# The Innovation Paradox: Concept Space Expansion with Diminishing Originality and the Promise of Creative AI

arXiv: https://arxiv.org/abs/2303.13300 · HTML: https://mlsanity.com/paper/2303.13300/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 72 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Generative Transformers for Design Concept Generation](https://mlsanity.com/paper/2211.03468/) (2022)
- [Engineering Knowledge Graph from Patent Database](https://mlsanity.com/paper/2106.06739/) (2021)
- [Generative Pre-Trained Transformer for Design Concept Generation: An Exploration](https://mlsanity.com/paper/2111.08489/) (2021)
- [Natural Language Processing in-and-for Design Research](https://mlsanity.com/paper/2111.13827/) (2021)
- [TechNet: Technology Semantic Network Based on Patent Data](https://mlsanity.com/paper/1906.00411/) (2019)
- [Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers](https://mlsanity.com/paper/2212.13196/) (2022)
- [Data-Driven Innovation: What Is It](https://mlsanity.com/paper/2201.08184/) (2022)
- [Guiding Data-Driven Design Ideation by Knowledge Distance](https://mlsanity.com/paper/2210.10104/) (2022)
- [Enhancing user creativity: Semantic measures for idea generation](https://mlsanity.com/paper/2106.10131/) (2021)
- [Patent Data for Engineering Design: A Critical Review and Future Directions](https://mlsanity.com/paper/2111.08500/) (2021)
- [Generative Design Ideation: A Natural Language Generation Approach](https://mlsanity.com/paper/2204.09658/) (2022)
- [Design Representation as Semantic Networks](https://mlsanity.com/paper/2210.11533/) (2022)
- [Creative Exploration Using Topic Based Bisociative Networks](https://mlsanity.com/paper/1801.10084/) (2018)
- [Technological improvement rate estimates for all technologies: Use of patent data and an extended domain description](https://mlsanity.com/paper/2004.13919/) (2020)
- [Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Data](https://mlsanity.com/paper/2110.13624/) (2021)
- [Generative Pre-Trained Transformers for Biologically Inspired Design](https://mlsanity.com/paper/2204.09714/) (2022)

## Built on by (most cited first, 2 of 2)

- [Large Language Model for Patent Concept Generation](https://mlsanity.com/paper/2409.00092/) (2024)
- [The Variance Paradox: How AI Reduces Diversity but Increases Novelty](https://mlsanity.com/paper/2508.19264/) (2025)

## Abstract (arXiv)

Innovation, typically spurred by reusing, recombining, and synthesizing existing concepts, is expected to result in an exponential growth of the concept space over time. However, our statistical analysis of TechNet, which is a comprehensive technology semantic network encompassing over four million concepts derived from patent texts, reveals a linear rather than exponential expansion of the overall technological concept space. Moreover, there is a notable decline in the originality of newly created concepts. These trends can be attributed to the constraints of human cognitive abilities to innovate beyond an ever-growing space of prior art, among other factors. Integrating creative artificial intelligence (CAI) into the innovation process holds the potential to overcome these limitations and alter the observed trends in the future.