IoT Device Activation Verification via Hazard Scoring

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Solution Overview

Problem

Unintended activations of networked IoT devices can lead to costly and dangerous situations, such as pipe damage from unheated homes during winter, due to accidental device control from user devices.

Innovation Solution

An activation verification system that detects device activations, calculates a hazard score and activation confidence score, and initiates deactivation if the adjusted confidence score exceeds a threshold, using a combination of device data, user data, and machine learning to differentiate between intended and unintended activations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If device activation is allowed without verification, then ease of operation is improved, but unintended activations cause harmful effects

Engineering Contradiction:

Improveease of device controlVSAvoidharmful effects from unintended activation

Core Design Contradiction:

Ease of operationVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary verification of device activations by analyzing activation patterns, user behavior data, and contextual information before executing the device command. This preliminary action distinguishes between intended and unintended activations, allowing legitimate operations to proceed while blocking harmful ones.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

An intermediary verification system is introduced between the user device and the networked device. This intermediary analyzes activation requests using machine learning models and hazard scores, acting as a mediator that allows legitimate activations while preventing unintended harmful activations without requiring direct user intervention.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Object-affected harmful factors

If activation verification is implemented, then harmful factors are reduced, but device complexity increases

Engineering Contradiction:

Improveharmful effects from unintended activationVSAvoidcomplexity of activation verification system

Core Design Contradiction:

Object-affected harmful factorsVSDevice complexity

Solution Approach 1:

The verification system operates autonomously by automatically analyzing activation patterns, computing hazard scores, and making decisions about whether to allow or block activations. The machine learning models self-adjust based on accumulated data, reducing the need for manual configuration and complex system management.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses dynamic parameter adjustment by computing hazard scores based on multiple variables including device type, user behavior patterns, temporal context, and spatial information. These parameters are continuously adjusted and reweighted by the machine learning model to optimize verification accuracy while maintaining system simplicity.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If hazard scoring is used to prevent unintended activations, then reliability is improved, but loss of time occurs due to verification processing

Engineering Contradiction:

Improveaccuracy of activation verificationVSAvoidtime for verification processing

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system applies partial verification by computing hazard scores based on the most critical factors first (device type, user pattern matching) and only performing more intensive analysis when initial indicators suggest potential issues. This allows most legitimate activations to be verified quickly while applying more thorough scrutiny only when necessary.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

User behavior patterns and device activation histories are pre-analyzed and stored as reference data before actual activation events occur. When an activation request is received, the system quickly compares it against pre-computed patterns rather than analyzing everything from scratch, significantly reducing verification time while maintaining high reliability.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11463437B2Device activation verification

Publication Date: 2022.10.04 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A method can include detecting an activation of a networked device. The method can further include obtaining a hazard score corresponding to a degree of hazard associated with the networked device. The method can further include determining an activation confidence score corresponding to the activation of the networked device. The method can further include determining, based at least in part on the hazard score and the activation confidence score, an adjusted activation confidence score. The method can further include determining that the adjusted activation confidence score exceeds a threshold. The method can further include initiating a deactivation of the networked device in response to the determining that the adjusted confidence score exceeds the threshold.