With the development of technology, more and more technical issues have been exposed, such as technical disputes, technical barriers and technical crisis. Thus, it is necessary to warn enterprises about technical deviation and predict future technology crises. Patent data can contain much information about technologies and would be useful in this setting. This paper proposes a technology early warning model based on patent data. This model helps enterprises analyse the technical crisis level and trends from four different perspectives (technical stability, technical monopoly, technical security and technical prospects).
Author(s): Ganlu Sun, Ying Guo, Fan Yang
Organization(s): Beijing Institute of Technology
Source: Proceedings of the Second International Workshop on Patent Mining and
its Applications (IPAMIN)
This paper presents the results of research to develop new data sources and methods that can be combined with existing information for real-time intelligence to understand and map enterprise development and commercialisation in a rapidly emerging and growing new technology. As a demonstration case, the study examines enterprise development and commercialisation strategies in graphene, focusing on a set of 65 graphenebased small and medium-sized enterprises located in 16 different countries. We draw on available secondary sources and bibliometric methods to profile developments in graphene. We then use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites. We identify groups of graphene small and medium-sized enterprises differentiated by how they became involved with graphene, the materials they target, whether they make equipment, and their orientation towards science and intellectual property. In general, access to finance and the firms’ location are significant factors that are associated with graphene product introductions. We also find that patents and scientific publications are not statistically significant predictors of product development in our sample of graphene SMEs. We show that the UK has a cohort of graphene-oriented SMEs that is signalling plans to develop intermediate graphene products that should have higher value in the marketplace. Our findings suggest that UK policy needs to ensure attention to the introduction and scale-up of downstream intermediate and final graphene products and associated financial, intermediary, and market identification support.
Author(s): Philip Shapira, Abdullah Gök, and Fatemeh Salehi Yazdi
Organization(s): Manchester Institute of Innovation Research, University of Manchester
Source: Nesta Working Paper Series
This paper aims to assess a method that applies scientometric and patentometric indicators in the selection process of projects by seed capital funds. There is increasing interest in technology-based enterprises, for their capacity to contribute to economic and social development. Nevertheless, in practice, there is some difficulty in assessing non-financial criteria associated with technology for the purposes of choosing investment opportunities.
The literature has presented various methods to instrumentalize the process of evaluation and selection of investment projects. This study focuses on an enterprise that received an investment by the largest seed capital fund in Brazil, to assess to what extent scientific and technological indicators can contribute to understanding the market potential of the firm’s technology.
The results show that the use of scientometric and patentometric indicators favors the process of judging non-financial criteria, in particular those related to technology, market, divestment and team.
The originality of this paper is in the evaluation of a patento-scientometric approach for the selection process of projects by seed capital funds.
Author(s): Gustavo da Silva Motta, Rogério Hermida Quintella, Pauli Adriano de Almada Garcia
Organization(s):Universidade Federal Fluminense, Universidade Federal da Bahia
Competitive technical intelligence addresses the landscape of both opportunities and competition for emerging technologies, as the boom of newly emerging science & technology (NEST)—characterized by a challenging combination of great uncertainty and great potential—has become a significant feature of the globalized world. We have been focusing on the construction of a “NEST Competitive Intelligence” methodology that blends bibliometric and text mining methods to explore key technological system components, current R&D emphases, and key players for a particular NEST. Continue reading How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: “problem & solution” pattern based semantic TRIZ tool and case study
Business intelligence is critical in defining the strategy and roadmap of an organization. However, business intelligence covers too much to consider all in such relevant fields as data analytics, text mining, predictive analytics, and so on. Continue reading User-centered innovative technology analysis and prediction application in mobile environment
This development responds to a challenge. Text mining software can conveniently generate very large sets of terms or phrases. Our examples draw from use of VantagePoint (or equivalently, Thomson Data Analyzer – TDA) software  to analyze abstract record sets. Continue reading Text Clumping for Technical Intelligence
Ce travail est un trait d’union entre les sciences de l’information et de la communication. Une robuste méthodologie et des outils performants d’analyses bibliométriques sont utilisés pour des études scientométriques et médiamétriques. Pour cela, nous avons étudié la production scientifique d’une organisation publique de recherche et développement, l’Entreprise Brésilienne de Recherche Agronomique (Embrapa), les compétences de ses chercheurs et enfin nous avons évalué la performance de cette organisation et ses 40 centres de recherche dans les médias. Continue reading Création de Systèmes d’Intelligence dans une Organisation de Recherche et Développement avec la Scientométrie et la Médiamétrie (Creation of Intelligence Systems in a Research and Development Organisation with Scientometrics and Mediametrics)
The use of enzymes in the pulp and paper industry was introduced in the 1986. However, their use has been relatively minor. This prospective study aims at enhancing the understanding of the most important advances regarding the use of enzymes in this industry and to identify the future trends of this technology. Information gathered from the Web of Science shows a growing number of papers published on this topic indicating an increased interest in this issue. A study on patents also displayed a high number documents related to this technology. Cellulase, xylanase, laccase and lipase are the most important enzymes that can be used in the pulp and paper processes. Furthermore, the key objectives of enzymes development have been in the bleaching boosting with xylanases and fiber modification with cellulases. The current and future trends on the development of enzymes are focused on increasing their thermostability and their alkalinity strength. Continue reading Technology Prospecting on Enzymes for the Pulp and Paper Industry
The change in technological environment presents threats as well as opportunities to companies in the related fields. Existing Technology Intelligence procedures require complicated techniques and high-skilled labor results. Large expert-interviews and manual work is also needed, so single small companies can not undertake this alone. To spread and activate Technology Intelligence in research and industrial fields, we propose shallow, but automated, Technology Intelligence services based on Semantic Web technologies, which can reduce the amount of labor required from experts. Continue reading Using Semantic Web Technologies for Technology Intelligence Services