Research on Copyright Identification and Rights Allocation Mechanisms for Generative AI Content: A Case Study of Prompt-Based AIGC

Zimeng Wang

doi:10.54691/wj4es922

Authors

Zimeng Wang Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province, 210000, China

DOI:

https://doi.org/10.54691/wj4es922

Keywords:

Generative Artificial Intelligence; Prompt-Based AIGC; Copyright Determination; Rights Allocation; Neighboring Rights.

Abstract

The current determination of copyright for Prompt-Based AIGC (Artificial Intelligence-Generated Content) based on prompts faces two major challenges: first, the standard for "originality" remains ambiguous; second, the application of neighboring rights is still uncharted. Centered on the core perspective of "educational contribution," this paper proposes a comprehensive framework for assessing originality across three dimensions: the specificity of instructions, the depth of parameter tuning, and the difficulty of result selection. Regarding rights allocation, the paper argues that users should hold copyright, while AI platforms are granted neighboring rights, with developers earning revenue through algorithm licensing. This framework not only helps unify judicial standards but also strikes a balance between technological innovation and intellectual property protection, thereby providing new theoretical foundations for improving China's copyright system.

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