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

arXiv: https://arxiv.org/abs/2110.13624 · HTML: https://mlsanity.com/paper/2110.13624/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Data-Driven Design-by-Analogy: State of the Art and Future Directions](https://mlsanity.com/paper/2106.01592/) (2021)
- [Technological improvement rate estimates for all technologies: Use of patent data and an extended domain description](https://mlsanity.com/paper/2004.13919/) (2020)

## Built on by (most cited first, 1 of 1)

- [The Innovation Paradox: Concept Space Expansion with Diminishing Originality and the Promise of Creative AI](https://mlsanity.com/paper/2303.13300/) (2023)

## Abstract (arXiv)

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.