广东省贸易摩擦应对综合服务平台
（海外知识产权）

The US Court of Appeals for the Federal Circuit affirmed a district court’s ruling that patents applying established machine learning methods to new data are not patent eligible under 35 U.S.C. §101. Recentive Analytics, Inc. v. Fox Corp. et al., Case No. 23-2437 (Fed. Cir. Apr. 18, 2025) (Dyk, Prost, Goldberg, JJ.)

美国联邦巡回上诉法院维持了地区法院的一项裁决，即根据美国《专利法》第35编第101条，将已有的机器学习方法应用于新数据的专利不具备可专利性。Recentive Analytics诉Fox Corp. 案，案号：23-2437（联邦巡回上诉法院，2025年4月18日）（戴克、普罗斯特、戈德堡法官审理）。 

Recentive sued Fox, alleging infringement of four patents designed to tackle long-standing challenges in the entertainment industry – namely, optimizing the scheduling of live events and refining “network maps,” which determine the content aired on specific channels across various geographic markets at set times. These patents aim to streamline broadcast operations and enhance programming efficiency.

Recentive公司起诉了Fox公司，指控其侵犯了四项专利。这些专利旨在应对娱乐行业长期存在的挑战，具体而言，就是优化直播活动的日程安排，以及完善“网络映射”，该“网络映射”可确定在特定时间各个地理市场中特定频道所播出的内容。这些专利旨在简化广播运营流程并提高节目编排效率。 

The patents at issue can be divided into two categories: network maps and machine learning training. The machine learning training patents focus on generating optimized event schedules by training machine learning models with parameters such as venue availability, ticket prices, performer fees, and other relevant factors. The network map patents describe methods for dynamically generating network maps that assign live events to television stations across different geographic regions. These methods utilize machine learning to optimize television ratings by mapping events to stations and updating the network map in real time based on changes to the schedule or underlying criteria. The patents’ specifications explain that the methods employ “any suitable machine learning technique” using generic computing machines.

涉案专利可分为两类：网络映射类和机器学习训练类。机器学习训练类专利侧重于通过利用诸如场地可用性、票价、表演者酬金以及其他相关因素等参数来训练机器学习模型，从而生成优化后的活动日程安排。网络映射类专利描述了动态生成网络映射的方法，这些方法会将直播活动分配给不同地理区域的电视台。这些方法利用机器学习，通过将活动映射到电视台，并根据日程安排或基础标准的变化实时更新网络映射，来优化电视收视率。这些专利的说明书解释称，这些方法使用通用计算机采用“任何合适的机器学习技术”。 

Fox moved to dismiss on the grounds that the patents were subject matter ineligible under § 101. Recentive acknowledged that the concept of preparing network maps had existed for a long time. Recentive also recognized that the patents did not claim the machine learning technique. Nonetheless, Recentive argued that its patents claimed eligible subject matter because they involve using machine learning to generate custom algorithms based on training the machine learning model. Recentive characterized its patents as introducing “the application of machine learning models to the unsophisticated, and equally niche, prior art field of generating network maps for broadcasting live events and live event schedules.”

Fox公司提出动议要求驳回诉讼，理由是这些专利根据美国《专利法》第101条属于不可授予专利的主题。Recentive公司承认，制作网络映射这一概念早已存在。Recentive公司也认识到这些专利并未主张特定的机器学习技术。尽管如此，Recentive公司辩称其专利主张的是符合条件的专利主题，因为这些专利涉及利用机器学习，通过训练机器学习模型来生成定制算法。Recentive公司将其专利描述为“把机器学习模型应用于此前技术水平不高且同样小众的领域，即生成用于直播活动和直播活动日程安排的广播网络映射”。 

The district court disagreed and granted Fox’s motion. Applying the Alice framework, at step one, the court determined that the asserted claims were “directed to the abstract ideas of producing network maps and event schedules, respectively, using known generic mathematical techniques.” At step two, the court determined that the machine learning limitations were no more than “broad, functionally described, well-known techniques” that claimed “only generic and conventional computing devices.” The court denied Recentive’s request for leave to amend because it determined that any amendment would be futile. Recentive appealed.

地区法院不同意该观点并批准了福克斯公司的动议。运用Alice案框架，在第一步分析中，法院认定涉案权利要求“分别指向运用已知的通用数学技术制作网络映射和活动日程安排的抽象概念”。在第二步分析中，法院认定机器学习方面的限定条件只不过是 “宽泛的、功能性描述的、众所周知的技术”，所主张的“仅仅是通用的、常规的计算设备”。法院驳回了Recentive公司提出的修改权利要求的申请，因为法院认定任何修改都是徒劳无功的。随后，Recentive公司提出了上诉。 

For the Federal Circuit, this case presented a question of first impression: whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible.

对于美国联邦巡回上诉法院而言，本案提出了一个前所未有的问题：那些仅仅是将已有的机器学习方法应用于新的数据环境的权利要求，是否具备可专利性。 

第一步：

While Recentive claimed that its machine learning approach was uniquely dynamic and capable of uncovering hidden patterns in real time, the Federal Circuit found these features to be merely standard aspects of how machine learning operates. The Court explained that iterative training and model updates are not breakthroughs but rather are fundamental to the technology itself. Recentive conceded that its patents did not disclose any novel method for enhancing machine learning algorithms – just their routine application. Recentive also conceded that before the advent of machine learning, event planners relied on “event parameters” such as ticket sales, weather forecasts, and other data to guide scheduling decisions, a process the patents themselves acknowledge was traditionally manual and inflexible. The same is true for network maps, which were historically crafted by humans to determine content placement across channels. The Court found that Recentive’s assertion that applying machine learning to this context was more than an abstract concept and therefore rendered the claims patent eligible lacked merit. Courts have consistently held that claims that simply place an abstract idea into a new field of use do not transform it into a patent-eligible invention.

虽然Recentive公司声称其机器学习方法具有独特的动态性，并且能够实时发现隐藏的模式，但美国联邦巡回上诉法院认为这些特点仅仅是机器学习运行方式的标准特征。法院解释称，迭代训练和模型更新并非突破性进展，而是这项技术本身的基本要素。Recentive公司承认其专利并未披露任何用于改进机器学习算法的新颖方法，仅仅是对这些算法的常规应用。Recentive公司还承认，在机器学习出现之前，活动策划者依靠诸如门票销售情况、天气预报和其他数据等 “活动参数”来指导日程安排决策，而这些专利本身也承认这一过程在传统上是人工操作且缺乏灵活性的。网络映射的情况也是如此，在过去，网络映射是由人工制作，以确定各频道的内容编排。法院认定，Recentive公司主张将机器学习应用于这一情境不仅仅是一个抽象概念，因此其权利要求具备可专利性，这一主张缺乏依据。法院一直认为，仅仅是将抽象概念应用于新的使用领域的权利要求，并不能将其转变为具备可专利性的发明。 

The Federal Circuit made clear that applying existing technology to a new dataset or context does not, on its own, confer eligibility. Federal Circuit precedent teaches that true innovation demands more than repackaging conventional methods within a different domain, regardless of how novel the application may seem. The Court noted that Recentive’s claim that its patents qualified merely because they incorporate machine learning into event planning and network mapping stood in direct contradiction to settled § 101 jurisprudence.

美国联邦巡回上诉法院明确表示，仅将现有技术应用于新的数据集或新情境本身，并不能使其具备可专利性。联邦巡回上诉法院的先例表明，真正的创新所要求的不仅仅是将传统方法重新包装后应用于不同领域，无论这种应用看起来多么新颖。法院指出，Recentive公司声称其专利符合可专利性要求仅仅是因为它们将机器学习融入了活动策划和网络映射绘制中，这与已确立的关于美国《专利法》第101条的司法判例直接相悖。 

第二步：

Recentive claimed that its patents involved an inventive concept by using machine learning to dynamically create optimized maps and schedules based on real-time data, updating them as conditions changed. The Federal Circuit disagreed, affirming the district court’s decision that this merely described the abstract idea itself. The Court found nothing in the patent or the claims that added anything more to transform the abstract concept of generating event schedules and network maps using machine learning into a patent-eligible invention.

Recentive公司声称，其专利涉及一项发明构思，即利用机器学习，根据实时数据动态创建经过优化的地图和日程安排，并随着条件的变化对其进行更新。美国联邦巡回上诉法院对此持不同意见，维持了地区法院的判决，即这仅仅描述了抽象概念本身。法院认为，无论是在该专利还是其权利要求中，都没有任何额外的内容能够将利用机器学习生成活动日程安排和网络映射这一抽象概念转化为一项具备可专利性的发明。 

The Federal Circuit rejected Recentive’s argument that the district court should have granted leave to amend its complaint, noting that Recentive neither proposed specific amendments nor identified factual issues that would impact the § 101 analysis.

美国联邦巡回上诉法院驳回了Recentive公司提出的地区法院本应批准其修改诉状的请求，指出Recentive公司既没有提出具体的修改方案，也没有指明会对美国《专利法》第101条的分析产生影响的事实问题。