Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Data

TL;DR

This paper constructs a technology fitness landscape using deep neural embeddings of patent data, revealing the structure of technological evolution and aiding innovators in identifying new directions for design breakthroughs.

Contribution

It introduces a novel deep neural embedding approach to map the technology landscape from patent data, highlighting its structure and potential for guiding innovation.

Findings

Identified a high hill in ICT domains indicating rapid improvement

Mapped a vast low plain representing slower-evolving domains

Provided a biological analogy for understanding technology evolution

Abstract

Technology is essential to innovation and economic prosperity. Understanding technological changes can guide innovators to find new directions of design innovation and thus make breakthroughs. In this work, we construct a technology fitness landscape via deep neural embeddings of patent data. The landscape consists of 1,757 technology domains and their respective improvement rates. In the landscape, we found a high hill related to information and communication technologies (ICT) and a vast low plain of the remaining domains. The landscape presents a bird's eye view of the structure of the total technology space, providing a new way for innovators to interpret technology evolution with a biological analogy, and a biologically-inspired inference to the next innovation.