{"title":"Will AI solve the patent classification problem?","authors":"Eleni Kamateri , Michail Salampasis , Eduardo Perez-Molina","doi":"10.1016/j.wpi.2024.102294","DOIUrl":null,"url":null,"abstract":"<div><p>This paper scrutinizes the act of patent classification as it is performed by specialists, namely patent examiners, and currently supported by automated systems in patent offices for assigning classification codes to patent application documents. It collectively discusses aspects of the patent classification operation, some of them not very visible, which are not commonly encountered in other document and text classification tasks. The advent of Deep Learning (DL) and, especially, Large Language Models (LLMs) offer a new perspective on the development of automated systems addressing these inherent aspects of patent classification. Towards this direction, the paper analyses how these technologies can address the patent classification problems and concludes with a discussion of potential challenges and benefits that the application of Artificial Intelligence (AI) technologies may bring to the task of patent classification.</p></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"78 ","pages":"Article 102294"},"PeriodicalIF":2.2000,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Patent Information","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0172219024000346","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This paper scrutinizes the act of patent classification as it is performed by specialists, namely patent examiners, and currently supported by automated systems in patent offices for assigning classification codes to patent application documents. It collectively discusses aspects of the patent classification operation, some of them not very visible, which are not commonly encountered in other document and text classification tasks. The advent of Deep Learning (DL) and, especially, Large Language Models (LLMs) offer a new perspective on the development of automated systems addressing these inherent aspects of patent classification. Towards this direction, the paper analyses how these technologies can address the patent classification problems and concludes with a discussion of potential challenges and benefits that the application of Artificial Intelligence (AI) technologies may bring to the task of patent classification.
期刊介绍:
The aim of World Patent Information is to provide a worldwide forum for the exchange of information between people working professionally in the field of Industrial Property information and documentation and to promote the widest possible use of the associated literature. Regular features include: papers concerned with all aspects of Industrial Property information and documentation; new regulations pertinent to Industrial Property information and documentation; short reports on relevant meetings and conferences; bibliographies, together with book and literature reviews.