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Analysis of technological innovation based on citation information

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Abstract

The paper discusses the dynamic properties of the patent network. Technological innovation occurs frequently, and predicting where it will happen is difficult because an economic system can adapt to changing technology. We construct a patent network based on the cited relations between patents and analyze the properties of the patent network from January 1976 to December 2005 by using USPTO patent data. We find that technology trends, which are calculated by our measures, are similar with historical trends of technology, showing that our measures would be useful to predict future technology relations. Also, we find that the change of similarity between patents shows meaningful results in terms of technological innovation.

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Notes

  1. https://en.wikipedia.org/wiki/H-index.

  2. NBER Patent Data Project, https://sites.google.com/site/patentdataproject/Home.

  3. We manually select sample patents from the total dataset. Since this study focuses mainly on citation information, we choose the patents according to the number of own cited patents. If a citing patent was citing other patents over the threshold, which is the number of cited patents for 1000th patent, then the citing patent would be selected. The reason why we use the threshhold based on the 1000th patent is that while the total number of patents in each year is at least over 3000, most of the patents have 1 citation. To calcaulate the similarity between patents, we should need the patents that have large citations. The patents that have small citation may distort our similarity values.

  4. Six technological groups are based on HJT tech categories.

  5. Sudden drop is a problem in patent dataset, called the truncation problem (Hall et al. 2001), which is that older patents may have more citations than newer patents. This problem would sometimes critically affect the result of citation analysis, however, since our measures not compare the nominal values of the number of citations among technological groups, but do the relative proportions of it among them to analyze trend of the citations of patents in a technological group, our results are able to avoid from the truncation problem.

  6. One patent pair has the maximum similarity value of 100. Therefore, since we use 1000 patents, the maximum similarity value of a patent is 1000*100. If there are 10 patents in a technological group and each patent has the similarity values of 100,000, then the average value of similarity in the technological group is 100,000. We use the similarity value as a weight of adjacency matrix in network theory. While similarity network is undirected network, which means that both the similarity of node 1 to node 2 and that of node 2 to node 1 are equal, both values are important in our results.

  7. H-Index measured in the Similarity network (HIS), H-Index measured in the Citation network (HIC), Degree of Centrality measured in the Similarity network (DCS) and Degree of Centrality measured in the Citation network (DCC).

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Acknowledgements

This work was supported by research funds from Chosun University, Korea, 2015.

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Authors and Affiliations

  1. Division of Business Administration, Chosun University, Gwangju, South Korea

    Gabjin Oh, Ho-Yong Kim & Ayoung Park

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  1. Gabjin Oh
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  2. Ho-Yong Kim
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  3. Ayoung Park
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Correspondence to Gabjin Oh.

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Oh, G., Kim, HY. & Park, A. Analysis of technological innovation based on citation information. Qual Quant 51, 1065–1091 (2017). https://doi.org/10.1007/s11135-016-0460-9

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