Computer-Assisted Categorization of Patent Documents in the International Patent Classification

The World Intellectual Property Organization is currently developing a system for assisting users in categorizing patent documents in the International Patent Classification (IPC). The system should support the classification of documents in several languages and aims to assist users in locating relevant IPC symbols by providing them with a convenient web-based service. The approach taken for developing such a system relies on powerful machine learning algorithms that are trained on manually classified documents to recognize IPC topics. We detail in-house results of applying a custom-built state-of-the-art computer-assisted categorizer to English, French, Russian, and Germanlanguage patent documents. We find that reliable computer-assisted categorization at IPC subclass level is an achievable goal for the statistical methods employed here. A categorization system suggesting three IPC symbols for each document can predict the main IPC class correctly for around 90% of documents, and the main IPC subclass for about 85% of documents. The accuracy of the system at main group level is enhanced if the user first validates the correct IPC class.