The emergence of Generative Artificial Intelligence (GAI), particularly Large Language Models (LLMs), offers novel opportunities for enhancing intellectual property (IP) creation and protection in engineering. Technical Patent Circumvention (TPC) refers to the legal design-around of existing patents by identifying alternative technical solutions that avoid infringement, including under the Doctrine of Equivalents. Traditionally, TPC is employed after a patent is granted, but it can also be applied proactively during the early stages of innovation to strengthen patent applications against future circumvention. This anticipatory use of TPC can significantly enhance the robustness of patent protection. However, conventional TPC methods, such as those based on the Theory of Inventive Problem Solving (TRIZ), require extensive training and domain expertise, limiting their practicality for preventive application. By leveraging the capabilities of LLMs, AI-aided TPC offers a more accessible and efficient approach to anticipatory patent protection. This study introduces a workflow for AI-assisted patent circumvention, leveraging OpenAI’s ChatGPT and Google’s Gemini to explore and compare various prompting strategies. The results are evaluated from the perspective of a patent attorney, demonstrating that generative AI can efficiently and effectively uncover viable, non-infringing design alternatives across selected case studies. The paper offers a systematic overview of legal and technical aspects of TPC, outlines traditional and TRIZ-based inventive approaches to TPC, and compares the application of the Doctrine of Equivalents in European and U.S. jurisdictions. It also presents a general algorithm for inventive anticipatory patent circumvention.