Bid Target Performance Prediction Using Context Adjustment Factors

The patent applies universality by creating a single prediction model that can handle multiple bid targets and context factor combinations. Instead of building dedicated models for each bid target-context factor pair, one model is trained to predict performance across all combinations, making the model universal and multi-functional. This reduces the number of models from potentially hundreds to just one, while still maintaining the ability to provide accurate predictions for any specific bid target and context combination.

The patent merges the training data from all bid targets and context factor combinations into a single unified dataset for training one prediction model. Instead of having separate small datasets for each bid target-context combination (which leads to sparsity), all historical observations are combined and used together. This pooling of data significantly increases the number of historical observations available for training, improving the reliability of predictions even though the model must handle multiple different scenarios.

The patent eliminates the need to maintain multiple dedicated models by implementing a single universal prediction model that can predict performance for any bid target and context factor combination. This single model performs the work that would otherwise require many separate models, significantly reducing computational resources and processing requirements. The model achieves this by being trained on diverse data covering all possible combinations, enabling it to generalize across different scenarios without requiring separate computational infrastructure for each.