案件資訊：原告/上訴人/專利權人：RECENTIVE ANALYTICS, INC.
被告/被上訴人/侵權被告：FOX CORP., FOX BROADCASTING COMPANY, LLC, FOX SPORTS PRODUCTIONS, LLC
系爭專利：US10,911,811、US10,958,957、US11,386,367、US11,537,960
判決日期：April 18, 2025
系爭專利關於機器學習方法，用以優化現場直播的時間表、映射圖表等，四件系爭專利分為機器學習訓練方法與映射圖表。專利權人Recentive Analystics對Fox提出侵權告訴，而Fox則主張系爭專利不具專利適格性（35U.S.C.§101）。
列舉'367專利，'367關於判斷事件時間表的方法，其中運用電腦實現的方法以動態產生事件時間表，方法如Claim 1記載，包括接收直播事件參數，接收連結這些事件的事件目標特徵，將以上收集的資訊傳送到機器學習模型，迭代訓練機器學習模型以識別出不同事件參數與事件目標特徵之間的關聯性，接著從使用者接收使用設定對於未來在多個地點的事件的事件參數，以及優先事件的特徵，以能提供相應參數與權重至訓練完成的機器學習模型，於是通過機器學習模型生成未來事件的時間表，經偵測即時事件參數的改變，可以提供改變至模型以改善模型，並且通過機器學習模型更新未來事件的時間表。
1. A computer-implemented method of dynamically generating an event schedule, the method comprising:receiving one or more event parameters for series of live events, wherein the one or more event parameters comprise at least one of venue availability, venue locations, proposed ticket prices, performer fees, venue fees, scheduled performances by one or more performers, or any combination thereof;
receiving one or more event target features associated with the series of live events, wherein the one or more event target features comprise at least one of event attendance, event profit, event revenue, event expenses, or any combination thereof;
providing the one or more event parameters and the one or more event target features to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector ML model;
iteratively training the ML model to identify relationships between different event parameters and the one or more event target features using historical data corresponding to one or more previous series of live events, wherein such iterative training improves the accuracy of the ML model;
receiving, from a user, one or more user-specific event parameters for a future series of live events to be held in a plurality of geographic regions;
receiving, from the user, one or more user-specific event weights representing one or more prioritized event target features associated with the future series of live events;
providing the one or more user-specific event parameters and the one or more user-specific event weights to the trained ML model;
generating, via the trained ML model, a schedule for the future series of live events that is optimized relative to the one or more prioritized event target features;
detecting a real-time change to the one or more user-specific event parameters;
providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; and
updating, via the trained ML model, the schedule for the future series of live events such that the schedule remains optimized relative to the one or more prioritized event target features in view of the real-time change to the one or more user-specific event parameters.
專利說明書即揭露了機器學習模型的運作，例如運用歷史資料進行訓練，經訓練的機器學習模型將可優化、擴大或變小目標特徵，並產生經優化的時間表。
列舉另一系爭專利'811的Claim 1，描述動態建立映射圖表的電腦實現方法，方法包括接收事件的時間表，產生映射這些事件時間表的圖表，其中記載有每個城市的電視台的直播事件，運用機器學習技術優化電視的直播收視排行，根據即時變動自動更新圖表，最後使用映射圖表判斷每個電視台顯示的事件。
1. A computer-implemented method for dynamically generating a network map, the method comprising:receiving a schedule for a first plurality of live events scheduled to start at a first time and a second plurality of live events scheduled to start at a second time;
generating, based on the schedule, a
network map mapping the first plurality of live events and the second plurality of live events to a plurality of television stations for a plurality of cities,
wherein each station from the plurality of stations corresponds to a respective city from the plurality of cities,
wherein the network map identifies for each station (i) a first live event from the first plurality of live events that will be displayed at the first time and (ii) a second live event from the second plurality of live events that will be displayed at the second time, and
wherein generating the network map comprises using a machine learning technique to optimize an overall television rating across the first plurality of live events and the second plurality of live events;
automatically updating the network map on demand and in real time based on a change to at least one of (i) the schedule and (ii) underlying criteria,
wherein updating the network map comprises updating the mapping of the first plurality of live events and the second plurality of live events to the plurality of television stations; and
using the network map to determine for each station (i) the first live event from the first plurality of live events that will be displayed at the first time and (ii) the second live event from the second plurality of live events that will be displayed at the second time.
對於Fox主張專利不具專利適格性的意見，Recentive理解運用圖表已經是久遠的事（應指以紙筆繪製關於電視台位置），但也理解系爭專利並非主張機器學習技術的本身，而是運用模型生成時間表與圖表的技術。不過，關於訓練機器學習模型，包括以演算法使用數據訓練模型、更新與優化模型都是屬於一般模型訓練的技術，而其強調的是發明特徵在於應用機器學習模型生成直播事件的時間表與圖表。
對此，地方法院運用TWO-STEP專利適格性判斷法則，認為系爭專利範圍運用一般數學技術生成圖表與間表涉及抽象概念，專利範圍也沒有進步概念（要有額外元件形成進步概念）可以讓發明實質超越抽象概念，主要理由是：機器學習技術並沒有超越已知技術，使得專利範圍僅涉及習知的電腦裝置。
Recentive上訴CAFC。
CAFC同樣地運用TWO-STEP專利適格性判斷法則：
step one: 系爭專利涉及不可專利概念，以每項專利範圍的元件的個別或其組合判斷是否具有進步概念（inventive concept）以足以讓專利實質超越其不可專利概念的本身。
實際的判斷是，在step one，判斷專利範圍所著重在電腦能力的改善，或是僅讓電腦是一個運行抽象概念的工具？經判斷，法院認為本案僅是使用一般機器學習技術實現產生事件時間表與映射圖表的技術，經參照說明書，可知其中運用的機器學習技術是習知的技術。
進一步地，法院認為系爭專利範圍中訓練模型的技術並沒有任何技術的改善方案，加上專利說明書也都沒有描述如何實現所述技術的改善，並且，根據過去的案例可知，如本案運用在電視台事件的時間表與映射圖表的新領域，法院表示抽象概念不會因為限制到某個領域或是技術環境而變成非抽象。
也就是說，僅是運用機器學習技術在新的領域仍不具專利適格性，特別是因為如果通過這個技術僅是加速了人工執行的速度，更是不足以將抽象概念轉變為非抽象的技術。
step two: 考慮專利範圍的元件的個別與組合判斷是否有額外元件轉換不可專利標的為可專利的應用？
如此，需判斷專利範圍中是否有足以轉換抽象概念為可專利應用的進步概念（判斷是否有additional elements），對此，專利權人是主張發明可即時而動態產生經優化的圖表與時間表，但仍判定是沒有超越抽象概念本身，理由是，從專利範圍中找不到可以轉換機器學習技術（用以產生圖表與時間表）實質超越抽象概念（數學方法）的元件。
CAFC判決如下，也算是CAFC目前的一個態度，對於沒有超越一般機器學習應用到新的數據領域的專利，並沒有改善所應用的機器學習模型，發明也就不具專利適格性。
"Today, we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101."
Ron
