Patent Madenciliği

Year 2021, Volume: 24 Issue: 2, 745 - 753, 01.06.2021

Ahmet Kayakökü , Diyar Akay

https://doi.org/10.2339/politeknik.842663

Cited By: 1

Abstract

Patent veri tabanları sürekli ve süratli bir şekilde büyüyen hacmiyle, günümüzün en önemli teknik bilgi kaynağı konumundadır. Oldukça geniş ve detaylı olan patent veri tabanlarından bilgi elde edebilmek için, gelişen veri analiz yöntemlerine başvurmak kaçınılmaz hale gelmiştir. Patent verisinden bilgi elde etmeyi ifade etmek üzere son zamanlarda patent madenciliği tabiri kullanılmaya başlanmıştır. Patent veri tabanlarının hem yapısal hem de yapısal olmayan karakteri sebebiyle, patent madenciliğinde veri madenciliği tekniklerine de metin madenciliği tekniklerine de ihtiyaç duyulmaktadır. Ancak patent dokümanları buluşların teknik yönlerinin açıklandığı metinlerden oluştuğundan, metin madenciliği uygulamalarının bu alandaki işlevi daha fazladır. Bu çalışmada patent dokümanlarının özelliklerinden bahsedilerek, metin madenciliği ile elde edilebilecek sonuçlara değinilmiştir. Literatürde patent metinleri üzerinde kullanılan metin madenciliği yöntemlerinden örnekler verilmiş ve gelecekte yapılacak çalışmalara yön vermek açısından patent dokümanları arasında benzerlik tespitinin neden önemli olduğu açıklanmıştır.

Keywords

Patent, patent madenciliği, metin madenciliği, benzerlik tespiti

Patent Mining

Abstract

Patent databases are the most significant technical information source of today with their continuously and rapidly growing volume. It becomes inevitable to apply developing data analysis method in order to obtain knowledge from quite large and detailed patent databases. Recently, in order to express knowledge discovery from patent data, the term - patent mining is used. Due to both the structural and non-structural character of patent databases, data mining techniques as well as text mining techniques are required for patent mining. Besides, since the patent documents consist of texts explaining the technical aspects of the inventions, text mining applications plays a more important role in this field. In this study, by referrring the properties of patent documents, the results that can be obtained with text mining are focused. Examples in the literature are given about text mining methods applied on patent documents and the significance of similarity detection between patent documents is explanied in order to leading future studies.

Keywords

patent, patent mining, text mining, similarity detection

References

• [1] “World Intellectual Property Indicators 2019” World Intellectual Property Organization (WIPO), 2019,
• [2] Schwander P.: “An evaluation of patent searching resources: comparing the professional and free on-line databases”, World Pat Inf, 22(3): 47-165, (2000)
• [3] Björklund, L.G.: “Online patent information: Perspectives for the future”, World Pat Inf, 13(4): 206-208, (1991)
• [4] Asche G.: ““80% of technical information found only in patents” – Is there proof of this [1]?”, World Pat Inf, 48: 16-28, (2017)
• [5] Allen J., and Oppenheim C.: “The overlap of U.S. and Canadian patent literature with journal literature literature with journal literature”, World Pat Inf, 1(2): 77-80, (1979)
• [6] https://www3.wipo.int/ipstats/index.htm?tab=patent
• [7] Kayakökü A., Demirbaş Ş., “Patent Arama Motorlarının Kullanımı Üzerine Bir İnceleme”, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji, 5(3): 149-165, (2017)
• [8] Shalaby W., and Zadrozny W.: “Patent retrieval: a literature review”, Knowl. Inf. Systems. Syst., 61(2): 631-660, (2019)
• [9] Rodriguez-Esteban R., and Bundschus M.: “Text mining patents for biomedical knowledge”, Drug Discov Today, 21(6): 997-1002, (2016)
• [10] Madani F., and Weber C.: “The evolution of patent mining: Applying bibliometrics analysis and keyword network analysis”, World Pat Inf, 46: 32-48, (2016)
• [11] Abbas A., Zhang L., and Khan S.U.: “A literature review on the state-of-the-art in patent analysis”, World Pat Inf, 37: 3-13, (2014)
• [12] Tekinalp, Ü. “Fikri Mülkiyet Hukuku”, Vedat Kitapçılık, 2012. 2012)
• [13] Bonino D., Ciaramella A., and Corno F.: “Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics”, World Pat Inf, 32(1): 30-38, (2010)
• [14] Chiavetta D., and Porter A.: “Tech mining for innovation management”, Technol Anal Strategy, 25(6): 617-618, (2013)
• [15] Chen H., Zhang G., Lu J., and Zhu D.: “A fuzzy approach for measuring development of topics in patents using Latent Dirichlet Allocation”, IEEE International Conference on Fuzzy Systems , 1-7, (2015)
• [16] Hido S., Suzuki S., Nishiyama R., Imamichi T., Takahashi R., Nasukawa T., Id Eacute, Tsuyoshi, Kanehira Y., Yohda R., Ueno T., Tajima A., and Watanabe T.: “Modeling Patent Quality: A System for Large-scale Patentability Analysis using Text Mining”, Journal of Information Processing, 20(3): 655-666, (2012)
• [17] Wang J., and Chen Y.-J.: “A novelty detection patent mining approach for analyzing technological opportunities”, Adv Eng Inform, 42: 100941, (2019)
• [18] Lei L., Qi J.J., and Zheng K.: “Patent Analytics Based on Feature Vector Space Model: A Case of IoT”, Ieee Access, 7:45705-45715, (2019)
• [19] Wang X.F., Ren H.C., Chen Y., Liu Y.Q., Qiao Y.L., and Huang Y.: “Measuring patent similarity with SAO semantic analysis”, Scientometrics, 121(1): 1-23, (2019)
• [20] Park H., Yoon J., and Kim K.: “Identifying patent infringement using SAO based semantic technological similarities”, Scientometrics, 90(2): 515-529, (2012)
• [21] Zou S.Y., Zheng W.X., and Wu M.: “Research on Chinese Patent Infringement Detection Algorithm Based on Semantic Extended Vector Space Model”, Clausius Scientific Pr. Inc., (2019)
• [22] Chen Y.-L., and Chang Y.-C.: “A three-phase method for patent classification”, Information Processing & Management, 48(6): 1017-1030, (2012)
• [23]https://www.wipo.int/classifications/ipc/en/ITsupport/Categorization/dataset/wipo-alpha-readme.html2020
• [24] Li S.B., Hu J., Cui Y.X., and Hu J.J.: “DeepPatent: patent classification with convolutional neural networks and word embedding”, Scientometrics, 117(2): 721-744, (2018)
• [25]https://www.wipo.int/classifications/ipc/ipcpub/?notion=search&version=20200101&symbol=none&menulang=en&lang=en&viewmode=f&fipcpc=no&showdeleted=yes&indexes=no&headings=yes&notes=yes&direction=o2n&i nitial=A&cwid=none&tree=no&searchmode=ipccat
• [26] Drazic M., Kukolj D., Vitas M., Pokric M., Manojlovic S., and Tekic Z.: “Technology Matching of the Patent Documents Using Clustering Algorithms”, Int Symp Comp Intell, 405-409, (2013)
• [27] Trappey A.J.C. and Trappey C.V.: “An R&D knowledge management method for patent document summarization”, Ind Manage Data Syst, 108(2): 245-257, (2008)
• [28] Trappey A.J.C., Trappey C.V. and Wu C.-Y.: “Automatic patent document summarization for collaborative knowledge systems and services”, Journal of Systems Science and Systems Engineering, 18(1): 71-94, (2009)
• [29] Trappey A.J.C., Trappey C.V., and Wu C.-Y.: “A Semantic Based Approach for Automatic Patent Document Summarization”, Advanced Concurrent Engineering, Springer London, pp. 485-494
• [30] Yang C., Huang C., and Su J.: “An improved SAO network-based method for technology trend analysis: A case study of graphene”, J Informetr., 12(1): 271-286, (2018)
• [31] Li X., Xie Q.Q., Daim T., and Huang L.C.: “Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology”, Technology Forecast Soc, 146: 432-449, (2019)
• [32] Ma J., and Porter A.L.: “Analyzing patent topical information to identify technology pathways and potential opportunities”, Scientometrics, 102(1): 811-827, (2015)
• [33] Yu Y.S., Han H.Q., and Li Z.: “The Method for Discovering Technology Competitor Groups Based on Graph Clustering”, Aer Adv Eng Res, 131: 484-489, (2017)
• [34] Jeon J., Lee C., and Park Y.: “How to Use Patent Information to Search Potential Technology Partners in Open Innovation”, J Intellect Prop Rig, 16(5): 385-393, (2011)
• [35] Park S., Lee S.-J., and Jun S.: “Patent Big Data Analysis using Fuzzy Learning”, International Journal of Fuzzy Systems, 19(4): 1158-1167, (2017)
• [36] Kim K.H., Han Y.J., Lee S., Cho S.W., and Lee C.: “Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer”, Sustainability-Basel, 11(22), (2019)
• [37] Deniz N.: “Teknoloji Yönetiminde Moora Ve Aras Çok Ölçütlü Karar Verme Teknikleri İle Patent Değerleme”, Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 64: 191-207, (2020)
• [38] Yavuz N., Birdoğan. B.: “Patent Değerlerinin Çok Kriterli Karar Verme Yöntemleri İle Sıralanması: Otomotiv Sektöründe Bir Uygulama”, Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi, 17, (2019)
• [39] Kasravi K., and Risov M.: “Patent Mining - Discovery of Business Value from Patent Repositories”, 40th Annual Hawaii International Conference on System Sciences, (2007)
• [40] Han E.J., and Sohn S.Y.: “Patent valuation based on text mining and survival analysis”, The Journal of Technology Transfer, 40(5): 821-839, (2015)
• [41] Atal V., and Bar T.: “Prior art: To search or not to search”, International Journal of Industrial Organization, 28(5): 507-521, (2010)