The transition to a bio-based economy requires the development and diffusion of (technological) innovations. Tracking innovation in the bioeconomy is challenging due to its multidimensional and cross-sectoral nature. To address this problem, we developed a comprehensive dataset of patents related to the bioeconomy, leveraging artificial intelligence (AI). Patents are a common indicator for knowledge development and innovation. Traditional methods for identifying bioeconomy-related patents have significant limitations. Static technology classifications and keyword searches in patent abstracts, while widely used, are prone to inaccuracies. For instance, bio-based innovations may be misclassified under unrelated categories or overlooked entirely due to variations in terminology across languages and disciplines. These shortcomings in traditional methodologies highlighted the need for a more adaptable and dynamic approach to accurately reflect the evolving nature of the bioeconomy. 

 

To overcome these challenges, we fine-tuned a pre-trained large language model (LLM) using manually annotated patent abstracts. This model was designed to identify bio-based products, services, and processes with greater accuracy and comprehensiveness. We analyzed a dataset of 67 million patents and successfully identified 5.6 million as bioeconomy-related. This approach transcends the limitations of traditional methods by accommodating linguistic and contextual variations in patent descriptions. 

 

To map innovations within the bioeconomy, we applied topic modeling, a technique that groups text data into thematic clusters. These topics were visualized through a detailed map of all bioeconomy-related patents, showcasing thematic clusters and their interconnections. This visualization enables researchers and policymakers to explore inventions in the bioeconomy. 

 

Further details on data processing are provided in the corresponding publication, where the data set is also available for download. We hope that interested researchers will use this data to answer a wide range of open research questions on innovations in the bioeconomy. Please cite the paper when using the data. 

 

Kriesch, L., & Losacker, S. (2024). A global patent dataset of bioeconomy-related inventions. Scientific Data, 11(1), 1308. http://dx.doi.org/10.1038/s41597-024-04163-6 

 

The datamap below (available in desktop view) enables an exploration of bioeconomy related-inventions. The topic labels are created automatically using a large language model based on the patent abstracts and may therefore contain technological inaccuracies. The data explorer can also be accessed on GitHub in full screen mode.