(一)在查阅相关资料后,发现了有一篇 F.NARIN 文章,名为 Patent Bibliometrics , 很好奇,觉得既然是专利分析鼻祖的文章,一定要拿来读一读。这篇文章发表于 Scientometrics, Vol. 30, No.1 (1994) 147-155. (二)该篇论文的主要大意是说:它觉得专利与文献在 national productivity, inventor productivity, referencing cycles, citation impact, within country citation preferences 五个方面的属性上具有高度的一致性。因此,文献研究的方法也可以应用于技术文献的研究上。 (三)这篇文章回顾了很多第一: 1. 第一篇计量科学研究的论文是: F.J.Cole, N.B.EALES, THE HISTORY OF COMPARATIVE ANATOMY, SCIENCE PROGRESS,11(1917)578-596. 2. 第一个将科技文献数量评价引入经济学的人是: DEREK DE SOLLA PRICE.PRICE 提出一国科技出版物的数量与该国的经济规模有关(尤其是 GDP ),而与该国的地理面积、人口和其他参数无关。 3. 科学生产率的第一人是 LOTKA, 和他的 LOTKAS LAW (四)里面有一句话印象很深: 【原文】The old linear model of the way in which science, technology, and economics develop, postulates that scientists do research, publish, and place that information in the public domain. It then gets codified, taught, and eventually works its way into technology and is utilized by inventors who produce patented inventions, which are manufactured and sold, with a consequent contribution to economics. This linear model, science, then technology, then economics is simplistic and clearly inaccurate, and ignores the much more intimate relationship between technology and science, with many feedback loops. 【翻译】传统的科学技术经济的线性发展模式假定:科学家做实验,发表,将科技信息公之于众,然后,该信息被汇编、被讲授,并最终形成技术,并被发明家用于发明专利,通过专利产品的制作与销售,随之而来的是对于经济产生贡献。这种先科学后技术再经济的线性模式,忽略了科学、技术及其副产品之间的紧密关系,是简单且错误的。 (五)该文献提出了一个很早期的关于文献与专利共引,或者说自引的证据。 附全文的链接 patent bibliometrics 2010年9月18日
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参考文献: Visualization of patent analysis for emerging technology (SD) References and further reading may be available for this article. To view references and further reading you must purchase this article. Young Gil Kim a , Jong Hwan Suh a and Sang Chan Park Abstract Many methods have been developed to recognize those progresses of technologies, and one of them is to analyze patent information. And visualization methods are considered to be proper for representing patent information and its analysis results. However, current visualization methods for patent analysis patent maps have some drawbacks. Therefore, we propose an alternative visualization method in this paper. With colleted keywords from patent documents of a target technology field, we cluster patent documents by the k -Means algorithm. With the clustering results, we form a semantic network of keywords without respect of filing dates. And then we build up a patent map by rearranging each keyword node of the semantic network according to its earliest filing date and frequency in patent documents. Our approach contributes to establishing a patent map which considers both structured and unstructured items of a patent document. Besides, differently from previous visualization methods for patent analysis, ours is based on forming a semantic network of keywords from patent documents. And thereby it visualizes a clear overview of patent information in a more comprehensible way. And as a result of those contributions, it enables us to understand advances of emerging technologies and forecast its trend in the future. Keywords: Visualization; Patent analysis; k -Means clustering; Semantic network; Ubiquitous computing technology A New Visualization Method for Patent Map: Application to Ubiquitous Computing Technology (sp) JongHwanSuh 1 and SangChanPark 1, 2 Abstract As technologies develop in faster and more complicated ways, it is getting more important to expect the direction of technological progresses. So many methods are being proposed all around world and one of them is to use patent information. Moreover, with efforts of governments in many countries, many patent analysis methods have been exploited and suggested usually on the basis of patent documents. However, current patent analysis methods have some limitations. In this paper, we suggest a new visualization method for a patent map, which represents patent analysis results with considering both structured and unstructured items of each patent document. And by the adoption of the k-means clustering algorithm and semantic networks, we suggest concrete steps to make a patent map which gives a clear and instinctive insight on the targeted technology. In application, we built up a patent map for the ubiquitous computing technology and discussed an overall view of its progresses.