Patent Language Model

Patent language models aim to leverage the power of natural language processing to analyze and generate patent text, improving efficiency and insight in patent analysis and drafting. Current research focuses on developing and evaluating models, including hierarchical attention networks and large language models like GPT-4, often tailored specifically for the structured language of patents, and assessing their performance through metrics such as keystroke reduction and accuracy in predicting citation counts. These models offer the potential to enhance patent searching, analysis of technological trends, and the automation of aspects of patent writing, ultimately accelerating innovation and improving intellectual property management.