建立產業專利分類系統－－共同引証分析的觀點

Establishing an Industry Classification System-- From the Viewpoint of Patent Co-Citation

IPC 或UPC 是各國專利局使用的專利分類系統。由於IPC 或UPC 是產業通用的分類系統，以分類結果進行產業專利分析時，分析結果無法有效的為企業進行研發規劃、技術定位、專利策略的制定等管理工作。因此，配合產業技術特性，發展產業專用的專利分類系統是專利管理的基本課題。本研究提出的專利共同引証分析(Patent Co-Citation Approach, PCA)是使用書目計量學文獻分析的技巧，以定義專利分類系統的方法論。分析過程分三階段進行：第一階段，依研究對象及研究目的選擇合適的專利資料庫以檢索專利，並篩選基礎專利。第二階段，以基礎專利對共同被引証的次數衡量基礎專利的相似性。第三階段，採用因素分析定義分類系統並評估分類系統的分類績效。本研究主要貢獻是提出一個以專利相似性為分類基礎的專利分類系統，以協助專利管理人員了解產業基礎專利、技術關聯性及技術演進的過程。

The paper proposes a new approach to create a patent classification system to replace the IPC or UPC system for conducting patent analysis and management. The traditional approach for management of patents, which is based on either the IPC or UPC, is too general to meet the needs of specific industries. In addition, some patents are placed in incorrect categories, making it difficult for enterprises to carry out R&D planning, technology positioning, patent strategy-making and technology forecasting. Therefore, it is essential to develop a patent classification system that is adaptive to the characteristics of a specific industry. In bibliometrics, the use of citation approach for the assessment of similarity for the classification of documents is a mature methodology. Kessler (1963) proposed the approach of bibliographic coupling, and Small (1973) proposed the co-citation approach. The degree of bibliographic coupling for documents A and B is reflected in the frequency of the documents that are co-cited by both A and B. The focus of the co-citation analysis is on the documents cited, by calculating the frequency of A and B that are co-cited by specific documents. This study uses the co-citation analysis, which is applicable to patents, to propose an approach called the Patent Co-citation Approach (PCA) to create a patent classification system The analysis of this approach is divided into 3 phases. Phase I selects appropriate databases to conduct patent searches according to the subject and objective of this study and then select basic patents. Phase II uses the co-cited frequency of the basic patent pairs to assess their similarity. Phase III uses factor analysis to establish a classification system and assess the efficiency of the proposed approach. In order to give a clearer picture on the conception of the PCA approach, this study demonstrates the analytical process a of this approach by using a set of simulation materials to demonstrate the concepts of the PCA . We discovered that the PCA approach is subject to two major problems: multiple classifications and non-classification and this study discusses their effects on the performance of the patent management and provides solution thereto. Future research will use the classification system for research planning and the analysis of patent portfolio and technology positions in industry, so as to provide more applicable information for the industry.