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  1. Buch ; Online: Multi label classification of Artificial Intelligence related patents using Modified D2SBERT and Sentence Attention mechanism

    Yoo, Yongmin / Heo, Tak-Sung / Lim, Dongjin / Seo, Deaho

    2023  

    Abstract: Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days. However, artificial intelligence- ... ...

    Abstract Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days. However, artificial intelligence-related patents are very difficult to classify because it is a mixture of complex technologies and legal terms. Moreover, due to the unsatisfactory performance of current algorithms, it is still mostly done manually, wasting a lot of time and money. Therefore, we present a method for classifying artificial intelligence-related patents published by the USPTO using natural language processing technique and deep learning methodology. We use deformed BERT and sentence attention overcome the limitations of BERT. Our experiment result is highest performance compared to other deep learning methods.
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 401 ; 006
    Erscheinungsdatum 2023-03-03
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Buch ; Online: A Novel Patent Similarity Measurement Methodology

    Yoo, Yongmin / Jeong, Cheonkam / Gim, Sanguk / Lee, Junwon / Schimke, Zachary / Seo, Deaho

    Semantic Distance and Technological Distance

    2023  

    Abstract: Patent similarity analysis plays a crucial role in evaluating the risk of patent infringement. Nonetheless, this analysis is predominantly conducted manually by legal experts, often resulting in a time-consuming process. Recent advances in natural ... ...

    Abstract Patent similarity analysis plays a crucial role in evaluating the risk of patent infringement. Nonetheless, this analysis is predominantly conducted manually by legal experts, often resulting in a time-consuming process. Recent advances in natural language processing technology offer a promising avenue for automating this process. However, methods for measuring similarity between patents still rely on experts manually classifying patents. Due to the recent development of artificial intelligence technology, a lot of research is being conducted focusing on the semantic similarity of patents using natural language processing technology. However, it is difficult to accurately analyze patent data, which are legal documents representing complex technologies, using existing natural language processing technologies. To address these limitations, we propose a hybrid methodology that takes into account bibliographic similarity, measures the similarity between patents by considering the semantic similarity of patents, the technical similarity between patents, and the bibliographic information of patents. Using natural language processing techniques, we measure semantic similarity based on patent text and calculate technical similarity through the degree of coexistence of International patent classification (IPC) codes. The similarity of bibliographic information of a patent is calculated using the special characteristics of the patent: citation information, inventor information, and assignee information. We propose a model that assigns reasonable weights to each similarity method considered. With the help of experts, we performed manual similarity evaluations on 420 pairs and evaluated the performance of our model based on this data. We have empirically shown that our method outperforms recent natural language processing techniques.
    Schlagwörter Computer Science - Information Retrieval ; Computer Science - Artificial Intelligence ; Computer Science - Social and Information Networks
    Thema/Rubrik (Code) 303
    Erscheinungsdatum 2023-03-23
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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    Fernleihe an ZB MED

    Sie können sich den gewünschten Titel als lokale Nutzerin oder lokaler Nutzer von ZB MED direkt an den Standort Köln schicken lassen.

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