Model-Based Homogeneity Determination for Detection Data
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Solution Overview
Problem
Existing methods struggle to accurately determine whether a dataset conforms to a specific statistical distribution, such as a Gaussian distribution, due to noise and complexities introduced by measurement errors, environmental variability, and instrument limitations.
Innovation Solution
The system and method involve extracting features from the dataset, such as the maximum normalized height of peaks, and inputting these features into a trained model, like a regression model or machine learning model, to generate a score that determines compatibility with the statistical distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional statistical methods are used to determine distribution conformity, then measurement precision is maintained, but reliability deteriorates due to noise and experimental complexities
Solution Approach 1:
The patent segments the determination process into distinct phases: data collection, feature extraction, model evaluation, and distribution determination. By dividing the complex task of assessing distribution conformity into manageable segments, the system can apply specific analytical techniques to each phase, improving overall reliability while managing precision requirements.
Solution Approach 2:
The patent introduces statistical models and computational algorithms as intermediary tools between raw experimental data and distribution determination. These intermediaries process the noisy data through standardized frameworks (such as goodness-of-fit tests and visual inspection methods), filtering out experimental variations while preserving the essential distributional characteristics.
2Productivity
If automated determination systems are implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service through automated data processing pipelines that automatically collect, analyze, and interpret experimental data without requiring manual statistical analysis. The system performs goodness-of-fit tests, generates visualizations, and determines distribution conformity autonomously, significantly improving productivity while the modular architecture manages complexity.
Solution Approach 2:
The patent creates a universal determination system that can evaluate multiple statistical distributions (Gaussian, Poisson, binomial, etc.) using a single integrated platform. This multi-functional approach improves productivity by eliminating the need for separate analysis tools for each distribution type, while the standardized framework actually reduces overall system complexity compared to maintaining multiple specialized systems.
Data Source
AI summary
A computing device may receive a detection dataset comprising a set of detection values corresponding to measurements of target entities generated from a sample in a detection analysis. Each detection value in the set corresponds to a measurement of one or more target entities. The computing device may extract one or more features of the detection value in the detection dataset, wherein at least one feature is extracted from a version of a particular detection value. The computing device may input the one or more features of the peaks into a model that is trained based on training samples of past detection datasets. The computing device may generate a computer-automated determination of homogeneity of the target entities in the sample based on an output of the model.