University Research, Industrial R&D, and the Anchor Tenant Hypothesis

We examine geographic concentration, agglomeration, and co-location of university research and industrial R&D in three technological areas: medical imaging, neural networks, and signal processing. Using data on scientific publications and patents as indicators of university research and industrial R&D, we find strong evidence of geographic concentration in both activities at the level of MSAs. While evidence for agglomeration (in the sense of excess' concentration relative to the size of MSAs and the size distribution of research labs) of research in these fields is mixed, we do find strong evidence of co-location of upstream and downstream activity. We view such co-located vertically connected activities as constituents of a local innovation system,' and these appear to vary markedly in their ability to convert local academic research into local commercial innovation. We develop and test the hypothesis that the presence of a large, local, R&D-intensive firm an anchor tenant' enhances the productivity of local innovation systems by making local university research more likely to be absorbed by and to stimulate local industrial R&D. Presence of anchor tenant firms may be an important factor in stimulating both the demand and supply sides of local markets for innovation and may be an important channel for transmission of spillovers. While our empirical results are preliminary, they indicate that anchor tenant technology firms may be an economically important aspect of the institutional structure of local economies.

Community Detection and Growth Potential Prediction from Patent Citation Networks

The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author

Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks

Scoring patent documents is very useful for technology management. However, conventional methods are based on static models and, thus, do not reflect the growth potential of the technology cluster of the patent. Because even if the cluster of a patent has no hope of growing, we recognize the patent is important if PageRank or other ranking score is high. Therefore, there arises a necessity of developing citation network clustering and prediction of future citations. In our research, clustering of patent citation networks by Stochastic Block Model was done with the aim of enabling corporate managers and investors to evaluate the scale and life cycle of technology. As a result, we confirmed nested SBM is appropriate for graph clustering of patent citation networks. Also, a high MAPE value was obtained and the direction accuracy achieved a value greater than 50% when predicting growth potential for each cluster by using LSTM.Comment: arXiv admin note: substantial text overlap with arXiv:1904.1204

Research and Development Workstation Environment: the new class of Current Research Information Systems

Against the backdrop of the development of modern technologies in the field of scientific research the new class of Current Research Information Systems (CRIS) and related intelligent information technologies has arisen. It was called - Research and Development Workstation Environment (RDWE) - the comprehensive problem-oriented information systems for scientific research and development lifecycle support. The given paper describes design and development fundamentals of the RDWE class systems. The RDWE class system's generalized information model is represented in the article as a three-tuple composite web service that include: a set of atomic web services, each of them can be designed and developed as a microservice or a desktop application, that allows them to be used as an independent software separately; a set of functions, the functional filling-up of the Research and Development Workstation Environment; a subset of atomic web services that are required to implement function of composite web service. In accordance with the fundamental information model of the RDWE class the system for supporting research in the field of ontology engineering - the automated building of applied ontology in an arbitrary domain area, scientific and technical creativity - the automated preparation of application documents for patenting inventions in Ukraine was developed. It was called - Personal Research Information System. A distinctive feature of such systems is the possibility of their problematic orientation to various types of scientific activities by combining on a variety of functional services and adding new ones within the cloud integrated environment. The main results of our work are focused on enhancing the effectiveness of the scientist's research and development lifecycle in the arbitrary domain area.Comment: In English, 13 pages, 1 figure, 1 table, added references in Russian. Published. Prepared for special issue (UkrPROG 2018 conference) of the scientific journal "Problems of programming" (Founder: National Academy of Sciences of Ukraine, Institute of Software Systems of NAS Ukraine

Me, Myself, and A.I.: Should Kenya’s Patent Law Be Amended to Recognise Machine Learning Systems as Inventors?

On 28 July 2021, South Africa set the record for being the first country in the world to grant a patent to an artificial intelligence (AI) system known as ‘Device for the Autonomous Bootstrapping of Unified Sentience’ (DABUS). Although DABUS is not the first AI system to produce patentable products, it is the first AI system to be listed as an inventor in a patent application, attracting worldwide interest. Against this backdrop, this article seeks to analyse whether Kenya’s Industrial Property Act, 2001 (IPA) should evolve to recognise machine learning (ML) systems as inventors. It submits that some ML systems are capable of inventive activity that is equivalent to or superior to that of the human intellect and that such systems should be recognised as inventors. This paper illustrates that Kenya's IPA, however, is unable to recognise ML systems since it is based on anthropocentric standards that, when put into practice, preclude the acknowledgement of non-human inventors. Therefore, this article makes several recommendations aimed at overhauling not only Kenya's IPA but also the country’s patent system

Weak signals in Science and Technologies 2019: Analysis and recommendations: Technologies at a very early stage of development that could impact the future

JRC has developed a quantitative methodology to detect very early signs of emerging technologies, so called "weak signals of technology development". Using text mining and scientometric indicators, 256 of these weak signals have been identified on the basis of scientific literature and have been reported earlier this year in a JRC technical report. The purpose of this follow-up report is to provide a European perspective and to provide recommendations for policy makers. Europe shows vulnerabilities in 179 of these weak signals, further analysed in the present report.JRC.I.3-Text and Data Minin

The emergence of new technologies in the ICT field: main actors, geographical distribution and knowledge sources

This paper examines the emergence of technologies, applications and platforms in the area of information and communication technologies (ITC), using patent data. It detects new technologies/applications/products using patents' abstracts and describes them looking at their degree of "hybridisation", in terms of technological domains and knowledge base, at the role of firms in driving the innovation activity, and at the geographical distribution of the innovation. The results show that in emerging technologies in ITC are more concentrated across technological classes and across firms than non emerging ones, and that this pattern is invariant across major countries. Furthermore, a preliminary analysis on patent citations show that in emerging technologies knowledge sources are more specific in terms of technological classes and more dispersed in terms of cited institutions. Also there is evidence of a role for universities and public research centres as sources of knowledge

A new approach for designing self-organizing systems and application to adaptive control

There is tremendous interest in the design of intelligent machines capable of autonomous learning and skillful performance under complex environments. A major task in designing such systems is to make the system plastic and adaptive when presented with new and useful information and stable in response to irrelevant events. A great body of knowledge, based on neuro-physiological concepts, has evolved as a possible solution to this problem. Adaptive resonance theory (ART) is a classical example under this category. The system dynamics of an ART network is described by a set of differential equations with nonlinear functions. An approach for designing self-organizing networks characterized by nonlinear differential equations is proposed