Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy

Xi Xi, Feifei Ren, Lean Yu and Jing Yang

Technological Forecasting and Social Change, 2023, vol. 195, issue C

Abstract: Detecting and identifying the future trends of disruptive technologies is crucial for both government R&D strategic planning and firms' practices. To monitor disruptive technology trends and grasp technological opportunities, this paper proposes an analytical framework that integrates appropriate tech mining and semantic analysis methods based on HiDS (high-dimensional and sparse)-trait patent abstract data and then visualizes the technology evolutionary pathway. Deep Learning technology is selected as a case study. In this case, tech mining methods are applied to analyze the Deep Learning technology evolutionary pathway and then refine Deep Learning technological prospects. Results indicate that Deep Learning has formulated three progressive clusters during its evolution, which strongly demonstrate the high application value of Deep Learning technology. In addition, Deep Learning technology will continue to maintain a rapid development stage, and Deep Learning Accelerator, Transfer Learning, Image Classification, Object Detection, and Deep Reinforcement Learning are the key technical topics in the future. Our study contributes to the technology forecast field by suggesting a data-trait-driven framework for HiDS data, which introduces data-trait-driven idea into technological evolution detection and combines it with technology life cycle theory, and helps experts make more precise and in-depth tech mining in technological evolution research.

Keywords: Deep learning technology; Tech mining; Technological evolution; Data-trait-driven modeling (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:195:y:2023:i:c:s0040162523004626

DOI: 10.1016/j.techfore.2023.122777

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