Improving Utility Patent Value by Design: Future Business Competitiveness Through Multi-Stakeholder Engagement for Patent Content Creation
Bibliographic record
Abstract
Growth through innovation is critical to Small and Medium-sized Enterprises (SMEs) and the use of patents is necessary to attract needed funding.SMEs' use of the patent system is not as effective as larger companies and Canadian SMEs' patent success underperforms compared to those from other industrialized countries (Nikzad, 2015).This research inquiry considers product development stakeholder's knowledge to increase the relevance of patent disclosures for more successful patents and business competitiveness.The research uses a mixed method questionnaire to gain insight from a broad selection of product development experts that include design, marketing, engineering, upper management, entrepreneur and legal representation.Stakeholder salience concepts are applied to identify stakeholders with forward looking patent attribute awareness.Insight generation techniques applied to the quantitative and qualitative data indicate that extended stakeholders have important knowledge to improve SME patent disclosures.Key barriers to the acquisition of this knowledge are identified as well as solution approaches that include interdisciplinary activities.The industrial designer is identified as having attributes and knowledge that could effectively assist the cross-boundary communication needed to acquire new knowledge from extended product development stakeholders.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".