Antigen Binding Specificity Metric via Beta Distribution

The patent transforms the specificity assessment from a traditional binary classification (binding vs non-binding) to a continuous probabilistic parameter using beta distribution. By modeling the binding specificity as a continuous variable with associated uncertainty, the system can process multiple cells simultaneously while maintaining precision through probabilistic metrics rather than requiring exhaustive traditional analysis of each cell individually

The patent creates a computational model that replicates and generalizes findings from a subset of cells to the entire population. By using beta distribution parameters estimated from measured cells, the system infers specificity metrics for all cells without performing exhaustive traditional assays on each one, thereby scaling throughput while maintaining measurement precision

The patent performs preliminary computational preparation by establishing beta distribution models and specificity thresholds before actual cell analysis. These pre-computed reference frameworks enable rapid assessment of new cells without requiring time-consuming traditional methods for each evaluation, thus reducing overall assessment time while maintaining identification accuracy

The patent replaces traditional mechanical/experimental verification methods with computational probabilistic modeling. Instead of relying on time-consuming experimental validation for each cell, the system uses beta distribution-based computational algorithms to rapidly assess specificity, substituting physical measurement processes with efficient mathematical calculations that maintain reliability