학술논문
Technology Forecasting Model Based on Trends of Engineering System Evolution (TESE) and Big Data for 4IR
Document Type
Conference
Author
Source
2020 IEEE Student Conference on Research and Development (SCOReD) Research and Development (SCOReD), 2020 IEEE Student Conference on. :237-242 Sep, 2020
Subject
Language
ISSN
2643-2447
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.