Building Model Anomaly Detection Using Gaussian Vision Analysis
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Solution Overview
Problem
Existing systems fail to accurately detect anomalous building models, which can lead to poor-quality models being delivered to customers without adequate review, especially in systems with massive data volumes where manual inspection is not feasible.
Innovation Solution
Computer vision systems and methods using independent univariate and multivariate Gaussian algorithms, frequency histogram algorithms, and bin frequency models to automatically analyze building models and determine anomalies based on probability thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of building models is performed, then detection accuracy is improved, but productivity deteriorates due to the inability to review all models in massive data volumes
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated computer vision system that uses machine learning algorithms to detect anomalies in building models. The system processes models automatically without human intervention, achieving both high detection accuracy and high productivity by eliminating the bottleneck of manual review while maintaining quality control standards.
2Productivity
If automated anomaly detection is implemented, then productivity is improved by processing massive data volumes, but measurement precision deteriorates without adequate model review
Solution Approach 1:
The patent implements a self-service automated system where the computer vision algorithm independently analyzes building models for anomalies without requiring external manual verification. The system uses trained machine learning models to automatically identify deviations from normal building patterns, achieving both high productivity in processing massive data volumes and maintained detection accuracy through sophisticated automated analysis capabilities.
3Reliability
If comprehensive quality control is applied to all models, then reliability is improved, but loss of time increases due to thorough review requirements
Solution Approach 1:
The patent replaces time-consuming manual quality control processes with automated computer vision analysis that can evaluate multiple building models simultaneously. The system maintains comprehensive quality control by detecting anomalies in all submitted models while reducing the time investment from hours of manual review to seconds of automated processing, thereby improving reliability without sacrificing time efficiency.
Data Source
AI summary
Computer vision systems and methods for detecting anomalous building models are provided. The systems and methods can detect anomalies in building models using one or more of an independent univariate Gaussian algorithm, a multivariate Gaussian algorithm, a combination of a multivariate Gaussian algorithm for continuous features and a frequency histogram algorithm for discrete features, and/or a bin frequency model. The system automatically processes computerized models to determine anomalies, and indicates whether the models are accurate and whether correction is required.