Unveiling the inventive process from patents by extracting problems, solutions and advantages with natural language processing

Patents are the main means for disclosing an invention. These documents encompass many steps of the inventive process starting with the definition of the problem to be solved and ending with the identification of a solution. In this study we focus on three fundamental concepts of the inventive process: (A) technical problems; (B) solutions; and (C) advantageous effects of the invention, which, based on the WIPO guidelines, any patent should include. We propose a system based on Natural Language Processing (NLP) pipeline that uses transformer language models to identify technical problems, solutions and advantageous effects from patents. We use a training dataset composed of 480,000 patents sentences contained in sections manually labelled by inventors or attorneys. Our model reaches a F1 score of 90%. The model is evaluated on a random set of patents to assess its deployability in a real-world scenario. The proposed model can be used as a novel tool for prior art mapping, novel ideas generation and technological evolution identification and can help to disclose valuable information hidden in patent documents.