Technology Forecasting Model Based on Trends of Engineering System Evolution (TESE) and Big Data for 4IR

Abstract

This article presented a research work to enhance one of the TRIZ tools: Trends of Engineering System Evolution (TESE) which is useful to assess the evolution direction of technical systems in 4th industrial revolution (4IR) for forecasting technological trends. TESE has hierarchical levels of multiple trends and sub-trends for forecasting the technological evolution and was well-established in product innovation but has no link to the data in patent information. Patent data is growing exponentially annually and is Big Data that can be mined and integrated with TESE. In this paper, a novel model using Big Data technologies was proposed to extract semistructured data in U.S. Patents Data where the basis of classification and sorting of patents were done based on the trends and sub-trends of TESE for product innovation. Initial experiments were conducted to demonstrate the potential efficacy of the novel model.

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