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Title: Market complexity evaluation to enhance the effectiveness of TRIZ outputs
Authors: Carrara, Paolo
Russo, Davide
Bennato, Anna R.
Keywords: TRIZ
Patent thicket
Business intelligence
Market structure
Issue Date: 2018
Publisher: Springer Nature Switzerland AG © IFIP International Federation for Information Processing
Citation: CARRARA, P., RUSSO, D. and BENNATO, A.R., 2018. Market complexity evaluation to enhance the effectiveness of TRIZ outputs. IN: Cavallucci, D., De Guio, R. and Koziolek, S. (eds). Automated Invention for Smart Industries. Proceedings of the 18th International TRIZ Future Conference (TFC 2018), Strasbourg, France, 29-31 October 2018, pp.66-74.
Series/Report no.: IFIP Advances in Information and Communication Technology;541
Abstract: In the context of innovation consulting activity, it may happen working in technical fields characterized by a high competitiveness level. Although TRIZ allows reaching innovative ideas in any kind of industry, it does not suggest any tool in order to evaluate the success rate of the invention in the reference market. During the last years, TRIZ got methodological contributes to sharpen the matching between the inventive idea and the actual needs of the market, for example the market potential tool. In order to support TRIZ experts in selecting the best innovation strategy, this paper introduces a new tool for the TRIZ toolbox that takes into account the competitiveness level of the market. Several economics works disclose the correlation between the patent-citation triadic relationships and the presence of dominant positions of few competitors. A patent analysis, focused on triads in patent citation, can inform the TRIZ expert about potential critical situation able to prevent the success of an inventive solution. It can generate an important indicator that helps him in selecting the most promising innovation strategy. The method could be integrated in a classic TRIZ activity, using commercial patent searching tools. The case study shows how to extract this kind of indicator from patent citation environment in Machine Learning field.
Description: This paper is closed access until 12 October 2019.
Version: Accepted for publication
DOI: 10.1007/978-3-030-02456-7_6
URI: https://dspace.lboro.ac.uk/2134/35618
Publisher Link: https://doi.org/10.1007/978-3-030-02456-7_6
ISBN: 9783030024550
Appears in Collections:Closed Access (Economics)

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