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Technological upgradation is the driving force of social and economic development. Although technological upgradation is extensively needed, it is characterised as high intelligence, high investment, and high risk. In the face of complex market competition patterns, industrial enterprises urgently need to seek external cooperation and achieve sustainable competitive advantage. However, the current digital systems of patents fail to fulfil efficiency improvement from technology transfer to research and development (R&D) cooperation for industrial enterprise. Nor does it provide adequate support for industrial enterprises' technological upgradation. Therefore, this research proposes a systematic framework that combines nautral language processing (NLP) and machine learning (ML) technologies, including the patent recommendation model, patent transferability evaluation model, and research team detection model. Such a digital system of patents improves the efficiency of technology transactions and R&D cooperation with research institutions for industrial enterprises to identify related authorised patents and locate technology research teams. It is also expected that the developed system will enable research institutions to recommend valuable patents and transfer innovative technologies effectively. [ABSTRACT FROM AUTHOR]

The identification of the technical cooperation mode in the field of hydrogen energy is of great importance in guiding technological innovation. Based on the social network analysis (SNA) and spatial network method, this paper constructs the technical cooperation graph of China's industry-university-research (IUR) tripartites in the field of hydrogen technology. Based on the performed graphs, it shows the game and evolution of hydrogen technology innovation in three stages, respectively 2000–2010, 2011–2015 and 2016–2019. Meanwhile, this paper calculates the changes of network indicators and found that: (1) the network density showed a downward trend, from the initial 0.062 to 0.003. (2) The degree centrality of the network decreased from 14.7% to 5.7%. (3) The average path length and clustering coefficient indicate that the internal connections within the network were relatively close from 2000 to 2010, and had small-world characteristics. From 2016 to 2019, although the clustering coefficient increased, the average path length also increased significantly, indicating that the internal connections of the network did not become closer. From the network characteristics, the collaborative innovation network of China's hydrogen energy industry continues to expand in scale and scope, and the cooperation relationship is stable. However, it shows the characteristics of low network density and loose cooperation relationship, gradually developing into a scale-free network. Developed provinces and cities in North and East China occupy core positions in the industry-university-research network, while other regions are gradually expanding their cooperation efficiency; however, the overall cooperation frequency is relatively low in Northeast China, and most provinces and cities in South and Northwest China are in edge positions with poor collaboration capabilities. • Network analysis sheds light on China's hydrogen energy tech cooperation via patent. • Collaborative innovation in hydrogen technology evolves through three stages. • Tight internal connections with small-world properties seen in network (2000–2010). • Network's internal connections didn't become closer (2016–2019). • Network transforms from a stable and expanding structure into a scale-free network. [ABSTRACT FROM AUTHOR]

The article investigates the impact of various funding sources on Technology Transfer Entities (TTEs), including government support, private sector investments, grants, and European funds. It emphasizes the crucial role these financial mechanisms play in enabling TTEs to bridge the gap between academic research and commercial application. By analyzing the influence of government funding, the significance of licensing and patent revenues, and the benefits of private and European investments, the article demonstrates how these resources assist TTEs in achieving financial stability, fostering innovation commercialization, and navigating the market. It also highlights the importance of aligning TTEs' goals with broader societal and economic objectives. Through case studies, the research illustrates the essential need for a diverse funding strategy and effective management for TTEs to optimize their contribution to the technological innovation ecosystem, economic growth, and societal well-being. [ABSTRACT FROM AUTHOR]