Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Data
This provides innovators with a new way to interpret technology evolution using a biological analogy, potentially aiding in identifying innovation directions, but it is incremental as it applies an existing method (deep neural embeddings) to new data (patent data).
The authors tackled the problem of understanding technological changes to guide design innovation by constructing a technology fitness landscape using deep neural embeddings of patent data, resulting in a landscape with 1,757 technology domains and their improvement rates, revealing a high hill for ICT and a vast low plain for other domains.
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.