The increasing complexity and diversity of natural language inputs have posed significant challenges to the contextual understanding capabilities of contemporary language models. Dynamic Pattern Alignment (DPA) emerges as a novel approach that dynamically aligns detected semantic patterns within input data with the model's internal representations, thereby enhancing semantic transfer and context-aware processing. This study presents a comprehensive examination of DPA's integration into an open-source language model, detailing the algorithmic framework and modifications to the model architecture. An automated testing environment was employed to evaluate performance gains and context consistency, utilizing diverse datasets and standardized preprocessing techniques. The experimental results demonstrate that DPA significantly improves semantic retention across contextually dynamic inputs, reduces computational load, and enhances response times. Comparative analyses with baseline models demonstrate DPA's superiority in semantic transfer and contextual relevance. These findings suggest that DPA offers a promising advancement in the development of context-aware language models, with potential applications extending beyond the current scope.