Design Around Bundle Patent Portfolio Based on Technological Evolution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Product innovation can be achieved by analyzing leading products patents in the market. Different methods have been proposed for design around patent, commonly using the elimination or replacement of a single patent element. However, the existing research fails to restore the position and function of the design around object in the original patent portfolio of enterprises, which often leads to the phenomenon of evading one patent and violating another. This paper proposes a method for design around patent through using the fusion of technologies of the evolution theory and bundle-type patent portfolio analysis in the initial stage of product development. The object system is analyzed to select technical opportunities through the evolutionary path of technologies and functional trimming methods to achieve circumvent barriers of bundle-type patents. The bundle patent portfolio is analyzed for the product evolution with a radar map. The technological evolution path is combined with the TRIZ innovation method to identify and solve the design problem. Patentability of the new design is evaluated using the patent system rules for innovative scheme difference from the original patent portfolio. The method is verified in a case study for the design of a glass-wiping robot. The design solution has been patented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it