Developing a predictive model for anticipating technology convergence: A transformer-based model and supervised learning approach
Mokh Afifuddin, Wonchul Seo
TL;DR
This paper introduces a new method using transformer models and machine learning to predict technology convergence in the bio-healthcare sector.
Contribution
The study offers a novel methodology combining transformer-based text mining and supervised learning for anticipating technology convergence.
Findings
The proposed method effectively identifies potential technology convergence opportunities.
Integration of transformer models and supervised learning improves prediction accuracy.
The approach reveals emerging trends and interdisciplinary collaboration potential.
Abstract
This study proposes a novel approach to anticipating technology convergence in the bio-healthcare sector by integrating text mining based on transformer models and supervised learning methodologies. The overarching goal is to develop a robust method for predicting technology convergence, leveraging the interrelationships between technology topics extracted from patents and research articles. Through the application of advanced techniques and by leveraging the strengths of transformer-based models such as BERTopic with KeyBERT and OpenAI integration to generate technology topics, we identified potential convergence opportunities and explored emerging trends within the dataset. The proposed method seeks to predict technology convergence effectively by employing various machine learning and deep learning techniques to train prediction models by integrating technological similarity, link…
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Taxonomy
TopicsIntellectual Property and Patents · Innovation Diffusion and Forecasting · Innovation and Knowledge Management