Design trend forecasting by combining conceptual analysis and semantic projections: New tools for open innovation

• Published in: Journal of open innovation Vol. 7; no. 1; pp. 1 - 26
• Main Authors: Manetti, Alessandro, Ferrer-Sapena, Antonia, Sánchez Pérez, Enrique A, Lara-Navarra, Pablo
• Format: Journal Article
• Language: English
• Published: Basel MDPI 01-03-2021; Elsevier Ltd; Elsevier Limited; Elsevier
• Subjects: Acceptance; Artificial intelligence; Big Data; Collaboration; Conceptual analysis; Concrete; Datasets; Design; Designers; Forecasting; fuzzy set; Fuzzy sets; Innovations; Internet; Lipschitz function; Metric space; Ontology; reinforcement learning; Semantics; Set theory; trend; Trend analysis; Trends; Universe; Lipschitz function; fuzzy set; trend; forecasting; reinforcement learning
• ISSN: 2199-8531; 2199-8531
• DOI: 10.3390/joitmc7010092

In this paper, we describe a new trend analysis and forecasting method (Deflexor), which is intended to help inform decisions in almost any field of human social activity, including, for example, business, art and design. As a result of the combination of conceptual analysis, fuzzy mathematics and some new reinforcing learning methods, we propose an automatic procedure based on Big Data that provides an assessment of the evolution of design trends. The resulting tool can be used to study general trends in any field - depending on the data sets used - while allowing the evaluation of the future acceptance of a particular design product, becoming in this way, a new instrument for Open Innovation. The mathematical characterization of what is a semantic projection, together with the use of the theory of Lipschitz functions in metric spaces, provides a broad-spectrum predictive tool. Although the results depend on the data sets used, the periods of updating and the sources of general information, our model allows for the creation of specific tools for trend analysis in particular fields that are adaptable to different environments.