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| Accurate Identification and Prediction of Patent Transformation Features：An Example of Artificial Intelligence Chip |
| Jiang Nan1,Li Yifan1,Liu Qian2,Liu Xing1 |
| (1.Shanghai International College of Intellectual Property, Tongji University, Shanghai 200092, China;2.Business School of Hohai University，Nanjing 210024, China) |
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Abstract Although there has been an exponential growth of patent applications in China, the commercialization potential of the majority of patents registered in the field of science and technology has not been exploited. Based on this background, the paper studies the identification factors of patent transformation in order to better promote patent transformation. The commercialization value of patents mainly depends on patent protection and patent policy. Given the current government policies on patents, more data are needed to predict patent technology transformation in emerging frontier fields, help to solve the problems that China has to rely on foreign key core technologies for the production of some high-tech products and realize the commercialization potential of science and technology patents in China. The paper aims to better promote the patent transformation of key core technologies and focuses on artificial intelligence (AI) chips, given their current importance.#br#In this study, AI chips patent data is from the Derwent World Patents Index Database. We employ logical regression, support vector machine, random forest and AdaBoost algorithms to carry out comparative method analysis. After decomposing the patent transformation indicators into three dimensions (technology, law and economy), we select 17 representative indicators in the field of AI chips and adopt the machine learning method to identify an optimal transformation prediction scheme and the factors influencing successful patent transformation in China and in other countries. We discuss the main characteristics of successful patent transformation in different areas and fields with various application bodies in China and abroad.#br#This study applies different algorithms to analyse patent transformation in the field of AI chips in China and a number of developed countries. These algorithms help to predict the main factors influencing the successful transformation of patent technology in the field of AI chips. Among the four algorithms (logical regression, support vector machine, random forest and AdaBoost) used to predict the drivers of successful patent transformation, the random forest algorithm is the best in predictive ability . The probability of patent transformation in the field of AI chips is distributed in a logarithmic curve, which is consistent with the general distribution curve of patent value. The top three factors with the greatest impacts on successful patent transformation for patent applications submitted by universities or scientific research institutions are the maintenance time, number of claim characters and number of inventors. The top three factors with the greatest impacts on successful patent transformation for patent applications submitted by enterprises are the number of claim characters, maintenance time and number of claims. Meanwhile, it is found that the factors affecting successful patent transformation in China are different with that in other countries.#br#In conclusion, this study investigates the usefulness of four machine learning algorithms in predicting the factors influencing successful patent transformation, specifically in relation to AI chips. Based on the findings, it is suggested that universities and scientific research institutions can improve their chances of successful patent transformation by strengthening relationships with key enterprises (i.e. enterprises with specific skills and scientific research strength). The study provides suggestions on how universities and scientific research institutions can jointly tackle key technical problems to gain a high-value patent portfolio, implement high-value patent mining and cultivation projects and strengthen patent management in the whole process. The enterprises intended to improve patent transformation work should pay more attention to the technical quality and writing quality of patents applications. Furthermore, the government should guide enterprises to make good use of preferential examination and rapid examination policies, high-value patent cultivation projects and patent navigation projects in various provinces and cities.#br#
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Received: 06 December 2021
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