Unsupervised Clustering for Trend Analysis of Muscle-Related Patents using Natural Language Processing and Machine Learning

Background The rapid growth of the aging population has increased the demand for technologies related to muscle health, assessment, prevention, and functional support. Purpose This study aimed to analyze technology trends in muscle-related innovations using natural language processing and machine learning applied to South Korean patent and utility model data. Study design Descriptive study using unsupervised machine learning Methods A total of 2,836 records were analyzed using combined title and abstract texts. Semantic embeddings were generated using Sentence-BERT, followed by dimensionality reduction with Uniform Manifold Approximation and Projection and clustering with Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Keywords for each cluster were extracted using term frequency-inverse document frequency, and annual filing patterns were examined to identify the trends. Results Two dominant macro-domains were identified, including a Wearable and Sensor Technology domain (n=1,695) and a Biological and Preventive Technology domain (n=903). HDBSCAN yielded substantially better cluster validity, with a silhouette coefficient of 0.610 and Davies–Bouldin index of 0.525. Within these macro-domains, sub-domains related to stem cell and regenerative therapy, functional food and bioactive extracts, antibody and antiviral therapeutics, aging and sarcopenia mechanisms, and massage devices were identified. Conclusions These findings provide an overview of muscle-related technology development and demonstrate the value of machine learning–based patent text analysis for technology landscape mapping and innovation assessment.