Alongside Patent Super Aggregators represented by Intellectual Ventures in the United States, there is a trend to construct patent intermediary in the context of platform ecosystem. Accordingly, patent operation platforms (POPs) have emerged recently in China, yet few studies focus on uncovering their structures and operating mechanisms. This article aims to explore them based on two in-depth case studies with the application of a four-dimensional service innovation framework. Findings pinpoint that POP consists of ‘Patent Plus’ database, patent service platform and two-sided patent platform, as a closed loop. In this structure, ICT plays a prominent role, connected with new service concepts, service delivery system and client interface, to operate the platform. Our article also shows implications to POP related theory and practice.

In the context of the application of artificial intelligence in an intellectual property trading platform, the number of demanders and suppliers that exchange scarce resources is growing continuously. Improvement of computational power promotes matching efficiency significantly. It is necessary to greatly reduce energy consumption in order to realize the machine learning process in terminals and microprocessors in edge computing (smart phones, wearable devices, automobiles, IoT devices, etc.) and reduce the resource burden of data centers. Machine learning algorithms generated in an open community lack standardization in practice, and hence require open innovation participation to reduce computing cost, shorten algorithm running time, and improve human-machine collaborative competitiveness. The purpose of this study was to find an economic range of the granularity in a decision tree, a popular machine learning algorithm. This work addresses the research questions of what the economic tree depth interval is and what the corresponding time cost is with increasing granularity given the number of matches. This study also aimed to balance the efficiency and cost via simulation. Results show that the benefit of decreasing the tree search depth brought by the increased evaluation granularity is not linear, which means that, in a given number of candidate matches, the granularity has a definite and relatively economical range. The selection of specific evaluation granularity in this range can obtain a smaller tree depth and avoid the occurrence of low efficiency, which is the excessive increase in the time cost. Hence, the standardization of an AI algorithm is applicable to edge computing scenarios, such as an intellectual property trading platform. The economic granularity interval can not only save computing resource costs but also save AI decision-making time and avoid human decision-maker time cost.

In an effort to tackle climate change, the “Dual Carbon” target raised by the Chinese government aims to reach peak carbon dioxide emissions by 2030 and to achieve carbon neutrality by 2060. Accordingly, policy incentives have accelerated the new energy vehicle (NEV) sector. Whilst previous studies have focused on the bilateral game between governments and manufacturers, NEV development has witnessed interaction among multiple players. In this paper, we construct a quadrilateral evolutionary game model, considering the impact of government policies, manufacturers’ R&D investments, dealers’ support, and consumer choice on the evolutionary stabilization strategy (ESS) in the context of China. The results show that: (1) in the absence of government incentives, there is no motivation for manufacturers, dealers and consumers to consider the development of NEVs; (2) government incentives affect manufacturers and consumers on the evolutionary paths in the short term. In the long term, benefit- and utility-based limited rationality has a dominant role in the ESS. This study contributes to the understanding of the multilateral dynamics of NEV innovation and provides important implications to practitioners and policy makers.

This paper explores the cultivation paths of innovation ecosystems in the digital age by constructing a theoretical framework consisting of innovators, elements of innovation, platforms and digital governance. Four cities in China, Shenzhen, Hangzhou, Xuzhou and Weifang, were selected for in-depth case studies. Findings revealed that: (1) regions with ‘head enterprises’ and a strong industrial base achieve a competitive advantage via the synergy of digital industrialisation and industrial digitalisation; (2) digital technology pioneer regions can adopt digital industrialisation; (3) regions with a strong industrial base and traditional manufacturing industries can prioritise industrial digitalisation and (4) manufacturing regions with a large number of small and mid-size enterprises (SMEs) who can combine industrial digitalisation and digital value. This study provides theoretical and practical value for the cultivation of regional innovation ecosystems.