This study proposes a neural network-based technology opportunity analysis of patent data.
First, based on learning of the content characteristics of the data by deep learning algorithm, the extraction keyword model was formed. Content similarity between keywords was calculated based on the contents. Second, patent technology network was built based on the keywords. The link prediction algorithm was used to calculate the topology features of the network, including similarity features based on local information, random walk and path. Finally, the three types of network topology features and content features were input to the BP neural network model to predict potential technology opportunities.
The field of biological nanomedicine was selected as the empirical field. The top 10 technological opportunities with the highest probability score in the patented technology network were identified in the field of biomedicine.
The results were analyzed and evaluated through the literature retrospective method, and the latest literature found the research results on these technical opportunities, but they have not successfully applied for patents.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |