Patent Dokümanlarının Anlamsal Benzerliğinin Tespiti Üzerine Bir İnceleme

Year 2024, EARLY VIEW, 1 - 1

Ahmet Kayakökü , Aslıhan Tüfekci

https://doi.org/10.2339/politeknik.1294789

Abstract

Teknik anlamda en güncel bilgileri barındıran, yüksek hacmiyle bilgi keşfi açısından müthiş bir potansiyele sahip olan ve teknoloji yönetimi alanında kilit bir rol üstlenen patent verisinin işlenmesinde patent madenciliği çalışmaları giderek önem kazanmaktadır. Patent verisi içerisinde bulunan yapısal veya yapısal olmayan verilerin hepsi önemli olsa da, patent madenciliği çalışmalarının en kritik hedefi patent dokümanlarının anlamsal benzerliğini tespit edebilmektir. Patentlerin anlamsal benzerlik tespiti ile patent başvuru sürecinin en zor ve en çok vakit alan safhası olan patentlenebilirlik kriterlerinin tespitinin otomatik olarak yapılabilmesi mümkün olacaktır. Patent metinlerinin, metin madenciliği yöntemleri ile yapısal hale getirilerek birbirine ne kadar benzediklerini tespit etmek için küme teorisi yaklaşımları, vektör uzay modeli yaklaşımları veya ontoloji vb. bilgi kaynaklarından faydalanılan yaklaşımlar mevcuttur. Ancak patent metinlerinin karmaşık yapısı ve kendine has terminolojisi sebebiyle bu yöntemlerden hedeflenen verim alınamamaktadır. Bu eksikliği gidermek için kullanıldığı her alanda büyük başarılar ortaya koyan derin öğrenme yöntemlerinden, patent metinlerinin anlamsal olarak karşılaştırılmasında da faydalanılması gerekmektedir. Bu alanda çalışmalar yapılmasına rağmen etkin bir şekilde patentlenebilirlik tespiti yapabilen modeller henüz başlangıç aşamasındadır. Nitelikli bir model geliştirilerek patentlenebilirlik tespiti yapıldıktan sonra patent araştırma raporunun otomatik olarak hazırlanması teknoloji yönetimi alanındaki büyük ihtiyacın karşılanabilmesi adına önemli bir adım olacaktır.

Keywords

patent, patent madenciliği, metin madenciliği, anlamsal benzerlik, patentlenebilirlik, derin öğrenme

A Review on the Determination of Semantic Similarity of Patent Documents

Abstract

Patent mining studies are gaining importance in the processing of patent data, which contains the most up-to-date technical information, has a great potential in terms of information discovery with its high volume, and plays a key role in the field of technology management. Although all the structured or unstructured data in the patent data are important, the most critical goal of patent mining studies is to determine the semantic similarity of patent documents. With the semantic similarity detection of patents, it will be possible to automatically determine the patentability criteria, which is the most difficult and time-consuming phase of the patent application process. Set theory approaches, vector space model approaches or ontology etc. are used to determine how similar patent texts are to each other by structuralizing them with text mining methods. There are approaches that make use of information sources. However, due to the complex structure and unique terminology of patent texts, the targeted efficiency cannot be obtained from these methods. In order to overcome this deficiency, deep learning methods, which have shown great success in every field they are used, should also be utilized in the semantic comparison of patent texts. Although studies have been carried out in this area, models that can effectively detect patentability are still in their infancy. After a qualified model is developed and patentability is determined, the automatic preparation of the patent search report will be an important step in meeting the great need in the field of technology management.

Keywords

patent, patent mining, text mining, semantic similarity, patentability, deep learning

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