Interpretation of TRIZ Principles in a Service Related Context
Bibliographic record
Abstract
A systematic innovation method such as the Theory of Inventive Problem Solving (TRIZ) has powerful tools that can be used to solve contradiction problems in technical or non-technical systems. The most common tool used in TRIZ is 40 inventive principles (IPs). The purpose of this paper is to interpret these IPs from a service prospective. The data was collected from many resources found in the literature. A case study was conducted to prove the feasibility of interpretative IPs. The outcome of this study enhanced the usability of the 40 IPs by including new synonyms for some principles, comprehensive descriptions, and providing suggestions and examples for each principle. The interpretative principles focused on a service process used to fulfill customer demands. An Interpretation of TRIZ tool such as the 40 IPs in a service related context improves the understanding of these principles by researchers or service designers.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".