The article addresses the challenge of automating the extraction of component structures from Russian-language patent descriptions of technical devices. The proposed approach leverages state-of-the-art neural network models for natural language processing (NLP). The paper outlines the full development lifecycle of the software module—from domain analysis and formal requirements specification to implementation, training, and evaluation on real-world patent texts. Particular attention is devoted to annotation schema design, system architecture, visualization of extracted component structures, and comparative benchmarking against widely used pre-trained language models. Experimental results demonstrate that the proposed architecture outperforms existing alternatives in both completeness and accuracy of structural element extraction. The findings confirm that the developed module can significantly accelerate patent analysis workflows, enhance the reliability and depth of expert assessments, and support the construction of structured knowledge bases for technical systems and devices.