DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments filed 09/03/2025 have been fully considered, but they are not persuasive. The amendment incorporates language into claim 1 similar to previous claim 3, which has been canceled. Applicant argues that Gunay fails to disclose providing the classification result using the classification model based on the shape of the curve. Applicant argues that “the data-based classification model uses the shape of the qPCR curve to provide the classification result which indicates one of a presence and a nonpresence of a DNA strand segment to be detected.” Applicant argues the rejection of previous claim 3 “mentioned the K-Means approach of Gunay. The K-Means approach of Gunay separates negative and positive sample clusters.” Applicant argues that there “is no disclosure in Gunay that the K-Means approach evaluates the shape of the qPCR curve, as required by amended claim 1, to provide the specifically recited classification result concerning the DNA strand segment to be detected.” Applicant argues that “Gunay discloses that machine learning is used to determine a CT value, and does not disclose that machine learning is used to indicate a presence and a non-presence of a DNA strand to be detected”.
This argument is not persuasive. While it is true that Gunay states the model “tries to determine the CT value” (page 2, left column, last paragraph), Gunay also states that “[t]he algorithm introduced in this study first tries to isolate positive samples by identifying signals that produce no sigmoid curve” (id.). Gunay also states (id.), “During this process three cases of signal is observed; no sigmoid, partial sigmoid and full sigmoid”. Gunay states (page 2, right column, first paragraph): “The model fits modified sigmoid function (See Equation 2) to the signal and estimates the parameters of the function.” These parameters are in turn used to predict the CT value.1 The function (equation) is the mathematical expression that defines the shape of the curve. That is to say, the model is determining whether the shape of the curve fits a sigmoid pattern based on the signal values. There is no requirement in the claim that the model evaluates the shape in a “visual” way, rather than in a mathematical way. Gunay further states (page 3, left column, first sentence under section III): “The determination of the CT is done in two steps: a) the isolation of negative samples, b) the prediction of CT values for positive samples.” Thus, Gunay’s model evaluates the shape of the curve (mathematically), and if the curve is not sigmoid, it returns no CT value, which is “negative”, indicating a “non-presence” of the DNA strand to be detected (see last step in algorithm, right column, page 3: “If not full or partial sigmoid curve then negative”). If, however, the curve is sigmoid or partial sigmoid, the algorithm will predict a CT value, which indicates a “positive” sample, indicating “presence” of the DNA strand to be detected (page 4, left column, last full paragraph: “Once the curve is decided to be either the partial or full sigmoid curve, the CT value is calculated…”. Therefore, by returning no CT value, the algorithm has concluded the shape of the curve is not sigmoid. This “indicates” a non-presence of a DNA strand segment to be detected. By returning a CT value, the algorithm has concluded the shape of the curve is sigmoid or at least partially sigmoid. This “indicates” a presence of a DNA strand segment to be detected. The process starts with separating positive and negative samples. Gunay used K-means in training the algorithm (paragraph spanning pages 3-4), which resulted in the determination of the threshold value 0.01 used in line 4 of the algorithm to separate positive from negative samples. Thus, Gunay’s algorithm represents a trained model (data-based classification model) which has been trained to provide a classification result (positive or negative) depending on the shape (sigmoidal or not) of the qPCR curve. Note that to get to line 4 in the algorithm, the first derivative (y’) must be determined, which in turn is based on estimating the parameters from fitting the data to the modified sigmoid function (equation (2)). Therefore, the process is based on the shape (in mathematical terms) of the qPCR curve.
The rejections are therefore maintained.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 and 7 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gunay (2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), December 2016; IDS reference).
Gunay disclosed a machine-learning method to predict Ct value in qPCR (see title). Gunay’s method involved cyclically executing qPCR cycles, measuring an intensity value of a fluorescence at each qPCR cycle, and obtaining a qPCR curve composed of intensity values; this is so, because Gunay’s method used the shape of said curve (second page, section II. Theoretical Model: “…by identifying signals the produce no sigmoid curve…”; fourth page, first column, last full paragraph: “Once the curve is decided to be either the partial or full sigmoid curve, the CT value is calculated…”).
See also fifth page, last paragraph prior to references section: “To separate negative and positive sample clusters, we used K-Means.” K-Means is an unsupervised machine learning algorithm). This meets the limitation of claim 1, since a “negative” sample indicates the absence of a DNA strand to be detected, while a “positive” sample indicates a presence of a DNA strand to be detected.
As to “conducting the qPCR process depending on the classification result from the evaluation of the shape of the qPCR curve”, Gunay’s method calculates the Ct value for positive samples, as noted above. This meets the limitations of claim 7, since calculating a C-t value inherently signals that a C-t value is “determinable”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gunay (2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), December 2016; IDS reference).
The method of Gunay has been discussed. While Gunay did not explicitly mention a “device” for carrying out the PCR and for carrying out the evaluation of the curve, it would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the application that a “device” was used to perform the PCR, and that a computer was used to carry out the algorithm (shown on page 3). Furthermore, it would have been obvious to integrate the software for running the PCR device and the software for performing the data analysis (i.e. a non-transitory electronic storage medium) on a single computer (i.e. a “device”), thereby “executing a computer program”. Such automation of operating PCR instruments and analyzing data was routine in the prior art.
Claim(s) 2 and 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gunay (2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), December 2016; IDS reference) in view of Walters (US 20200012886, previously cited).
The method of Gunay has been discussed. Gunay used K-Means for data clustering (i.e. clustering positive and negative samples). Gunay did not mention neural network, deep neural network, recurrent neural network, or LSTM.
Walters disclosed (paragraph [0002]): “The need for efficient and effective systems to classify and cluster data arises in many fields, including data management, science, finance, engineering, environmental monitoring, water supply systems, climate studies, health care, and many other areas of human activity.”
Walters further disclosed (paragraph [0068]) that machine-learning models could include mention neural network, deep neural network, recurrent neural network, or LSTM, as well as K-Means clustering.
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the application to substitute any of neural network, deep neural network, recurrent neural network, or LSTM for the K-Means clustering in Gunay’s method, as it is obvious to substitute equivalents known for the same purpose (MPEP 2144.06), and Walters shows that all of these types of machine learning tools were known for the purpose of classifying and clustering data.
Claim(s) 4-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gunay (2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), December 2016; IDS reference) in view of Robinson (US 20180285624, previously cited).
The method of Gunay has been discussed. Gunay used K-Means for data clustering (i.e. clustering positive and negative samples). Gunay did not mention determining residual error plots.
Robinson disclosed that for K-Means clustering, different values of k could be tested to find a clustering having residual errors or other goodness-of-fit indicators meeting selected criteria (paragraph [0092]).
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the application to determine residual error plots for various values of k when practicing Gunay’s method in order to determine whether the clustering goodness-of-fit was acceptable, as disclosed by Robinson.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL C WOOLWINE whose telephone number is (571)272-1144. The examiner can normally be reached 9am-5:30pm.
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/SAMUEL C WOOLWINE/Primary Examiner, Art Unit 1681
1 The algorithm estimates the parameters: a, k, x0 and c; see paragraph between equations (2) and (3) on page 2. Thereafter, the first (y’) and second (y’’) are determined as shown in the discussion following equation (2) and ending at equation (6). These first and second derivatives are used in the CT prediction algorithm as discussed in section III, page 3 and shown in the algorithm illustrated in the right column of page 3.