Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
