Identification of Innovative Opportunities Based on Product Scenario Evolution
Bibliographic record
Abstract
Innovation is a key factor for product development. Identifying innovative opportunities is the first step in innovative product design. Traditional methods of identifying innovative opportunities, such as market surveys and brainstorming, are limited by product users’ and designers’ experiences and lack systematic approaches to generate breakthrough innovations. This paper proposes a method to identify innovative opportunities based on product scenario evolution. The method models a product scenario based on product scenario elements, states, and behaviors. A Type II hierarchical function model is constructed based on the transformation and abstraction hierarchy of the product function model to identify target elements for the scenario evolution. Based on the theory of basic element extension and needs evolution characteristics, the method of extending target scenario elements is proposed. Based on the new scenario element sets and their impact, diffusion, identification, and evaluation methods are proposed for innovation opportunities. Potential opportunities are explored for product innovation from a scenario evolutionary perspective, which updates knowledge and technology reserves and finds new market opportunities for industries. The feasibility and effectiveness of the method are verified using the innovative design of a polyethylene (PE) pipeline hot-melt welding machine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".