Development of a patent document classification and search platform using a back-propagation network

Abstract

In order to process large numbers of explicit knowledge documents such as patents in an organized manner, automatic document categorization and search are required. In this paper, we develop a document classification and search methodology based on neural network technology that helps companies manage patent documents more effectively. The classification process begins by extracting key phrases from the document set by means of automatic text processing and determining the significance of key phrases according to their frequency in text. In order to maintain a manageable number of independent key phrases, correlation analysis is applied to compute the similarities between key phrases. Phrases with higher correlations are synthesized into a smaller set of phrases. Finally, the back-propagation network model is adopted as a classifier. The target output identifies a patent document’s category based on a hierarchical classification scheme, in this case, the international patent classification (IPC) standard. The methodology is tested using patents related to the design of power hand-tools. Related patents are automatically classified using pre-trained neural network models. In the prototype system, two modules are used for patent document management. The automatic classification module helps the user classify patent documents and the search module helps users find relevant and related patent documents. The result shows an improvement in document classification and identification over previously published methods of patent document management.

Introduction

With the rapid development of information technology, the number of electronic documents and the digital content of documents exceed the capacity of manual control and management. People are increasingly required to handle wide ranges of information from multiple sources. As a result, knowledge management systems are implemented by enterprises and organizations to manage their information and knowledge more effectively. Knowledge management includes sorting useful knowledge from information, storing knowledge in good order, and finding knowledge in an existing knowledge base (Turban & Aronson, 2001). In this research, we focus on explicit knowledge management, i.e., management of well-structured documents such as patent documents (called “patents” in brief). Patents provide exclusive rights and legal protection for patent inventors. In addition, patents play an important role in the advancement and diffusion of technology. The objective of this research is to develop an effective methodology to automatically classify and identify patent documents. Furthermore, a prototype system is implemented and tested using hand tools patents sourced from the World Intellectual Property Organization (WIPO) database for scenario demonstration.

There have been many research efforts devoted to automatic document classification. Some of the classification methodologies are difficult to implement, and others are neither efficient nor effective, requiring developers of knowledge management systems to expend considerable resources testing and evaluating algorithms. The purpose of this research is to develop a document classification method based on neural networks and benchmark the performance against published standards. Through the implementation of a document classification module and a document search module, a prototype patent document management system is created. The patent document management system automates the classification of patent documents and improves the search for documents.

The automatic document classification methodology is described in the following steps: First, significant terms are abstracted from patent documents and are used to build a key phrase database. Second, the similarities between phrases are computed and depicted in a correlation matrix in order to synthesize phrases into a smaller set representing key concepts within the patent domain. After the steps of key phrase extraction and synthesis, a consolidated set of key phrases are treated as inputs of the back-propagation network model. The neural network model is trained using key phrases and the frequency of key phrases from the sample documents. The trained model is assessed until it reaches a satisfactory level of accuracy. After the network model is trained, the final step is to use the model for automated patent document classification and search.