# Knowledge flows from science to AI technology: Identifying core and brokerage technological roles

## TL;DR

This paper explores how scientific knowledge influences AI technology development by analyzing patents and scientific publications from 2002 to 2021.

## Contribution

The study introduces a semantic science-technology exploration framework tailored for AI, combining patent classification and topic modeling.

## Key findings

- AI patents are categorized into four groups based on CPC co-occurrence networks.
- BERTopic modeling reveals key scientific topics influencing AI technological trends.
- The framework identifies structural pathways of science-to-technology knowledge flow in AI.

## Abstract

The rapid advancement of artificial intelligence (AI) technologies has not only driven convergence with diverse technological domains but also swiftly spread across various industrial sectors. As a knowledge-intensive field, AI is particularly shaped by the flow of knowledge from scientific research to technological development, yet remains insufficiently examined in a systematic and structural way. This study addresses this gap by investigating science-to-technology knowledge flow that underpins AI’s technological evolution. We propose a semantic science-technology exploration framework specifically designed for the AI domain, consisting of the two stages: technology classification and semantic topic exploration. First, AI patents are classified into four categories using centrality measures derived from a CPC co-occurrence network. Then, we extract abstracts from both patents and their cited scientific publications to apply BERTopic modelling and generate topic labels using generative AI. Analyzing AI-related patents filed from 2002 to 2021, we trace key technological trends and elucidate the structural pathways of knowledge flow science to technology. The findings offer practical implications for corporate R&D strategies and innovation policy design in the era of AI.