Shaping the Future of Intellectual Property in the Digital Era

Growing patent applications globally challenge manual examination and prior art search. This chapter presents an AI-based patent analysis framework using natural language processing and machine learning, including BERT transformers and ensemble algorithms (Random Forest, Gradient Boosting, SVM). The framework addresses automated patent classification via Cooperative Patent Classification codes, prior art detection, semantic similarity measurement, and citation network analysis. Experimental validation on 1,847,203 USPTO patents (2010-2020) demonstrates 96.2% F1-score in classification, 0.862 NDCG@10 in prior art retrieval, with production-ready computational efficiency. The study contributes patent-specific language model refinement, neural and traditional feature engineering, multi-task evaluation, attention visualization, and deployment recommendations. Results indicate AI systems can effectively supplement human patent review with expert-level accuracy while handling large-scale document volumes.