Assay Calibration Model With Variable Hill Slope Accuracy

The patent applies parameter changes by using a modified four-parameter logistic regression equation where the Hill's slope is made variable instead of constant. The calibration model incorporates quantity-dependent Hill's slope values that are derived from calibration data, allowing the system to adapt the slope parameter dynamically based on the analyte quantity being measured. This improves measurement precision by better fitting the assay response curve while maintaining operational simplicity through automated parameter calculation.

The calibration model performs self-service by automatically calculating the quantity-dependent Hill's slope values during the calibration process. The system uses the calibration data points to compute the appropriate slope values for different quantity ranges without requiring manual intervention or complex external calculations. This automated approach improves accuracy while managing model complexity through internal self-calculation rather than external complexity.

The patent implements feedback by using the calibration data to dynamically adjust the Hill's slope parameter in the logistic regression equation. The system continuously refines the calibration model based on the actual calibration sample measurements, creating a feedback loop where the calibration results directly inform the parameters used for subsequent sample measurements. This feedback mechanism improves reliability by ensuring the calibration model accurately reflects the specific assay conditions, while the automated nature of the process manages complexity.