Monte Carlo Risk Loss Estimation to Correct Double Counting

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing risk assessment methods rely on ordinal measures that are not accurate and do not provide optimized results for quantifying the likelihood and impact of risk events, lacking the ability to assign monetary values to risk impacts.

Innovation Solution

A system utilizing a Monte Carlo simulation to correct inflated estimates by generating non-inflated risk event losses through random cause and event generation, followed by consequence computation and objective value reduction, ensuring accurate risk assessment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If Monte Carlo simulation is applied to risk assessment, then measurement precision of risk estimates is improved, but device complexity increases

Engineering Contradiction:

Improveprecision of risk loss estimatesVSAvoidcomplexity of simulation system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical/manual risk assessment methods with a computational Monte Carlo simulation system. The processor automatically performs thousands of simulated risk events using random sampling and probability distributions, substituting human judgment and manual calculations with algorithmic computation to achieve higher precision in risk loss estimates.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates virtual copies of risk events through simulation trials. Instead of analyzing one actual risk scenario, the Monte Carlo method generates numerous simulated instances of risk events with varying parameters, allowing statistical analysis of potential outcomes and providing more robust risk estimates based on aggregated simulation data.

Inventive Principle:

Principle #26Copying

2Ease of operation

If traditional ordinal measures are used for risk assessment, then ease of operation is maintained, but measurement precision deteriorates

Engineering Contradiction:

Improvesimplicity of risk assessmentVSAvoidaccuracy of risk quantification

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent transforms the measurement parameters from ordinal scales (1-5 ratings) to continuous probability distributions and monetary values. By changing the parameter type from discrete ordinal categories to continuous numerical variables, the system enables much finer granularity in risk assessment while maintaining user-friendly input interfaces through structured data collection forms.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12367327B2System and a method for correcting inflated computed risk estimates by employing Monte Carlo simulations

Publication Date: 2025.07.22 FORMAN ERNEST

AI summary

Disclosed is a system for correcting inflated computed estimates of event loses derived from computed likelihoods and impacts of the events. The system includes a processor, an input unit, a display unit, a Monte Carlo simulation trials to generate non-inflated estimates of event loses and a data storage. The processor is configured to receive computed values of likelihood of causes, receive computed values of likelihoods of the events given causes, receive consequence of the events on objectives, receive computed value of the objectives, apply the Monte Carlo simulation trials on the causes, the events given the causes, and consequences of the events on the objectives, and compute the average loss to the objectives and the chance that the loss will exceed a pre-defined percentage. The Monte Carlo Simulation trial results in determining the impact of the event by taking the sum product of the total loss to each objective and the computed value of the objectives.