计算机科学
生成语法
专利分析
数据科学
构造(python库)
工业工程
余弦相似度
数据挖掘
人工智能
模式识别(心理学)
工程类
程序设计语言
作者
Zhenfeng Liu,Feng Jian,Lorna Uden
标识
DOI:10.1016/j.techfore.2023.122565
摘要
Technology opportunity analysis (TOA) with ideas generation has been recognized an important activity to remain competitive and lead the industry in the future. However, there are several issues with existing TOA, such as an unclear path from technology opportunities to ideas generation, a fuzzy integration between automated TOA techniques and expert-based methods, and a lack of detailed schemes for technology opportunities. This study proposes a new systematic approach to show the way from technology opportunities to ideas generation via cross-cutting patent analysis. The proposed approach is comprised of three stages: 1) establishing a cross-cutting relationship between the target and reference technologies through the results of F-terms; 2) collecting and processing patents to construct patent-keyword vector matrices of the target and reference technologies, respectively; and 3) migrating corresponding ideas via cosine similarity and link prediction for the target technology opportunities that are discovered based on generative topographic mapping (GTM). The feasibility and effectiveness of the proposed approach is demonstrated by empirical research on the exploitation technology in both the natural gas hydrate (NGH) and the coal bed methane (CBM) fields. This study represents a contribution to expand the existing TOA research into generating creative ideas by providing more detailed schemes for technology opportunities.
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