基于机器学习的高效研发主题选择方法研究

Masashi Shibata, Koichi Inoue, Masakazu Takahashi
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引用次数: 1

摘要

本文提出了一种研发主题选择方法。选题方法有专利分析法、德尔菲调查法等。专利和同行评议的论文经常被用作主题选择的材料。一般而言,企业研发主题的选择分为三个阶段,即短期研发主题选择、长期研发主题选择和中期研发主题选择。中期研发主题的选择通常是在5年内实现,如探索性技术主题。由于它依赖于启发式知识和技术趋势,因此在业务领域中需要一种有效的选择方法。本文提出了一种基于公开信息的链接挖掘和机器学习相结合的研发主题选择方法。结果表明,满足对5年后技术结构的预测。
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A Study on the Efficient R&D Theme Selection Method with Machine Learning
This paper proposes an R&D theme selection method. There are various methods for the theme selection such as the patent analysis and the delphi investigation. The patents and the peer reviewed papers are frequently used as material for the theme selection. Generally, there are three phases for the R&D term selection such as the short-term R&D theme selection, the long-term R&D theme selection, and the medium-term R&D theme selection. The medium-term R&D theme selection is often aimed implementation within 5 years such as an exploratory technology theme. Since it relies on the heuristics knowledge with the technology trends, an efficient selection method is required among the business field. In this paper, we propose a method of selecting the R&D theme using combination of link mining and machine learning based on the public information. As a result, we satisfy predicting technology structure of 5 years later.
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