A Structured Approach Integrating Artificial intelintelligence and the 8 Trends of Technical Evolution for Innovative Solutions
Bibliographic record
Abstract
The infusion of Creative Innovation Techniques, along with TRIZ, the Theory of Inventive Problem Solving, into almost every industry, has had long-standing effects on the advancement of technology and process. However, the very complexity and domain-specific knowledge attached to TRIZ methodologies inherently limit their use by practitioners at the novice level. The objective of this paper is to enhance the usability and effectiveness of a TRIZ tool named the 8 Trends of Technical Evolution by integrating it with AI-driven tools, such as ChatGPT. The new framework aims to streamline the innovation process and generate more practical and innovative solutions, particularly focusing on overcoming psychological inertia during creative processes like brainstorming. The latter enables the users to solve the most complicated issues and come up with innovative solutions by leading them through well-structured questions embedding TRIZ principles and the 8 Trends of Technical Evolution. To demonstrate this approach, a case study on the development of Non-Slippery Shoe Outsole Design and Material Selection is presented. The results reveal how the integration of AI with TRIZ significantly enhances innovation practices, increasing the potential for broader adoption across industries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".