Empirical Analysis of Patent Litigation: A Comparison Study between Japan and China

　In this paper, we gathered the data from public sources for analyzing the outcomes of 531 cases and 785 cases decided respectively by Japanese and Chinese courts at the first trial of patent litigations between 2004 and 2016. Using these data, we implemented a comparison analysis on recent patent litigations between Japan and China. Moreover, combining with information from Patstat, a patent database, for the patents infringed, we did an empirical analysis on determinants of trail win rate and rewards in patent litigations both for Japan and China. 　Our estimated results suggest that China has the determinants on rates of success and appeal which very similar to those in Japanese patent suits. On the other hand, however, for infringement awards, those that influence the outcome of the courts are quite different between the two countries

University-Industry Technology Transfer: Empirical Findings from Chinese Industrial Firms

The knowledge and innovation generated by researchers at universities is transferred to industries through patent licensing, leading to the commercialization of academic output. In order to investigate the development of Chinese university-industry technology transfer and whether this kind of collaboration may affect a firm's innovation output, we collected approximately 6400 license contracts made between more than 4000 Chinese firms and 300 Chinese universities for the period between 2009 and 2014. This is the first study on Chinese university-industry knowledge transfer using a bipartite social network analysis (SNA) method, which emphasizes centrality estimates. We are able to investigate empirically how patent license transfer behavior may affect each firm's innovative output by allocating a centrality score to each firm in the university-firm technology transfer network. We elucidate the academic-industry knowledge by visualizing flow patterns for different regions with the SNA tool, Gephi. We find that innovation capabilities, R&D resources, and technology transfer performance all vary across China, and that patent licensing networks present clear small-world phenomena. We also highlight the Bipartite Graph Reinforcement Model (BGRM) and BiRank centrality in the bipartite network. Our empirical results reveal that firms with high BGRM and BiRank centrality scores, long history, and fewer employees have greater innovative output

Retrieval Technology of Enterprise Data Center Resources Based on Density Peak Clustering Algorithm

In order to effectively ensure the retrieval effect of enterprise data center resources, improve the retrieval accuracy of enterprise data center resources, and shorten the retrieval time of enterprise data center resources, a retrieval technology of enterprise data center resources based on density peak clustering algorithm is proposed. Analytical clustering algorithms, density clustering algorithms, and density peak clustering algorithms are all types of clustering algorithms. To reduce the dimensionality of enterprise data center resources, the kernel principal component analysis method is used. The structure of the enterprise data center resource set is reorganized and the feature quantity of the enterprise data center resource distribution is extracted using feature space reorganization technology. On this basis, the density peak clustering is carried out on the data center resource set of enterprise, and the semantic association distribution model of data center resource retrieval in enterprise is constructed. Through the semantic registration and weighted vector combination control method, the retrieval of enterprise data center resources is realized. The experimental results show that the proposed algorithm has a good effect on the retrieval of enterprise data center resources, which can effectively improve the resource retrieval accuracy and shorten the resource retrieval time