IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v164y2021ics0040162520313378.html
   My bibliography  Save this article

An intelligent patent recommender adopting machine learning approach for natural language processing: A case study for smart machinery technology mining

Author

Listed:
  • Trappey, Amy
  • Trappey, Charles V.
  • Hsieh, Alex

Abstract

Recommendation systems are widely applied in many fields, such as online customized product searches and customer-centric advertisements. This research develops the methodology for a patent recommender to discover semantically relevant patents for further technology mining and trend analysis. The proposed recommender adopts machine learning (ML) algorithms for natural language processing (NLP) to represent patent documents in vector space and to enable semantic analyses of the patent documents. The ML approach of neural network (NN) language models, trained by domain patent documents (text) as a training set, convert patent documents into vectors and, thus, can identify semantically similar patents using document similarity measures. In particular, the proposed recommender is deployed to in-depth case studies for advanced patent recommendations. The case domain of smart machinery is used to better enable smart manufacturing by incorporating innovative technologies, such as intelligent sensors, intelligent controllers, and intelligent decision making. The research uses six sub-domains in smart machinery technologies as the case studies to verify the superior accuracy and efficacy of the recommender system and methodologies.

Suggested Citation

  • Trappey, Amy & Trappey, Charles V. & Hsieh, Alex, 2021. "An intelligent patent recommender adopting machine learning approach for natural language processing: A case study for smart machinery technology mining," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    • Handle: RePEc:eee:tefoso:v:164:y:2021:i:c:s0040162520313378
    DOI: 10.1016/j.techfore.2020.120511
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162520313378
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2020.120511?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    2. Shutian Ma & Chengzhi Zhang & Xiaozhong Liu, 2020. "A review of citation recommendation: from textual content to enriched context," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1445-1472, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kraus, Sascha & Kumar, Satish & Lim, Weng Marc & Kaur, Jaspreet & Sharma, Anuj & Schiavone, Francesco, 2023. "From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    2. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    3. Chiarello, Filippo & Fantoni, Gualtiero & Hogarth, Terence & Giordano, Vito & Baltina, Liga & Spada, Irene, 2021. "Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    4. Eachempati, Prajwal & Srivastava, Praveen Ranjan & Kumar, Ajay & Muñoz de Prat, Javier & Delen, Dursun, 2022. "Can customer sentiment impact firm value? An integrated text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    5. Just, Julian, 2024. "Natural language processing for innovation search – Reviewing an emerging non-human innovation intermediary," Technovation, Elsevier, vol. 129(C).
    6. Jaewoong Choi & Jiho Lee & Janghyeok Yoon & Sion Jang & Jaeyoung Kim & Sungchul Choi, 2022. "A two-stage deep learning-based system for patent citation recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6615-6636, November.
    7. Jeon, Daeseong & Ahn, Joon Mo & Kim, Juram & Lee, Changyong, 2022. "A doc2vec and local outlier factor approach to measuring the novelty of patents," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    8. Jauhar, Sunil Kumar & Sethi, Sunil & Kamble, Sachin S. & Mathew, Shawn & Belhadi, Amine, 2024. "Artificial intelligence and machine learning-based decision support system for forecasting electric vehicles' power requirement," Technological Forecasting and Social Change, Elsevier, vol. 204(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Yang & Yongquan, Yang & Jian, Ma & Lu, Angela & Xuanhua, Xu, 2024. "Policy-induced cooperative knowledge network, university-industry collaboration and firm innovation: Evidence from the Greater Bay Area," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    3. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    4. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
    5. Rodrigo Nogueira & Zhiying Jiang & Kyunghyun Cho & Jimmy Lin, 2020. "Navigation-based candidate expansion and pretrained language models for citation recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 3001-3016, December.
    6. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2021. "An in-text citation classification predictive model for a scholarly search system," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5509-5529, July.
    7. Gupta, Nitish & Park, Hyunkyu & Phaal, Rob, 2022. "The portfolio planning, implementing, and governing process: An inductive approach," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    8. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    9. Jeon, Eunji & Yoon, Naeun & Sohn, So Young, 2023. "Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    10. Orlando Fonseca Guilarte & Simone Diniz Junqueira Barbosa & Sinesio Pesco, 2021. "RelPath: an interactive tool to visualize branches of studies and quantify the expertise of authors by citation paths," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4871-4897, June.
    11. Seunghyun Oh & Jaewoong Choi & Namuk Ko & Janghyeok Yoon, 2020. "Predicting product development directions for new product planning using patent classification-based link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1833-1876, December.
    12. Choi, Seokkyu & Lee, Hyeonju & Park, Eunjeong & Choi, Sungchul, 2022. "Deep learning for patent landscaping using transformer and graph embedding," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    13. Zhang, JingJing & Yan, Yan & Guan, JianCheng, 2019. "Recombinant distance, network governance and recombinant innovation," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 260-272.
    14. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2021. "The use of citation context to detect the evolution of research topics: a large-scale analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2971-2989, April.
    15. Ba, Zhichao & Meng, Kai & Ma, Yaxue & Xia, Yikun, 2024. "Discovering technological opportunities by identifying dynamic structure-coupling patterns and lead-lag distance between science and technology," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    16. Lee, MyoungHoon & Kim, Suhyeon & Kim, Hangyeol & Lee, Junghye, 2022. "Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    17. Tadeusz A. Grzeszczyk & Michal K. Grzeszczyk, 2021. "Improving the Discovery of Technological Opportunities Using Patent Classification Based on Explainable Neural Networks," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 402-409.
    18. Pengcheng Li & Wei Lu & Qikai Cheng, 2022. "Generating a related work section for scientific papers: an optimized approach with adopting problem and method information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4397-4417, August.
    19. Jinho Choi & Yong Sik Chang, 2020. "Development of a New Methodology to Identity Promising Technology Areas Using M&A Information," Sustainability, MDPI, vol. 12(14), pages 1-25, July.
    20. Wei Du & Yibo Wang & Wei Xu & Jian Ma, 2021. "A personalized recommendation system for high-quality patent trading by leveraging hybrid patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9369-9391, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:164:y:2021:i:c:s0040162520313378. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.