A Supervised Requirement-oriented Patent Classification Scheme Based on the Combination of Metadata and Citation Information

Patent classification systems are applied extensively in innovative analysis. Existing patent classification schemes are either technology-dependent or TRIZ-based. The former ones, such as the IPC and UPC, are normally developed by different patent offices in the world mainly for the purpose of patentability examination and patent retrieval, while the latter is for TRIZ users and analysts with no more than 40 categories. These static classifications are too complex and general to meet the in-depth patent classification requirements of a specific technology area or organization. To tackle these drawbacks, in this paper, we propose an automatic requirement-oriented patent classification scheme as a complementary method using supervised machine learning techniques to classify patent dataset into a user-defined taxonomy. The requirement-oriented patent taxonomy can be technology-dependent, application-dependent or a mixture of both tailored to specific business objectives. It is more comprehensible and adaptable to various patent management requirements. Through a set of experiments on a collection of 14,414 patents in a case study in the technology area of system on a chip (SoC), we recommend using the combination of the metadata and citation information as the document representation for the new method since it can obtain relatively high classification accuracy with a dramatically simplified document preprocessing process.