusting risk mitigation strategies based on real-time data and user-specific knowledge.

Innovation Solution

A computer-implemented method leveraging machine learning and combinatorial optimization techniques to detect critical situations, locate users, retrieve relevant knowledge corpora, create and execute risk mitigation action plans, and dynamically adjust strategies based on available IoT devices and external factors.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If existing IoT-based security management systems are used, then basic security monitoring is provided, but the capability to detect and mitigate critical situations dynamically is insufficient

Engineering Contradiction:

Improvecapability to detect and mitigate critical situationsVSAvoiddynamic adjustment of risk mitigation strategies

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system dynamically adjusts risk mitigation strategies by continuously monitoring critical situations and adapting responses in real-time. The machine learning module learns from historical data and updates mitigation strategies dynamically, allowing the system to respond effectively to evolving emergency conditions rather than relying on static pre-programmed responses.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback mechanisms where the outcomes of mitigation actions are fed back into the machine learning model. This feedback loop enables the system to learn from previous actions and improve its decision-making capabilities, continuously optimizing its response to critical situations based on actual effectiveness rather than theoretical protocols.

Inventive Principle:

Principle #23Feedback

2Productivity

If machine learning and combinatorial optimization techniques are applied, then optimal risk mitigation plans are selected, but system complexity increases

Engineering Contradiction:

Improveoptimization of risk mitigation responsesVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the complex risk mitigation process into distinct functional modules: critical situation detection module, user location module, knowledge corpus retrieval module, action plan creation module, and optimization selection module. This segmentation allows each module to handle specific tasks independently, making the overall complex system more manageable and easier to implement while maintaining high optimization capability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The knowledge corpus acts as an intermediary between raw critical situation data and optimal action plans. Instead of directly processing complex optimization problems, the system retrieves pre-processed knowledge and guidelines from the corpus, which simplifies the optimization process and reduces computational complexity while still achieving high-quality mitigation strategies.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If real-time data from multiple IoT devices is processed, then accurate user location and situation detection is achieved, but data processing requirements increase

Engineering Contradiction:

Improveaccuracy of user location and critical situation detectionVSAvoiddata processing energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system extracts only the critical and relevant information from the海量 data generated by multiple IoT devices. Rather than processing all raw data, the machine learning model identifies and extracts key features such as user locations, critical situation indicators, and risk factors, significantly reducing the data processing burden while maintaining high detection accuracy.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system performs preliminary data processing and feature extraction in advance, pre-processing IoT device data to identify potential critical situations before they fully manifest. This preliminary action filters and prepares data in advance, reducing the computational load during actual emergency response while maintaining accurate detection capabilities.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11477230B2Home automation risk assessment and mitigation via machine learning

Publication Date: 2022.10.18 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

An approach for identifying mitigation solution based on critical situations is disclosed. The approach includes detecting one or more critical situations associated within a structure and detecting one or more location of one or more users in the structure. The approach retrieves a user-knowledge corpus based on one or more smart IoT devices or from existing database. Furthermore, the approach retrieves a critical situation knowledge corpus from various servers and creates risk mitigation action plans to address the one or more critical situations. The approach selects an optimal plan, by leveraging machine learning through combinatorial optimization technique, from the existing risk mitigation action plans and executing the optimal plan.