Abstract
In order to stimulate novel ideas and avoid patent infringement during new product development, R&D engineers need to obtain existing patent information related to the development domain accurately and in a timely matter. Further, patent documents if we organized and categorized, can provide IP managers with a clean view of the state-of-the-art technologies in an efficient and effective way. Equipped with IP knowledge, companies can set R&D directions and develop patent portfolio and territory strategy to stay competitive in the global market place. This thesis proposes a patent categorization methodology by using Artificial Neural Network (ANN) to classify patent documents based on pre-constructed ontology. The proposed methodology not only recognizes Web Ontology Language (OWL) created by protégé but also acquire probabilities which key phrases belong to the specific concepts in domain ontology. The procedure of the proposed methodology, firstly, extracts key phrases from documents based on Term Frequency - Inverse Document Frequency (TF-IDF) method, and then summarizes a probability matrix between key phrases and concepts to calculate the probability that a specific key phrase contains a certain concept. Because combining frequencies and probabilities of key phases, this study can get better representative input values for ANN model. In addition, this research provides document searching module by selecting key phrases and setting weights to execute IP document analysis. Finally, this research uses patents of Chemical Mechanical Polishing (CMP) and Radio Frequency Identification (RFID) domains as case examples to illustrate and demonstrate the proposed methodology at work with superior results.