Opportunity Cost Prediction Using Incomplete Revenue Data

The patent introduces an intermediary variable called 'opportunity cost proxy' that mediates between historical transaction data and opportunity cost predictions. Instead of directly predicting opportunity costs from limited data, the system first generates proxies from historical prices and transactions, then uses these proxies as training targets for the machine learning model. This intermediary approach allows the system to work effectively with insufficient data by creating surrogate measurements that capture the essential patterns needed for accurate prediction.

The system creates copies of historical transaction data in the form of opportunity cost proxies that represent future opportunity costs. By generating these proxy values from historical prices and transaction patterns, the system creates synthetic training data that mimics the structure and relationships of actual opportunity costs, enabling the model to learn from historical patterns without requiring extensive future data.

The patent replaces complex mechanical optimization systems with a machine learning-based predictive system. Instead of using iterative optimization algorithms that require complex mathematical computations and multiple iterations, the system trains a neural network to directly predict opportunity costs from input features. This substitution transforms a computationally intensive optimization problem into a more manageable pattern recognition task that can be implemented more easily in production environments.

The system changes the fundamental parameter being optimized from direct pricing decisions to opportunity cost predictions. By shifting the focus to predicting opportunity costs as an intermediate parameter, the system simplifies the overall pricing optimization process. The machine learning model learns to predict the key driver of optimal pricing (opportunity cost) rather than directly optimizing complex pricing functions, making the system easier to implement and more adaptable to changing conditions.

The patent extracts only the essential features and patterns from historical transaction data that are necessary for predicting opportunity costs. Instead of storing and processing entire historical datasets, the system extracts key elements such as historical prices, transaction volumes, and temporal patterns, then uses these extracted features to generate opportunity cost proxies. This extraction approach maintains prediction accuracy while significantly reducing data storage requirements and processing complexity.

The system segments historical data into meaningful components that can be independently processed and stored. By dividing the historical transaction data into distinct features (prices, volumes, timestamps, product categories) and processing them separately to generate opportunity cost proxies, the system reduces the burden of storing and managing large monolithic datasets. Each segment can be stored and processed independently, improving efficiency while maintaining overall prediction accuracy.