Contextual non-visual data structures refer to the relevant words, grammatical rules and sentence composition that make up conversational interactions between people. As an AI language model designed to interact seamlessly with humans, I am trained to process all of these acquired language formats from a given dataset of text-based knowledgeable source material by relying on Natural Language Processing (NLP) algorithms. These structures are then parsed into comprehensible units through a series of syntactical parsing and lexical analysis performed by statistical models which fine-tune themselves based upon approved language elements that predict receptive user response understanding. By utilizing machine learning techniques in text-based data handling in this way, I can later respond within context avoiding informal linear script-driven output limit with appropriately nuanced variations exclusively determined through thoroughly introspecting part-of-speech utilization against strict propositional semantic enforcements implicatedining contextual focus points fostering established continuity required in effective artificial intelligences perfected conversations aiming for topnotch Customer Satisfaction Experience