Clustered Job Pricing Models for Variable Completion-Time Estimates

The patent segments the job completion time distribution into multiple clusters, each representing different work scenarios (e.g., different personnel skill levels, environmental conditions). Instead of using a single mean or median value, the system divides the data into distinct groups and models each cluster separately, thereby capturing the variability and improving estimation accuracy while managing complexity through structured segmentation.

The patent performs preliminary clustering and model training using historical job completion data before actual pricing estimations are needed. By pre-processing the data to identify clusters and establish mixture models in advance, the system captures variability and improves accuracy while reducing the computational burden during real-time pricing operations, as the complex modeling work has already been completed.

The patent employs a universal mixture model framework that can accommodate multiple clusters with different characteristics (mean, standard deviation) while using a consistent mathematical structure. This multi-functional approach allows the model to adapt to various work conditions and personnel types through a single unified framework, capturing variability across different scenarios without proportionally increasing the number of parameters, as the same model structure serves multiple clustering purposes.