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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-23Cited by 0

生成AI時代における日本のIPコンテンツ保護および動的収益帰属分配インフラに関する考察 (Algorithmic Attribution and Dynamic Revenue-Routing Infrastructure for Japanese IP Content Preservation in the Generative AI Era)

A Kijinsuke

【概要】本論文は、生成AIの急速な発展に伴う日本のIPコンテンツ(アニメ、漫画、小説等)の非対称な流出(データ植民地化)を防止し、クリエイターへの正当な経済的還元を秒速で自動実行する新たな分配インフラ『クリエイター・レベニュー・ルーティング(CRR)』を提唱する。既存の製作委員会方式による合意形成の停滞を打破するため、トップダウンの「垂直統合型アーキテクチャ」を導入し、ゲーム理論のシャープレイ値の動的適用およびトランスフォーマーのアテンション・マップ逆解析を用いて、出力コンテンツに対する個別IPの残差寄与度をミリ秒単位で定量化する数理モデルを構築。さらに、多重帰属による画質汚損(アテンション飽和エラー)を回避する「包括的帰属認証マーク」とC2PAメタデータ層の分離仕様を提案し、権利保護とプレミアムなユーザー体験を両立する社会実装可能なマクロ経済的アライメントを確立する。 [Abstract]This paper proposes "Creator Revenue-Routing" (CRR), a novel distributional infrastructure engineered to resolve the structural antinomy in generative AI: the trade-off between the infringement risks of Japanese intellectual property (IP) and the hyper-inflation of hardware scaling.To overcome the paralysis in consensus-building induced by legacy "Production Committees," we introduce a top-down, vertically integrated architecture, formulated with a mathematical model capable of executing millisecond-scale inverse analysis of individual IP contributions relative to output tensors through the dynamic application of Shapley Values and transformer Attention Maps. Furthermore, we define a subtractive "Comprehensive Attribution Certification" to eliminate visual data saturation (attention saturation errors) without compromising pixel quality by separating the visual certification layer from C2PA metadata stacks, successfully anchoring a robust Nash Equilibrium that permanently secures creative sovereignty.

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