  We present a novel approach to exploring innovation problem and solution
domains using LLM fine-tuning with a custom idea database. By semantically
traversing the bi-directional problem and solution tree at different
temperature levels we achieve high diversity in solution edit distance while
still remaining close to the original problem statement semantically. In
addition to finding a variety of solutions to a given problem, this method can
also be used to refine and clarify the original problem statement. As further
validation of the approach, we implemented a proof-of-concept Slack bot to
serve as an innovation assistant.
