KaydedilenlerDerginizi ekleyin

Dergiler / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2026 / Cilt: 41 Sayı: 2

Patentometri ile teknoloji trendlerinin bütünleşik analizine yönelik bir yaklaşım

Serkan Altuntaş(YILDIZ TEKNİK ÜNİVERSİTESİ)0000-0003-4383-4710
Semih Kansu(YILDIZ TEKNİK ÜNİVERSİTESİ)0000-0003-2204-6942
Sayfa
1063–1078
Yayın Tarihi
2026-07-20
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Şirketlerin bu teknolojilere yönelik Ar-Ge yatırımlarının artırılması ve bu teknolojilerin iş süreçlerine entegrasyonuyla rekabet avantajı sağlanabilecektir.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Patentometri, teknolojilere yatırım yapma konusunda önemli bir yol haritası sunarak şirketlerin stratejik karar alma süreçlerini güçlendirir.
📊
Temel Bulgular & Veri:Çalışma kapsamında, “lens.org” veri tabanında son 20 yılda tescillenmiş 32.578 patent verisi kullanılmıştır.
💡
Sonuç & Katkı:Şirketlerin bu teknolojilere yönelik Ar-Ge yatırımlarının artırılması ve bu teknolojilerin iş süreçlerine entegrasyonuyla rekabet avantajı sağlanabilecektir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Patent analizi · kümeleme · patent kalitesi değerlendirme

Özet

Patentometri, teknolojilere yatırım yapma konusunda önemli bir yol haritası sunarak şirketlerin stratejik karar alma süreçlerini güçlendirir. Patent verileri şirketlerin teknoloji istihbaratı ve trendlerini belirlemek için kullanabilecekleri kaynaklardır. Patent verilerinin veri odaklı analizine dayanan patentometri ile teknolojik eğilimlerin belirlenmesi son derece önemlidir. Bu çalışmada, patent verileri kullanılarak K-means, Entropi tabanlı VIKOR ve ARIMA modeli ile teknoloji trendlerinin analizinde bütünleşik bir yaklaşım önerilmiştir. Önerilen yaklaşımın uygulamadaki başarısı tedarik zinciri teknolojileri ile ilgili patent verileri kullanılarak test edilmiştir. Çalışma kapsamında, “lens.org” veri tabanında son 20 yılda tescillenmiş 32.578 patent verisi kullanılmıştır. Uygulamanın sonuçları, “Makine Öğrenmesi ve Bilişsel Sistemler,” “Ağ Güvenliği ve Blok Zinciri,” ve “RFID ve Kimlik Tanıma Sistemleri” kümelerinin en yüksek kaliteye sahip kümeler olduğunu göstermiştir. Bununla birlikte, kümelerdeki patent sayılarının önümüzdeki 5 yılda artacağı öngörülmüştür. Şirketlerin bu teknolojilere yönelik Ar-Ge yatırımlarının artırılması ve bu teknolojilerin iş süreçlerine entegrasyonuyla rekabet avantajı sağlanabilecektir.

🔖

Bu Makaleye Atıf Yap

Altuntaş, S., Kansu, S. (2026). Patentometri ile teknoloji trendlerinin bütünleşik analizine yönelik bir yaklaşım. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 41 Sayı: 2, 1063–1078. https://doi.org/10.17341/gazimmfd.1655811

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (82)

  1. Abbas, A., Zhang, L., Khan, S. U., A literature review on the state-of-the-art in patent analysis, World Patent Information, 37, 3-13, 2014.
  2. Mogee, M. E., Using Patent Data for Technology Analysis and Planning, Research-Technology Management, 34 (4), 43-49, 1991.
  3. Ozcan, S., Unalan, S., Blockchain as a General-Purpose Technology: Patentometric Evidence of Science, Technologies, and Actors, IEEE Trans Eng Manag, 69 (3), 792-809, 2022.
  4. Pivar, J., Vugec, D. S., Patent Analysis-Systematic Literature Review on Methodology and Technological Fields, 2024 47th ICT and Electronics Convention, MIPRO 2024-Proceedings, Institute of Electrical and Electronics Engineers Inc., Opatija, Croatia, 988-993, 20-24 Mayıs, 2024.
  5. Yang, C., Zhu, D., Zhang, G., Semantic-based technology trend analysis, 10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015, Institute of Electrical and Electronics Engineers Inc., 222-228, Ocak, 2016.
  6. Huang, S. M., Yen, D. C., Yan, T. J., Yang, Y. T., An Intelligent Mechanism to Automatically Discover Emerging Technology Trends: Exploring Regulatory Technology, ACM Trans Manag Inf Syst, 13 (2), 2021.
  7. Segev, A., Jung, C., Jung, S., Analysis of technology trends based on big data, 2013 IEEE International Congress on Big Data, Silicon Valley, CA, USA, 6-9 Ekim, 419–420, 2013.
  8. Kim, J., Yoon, J., Park, E., Choi, S., Patent document clustering with deep embeddings, Scientometrics, 123 (2), 563-577, 2020.
  9. Buzard, K., Carlino, G. A., Hunt, R. M., Carr, J. K., Smith, T. E., Localized knowledge spillovers: Evidence from the spatial clustering of R&D labs and patent citations, Reg Sci Urban Econ, 81, 103490, 2020.
  10. Tseng, Y. H., Lin, C. J., Lin, Y. I., Text mining techniques for patent analysis, Inf Process Manag, 43 (5), 1216-1247, 2007.
  11. Huang, S. H., Ke, H. R., Yang, W. P., Structure clustering for Chinese patent documents, Expert Syst Appl, 34 (4), 2290-2297, 2008.
  12. Dereli, T., Durmuşoǧlu, A., Classifying technology patents to identify trends: Applying a fuzzy-based clustering approach in the Turkish textile industry, Technol Soc, 31 (3), 263-272, 2009.
  13. Dereli, T., Baykasoğlu, A., Durmuşoğlu, A., Durmuşoğlu, Z. D. U., Enhancing technology clustering through heuristics by using patent counts, Expert Syst Appl, 38 (12), 15383-15391, 2011.
  14. Jun, S., Park, S. S., Jang, D. S., Document clustering method using dimension reduction and support vector clustering to overcome sparseness, Expert Syst Appl, 41 (7), 3204-3212, 2014.
  15. Xie, Y., Hu, X., Zhang, Y., Li, S., Unsupervised keyword extraction method based on Chinese patent clustering, 10th IEEE International Conference on Big Knowledge, ICBK 2019, Institute of Electrical and Electronics Engineers Inc., Beijing, China, 302-309, 10-11 Kasım, 2019.
  16. Han, H., Yu, Y., Wang, L., Zhai, X., Ran, Y., Han, J., Disambiguating USPTO inventor names with semantic fingerprinting and DBSCAN clustering, Electronic Library, 37 (2), 225-239, 2019.
  17. Suh, J. W., Sohn, S. Y., Lee, B. K., Patent clustering and network analyses to explore nuclear waste management technologies, Energy Policy, 146, 111794, 2020.
  18. Skripnikova, T., Aras, H., Weißhaar, A., Blank, S., Zorn, H. P., Semantic views — Interactive hierarchical exploration for patent landscaping, World Patent Information, 65, 102043, 2021.
  19. Ramadhani, P. A., Wibawa, A. D., Endroyono, ICT Evolution Map Based on Indonesian Patent Documents Using Text Clustering Analysis, 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022, Institute of Electrical and Electronics Engineers Inc., 109-114, 13-14 Aralık, 2022.
  20. Wambsganss, A., Tomidei, L., Sick, N., Salomo, S., Ben Miled, E., Machine learning-based method to cluster a converging technology system: The case of printed electronics, World Paten Information, 78, 102301, 2024.
  21. Chang, A. C., Lin, J. S., Exploring technological applications through LDA-based patent mining: A metaverse ontology construction, World Patent Information, 79, 102314, 2024.
  22. Mazaheri, M., Bonnin Roca, J., Markus, A., Tur, E. M., Walrave, B., Maturity assessment of green patent clusters: Methodological implications, Technol Forecast Soc Change, 209, 123813, 2024.
  23. Kahraman, S. Y., Durmuşoğlu, A., Dereli, T., Patent classification with pre-trained Bert model, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (4), 2484–2496, 2024.
  24. Trappey, A. J. C., Trappey, C. V., Wu, C. Y., Lin, C. W., A patent quality analysis for innovative technology and product development, Advanced Engineering Informatics, 26 (1), 26-34, 2012.
  25. Van Pottelsberghe La Potterie, B., The quality factor in patent systems, Bruegel, Brussels, 2010/03, 2010. [Çevrimiçi]. Erişim adresi: https://hdl.handle.net/10419/77977
  26. Higham, K. W., De Rassenfosse, G., Jaffe, A. B., Patent Quality: Towards a Systematic Framework for Analysis and Measurement, Cambridge, 27598, 2020. [Çevrimiçi]. Erişim adresi: http://www.nber.org/papers/w27598
  27. Squicciarini, M., Dernis, H., Criscuolo, C., Measuring Patent Quality: Indicators of Technological and Economic Value, Paris, 2013/03, 2013.
  28. Lin, H., Wang, H., Du, D., Wu, H., Chang, B., Chen, E., Patent quality valuation with deep learning models, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 474-490, 2018.
  29. Zhang, L., Li, Q., Yu, J., Patent Quality Evaluation Model Based on Hesitant Fuzzy Soft Set, 24th International Conference on Industrial Engineering and Engineering Management 2018, Springer Singapore, 621-628, 2019.
  30. Hain, D. S., Jurowetzki, R., Buchmann, T., Wolf, P., A text-embedding-based approach to measuring patent-to-patent technological similarity, Technol Forecast Soc Change, 177, 2022.
  31. Yang, Y., Rapid Patent Quality Evaluation Method Based on Big Data Analysis: Chinese Invention Patentsas Sample, Advances in Economics, Management and Political Sciences, 7 (1), 343-349, 2023.
  32. Erdoğan, Z., Altuntas, S., Dereli, T, Predicting Patent Quality Based on Machine Learning Approach, IEEE Trans Eng Manag, 71, 3144-3157, 2024.
  33. Dereli, T., Durmuşoğlu, A., Alerting companies through on-line patent trend analysis, Cybern Syst, 41(5), 371-390, 2010.
  34. Wilson, R. M., Patent Analysis using Online Databases Technological Trend Analysis, World Patent Information, 9 (1), 18-26, 1987.
  35. Bigwood, M. P., Patent Trend Analysis: Incorporate Current Year Data, World Patent Information, 19 (4), 243-249, 1997.
  36. Shih, M. J., Liu, D. R., Hsu, M. L., Discovering competitive intelligence by mining changes in patent trends, Expert Syst Appl, 37 (4), 2882-2890, 2010.
  37. Smith, M., Agrawal, R., A Comparison of Time Series Model Forecasting Methods on Patent Groups, Midwest Artificial Intelligence and Cognitive Science Conference, North Carolina, USA, 25-26 Nisan, 2015.
  38. Hao, X., Xie, N., Sun, W., Analyzing Technology Development Trend Based on Patent Data, The 2016 3rd International Conference on Systems and Informatics (ICSAI 2016), Shanghai, China, 19-21 Kasım, 2016.
  39. Malpure, D., Botre, Y., Bhansali, D., Bhagi, R., Wagh, K. K., International Journal of Computer Science and Mobile Computing Patent Trend Analysis and Future Prediction, 2017. [Çevrimiçi]. Erişim adresi: www.ijcsmc.com
  40. You, H., Li, M., Hipel, K. W., Jiang, J., Ge, B., Duan, H., Development trend forecasting for coherent light generator technology based on patent citation network analysis, Scientometrics, 111 (1), 297-315, 2017.
  41. Mutlu N.G., Altuntaş S., Monitoring technological changes with statistical control charts based on patent data, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (4), 1875–1892, 2021.
  42. Noh, H., Jo, Y., Lee, S., Keyword selection and processing strategy for applying text mining to patent analysis, Expert Syst Appl, 42 (9), 4348-4360, 2015.
  43. Sokmen, N., Petrov, A. E., Forecasting of Radar Technologies Using Patent Information, 15th IEEE International Conference on Application of Information and Communication Technologies, AICT 2021, Institute of Electrical and Electronics Engineers Inc., 13-15 Ekim, 2021.
  44. Tarazi, A., Comparative Analysis of the Bibliographic Data Sources Using PubMed, Scopus, Web of Science, and Lens, High Yield Medical Reviews, 2 (1), 2024.
  45. Shokouhyar, S., Maghsoudi, M., Khanizadeh, S., Jorfi, S., Analyzing supply chain technology trends through network analysis and clustering techniques: a patent-based study, Ann Oper Res, 341, 313-348, 2024.
  46. Kang, H., The prevention and handling of the missing data, Korean J Anesthesiol, 64 (5), 402-406, 2013.
  47. Adams, S., Comparing the IPC and the US classification systems for the patent searcher, World Patent Information, 23, 15-23, 2001.
  48. Prasad, R., Searching bioremediation patents through Cooperative Patent Classification (CPC), Rev Environ Health, 31 (19), 53-56, 2016.
  49. Leydesdorff, L., Kogler, D. F., Yan, B., Mapping patent classifications: portfolio and statistical analysis, and the comparison of strengths and weaknesses, Scientometrics, 112 (3), 1573-1591, 2017.
  50. Dash, M., Koot, P. W., Feature Selection for Clustering”, içinde Encyclopedia of Database Systems, Editör: Liu, L, Özsu, M. T., Boston, MA: Springer, 2009.
  51. Kim, G., Bae, J., A novel approach to forecast promising technology through patent analysis, Technol Forecast Soc Change, 117, 228-237, 2017.
  52. Nam, S., Kim, K., Monitoring Newly Adopted Technologies Using Keyword Based Analysis of Cited Patents, IEEE Access, 5, 23086-23091, 2017.
  53. Pujari, S. C., Mantiuk, F., Giereth, M., Strötgen, J., Friedrich, A., Evaluating Neural Multi-Field Document Representations for Patent Classification, 2022. [Çevrimiçi]. Erişim adresi: https://www.cooperativepatentclassification.org
  54. Landau, S., Chis Ster, I., Cluster Analysis: OverviewInternational Encyclopedia of Education, Third Edition, Elsevier, 72-83, 2009.
  55. Rokach, L., Maimon, O., Clustering Methods, içinde Data Mining and Knowledge Discovery Handbook, Springer-Verlag, 321-352, 2006.
  56. Chong, B., K-means clustering algorithm: a brief review, Academic Journal of Computing & Information Science, 4 (5), 37-40, 2021.
  57. Anuranjana, Mittas, N., Mehrotra, D., Clustering the patent data using K-means approach, Advances in Intelligent Systems and Computing, Springer Verlag, 639-645, 2019.
  58. Sinaga, K. P., Yang, M. S., Unsupervised K-means clustering algorithm, IEEE Access, 8, 80716-80727, 2020.
  59. Chicco, D., Campagner, A., Spagnolo, A., Ciucci, D., Jurman, G., The Silhouette coefficient and the Davies-Bouldin index are more informative than Dunn index, Calinski-Harabasz index, Shannon entropy, and Gap statistic for unsupervised clustering internal evaluation of two convex clusters, PeerJ Computer Science, 11, e3309, 2025.
  60. Gagolewski, M., Bartoszuk, M., Cena, A., Are cluster validity measures (in) valid?, Information Sciences, 581, 620–636, 2021.
  61. Rousseeuw, P. J., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, 20, 53-65, 1987.
  62. Efron, B., Tibshirani, R., An Introduction to Bootstrap, 1st ed., 2, Chapman and Hall/CRC, New York, 1994. 63. Hubert, L., Arabic, P., Comparing Partitions, Journal of Classifications, 2, 193-218, 1985.
  63. Lu-cheng, H., Liu, C., Patent Quality Evaluation with Entropy Weight Method and Improved TOPSIS: The Case of Geriatric Technology, 2018 International Conference on Management Science & Engineering (25th), Frankfurt, Germany: Institute of Electrical and Electronics Engineers, 260, Ağustos, 2018.
  64. Chen, C. H., A hybrid multi-criteria decision-making approach based on anp-entropy topsis for building materials supplier selection, Entropy, 23 (12), 1597, 2021.
  65. Yavuz, N., Baki, B., Patent değerlerinin çok kriterli karar verme yöntemleri ile sıralanması: Otomotiv sektöründe bir uygulama, Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi, 9, 27-52, 2019.
  66. Deniz, N., Teknoloji yönetiminde MOORA ve ARAS çok ölçütlü karar verme teknikleri ile patent değerleme, Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 64, 191-207, 2020.
  67. Zhang, Y., Qian, Y., Huang, Y., Guo, Y., Zhang, G., Lu, J., An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation, Scientometrics, 111 (3), 1925-1946, 2017.
  68. Gul, M., Celik, E., Aydin, N., Taskin Gumus, A., Guneri, A. F., A state of the art literature review of VIKOR and its fuzzy extensions on applications, Applied Soft Computing, 46, 60-89, 2016.
  69. Opricovic, S., Tzeng, G. H., Extended VIKOR method in comparison with outranking methods, European Journal of Operations Research, 178 (29), 514-529, 2007.
  70. Chatterjee, P., Chakraborty, S., A comparative analysis of VIKOR method and its variants, Decision Science Letters, 5 (4), 469-486, 2016.
  71. Dev, S., Aherwar, A., Patnaik, A., Material Selection for Automotive Piston Component Using Entropy-VIKOR Method, Silicon, 12, 155-169, 2020.
  72. Fajdek-Bieda, A., Using Entropy-VIKOR method in chemical processes optimization, Procedia Computer Science, Elsevier B.V., 4208-4217, 2021.
  73. Shemshadi, A., Shirazi, H., Toreihi, M., Tarokh, M. J., A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting, Expert Syst Appl, 38 (10), 12160-12167, 2011.
  74. Zheng, Q., Chen, K., Kong, D., Liang, J., Wu, J., A Selection Model of Strategic Emerging Industries in Fujian Based on Entropy-VIKOR Algorithm, Discrete Dyn Nat Soc, Discrete Dynamics in Nature and Society, 2021, 2021.
  75. Donatelli, R. E., Park, J. A., Mathews, S. M., Lee, S. J., Time series analysis, Statistics and Research Design, 161 (4), 605-608, 2022.
  76. Ho, S. L., Xie, M., The use of ARIMA models for reliability forecasting and analysis, Computers and Industrial Engineering, 35 (1-2), 213-216, 1998.
  77. Kwon, K., Jun, S., Lee, Y. J., Choi, S., Lee, C. Logistics technology forecasting framework using patent analysis for technology roadmap, Sustainability, 14 (9), 5430, 2022.
  78. Lee, Y.-J., Han, Y. J., Kim, S.-S., Lee, C. Patent Data Analytics for Technology Forecasting of the Railway Main Transformer, Sustainability, 15 (1), 278, (2023).
  79. Smith, M., Agrawal, R. A Comparison of Time Series Model Forecasting Methods on Patent Groups. MAICS, 1353, 167-173, 2015.
  80. Meral H., Özdemir Y.E., Akbay C., Forecasting patent trends in the agricultural sector using the ARIMA model: A case study of IPC A01 class, Turkish Journal of Agricultural and Natural Sciences, 12 (4), 1056-1067, 2025.
  81. Altuntas, S., Erdogan, Z., Dereli, T. A clustering-based approach for the evaluation of candidate emerging technologies. Scientometrics 124, 1157–1177, 2020.
  82. Altuntaş, F., Yilmaz,M.K., Patent analizi ile teknoloji ağlarının oluşturulması, Girişimcilik ve İnovasyon Yönetimi Dergisi, 6 (2), 97-129, 2017.