Building Model Anomaly Screening with Gaussian Feature Distributions
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Solution Overview
Problem
Current solutions fail to accurately detect anomalous building models in large datasets without manual review, leading to potential delivery of inaccurate models to customers.
Innovation Solution
Implement computer vision systems using independent univariate and multivariate Gaussian algorithms, frequency histogram analysis, and bin frequency models to automatically identify anomalies in building models by analyzing feature distributions and correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated anomaly detection algorithms are implemented to review building models, then productivity increases by eliminating the need for manual review of all models, but measurement precision may worsen due to the challenge of accurately detecting anomalies without human judgment
Solution Approach 1:
The patent introduces an intermediary statistical framework that bridges automated processing and accurate anomaly detection. By using probability distributions (Gaussian for continuous features, histograms for discrete features) as intermediaries, the system translates raw model features into anomaly scores that can be automatically evaluated while maintaining detection precision comparable to manual review
Solution Approach 2:
The system transforms the anomaly detection problem by changing parameters from direct feature comparison to probability-based anomaly scoring. By modeling feature distributions and calculating how likely a model's features are under normal conditions, the system achieves high-precision automated detection that can scale to large datasets without sacrificing accuracy
2Measurement precision
If all building models are reviewed by quality control teams, then measurement precision improves through thorough manual inspection, but productivity deteriorates due to the massive time required to review large datasets
Solution Approach 1:
The patent extracts only the essential features from building models that are relevant for anomaly detection, rather than requiring comprehensive manual review of all model aspects. By identifying and analyzing key features (continuous features like area measurements and discrete features like building attributes), the system achieves accurate anomaly detection with automated processing at scale
Solution Approach 2:
The system enables building models to be self-evaluated through automated anomaly detection. Each model is independently assessed by comparing its feature distribution against learned normal patterns, eliminating the need for external human reviewers while maintaining detection quality through statistical rigor
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.