An Integrated Method to Acquire Technological Evolution Potential to Stimulate Innovative Product Design
Bibliographic record
Abstract
Fast and effective forecasting of the new generation of products is key to enhancing the competitiveness of a company in the market. Although the technological evolution laws in the theory of the solution of inventive problems (TRIZ) have been used to predict the potential states of products for innovation, there is a lack of effective methods to select the best technological evolution law consistently with product replacement and update, and acquiring potentially new technologies and solutions, which relies heavily on designers’ experience and makes it impossible for designers to efficiently use the technological evolution laws to stimulate product innovation. Aimed to bridge this gap, this paper proposes an integrated method consisting of three main steps, combining the technological evolution laws with back propagation neural network (BPNN), international patent classification (IPC) knowledge and company’s technological distance. The best technical evolution law is first searched by a BPNN. The functional verbs and effects in the IPC are then extracted and searched for potential technologies in the Spyder-integrated development environment. Finally, the company’s technological distance is used to select analogous sources of potential solutions in the patent database. The final innovative design is determined based on the ideality. The proposed method is applied in the development of a steel pipe-cutting machine to verify its feasibility. The proposed method reduces the dependence on designers’ experience and provides a way to access cross-domain technologies, providing a systematic approach for the technological evolution laws to motivate innovative product design.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".