Graphene serves as the most disruptive material in the twenty-first century and plays an unsubstitutable role in solving the sustainable development problems of energy crises, water shortages, and environmental pollution. Recently, obtaining a sustainable competitive advantage (SCA) in the field of graphene has gained increasing attention from both researchers and practitioners. However, few attempts have been made to summarize the SCA of this field by applying patent information. Basing on a patent-based multi-level perspective (MLP), this study aims to develop an approach to identify SCA in the target technological area by conducting a patent review from the comprehensive perspectives of the macro landscape, meso socio-technical system, and micro niches, and then integrate patent analysis with technology life cycle (TLC) theory to examine patents involving global technological competition. The effectiveness of the approach is verified with a case study on graphene. The results show that the graphene field is an emerging and fast-growing technological field, with an increasing number of patents over the year. The TLC of graphene technology demonstrated an approximate S shape, with China, the U.S., Korea, and Japan filing the largest number of graphene patents. Evidenced by Derwent Manual Codes, we found an increase in consideration given to technological application and material preparation topics over time, and research hotspots and fronts that have SCA. In terms of a leading country or region with SCA, the U.S., with a high foreign patent filing rate, large number of high forward citation patents, strong assignees’ competitive position, and large number of high-strength patents, was still the most powerful leader, with a higher SCA in the graphene industry. Korea also obtained a relatively higher SCA and will be a promising competitor in this field. Although China was shown to be catching-up very rapidly in the total number of graphene patents, the apparent innovation gaps in the foreign patent filing rate, high value patents, and Industry-University-Research Collaboration will obviously hamper Chinese catch-up efforts for obtaining SCA. As for patentees, the most powerful leaders with a higher SCA represented by Samsung Electronics Co., Ltd, International Business Machines Corp, and Nanotek Instruments Inc were identified. In addition, most of the high strength patents were owned by the above patentees. Further, valuable contributions to the understanding of SCA in graphene technology were summarized. First, the proposed patent-based MLP provides a new and comprehensive analytical framework for review research, as well as SCA analysis, and extends its research perspectives. Second, it introduces patent indicators to the previous MLP model, and provides a new theoretical perspective for the study of technological innovation in the previous MLP model. Third, this paper employs the TLC theory to explore the dynamic SCA in the given technology field, which further develops the concept of the MLP model from the temporal dimension. Finally, future research directions were demonstrated. To the best of the authors’ knowledge, this is the first systematic review of this field using patent analysis, comprehensively acknowledging the current technological competition and development in the graphene field and that of the future, and can be applied to various other emerging technology fields.

PurposeThe benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.Design/methodology/approachThis study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.FindingsThe I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.Originality/valueThe maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

The articular disc plays an important role as a stress absorber in joint movement, resulting in stress reduction and redistribution in the temporomandibular joint (TMJ). The flow of synovial fluid in the TMJ may follow a regular pattern during movement of the jaw. We hypothesised that the regular pattern is disrupted when the TMJ disc is perforated. By computed tomography arthrography, we studied the upper TMJ compartment in patients with small disc perforation during jaw opening-closing at positions from 0 to 3 cm. Finite element fluid dynamic modelling was accomplished to analyse the pattern of fluid flow and pressure distribution during the movements. The results showed that the fluid flow in the upper compartment generally formed an anticlockwise circulation but with local vortexes with the jaw opening up to 2 cm. However, when the jaw opening-closing reached 3 cm, an abnormal flow field and the fluid pressure change associated with the perforation may increase the risk of perforation expansion or rupture and is unfavourable for self-repair of the perforated disc.

Ultrafine-grained (UFG) commercially pure (CP) Ti with a grain size of about 200 nm was produced by ECAP up to 8 passes using route BC at room temperature. For ECAP processing a proper die set was designed and constructed with an internal channel angle Φ of 120° and an outer arc of curvature Ψ of 20°. Strain rate sensitivity of UFG CP-Ti and CG CP-Ti were investigated by compression tests in the temperature range of 298~673K and strain rate range of 10-4~100s-1 using Gleeble simulator machine. Evolution of the microstructure during compression testing was observed using optical microscopy (OM) and transmission electron microscopy (TEM). Strain rate sensitivity value m of the UFG CP-Ti has been measured and is found to increase with increasing temperature and decreasing strain rate, and is enhanced compared to that of CG CP-Ti. Result of the deformation activation energy determination of UFG CP-Ti indicates that the deformation mechanism in UFG CP-Ti is correlated to the grain boundaries.

Graphene, with high biocompatibility, physiological solubility and stability, has been reported as an emerging material for biomedical applications such as biosensors, drug delivery, and tissue engineering. Recently, identifying the technological competition (TC) of graphene biomedical technology has received worldwide attention from stakeholders. However, few studies have attached great importance to review the TC of this field by the analysis of patents. The main objective of this study is to develop a new and comprehensive method to investigate TC in a given technology field by conducting a patent review and then employing a patent roadmap to dig out the technology opportunity. The effectiveness of the approach is verified with the case study on graphene biomedical technology. Compared to previous research, this study makes the following important contributions. First, this study provides a new and systematic framework for the dynamic analysis of TC in a given technology field. It also extends the research perspectives of TC for industry, assignees, and technology, employs a patent roadmap to dig out technology opportunities, and enables stakeholders to understand TC from a dynamic perspective. Second, this study integrates patent analysis with a patent roadmap that has not appeared in existing methodologies of patent review. Third, it first introduces indicators (e.g., high value patent and competition position of top assignees) to the previous patent roadmap and provides a new methodology for patent roadmaps from a country level and assignee level. Finally, this study provides useful information for stakeholders interested in graphene biomedical technology, helps them to find new technology opportunities in this field, encourages them to determine the direction of future research, and has important significance for its application to diverse other emerging technologies.

No abstract

No abstract

In recent years, assessing patent risks has attracted fast-growing attention from both researchers and practitioners in studies of technological innovation. Following the existing literature on risks and intellectual property (IP) risks, we define patent risks as the lack of understanding of the distribution of patents that lead to losing a key patent, increased research and development costs, and, potentially, infringement litigation. This paper aims to propose an explorative approach to investigating patent risks in the target technology field by integrating social network analysis and patent analysis. Compared to previous research, this study makes an important contribution toward identifying patent risks in the overall technological field by employing a patent-based multi-level network model that has not appeared in existing methodologies of patent risks. In order to verify the effectiveness of this approach, we take artificial intelligence (AI) as an example. Data collected from the Derwent Innovation Index (DII) database were used to build the patent-based multi-level network on patent risks from market, technology, and assignee perspectives. The results indicate that the lack of international collaborations among assignees and industry–university–research collaboration may lead to patent collaboration risks. Regarding patent market risks, the lack of overseas patent applications, especially the lack of distribution in the main competitive markets, is a key factor. As for patent technology risks, most of the leading assignees lack awareness of the distribution in the following technological fields: industrial electric equipment, engineering instrumentation, and automotive electrics. In summary, assignees from the U.S. with first mover advantages are still powerful leaders in the AI technology field. Although China is catching up very rapidly in the total number of AI patents, the apparent patent risks under the perspectives of collaboration, market, and technology will obviously hamper the catch-up efforts of China’s AI industry. We conclude that, in practice, the proposed patent-based multi-level network model not only plays an important role in helping stakeholders in the AI technological field to prevent patent risks, find new technology opportunities, and obtain sustainable development, but also has significance for guiding the industrial development of various emerging technology fields.