The Use of AI to Classify Sustainability Patents About AM Metal Powders According to EU Environmental Objectives

Awareness of the issue of environmental sustainability of industrial products and processes is becoming increasingly topical, especially in EU countries, and with it the need to assess and certify the environmental impact of production activities. Among the most popular practices are methods based on life-cycle assessment (LCA), both current and prospective, which require the research of a vast amount of data, accurate, and up-to-date. Whereas in the past the scientific community relied on data reported in the scientific literature and in European projects, there is now increasing recognition of the importance of including patent information, which provides a more detailed view of the scalability of processes, use and end of life of a product. One of the main challenges in making the best use of patents as a source of technical data is managing the complexity of the database and language structure of the documents themselves. A crucial step in facilitating access to this data is through the accurate identification and classification of patents dealing with sustainability to circumscribe the search to a more delimited and accurate scope. Although the criteria for classification are well defined by European regulations, the techniques for actually operating this classification are still highly inefficient. In this paper, we compared traditional patent search methods by keywords and by using Cooperative Patent Classification, in terms of precision and recall, then proposing as an alternative the use of AI tools, such as next-generation NER using a bidirectional transformer encoder and RAG, to overcome the limitations that exist with traditional approaches. An exemplary case is conducted on a pool of patents on metal powders for AM.