Damage Assessment Confidence Aggregation for False Positive Filtering

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Solution Overview

Problem

Conventional damage analysis models lack the ability to quantify the probability of correctness for complex systems involving multiple intermediate outputs, leading to a high number of false positives and inefficient inspection processes.

Innovation Solution

A method for generating combined confidence metrics using machine learning models that aggregate the probability of correctness across multiple stages of a damage assessment pipeline, allowing for the rejection of predictions below a defined threshold.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional damage analysis models are used without combined confidence metrics, then the system can process damage assessments through multiple intermediate stages, but the system produces a high number of false positives and cannot quantify the probability of correctness

Engineering Contradiction:

Improveprobability of correctness quantificationVSAvoidfalse positive rate

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The patent segments the damage assessment system into multiple intermediate processing stages, each with its own confidence metric. Instead of treating the system as a black box, it divides the prediction process into discrete components (feature extraction, damage detection, severity assessment) that can be individually evaluated and aggregated to produce an overall confidence score, enabling quantification of probability of correctness.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements feedback by computing confidence metrics at each intermediate stage and using these metrics to adjust and refine the overall prediction. The confidence information flows back through the system, allowing the model to weigh different intermediate outputs appropriately and produce a final prediction with a quantified probability of correctness, thereby reducing false positives.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If complex multi-stage damage assessment systems are implemented, then comprehensive damage analysis can be performed, but it becomes extremely difficult to estimate confidence intervals for aggregated predictions

Engineering Contradiction:

Improvecomprehensive damage analysis capabilityVSAvoidconfidence estimation complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent merges the confidence estimation from multiple intermediate stages into a single combined confidence metric. By aggregating the confidence information from each processing stage using a unified mathematical framework, the system maintains comprehensive damage analysis capability while simplifying the complexity of confidence estimation into a manageable integrated measure.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent transforms the confidence estimation problem by changing the parameters from individual stage confidences to a combined confidence metric that reflects the overall prediction reliability. This parameter transformation enables the system to handle complex multi-stage assessments while providing a straightforward confidence interval estimation for the aggregated prediction.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If individual component predictions are evaluated separately, then each component has well-defined confidence metrics, but the aggregated system prediction lacks reliable confidence estimation

Engineering Contradiction:

Improveindividual component confidence metricVSAvoidaggregated prediction confidence information

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The patent introduces an intermediary confidence aggregation mechanism that bridges individual component confidence metrics and the final aggregated prediction. This intermediary layer processes the confidence information from each component through a structured combination method, preserving the precision of individual metrics while recovering the confidence information needed for the overall system prediction.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20260080371A1Generating combined confidence metrics for complex damage assessment systems

Publication Date: 2026.03.19 FUSION INC

AI summary

A computing system configured to process a plurality of intermediate outputs from machine learning models to generate final outputs may be maintained. A combined confidence metric that reflects a probability that the final outputs are accurate may be determined based on the intermediate outputs. A comprehensive damage assessment may be presented. The comprehensive damage assessment may include identified damage having combined confidence metrics determined to be above a threshold.