“数据”可否纳入知识产权客体范畴?

Can“Data”be Integrated into the Category of Objects of Intellectual Property Rights?

《民法总则》(草案)第108条将“数据信息”纳入知识产权客体范畴,虽然该规定最终未获通过,但却引发了学界和业界的热议,数据可否纳入知识产权客体范畴的讨论方兴未艾。根据“信息链”理论,事实、数据、信息、知识和情报之间是呈现一种由下至上逐步提炼加工的链式关系,数据与信息、知识、情报之间依次包含。但数据并不符合知识产权客体的一般特征,若将“数据”整体直接纳入知识产权客体的范畴,会冲击已有的权能和体系,影响知识产权客体家族的统一性和协调性。应对数据进行分类保护,在现有知识产权保护基础之上,增设数据产权保护模式,数据主体就个人原始数据享有数据人格权和数据财产权;企业就其加工处理取得的衍生数据享有数据财产权,企业这种财产权利取得的门槛低于知识产权取得,不必要求独创性、创造性或显著性等知识产权客体的一般特征,宜采取弱保护模式。

The proposed integration of“data information”into the category of objects of intellectual property rights in Article 108 of the draft to General Rules of Civil Law sparked significant debate within academia and industry.Though not ultimately passed,that provision instigated ascendant discussions regarding the eligibility of data to be integrated as an object of intellectual property rights.According to the“information chain”theory,facts,data,information,knowledge,and intelligence represent a sequential,bottom-up refinement,within this chain-like framework,data encapsulates elements of information,knowledge,and intelligence.However,data does not entirely conform to the general characteristics of objects of intellectual property rights.Directly integrating“data”into the category of objects of intellectual property rights as a whole could disrupt existing rights and systems,compromising the unity and coordination of the object family.Proposed is a classified protection system for data:establishing a data property rights protection model based upon existing intellectual property protections,under which data subjects are granted both personality rights and property rights over their individual original data;enterprises enjoy property rights for their processed derived data,the threshold for acquiring these rights is lower compared to the acquisition of traditional intellectual property rights,thus general characteristics associated with objects of intellectual property rights,such as originality,nonobviousness,or distinctiveness are not required,and a thin protection model is advisable.