MEANING GENERATION THEORY: A BAYESIAN APPROACH TO SIGN × CONTEXT = MEANING
DS 136: Proceedings of the Asia Design and Innovation Conference (ADIC) 2024
Year: 2024
Editor: Yong Se Kim; Yutaka Nomaguchi; Chun-Hsien Chen; Xiangyang Xin; Linna Hu; Meng Wang
Author: Kushi, Shotaro; Nakaji, Kouhei; Yanagisawa, Hideyoshi
Series: Other endorsed
Institution: NEW STANDARD inc, University of Tokyo, University of Toronto
Page(s): 019-027
Abstract
This paper proposes the “Sign × Context = Meaning (Meaning Value)” framework for ideation in design engineering. By integrating abduction and Bayesian inference, the framework generates new meanings by shifting the contexts of signs. Case studies conducted with NEW STANDARD, Inc., validated its application in product and customer experience (CX) development. While the framework demonstrates reproducible innovation, its effectiveness is influenced by cultural and contextual factors. Future research should explore AI-driven tools to enhance idea generation across diverse contexts.
Keywords: Idea generation theory, Bayesian Inference, Product Development, Innovation of Meaning