# A computational model and tool for generating more novel opportunities in professional innovation processes

arXiv: https://arxiv.org/abs/2510.20402 · HTML: https://mlsanity.com/paper/2510.20402/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 45 references that are in the map.
- First posted on arXiv 23 Oct 2025.

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

- [Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers](https://mlsanity.com/paper/2409.04109/) (2024)
- [LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions](https://mlsanity.com/paper/2411.05025/) (2024)
- [GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation](https://mlsanity.com/paper/2503.12600/) (2025)
- [Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability](https://mlsanity.com/paper/2504.12320/) (2025)

## Abstract (arXiv)

This paper presents a new computational model of creative outcomes, informed by creativity theories and techniques, which was implemented to generate more novel opportunities for innovation projects. The model implemented five functions that were developed to contribute to the generation of innovation opportunities with higher novelty without loss of usefulness. The model was evaluated using opportunities generated for an innovation project in the hospitality sector. The evaluation revealed that the computational model generated outcomes that were more novel and/or useful than outcomes from Notebook LM and ChatGPT4o. However, not all model functions contributed to the generation of more novel opportunities, leading to new directions for further model development