機器學習方法的專利適格性有解方嗎？ - Recentive案的功課

針對過去報導「僅運用機器學習技術在新的領域仍不具專利適格性 - Recentive Analytics, Inc. v. Fox Corp. (CAFC 2025)（https://enpan.blogspot.com/2025/05/recentive-analytics-inc-v-fox-corp-cafc.html）」案例，本篇啟發於"https://ipwatchdog.com/2025/06/11/ai-machine-learning-patents-doomed-recentive/"文中，提供這類應用機器學習方法的發明可專利性（專利適格性）一些想法（ipwatchdog的標題挺聳動的："Are AI and Machine Learning Patents Doomed After Recentive?"）。

以Recentive的“US11,386,367”為例，這是在USPTO審查階段面對35U.S.C.101核駁答辯獲准的範圍，仍是值得一探究竟，Claim 1如下，用顏色標出一些我覺得是重點特徵的元件，整體看來確實是很“機器學習”的演算方法，特徵主要是訓練模型的目標 - schedule。

1. A computer-implemented method of dynamically generating an event schedule, the method comprising:

(取得現場事件參數與目標特徵)

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;

(機器學習，學習事件參數與目標特徵之間的關聯性)

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;

(使用者介入學習，將使用者指定參數與權重輸入至機器學習模型)

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;

(對未來事件產生時刻表)

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;

(改善機器學習模型的精確度)

providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; and

(即時更新時刻表)

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.

此案在USPTO審查階段受到3次核駁與答辯，最後一次修正成現在的樣貌：

101核駁理由：

功課一：即便是機器學習演算法，“動詞”的規劃很重要，這個101駁回理由引用了claim中的動詞：defining, training, receiving, providing, generating...，這些算是演算法中很通常的動詞，確實"表面上"就是招惹101核駁意見的動詞 - 如此案前兩次101核駁意見所述，是人類智力上可以紙筆完成的步驟。

功課二：審查委員提供了克服101核駁意見的指導方針。不同於採用TWO-STEP檢測專利適格性法則，審查委員在本案受到3次101核駁意見中都不厭其煩地（copy and paste）提示發明不具專利適格性的理由以及可以符合101的方向，都是可學習的功課。

引用2019年改版的專利適格性指導方針，指出專利範圍中可整合法定不可專利標的（如抽象概念）為具體應用的“額外元件/additional elements”包括：改善電腦或特定技術領域的功能、醫藥類的特定治療與預防效果、轉換物品到不同的狀態或事物、超越法定不可專利項目的一般用途的有意義的方法。

反向地指出不能整合到具體應用的限制：僅是在電腦上實現抽象概念，或是以電腦作為工具執行抽象概念、加入微不足道的額外解方的活動、法定例外項目的一般使用。

功課三：本案是經過與審查委員面詢後，審查委員提供克服101核駁意見的方向：闡明機器學習模型為迭代訓練以改善模型精確度，顯然說明書支持這些描述，讓專利範圍更為明確地表達其特徵：使用之前現場活動的歷史資料迭代訓練機器學習模型以識別出事件參數與目標特徵之間的關聯性。

功課四：即便審查委員可能在面詢時也沒有說到滿，但申請人仍進一步修正專利範圍，闡述發明(1)根據即時更新使用者指定的事件參數以改進機器學習模型的精確度，以及(2)更新未來事件的行程表。也就是，通過改變專利範圍步驟（動詞：detecting, updating...）更明確地表達發明手段以能達成其目標功效。

（5/4/2022答辯意見：https://app.box.com/s/vvcobk42zdwi16mj2517qqmo5bljgg3l）

Ron