Exploring the Factors Influencing Patent Maintenance Time Based on an Algorith-mic Attribution Framework
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.
Patent maintenance timeAlgorithmic attributionSurvival analysisInterpretable machine learningPatent value
万方数据
维普
