Purpose -The purpose of this paper is to evaluate innovations in intellectual property rights (IPR) databases, techniques and software tools, with an emphasis on selected new developments and their contribution towards achieving advantages for IPR management (IPRM) and wider social benefits. Several industry buzzwords are addressed, such as IPR-linked open data (IPR LOD) databases, blockchain and IPR-related techniques, acknowledged for their contribution in moving towards artificial intelligence (AI) in IPRM. Design/methodology/approach -The evaluation, following an original framework developed by the authors, is based on a literature review, web analysis and interviews carried out with some of the top experts from IPR-savvy multinational companies. Findings -The paper presents the patent databases landscape, classifying patent offices according to the format of data provided and depicting the state-of-art in the IPR LOD. An examination of existing IPR tools shows that they are not yet fully developed, with limited usability for IPRM. After reviewing the techniques, it is clear that the current state-of-the-art is insufficient to fully address AI in IPR. Uses of blockchain in IPR show that they are yet to be fully exploited on a larger scale. Originality/value -A critical analysis of IPR tools, techniques and blockchain allows for the state-of-art to be assessed, and for their current and potential value with regard to the development of the economy and wider society to be considered. The paper also provides a novel classification of patent offices and an original IPR-linked open data landscape.
Although empirical studies show that suppliers’ innovativeness enhances original equipment manufacturers’ (OEM) total innovation performance, some evidence reveals that suppliers’ innovation affects OEM in quantitatively and qualitatively limited ways. This study aims to explore innovation systems of European automobile producers, i.e., OEM. Technological innovation systems (TIS) remain relatively underexplored, but the approach is especially valuable for explaining why and how sustainable and circular innovation develop and spread. We applied a mixed-method approach and conducted patent analyses and interviews with 20 respondents from Slovenia, Austria, and Hungary, which are representatives of suppliers for the automotive industry and automotive clusters. We confirm that the European OEMs build innovation ecosystems that are more closed than their Asian counterparts. Furthermore, we define three paths of how inventions of suppliers can reach the OEMs, with developmental suppliers (large companies) having the highest probability of influencing the innovation activity of OEMs. The entry of small and medium-sized enterprises (SME) and start-ups with their inventions is difficult. However, it is not impossible, especially if they develop new solutions connected to current disruptive trends in the automotive industry: electric cars, autonomous driving and digitalisation.
The volume of patent data is increasing, which is a big challenge to patent examiners as well as to all inventive companies and individuals. In this paper we take the view of individual inventors who believe they invented something new. Artificial intelligence brings a promise to support their prior art search for existing (similar) inventions with machine learning and deep learning algorithms. We discuss the potential of artificial intelligence in prior art searching. We present an experiment, based on a real-life invention, comparing relevant patents we got from Boolean keyword searching with those from the semantic search supported by artificial intelligence. We can confirm that artificial intelligence has great potential in this field. However, presently it is not yet able to make traditional patent search engines obsolete, hence it still fits better with the notions of augmented intelligence or expertise.
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