This study develops an algorithmic attribution framework,inspired by biological theory,to ex-plore the effects of various factors on patent maintenance time.It delves into the changing dynamics of these effects across different literature life cycle stages and different technology fields.Employing a combination of survival analysis and interpretable machine learning techniques,this study analyzes a dataset of patents granted in China between 2001 and 2017.Specifically,this study investigates the effects of innate,behav-ioral and environmental factors on patent maintenance time.The findings shows that the algorithmic attribu-tion framework is effective in accurately determining the relationship between diverse factors and patent ma-intenance time.Additionally,the study notes that the effects of these factors varies significantly across dif-ferent literature life cycle stages and among diverse technological domains.The findings can provide critical insights and innovative approaches for patent maintenance time prediction and patent value assessment.
关键词
专利维持时间/算法归因/生存分析/可解释机器学习/专利价值
Key words
Patent maintenance time/Algorithmic attribution/Survival analysis/Interpretable machine learning/Patent value
维普
万方数据