Innovation within networks – patent strategies for blockchain technology
Bibliographic record
Abstract
Purpose Understanding a technology’s patent landscape, including patent strategies, helps organizations position themselves regarding their innovation and provides insight about a technology’s future direction. This study aims to provide an overview of the blockchain technology patenting trends and outlines an exploratory framework of patenting strategies for blockchain. Design/methodology/approach A total of 3,234 registered patents are analyzed to determine the geographical distribution and identify key actors patenting around the globe. In addition, an empirical study consisting of multiple case studies in the form of ten in-depth interviews with owners/managers of organizations based in North America was conducted to understand organizations’ strategies for patenting the blockchain technology. Findings Several novel insights regarding the strategies are used for blockchain technology patenting. For example, the existence of strong anti-patent sentiment which results in a lack of patenting by start-up organizations or has led to a form of open source patenting strategy. Larger organizations appear to be patenting defensively, and small to medium organizations are primarily patenting to defend their competitive advantage. Practical implications Start-up organizations harboring anti-patent sentiment should consider the open-source patenting strategy to ensure that the collaborative innovation network can continue. They should also consider collaborating with other actors within the network to have a competitive position in the market. Originality/value To the authors’ knowledge, this paper is the first to conduct an empirical study with organizations currently using the blockchain technology to understand patenting strategies used for blockchain.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".