Patent Analysis of Deep Learning
| Autor: | Chen, Chi-Hsuan, 陳綺萱 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 The purpose of this study was to explore the development and the growth trend of deep learning in different countries. Also, the situation of deep learning in other related subjects and the application in different fields. This study used patent analysis and the content mining tool - CATAR to analyze the patents in the field of deep learning from 1976 to 2018 searching from USPTO. The findings of this paper are as follows: (1) The technology life cycle of deep learning is in the growth stage, and on average, the issue date is 1.75 years later than the applied date. (2) On patent assignee's nationality, the countries of high productivity are the US, Japan, Israel, South Korea, China, Germany, and Canada, and patents in these countries account for 93% of the total. On patent inventor's nationality, the countries of high productivity are the US, China, South Korea, Israel, Japan, India, and Canada. (3) Among 103 national groups of assignees and inventors, there are 78 groups related to the US. (4) Citations are mainly related to deep learning, neural networks, and speech recognition. (5) Applications focus on speech recognition, image analysis, image recognition, medical image, and vehicle control systems. (6) Taiwan can learn from Israel and South Korea, and research on medical image. Based on the findings of this study, there are three suggestions: (1) Add keywords. (2) Research on specific subjects intensively. (3) Research on papers in the field of deep learning. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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