The Patent Technology Development Characteristics Research of the Core Sporting Goods Enterprises—Taking Nike Company as an Example
Bibliographic record
Abstract
This paper from the perspective of patent analysis,with Nike,included in the Derwent Innovation Index the patent retrieval system 1694patents(basic patent)information in the database as the research object,and with the large literature processing software Bibexcel and the latest data visualization software CitespaceII statistic on the patent data information. The results show that since 2004,Nike's patented technology research and development into rapid development phase,the patent except in their home country to apply protection are mainly distributed in China,Japan,Canada,Australia,Germany,Brazil,UK.At present,it has formed A and W,T types of patent technology cluster,and patent technology mainly concentrated in the sports shoes,clothes,sports equipment components and design,match and training used in data processing and transmission devices as well as the sports injury appraisal,and other fields,including Nike elasticity and shock absorption,non-slip soles(air cushion technology) and golf clubs most mature technology field.In addition,the cooperation density of innovation team is very high,in all areas has its own professional research and development organization, greatly improve the efficiency of research and development From the field of patent in the core enterprise of sport to promote the development of the sports goods manufacturing industry in our country's patent technology has the reference value of competitive intelligence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".