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"confidence": 0.95, "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": "这篇评论把训练、输出和传播链条拆开，说明同一生成内容可能同时触发版权、商标与人格商业利用争议；它提供责任分配框架，不代表法院已经采纳其中结论。", "legalTopics": [ "copyright", "trademark", "personality-deepfake", "platform-liability" ], "aiRoles": [ "training-data", "model-output", "deepfake" ] } }, { "link": { "id": "issue-document-matter-02-matter-02:issue-memorized-outputs-knowledge-copyright-safety-generative-ai", "matterId": "matter-02", "issueId": "matter-02:issue-memorized-outputs", "documentId": "knowledge-copyright-safety-generative-ai", "relationship": "doctrinal-context", "relevanceSummaryZh": "为模型记忆、角色复现、训练去重与输出过滤等争议提供版权和工程交叉背景。 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": "mixed", "confidence": 0.8500000000000001, "assignedBy": "rule" }, "document": { "id": "knowledge-copyright-safety-generative-ai", "familyId": "knowledge-family-copyright-safety-generative-ai", "title": "生成式 AI 的版权安全", "originalTitle": "Copyright Safety for Generative AI", "url": "https://houstonlawreview.org/article/92126-copyright-safety-for-generative-ai", "kind": "academic-research", "sourceId": "houston-law-review", "publishedAt": "2023-12-11", "author": "Matthew Sag", "primaryOrSecondary": "secondary", "relationship": "independent", "identifiers": {}, "language": "en", "metadata": { "abstractZh": "区分通常的非表达性训练与模型记忆、近似复现等边缘风险，并提出去重、训练记录、敏感提示处理和输出过滤等版权安全措施。", "venue": "Houston Law Review 61: 295", "publisher": "Houston Law Review", "publicationState": "published", "peerReviewed": false, "paywalled": false }, "stableIdentifier": { "kind": "publisher-record", "value": "houston-law-review:61:2:295" }, "legalTopics": [ "copyright", "platform-liability" ], "aiRoles": [ "training-data", "model-output" ] } }, { "link": { "id": "issue-document-matter-02-matter-02:issue-memorized-outputs-knowledge-generative-ai-meets-copyright", "matterId": "matter-02", "issueId": "matter-02:issue-memorized-outputs", "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-02-matter-02:issue-memorized-outputs-news-context-ai-liability-ip-harms", "matterId": "matter-02", "issueId": "matter-02:issue-memorized-outputs", "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 3 个确定性争点词。", "viewpoint": "mixed", "confidence": 0.95, "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": "这篇评论把训练、输出和传播链条拆开，说明同一生成内容可能同时触发版权、商标与人格商业利用争议；它提供责任分配框架，不代表法院已经采纳其中结论。", "legalTopics": [ "copyright", "trademark", "personality-deepfake", "platform-liability" ], "aiRoles": [ "training-data", "model-output", "deepfake" ] } }, { "link": { "id": "issue-document-matter-02-matter-02:issue-market-substitution-knowledge-foundation-models-fair-use", "matterId": "matter-02", "issueId": "matter-02:issue-market-substitution", "documentId": "knowledge-foundation-models-fair-use", "relationship": "technical-context", "relevanceSummaryZh": "连接合理使用因素、输出相似度实验和技术缓解措施，适合解释训练与输出风险为何不能混为一谈。 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": "mixed", "confidence": 0.8500000000000001, "assignedBy": "rule" }, "document": { "id": "knowledge-foundation-models-fair-use", "familyId": "knowledge-family-foundation-models-fair-use", "title": "基础模型与合理使用", "originalTitle": "Foundation Models and Fair Use", "url": "https://jmlr.org/papers/v24/23-0569.html", "kind": "academic-research", "sourceId": "jmlr", "publishedAt": "2023-09", "author": "Peter Henderson; Xuechen Li; Dan Jurafsky; Tatsunori Hashimoto; Mark A. Lemley; Percy Liang", "primaryOrSecondary": "secondary", "relationship": "independent", "identifiers": {}, "language": "en", "metadata": { "abstractZh": "把合理使用 doctrine 与模型输出相似度实验放在同一框架中，认为训练和部署风险取决于输出、市场影响与缓解措施，法律和技术护栏需要共同演进。", "venue": "Journal of Machine Learning Research 24(400): 1–79", "publisher": "Journal of Machine Learning Research", "publicationState": "published", "peerReviewed": true, "paywalled": false }, "stableIdentifier": { "kind": "publisher-record", "value": "jmlr:24:400:23-0569" }, "legalTopics": [ "copyright" ], "aiRoles": [ "training-data", "model-output" ] } }, { "link": { "id": "issue-document-matter-02-matter-02:issue-market-substitution-knowledge-fair-learning", "matterId": "matter-02", "issueId": "matter-02:issue-market-substitution", "documentId": "knowledge-fair-learning", "relationship": "doctrinal-context", "relevanceSummaryZh": "为训练复制属于非表达性使用的主张提供基础 doctrine，同时明确表达性模仿可能改变结论。 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": "supportive", "confidence": 0.8500000000000001, "assignedBy": "rule" }, "document": { "id": "knowledge-fair-learning", "familyId": "knowledge-family-fair-learning", "title": "公平学习", "originalTitle": "Fair Learning", "url": "https://texaslawreview.org/fair-learning/", "kind": "academic-research", "sourceId": "texas-law-review", "publishedAt": "2021-02-23", "author": "Mark A. Lemley; Bryan Casey", "primaryOrSecondary": "secondary", "relationship": "independent", "identifiers": {}, "language": "en", "metadata": { "abstractZh": "主张机器为获取不受版权保护的事实、思想或功能而学习时，训练复制原则上应得到合理使用的有利评价；若训练目标是复现受保护表达，结论会更困难。", "venue": "Texas Law Review, Volume 99, Issue 4", "publisher": "Texas Law Review Association", "publicationState": "published", "peerReviewed": false, "paywalled": false }, "stableIdentifier": { "kind": "publisher-record", "value": "texas-law-review:99:4:fair-learning" }, "legalTopics": [ "copyright" ], "aiRoles": [ "training-data", "model-output" ] } }, { "link": { "id": "news-context-link-matter-02-matter-02:issue-market-substitution-news-context-ai-liability-ip-harms", "matterId": "matter-02", "issueId": "matter-02:issue-market-substitution", "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 3 个确定性争点词。", "viewpoint": "mixed", "confidence": 0.95, "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": "这篇评论把训练、输出和传播链条拆开，说明同一生成内容可能同时触发版权、商标与人格商业利用争议；它提供责任分配框架，不代表法院已经采纳其中结论。", "legalTopics": [ "copyright", "trademark", "personality-deepfake", "platform-liability" ], "aiRoles": [ "training-data", "model-output", "deepfake" ] } } ], "sources": [ { "id": "docketalarm", "slug": "docketalarm", "name": "Docket Alarm", "homepage": "https://www.docketalarm.com/", "type": "legal-database", "grade": "B", "primaryOrSecondary": "mixed", "score": { "traceability": 23, "corrections": 14, "ownershipTransparency": 17, "expertise": 17, "historicalAccuracy": 12, "total": 83 }, "rationale": "提供可回溯法院、案号与条目的案卷镜像；程序结论仍以法院正式文书为准。", "methodologyVersion": "1.0", "reviewedAt": "2026-07-15" }, { "id": "science", "slug": "science", "name": "Science", "homepage": "https://www.science.org/", "type": "academic", "primaryOrSecondary": "secondary", "rationale": "AAAS 出版的学术期刊，具有 DOI、作者和卷期记录；评论栏目不自动标记为同行评审研究。", "grade": "A", "score": { "traceability": 25, "corrections": 19, "ownershipTransparency": 20, "expertise": 20, "historicalAccuracy": 15, "total": 99 }, "methodologyVersion": "1.0", "reviewedAt": "2026-07-16" }, { "id": "jmlr", "slug": "jmlr", "name": "Journal of Machine Learning Research", "homepage": "https://www.jmlr.org/", "type": "academic", "primaryOrSecondary": "secondary", "rationale": "同行评审机器学习期刊，提供稳定卷期、作者、编辑和开放全文定位。", "grade": "A", "score": { "traceability": 25, "corrections": 18, "ownershipTransparency": 19, "expertise": 20, "historicalAccuracy": 14, "total": 96 }, "methodologyVersion": "1.0", "reviewedAt": "2026-07-16" }, { "id": "lawfare", "slug": "lawfare", "name": "Lawfare", "homepage": "https://www.lawfaremedia.org/", "type": "newsroom", "primaryOrSecondary": "secondary", "rationale": "作者和编辑准则公开，长期提供国家安全、平台治理和科技法分析；具体案件结论仍回溯官方材料。", "grade": "A", "score": { "traceability": 23, "corrections": 18, "ownershipTransparency": 19, "expertise": 19, "historicalAccuracy": 13, "total": 92 }, "methodologyVersion": "1.0", "reviewedAt": "2026-07-16", "ingestion": { "feedEnabled": false, "feedUrl": null, "verification": "not-verified", "allowedUse": "discovery-metadata-and-deep-links" } }, { "id": "houston-law-review", "slug": "houston-law-review", "name": "Houston Law Review", "homepage": "https://houstonlawreview.org/", "type": "academic", "primaryOrSecondary": "secondary", "rationale": "学生编辑法学评论，文章页提供卷期、引注、作者和公开全文；观点按学术评论展示。", "grade": "A", "score": { "traceability": 24, "corrections": 16, "ownershipTransparency": 18, "expertise": 17, "historicalAccuracy": 12, "total": 87 }, "methodologyVersion": "1.0", "reviewedAt": "2026-07-16" }, { "id": "texas-law-review", "slug": "texas-law-review", "name": "Texas Law Review", "homepage": "https://texaslawreview.org/", "type": "academic", "primaryOrSecondary": "secondary", "rationale": "学生编辑法学评论，保留卷期、作者、引注和公开更正入口；不等同于同行评审期刊。", "grade": "A", "score": { "traceability": 24, "corrections": 16, "ownershipTransparency": 18, "expertise": 17, "historicalAccuracy": 12, "total": 87 }, "methodologyVersion": "1.0", "reviewedAt": "2026-07-16" } ], "canonicalUrl": "/cases/new-york-times-v-microsoft-openai" }