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Comparative analysis of international KM responses highlights mainstream strategies such as moral/economic rights separation, collective authorization, safe-harbour reform, and patent-path exploration, but existing proposals show limitations in technical fit, legal compatibility, and ethical legitimacy. To address these gaps, the study proposes a technology-law-governance optimization framework using interpretable contribution tracking, causal mapping of assets, hierarchical rights confirmation, and smart-contract-based cross-border coordination.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Yujie Wu and Fangkun Xia  \nBusiness school of Shenzhen Technology University, China  \n[Wangzhe192612@163.com](Wangzhe192612@163.com)  \n[1271478903@qq.com](1271478903@qq.com)[ ](1271478903@qq.com)[214773518@qq.com](214773518@qq.com)  \nAbstract: With the in-depth application of generative artificial intelligence (Gen AI) in the field of knowledge management (KM), the traditional intellectual property (IP) system is facing serious challenges. The AI-driven knowledge production model blurs the clear boundaries of traditional IP ownership, resulting in increased uncertainty in the distribution of rights and an aggravation of the imbalance in the pattern of interests. This dilemma is further amplified by Gen AI's unique \"black box\"and \"autonomous\" characteristics, and it is difficult to determine a clear ownership subject for its automatically generated content. These challenges have exposed the fundamental limitations of the traditional IP framework, which is based on an anthropocentric knowledge production paradigm that is difficult to adapt to the characteristics of knowledge innovation in the AI era. By systematically combing the solutions proposed by various countries in the field of KM, combined with the analysis of international typical cases, this study focuses on the mainstream coping strategies, including the separation of moral rights and economic rights, the collective management authorization model, the transformation of the safe harbour principle, and the exploration of patent paths. The results show that the existing schemes have significant limitations: they are insufficient in terms of technical adaptability, legal compatibility and ethical legitimacy. These solutions focus too much on rights segmentation and ignore dynamic collaboration; Emphasis is placed on post-event relief, and there is a lack of source governance. In order to overcome these limitations, this study innovatively proposes an optimization framework of\"technology-law-governance\": develop an interpretable contribution tracking algorithm at the technical level, and construct a causal mapping of data-model-knowledge assets; At the legal level, a mixed ownership structure is designed to balance private rights protection and public access through hierarchical rights confirmation. At the governance level, a global coordination mechanism has been established to use smart contracts to achieve real-time distribution of cross-border IP rights. This integrated solution aims to reconstruct the IP system in the AI era, promote technological innovation while maintaining the sustainability and fairness of the knowledge ecosystem.  \nKeywords: Generative AI, Intellectual property, Knowledge management, Technology-Law-governance framework, Hybrid  property rights   \n1. Introduction  \nThe rapid development of the new generation of AI is profoundly changing the paradigm of KM, creating great opportunities and serious challenges. AI technology has become the core driving force for the digital transformation of various industries, but its rapid development has also completely shaken the foundation of the traditional IP system, exposing major shortcomings in the ownership identification and management mechanism of AI-generated content (AIGC) . The inherent \"black box\" nature of AI models leads to the opaque decision-making process, and its self-generation ability makes it difficult for the creator to identify the subject (Rezaei, 2025) . This situation has been further exacerbated by the global application of AI, and cross-border data flows and cooperation have had a huge impact on the traditional IP system based on territorial jurisdiction. With the wide application of Gen AI in key knowledge work areas such as legal document drafting and medical research, it is urgent to build flexible and fair IP management solutions. According to Priority Research, the global AIGC market will exce","cbCaibJxiNVBmtIL","https://ap.wps.com/l/cbCaibJxiNVBmtIL","pdf",957206,"English","# Introduction\n## Research Background and Challenges\n## Paper Aim and Tripartite Framework","[{\"question\":\"Why does generative AI complicate knowledge ownership and IP attribution?\",\"answer\":\"Generative AI blurs boundaries of traditional IP ownership because its outputs are hard to attribute to a clear subject, especially given the models’ black-box and autonomous generation characteristics.\"},{\"question\":\"What mainstream coping strategies are discussed for IP problems in knowledge management?\",\"answer\":\"The paper reviews strategies including separating moral and economic rights, using collective management authorization, transforming the safe-harbour principle, and exploring patent pathways.\"},{\"question\":\"What limitations do existing solutions have according to the study?\",\"answer\":\"Existing schemes are limited in technical adaptability, legal compatibility, and ethical legitimacy, and they tend to focus on rights segmentation while neglecting dynamic collaboration and source governance.\"}]","Generative AI's Impact on Knowledge Ownership - Ensuring Fair IP Attribution | PDF",1789940873]