Applied patent mining with topic models and meta-data: A comprehensive case study

Pölzlbauer, Georg; Auer, Erwin

doi:10.1016/j.wpi.2021.102065

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Wu et al constructed an enterprise-centered international patent classification (IPC) multilevel supply chain network to discover technological opportunities and support corporate R&D decisions [20]. Georg et al provided available metadata in the form of IPC symbols, trying to guide topics to easily identifiable labels, enabling experts to quickly acquire advanced technologies [21]. Because the content contained in the classification number is relatively broad, it cannot provide more details for the design.…”

Section: Patent Data Miningmentioning

confidence: 99%

Acquisition Method of User Requirements for Complex Products Based on Data Mining

et al. 2023

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The vigorous development of big data technology has changed the traditional user requirement acquisition mode of the manufacturing industry. Based on data mining, manufacturing enterprises have the innovation ability to respond quickly to market changes and user requirements. However, in the stage of complex product innovation design, a large amount of design data has not been effectively used, and there are some problems of low efficiency and lack of objectivity of user survey. Therefore, this paper proposes an acquisition method of user requirements based on patent data mining. By constructing a patent data knowledge base, this method combines the Latent Dirichlet Allocation topic model and a K-means algorithm to cluster patent text data to realize the mining of key functional requirements of products. Then, the importance of demand is determined by rough set theory, and the rationality of demand is verified by user importance performance analysis. In this paper, the proposed method is explained and verified by mining the machine tool patent data in CNKI. The results show that this method can effectively improve the efficiency and accuracy of user requirements acquisition, expand the innovative design approach of existing machine tool products, and be applied to other complex product fields with strong versatility.

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Section: Patent Data Miningmentioning

confidence: 99%