{ "schemaVersion": "2.0", "generatedAt": "2026-07-16T12:09:49.396Z", "compatibility": { "legacyMatterFieldsRetained": true, "issueDocumentsAreNotTimelineEvents": true }, "matter": { "id": "matter-cn-douyin-b612-ai-model", "slug": "cn-douyin-b612-ai-model", "titleZh": "抖音诉 B612 AI 模型结构与参数案", "formalName": "北京抖音科技有限公司诉亿睿科信息技术（北京）有限公司不正当竞争纠纷案", "aliases": [ "变身漫画特效案", "（2023）京73民终3802号", "（2023）京73民终3803号", "Douyin v. 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"matter-cn-douyin-b612-ai-model:document:appellate-transcript", "matter-cn-douyin-b612-ai-model:document:spc-typical-case" ] }, { "text": "法院没有按原告提出的著作权路径保护案涉模型，而以反不正当竞争法一般条款评价被诉使用行为。", "kind": "legal-holding", "status": "confirmed", "asOf": "2025-03-31", "id": "matter-cn-douyin-b612-ai-model:claim:copyright-limit", "matterId": "matter-cn-douyin-b612-ai-model", "eventId": "matter-cn-douyin-b612-ai-model:event:appeal-judgment", "documentIds": [ "matter-cn-douyin-b612-ai-model:document:appellate-transcript" ] }, { "text": "二审驳回上诉、维持原判；并行案件分别裁判，案内一审责任安排继续有效。", "kind": "procedural-fact", "status": "confirmed", "asOf": "2025-03-31", "id": "matter-cn-douyin-b612-ai-model:claim:appeal-outcome", "matterId": "matter-cn-douyin-b612-ai-model", "eventId": "matter-cn-douyin-b612-ai-model:event:appeal-judgment", "documentIds": [ "matter-cn-douyin-b612-ai-model:document:appellate-transcript" ] }, { "text": "最高人民法院于 2025 年 9 月 9 日将（2023）京73民终3802号列为反不正当竞争典型案例。", "kind": "procedural-fact", "status": 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"解释模型参数或功能如何可能经由接口被提取，为模型复制证据和防护争议提供技术背景，但不证明个案发生提取。 本次规则匹配仅使用本争点文本及其引用主张，命中 2 个确定性争点词；AI 作用分面只用于候选召回。", "viewpoint": "descriptive", "confidence": 0.9, "assignedBy": "rule" }, { "id": "news-context-link-matter-cn-douyin-b612-ai-model-matter-cn-douyin-b612-ai-model:issue:proof-of-use-news-context-ai-transparency-trade-secrets", "matterId": "matter-cn-douyin-b612-ai-model", "issueId": "matter-cn-douyin-b612-ai-model:issue:proof-of-use", "documentId": "news-context-ai-transparency-trade-secrets", "relationship": "news-context", "relevanceSummaryZh": "用于解释模型权重、架构、训练数据和测试方法如何满足商业秘密要件，以及保密与透明之间为何需要分别审查。 本次规则匹配仅使用本争点文本及其引用主张，命中 2 个确定性争点词。", "viewpoint": "mixed", "confidence": 0.9, "assignedBy": "rule" }, { "id": "issue-document-matter-cn-douyin-b612-ai-model-matter-cn-douyin-b612-ai-model:issue:copyright-route-knowledge-generative-ai-meets-copyright", "matterId": "matter-cn-douyin-b612-ai-model", "issueId": "matter-cn-douyin-b612-ai-model:issue:copyright-route", "documentId": "knowledge-generative-ai-meets-copyright", "relationship": "doctrinal-context", "relevanceSummaryZh": "用于概览生成式 AI 版权诉讼的主要请求与尚未形成定论的合理使用问题。 本次规则匹配仅使用本争点文本及其引用主张，命中 1 个确定性争点词；AI 作用分面只用于候选召回。", "viewpoint": "descriptive", "confidence": 0.8500000000000001, "assignedBy": "rule" }, { "id": "news-context-link-matter-cn-douyin-b612-ai-model-matter-cn-douyin-b612-ai-model:issue:copyright-route-news-context-ai-liability-ip-harms", "matterId": "matter-cn-douyin-b612-ai-model", "issueId": "matter-cn-douyin-b612-ai-model:issue:copyright-route", "documentId": "news-context-ai-liability-ip-harms", "relationship": "news-context", "relevanceSummaryZh": "用于比较生成式 AI 供应链中训练复制、近似输出、元数据去除、商标和公开权的不同责任入口。 本次规则匹配仅使用本争点文本及其引用主张，命中 2 个确定性争点词。", "viewpoint": "mixed", "confidence": 0.9, "assignedBy": "rule" } ] }, "issueDocuments": [ { "link": { "id": "issue-document-matter-cn-douyin-b612-ai-model-matter-cn-douyin-b612-ai-model:issue:competition-interest-knowledge-stealing-machine-learning-models", "matterId": "matter-cn-douyin-b612-ai-model", "issueId": "matter-cn-douyin-b612-ai-model:issue:competition-interest", "documentId": "knowledge-stealing-machine-learning-models", "relationship": "technical-context", "relevanceSummaryZh": "解释模型参数或功能如何可能经由接口被提取，为模型复制证据和防护争议提供技术背景，但不证明个案发生提取。 本次规则匹配仅使用本争点文本及其引用主张，命中 1 个确定性争点词；AI 作用分面只用于候选召回。", "viewpoint": "descriptive", "confidence": 0.8500000000000001, "assignedBy": "rule" }, "document": { "id": "knowledge-stealing-machine-learning-models", "familyId": "knowledge-family-stealing-machine-learning-models", "title": "通过预测 API 提取机器学习模型", "originalTitle": "Stealing Machine Learning Models via Prediction APIs", "url": "https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/tramer", "kind": "academic-research", "sourceId": "usenix-security", "publishedAt": "2016-08-10", "author": "Florian Tramèr; Fan Zhang; Ari Juels; Michael K. Reiter; Thomas Ristenpart", "primaryOrSecondary": "secondary", "relationship": "independent", "identifiers": {}, "language": "en", "metadata": { "abstractZh": "实证展示攻击者如何利用预测接口恢复模型参数或近似模型行为，并分析查询成本、模型类型和防护限制。它可解释模型复制的技术路径，但不能证明任何被告实施了提取。", "venue": "25th USENIX Security Symposium", "publisher": "USENIX Association", "publicationState": "published", "peerReviewed": true, "paywalled": false }, "stableIdentifier": { "kind": "repository-record", "value": "usenixsecurity16:tramer:model-extraction" }, "legalTopics": [ "trade-secret", "competition", "platform-liability" ], "aiRoles": [ "model-output", "training-data" ] } }, { "link": { "id": "issue-document-matter-cn-douyin-b612-ai-model-matter-cn-douyin-b612-ai-model:issue:proof-of-use-knowledge-stealing-machine-learning-models", "matterId": "matter-cn-douyin-b612-ai-model", "issueId": "matter-cn-douyin-b612-ai-model:issue:proof-of-use", "documentId": "knowledge-stealing-machine-learning-models", "relationship": "technical-context", "relevanceSummaryZh": "解释模型参数或功能如何可能经由接口被提取，为模型复制证据和防护争议提供技术背景，但不证明个案发生提取。 本次规则匹配仅使用本争点文本及其引用主张，命中 2 个确定性争点词；AI 作用分面只用于候选召回。", "viewpoint": "descriptive", "confidence": 0.9, "assignedBy": "rule" }, "document": { "id": "knowledge-stealing-machine-learning-models", "familyId": "knowledge-family-stealing-machine-learning-models", "title": "通过预测 API 提取机器学习模型", "originalTitle": "Stealing Machine Learning Models via Prediction APIs", "url": "https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/tramer", "kind": "academic-research", "sourceId": "usenix-security", "publishedAt": "2016-08-10", "author": "Florian Tramèr; Fan Zhang; Ari Juels; Michael K. Reiter; Thomas Ristenpart", "primaryOrSecondary": "secondary", "relationship": "independent", "identifiers": {}, "language": "en", "metadata": { "abstractZh": "实证展示攻击者如何利用预测接口恢复模型参数或近似模型行为，并分析查询成本、模型类型和防护限制。它可解释模型复制的技术路径，但不能证明任何被告实施了提取。", "venue": "25th USENIX Security Symposium", "publisher": "USENIX Association", "publicationState": "published", "peerReviewed": true, "paywalled": false }, "stableIdentifier": { "kind": "repository-record", "value": "usenixsecurity16:tramer:model-extraction" }, "legalTopics": [ "trade-secret", "competition", "platform-liability" ], "aiRoles": [ "model-output", "training-data" ] } }, { "link": { "id": "news-context-link-matter-cn-douyin-b612-ai-model-matter-cn-douyin-b612-ai-model:issue:proof-of-use-news-context-ai-transparency-trade-secrets", "matterId": "matter-cn-douyin-b612-ai-model", "issueId": "matter-cn-douyin-b612-ai-model:issue:proof-of-use", "documentId": "news-context-ai-transparency-trade-secrets", "relationship": "news-context", "relevanceSummaryZh": "用于解释模型权重、架构、训练数据和测试方法如何满足商业秘密要件，以及保密与透明之间为何需要分别审查。 本次规则匹配仅使用本争点文本及其引用主张，命中 2 个确定性争点词。", "viewpoint": "mixed", "confidence": 0.9, "assignedBy": "rule" }, "document": { "id": "news-context-ai-transparency-trade-secrets", "familyId": "news-family-ai-transparency-trade-secrets", "title": "AI 透明规则面临的商业秘密难题", "originalTitle": "New AI Transparency Rules Have a Trade Secrets Problem", "url": "https://www.lawfaremedia.org/article/new-ai-transparency-rules-have-a-trade-secrets-problem", "kind": "professional-commentary", "sourceId": "lawfare", "publishedAt": "2025-09-15", "author": "Julius Hattingh", "primaryOrSecondary": "secondary", "relationship": "independent", "identifiers": {}, "language": "en", "metadata": { "abstractZh": "文章说明模型权重、架构、训练数据、系统提示和安全测试方法可能同时具有商业秘密属性，并分析安全透明义务与保密利益之间的制度张力。", "venue": "Lawfare", "publisher": "Lawfare Institute", "publicationState": "published", "peerReviewed": false, "paywalled": false }, "editorialSummaryZh": "这篇评论提供了判断 AI 信息是否因秘密性而具有商业价值的背景，也提醒读者：商业秘密资格与公共披露义务是两个问题，不能只凭技术价值推定秘密成立。", "legalTopics": [ "trade-secret", "public-law-human-rights" ], "aiRoles": [ "training-data", "model-output" ] } }, { "link": { "id": "issue-document-matter-cn-douyin-b612-ai-model-matter-cn-douyin-b612-ai-model:issue:copyright-route-knowledge-generative-ai-meets-copyright", "matterId": "matter-cn-douyin-b612-ai-model", "issueId": "matter-cn-douyin-b612-ai-model:issue:copyright-route", "documentId": "knowledge-generative-ai-meets-copyright", "relationship": "doctrinal-context", "relevanceSummaryZh": "用于概览生成式 AI 版权诉讼的主要请求与尚未形成定论的合理使用问题。 本次规则匹配仅使用本争点文本及其引用主张，命中 1 个确定性争点词；AI 作用分面只用于候选召回。", "viewpoint": "descriptive", "confidence": 0.8500000000000001, "assignedBy": "rule" }, "document": { "id": "knowledge-generative-ai-meets-copyright", "familyId": "knowledge-family-generative-ai-meets-copyright", "title": "生成式 AI 遇上版权法", "originalTitle": "Generative AI Meets Copyright", "url": "https://doi.org/10.1126/science.adi0656", "kind": "academic-research", "sourceId": "science", "publishedAt": "2023-07-14", "author": "Pamela Samuelson", "primaryOrSecondary": "secondary", "relationship": "independent", "identifiers": { "doi": "10.1126/science.adi0656" }, "language": "en", "metadata": { "abstractZh": "概述生成式 AI 训练与输出引发的版权诉讼路径，强调合理使用是逐案判断，正在进行的案件会同时影响开发者、部署者和普通使用者。", "venue": "Science 381(6654): 158–161", "publisher": "American Association for the Advancement of Science", "publicationState": "published", "peerReviewed": false, "paywalled": true }, "stableIdentifier": { "kind": "doi", "value": "10.1126/science.adi0656" }, "legalTopics": [ "copyright" ], "aiRoles": [ "training-data", "model-output" ] } }, { "link": { "id": "news-context-link-matter-cn-douyin-b612-ai-model-matter-cn-douyin-b612-ai-model:issue:copyright-route-news-context-ai-liability-ip-harms", "matterId": "matter-cn-douyin-b612-ai-model", "issueId": "matter-cn-douyin-b612-ai-model:issue:copyright-route", "documentId": "news-context-ai-liability-ip-harms", "relationship": "news-context", "relevanceSummaryZh": "用于比较生成式 AI 供应链中训练复制、近似输出、元数据去除、商标和公开权的不同责任入口。 本次规则匹配仅使用本争点文本及其引用主张，命中 2 个确定性争点词。", "viewpoint": "mixed", "confidence": 0.9, "assignedBy": "rule" }, "document": { "id": "news-context-ai-liability-ip-harms", "familyId": "news-family-ai-liability-ip-harms", "title": "AI 造成知识产权损害时，责任如何分配", "originalTitle": "AI Liability for Intellectual Property Harms", "url": "https://www.lawfaremedia.org/article/ai-liability-for-intellectual-property-harms", "kind": "professional-commentary", "sourceId": "lawfare", "publishedAt": "2024-09-23", "author": "Katrina Geddes", "primaryOrSecondary": "secondary", "relationship": "independent", "identifiers": {}, "language": "en", "metadata": { "abstractZh": "文章分别分析未经许可训练、近似输出、版权管理信息、商标和形象商业利用，强调开发者、部署者与用户的责任不能用一个统一答案概括。", "venue": "Lawfare", "publisher": "Lawfare Institute", "publicationState": "published", "peerReviewed": false, "paywalled": false }, "editorialSummaryZh": 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