文/北京集佳知識產權代理有限公司 葛琛琛
近年來，隨著越來越多新興科技的涌現，專利申請的主題也變得廣泛。隨之而來的是，專利適格性的問題由于不斷變化而備受關注。2014年，美國最高院提出采用“兩步法”準則審查專利是否滿足專利法101條，也就是專利適格性問題。但是，“兩步法”準則對于審查是否具有專利適格性的操作要求并不明確。對此，在2019年1月，美國專利商標局向審查員發(fā)布了關于如何針對專利適格性審查權利要求的新指南，該指南重點關注專利適格性分析中的步驟2A。在步驟2A分析中，當審查員認定是抽象概念時，需要回答的關鍵問題是該抽象概念是否指向實際應用。
下面我們分享兩件Patent Trial and Appeal Board 審理決定的案例來了解步驟2A的審查實踐。
一、案例1：Appeal 2024-000087 Application 16/474,477【1】
案例涉及減速器的故障診斷裝置領域，權利要求1如下：
1.A failure diagnosis system comprising:
a mechanical apparatus including:
a motor;
a reduction gear driven by the motor, the reduction gear being configured to slow down rotation power of the motor and transmit the rotation power to an operating part of the mechanical apparatus;
an encoder configured to detect a rotational position of the motor; and
a sensor configured to detect a motor current supplied to the motor, the motor current being one of load current of the motor and a current value having a correlation with the load current;
a processor programmed to:
acquire rotation speed data of the motor based on a
signal from the encoder;
identify an acceleration/deceleration period during which operation of the mechanical apparatus accelerates and/or decelerates based on the acquired rotation speed data based on the signal from the encoder;
generate a group of time series rotation speed data by sequentially sampling the portion of the acquired rotation speed data from the identified acceleration/deceleration period acquired based on the signal from the encoder;
...
make a determination of whether the reduction gear indicates a sign of failure induced by abrasion based on a comparison between a given amplitude threshold and the extracted peak value in a change in frequency spectrum of the motor current with respect to a change in a rotation speed of the motor during the acceleration/deceleration period; and
an outputter that outputs a result of the determination of whether the reduction gear indicates the sign of failure.
審查員認為權1屬于抽象概念中的數學概念或心理活動，且該抽象概念沒有指向實際應用，因為權1中所述處理器（processor）僅是用于執(zhí)行通用計算機指令的通用計算機裝置，而至于權1中的motor, reduction gear, encoder, and sensor 僅是用于無關緊要的額外解決方案活動的數據收集步驟。
但PTAB 認為，權1不涉及數學概念。雖然權1中的一系列決策是基于數學概念，但并沒有出現數學概念本身。即使權1中涉及了抽象概念，但該抽象概念也指向改進減速器故障檢測技術的實際應用。雖然權1的處理器是一個通用的計算機組件，但執(zhí)行該處理器可以改進技術。所以，根據新修改指南在Step2A中的Prong 2的判斷，權1符合101的規(guī)定，屬于可專利的客體。
二、案例2：Appeal 2024-000046 Application 15/793,455【1】
案例涉及基于內核的機器學習系統生成輸入的方法領域，權利要求1如下：
1. A method, comprising:
performing a classification operation on a first item,
including:
generating, by processing circuitry of a computer configured to operate a kernel-based machine learning classifier, a plurality of diagonal matrices, each of the plurality of diagonal matrices having non-diagonal elements that are zero and diagonal elements that have values distributed according to a specified probability distribution function and having a dimension based on a specified dimension;
producing, by the processing circuitry, a plurality of orthogonal matrices, each of the plurality of orthogonal matrices having mutually orthogonal rows;
for each of the plurality of diagonal matrices, forming, by the processing circuitry, a plurality of matrix pairs, each of the plurality of matrix pairs including (i) that diagonal matrix, and (ii) a respective orthogonal matrix of the plurality of orthogonal matrices;
generating, by the processing circuitry, a product of each of the plurality of matrix pairs to produce a linear transformation matrix;
obtaining, by the processing circuitry, an input vector representing the first item from a database, the input vector having the specified dimension;
using the linear transformation matrix to produce an approximated feature vector for the input vector, the approximated feature vector including a nonlinear function of inner products of row vectors of the linear transformation matrix and the input vector; and
providing the approximated feature vector as input into the kernel-based machine learning classifier; and
determining, by the processing circuitry, whether the first item has a particular classification based on an output of the kernel-based machine learning classifier.
審查員將斜體部分認為是屬于抽象概念，黑體部分屬于高度概括的附加元素，下劃線部分是無關緊要的額外解決方案的活動。
PTAB則是通過以下步驟對權1進行判斷：
1）步驟 1(Statutory category法定分類)
根據101法條規(guī)定的可專利的四類中，可以確認屬于方法。
2）步驟 2A(i)（does the claim recite a judicial exception?權利要求是否屬于司法例外？）
部分權1特征如下：
“generating . . . a product of each of the plurality of matrix pairs to produce a linear transformation matrix . . .
using the linear transformation matrix to produce an approximated feature vector for the input vector . . .”
認同審查員意見為數學概念，但是僅是數學概念不足以被認為不具有專利適格性。
另有部分權1特征如下：
“performing a classification operation on a first item . . .
generating . . . a plurality of diagonal matrices . . .
producing . . . a plurality of orthogonal matrices . . .
for each of the plurality of diagonal matrices, forming . . . a plurality of matrix pairs . . .
providing the approximated feature vector as input . . . ; and
determining . . . whether the first item has a particular classification based on an output of the kernel-based machine learning classifier.”
PTAB假定這些特征屬于抽象概念中的心理活動。
3）步驟2A(ii)（is the judicial exception integrated into a practical application?該司法例外是否指向實際應用？）
PTAB同意審查員認為“processing circuitry”和“database”是高度概括的元素僅涉及執(zhí)行通用指令的通用的計算機，認為“obtaining . . . an input vector representing the first item . . . , the input vector having the specified dimension”是無關緊要的額外解決方案活動。
但是，PTAB認為“providing the approximated feature vector as input into the kernel-based machine learning classifier”和 “determining, by the processing circuitry, whether the first item has a particular classification based on an output of the kernel-based machine learning classifier”是將矩陣和向量的乘法這個抽象概念指向實際應用，即：根據稱為結構化正交隨機特征(SORF)的新框架生成針對高斯內核的無偏估計器；使用該無偏估計器來從數據庫中選擇的項目進行分類操作。這種實際應用提高了某一特定機器的性能。因此，這些額外的限制通過提高機器學習分類器的內存使用和準確性，為機器學習系統的技術領域提供了改進。所以根據新修改指南在Step2A中的Prong 2的判斷，權1符合101的規(guī)定，屬于可專利的客體。
三、總結
從上述兩個案例可以看出，當被審查員認為是抽象概念時，可以根據步驟2A的判斷步驟去整理答復思路，也就是該抽象概念是否指向實際應用，在這里，兩案例中的抽象概念指向的實際應用，均是在其技術或技術領域內取得有益效果或改進。新指南中也指出，如果抽象概念在應用領域沒有改進，則不被視為指向實際應用。
以上是近期PTAB針對101問題審查的兩件案例，希望能為代理人在處理相關問題時提供啟發(fā)。
參考文獻：
【1】https://developer.uspto.gov/ptab-web/#/search/decisions