This paper presents a theoretical framework that combines marginal utility, matrix factorization, and Key-Value cache to optimize inference in machine learning models. It is applied to automated extraction of structured information from geo-mining documents, with empirical results showing a high-performing hierarchical classifier trained on a large dataset.