CC BY-ND 4.0Martini, MelanieMelanieMartiniJohn, MarcusMarcusJohn2025-07-072025-07-072025https://doi.org/10.24406/publica-4852https://publica.fraunhofer.de/handle/publica/48920410.24406/publica-4852This research presents a novel framework for patent analysis aimed at enhancing technology foresight practices. We identified 739 relevant publications for using patent data analysis for data-driven foresight. We conducted a structured literature review to classify the publications according to the technology foresight use cases and the methods and data fields used. The framework is presented in detail with two specific use case profiles. The results encompass both low-requirement and high-requirement analytical methods, allowing for to selection of appropriate techniques based on their expertise and resource availability. While the framework provides valuable guidance for researchers and practitioners, it also emphasizes the need for ongoing evaluation and incorporation of recent literature. Future steps include expanding the dataset and making the use case profiles publicly accessible to further support datadriven decision-making in innovation management.enpatent analysistechnology foresightdata driven foresightpatent metadatabibliometricsstrategic decision-making000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::001 Wissenconference paper