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 .
Claims 1-15 are pending and currently under examination.
Response to Amendment
The Amendment filed 9/9/25 has been entered. Claims 1-15 are currently pending. Applicant’s amendments to claims 6-7 and the specification have overcome the 112(b) rejection and objections previously set forth in the Non-Final Office Action mailed 6/10/25.
Response to Arguments
Applicant’s arguments, see pages 6-8, filed 9/9/25, with respect to the rejections of claims 1-15 under 35 USC 103 and claim 1 under nonstatutory double patenting have been fully considered and are found unpersuasive, and the 103 and nonstatutory double patenting rejections documented in the Non-Final mailed on 6/10/25 have been revised to address claim amendments filed 9/9/25 in this Final Office Action. More detailed responses to Applicant’s arguments are provided at the end of each maintained rejection.
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.
Claims 1-2, 4, 6, and 12-14 remain/are rejected under 35 U.S.C. 103 as being unpatentable over Woo et al. (2018; US 10,002,229 B2; USPat citation B in PTO-892 filed 6/10/25).
This rejection is necessitated by claim amendments filed 9/9/25.
Woo et al. teaches “a system and methods for processing and evaluating data generated in real-time quantitative PCR” (column 1, lines 53-55).
Relevant to claim 1, Woo et al. teaches “In another embodiment, the invention comprises a method for quantifying nucleic acid sequences present in one or more amplification reactions to be collectively analyzed. The method further comprising the steps of: (a) acquiring intensity data for each reaction over a selected number of reaction intervals wherein the intensity data is indicative of a detected quantity of progeny sequences arising from each sequence; (b) assessing the intensity data over the selected number of reaction intervals to generate an amplification profile indicative of the change in quantity of the progeny sequences for each reaction interval; (c) evaluating each amplification profile to identify a corresponding exponential region, having upper and lower bounds; (d) determining a threshold based upon an intersection between at least one exponential region upper bound with at least one exponential region lower bound; (e) performing a polynomial fitting operation for each amplification profile that applies the threshold to determine a polynomial root which is thereafter associated with a threshold cycle for each reaction; and (f) quantifying the sequence for each reaction using the threshold cycle” (column 1, lines 66-67 continued to column 2, lines 1-19).
Further relevant to claim 1, Woo et al. teaches “In still another embodiment, the invention comprises a method for quantitating at least one nucleic acid target of unknown concentration. The method further comprising the steps of: (a) performing PCR-based amplification of each target using a detectable reporter construct; (b) acquiring detection information generated by the detectable reporter construct indicative of a change in the concentration of each target over the course of the amplification; (c) assembling a data set comprising at least a portion of the detection information to model amplification reaction characteristics; (d) identifying an exponential region for each target of the data set from the modeled amplification reaction characteristics; (e) identifying a baseline component based, in part, on the exponential region; (f) normalizing the data set using the baseline component; (g) determining a threshold based upon a comparison of the exponential regions for the targets of the data set; (h) identifying a polynomial equation whose root is identified using the threshold and wherein the root is assigned as a threshold cycle; and (i) quantifying each target using the threshold cycle” (column 2, lines 20-39).
These teachings read on claim 1 A method for conducting a quantitative polymerase chain reaction (qPCR), comprising: cyclically executing a predetermined plurality of qPCR cycles; measuring an intensity value after each of the predetermined plurality of qPCR cycles to obtain a measured portion of a qPCR curve of intensity values; estimating, after cycling the predetermined plurality of qPCR cycles, a remainder of the qPCR curve using the measured intensity values and a data-based trainable qPCR model; selecting one of a plurality of steps of the method based on the remainder of the plot of the qPCR curve; and conducting the selected one of the plurality of steps of the method.
Relevant to claim 2, Woo et al. Figure 1 depicts qPCR cycles ranging between 5 and 15 cycles. Further relevant to claim 2, Woo et al. teaches that “during the earlier cycles of a PCR reaction there may be an approximate doubling of the nucleotide strands with each cycle (exponential amplification). In the later cycles of the reaction, however, the efficiency of the amplification process may be diminished resulting in non-exponential amplification. Some of the factors that may affect the amplification efficiency include limiting quantities or depletion of reagents and competition for reaction products. The aforementioned changes in reaction kinetics may result in difficulties in determining the initial target concentration without performing detailed analysis of the reaction profile. In one aspect, it is desirable to monitor the reaction at various time or cycle intervals and acquire data which quantifies the emitted fluorescence of the reaction at these intervals” (column 4, lines 39-53).
These teachings read on claim 2 the predetermined plurality of qPCR cycles is specified between 5 and 15, as the skilled artisan would recognize that later cycles would result in detrimental effects to amplification efficiency.
Relevant to claim 4, Woo et al. teaches that “FIG. 6 illustrates one embodiment of a threshold cycle selection process 600 that may be used for determining the threshold cycle (CT) 140. In one aspect, the method 600 utilizes the threshold 135 previously determined in threshold analysis procedure 500 described in conjunction with FIG. 5 above. The method 600 commences in state 610 where a terminal cycle is identified. The terminal cycle is typically selected as the endpoint of the amplification reaction (cycle 40 in the illustrated amplification plot shown in FIG. 1), however, it will be appreciated that designation of the terminal cycle may be substantially any value within the plateau region 130 or the exponential region 124 of the amplification profile 117. In state 610, a current comparison cycle is selected by decrementing one cycle from the terminal cycle. The fluorescence intensity value of the current comparison cycle is then compared to the value of the threshold 135 in state 630” (column 15, lines 9-25).
This teaching reads on claim 4 the data-based trainable qPCR model is configured to recursively determine the qPCR curve using at least one of the measured intensity values and the at least one subsequent intensity value.
Relevant to claim 6, Woo et al. teaches “By evaluating these curves with respect to one another, a polynomial equation can be identified that describes the characteristics of at least a portion of the profile 117. In one aspect the ‘real’ root of the polynomial equation may be found to identify the threshold cycle 140. The threshold cycle 260 may then be used in subsequent calculations to quantitate the concentration of target present in the initial reaction. Unlike conventional methods which subjectively assess the amplification data to identify the threshold cycle 260, various embodiments of the present invention provide a means for more rapidly and reproducibly identifying exponential and baseline regions of the amplification profile 117 to facilitate subsequent identification of the threshold 135 and threshold cycle 140. Utilizing this method 200 may further improve the accuracy and reproducibility of the analysis and reduce or eliminate the need to visually inspect the intensity data which might otherwise introduce an undesirable subjective bias into the analysis” (column 10, lines 5-24).
This teaching reads on claim 6 the method includes the step of determining a ct value from the estimated remainder of the plot of the qPCR curve; signaling is effected when the ct value is determinable, and otherwise returning to the step of determining a ct value; and the method is terminated when a ct value has been determined.
Relevant to claim 12, Woo et al. teaches “a system and methods for processing and evaluating data generated in real-time quantitative PCR” (column 1, lines 53-55). Woo et al. also teaches “In another embodiment, the invention comprises a method for quantifying nucleic acid sequences present in one or more amplification reactions to be collectively analyzed. The method further comprising the steps of: (a) acquiring intensity data for each reaction over a selected number of reaction intervals wherein the intensity data is indicative of a detected quantity of progeny sequences arising from each sequence; (b) assessing the intensity data over the selected number of reaction intervals to generate an amplification profile indicative of the change in quantity of the progeny sequences for each reaction interval; (c) evaluating each amplification profile to identify a corresponding exponential region, having upper and lower bounds; (d) determining a threshold based upon an intersection between at least one exponential region upper bound with at least one exponential region lower bound; (e) performing a polynomial fitting operation for each amplification profile that applies the threshold to determine a polynomial root which is thereafter associated with a threshold cycle for each reaction; and (f) quantifying the sequence for each reaction using the threshold cycle” (column 1, lines 66-67 continued to column 2, lines 1-19).
Thus, the Woo et al. “system” and described “method” read on claim 12 A device for conducting a quantitative polymerase chain reaction (qPCR) method, wherein the device is designed to: cyclically execute a predetermined plurality of qPCR cycles; measure an intensity value after each of the predetermined plurality of qPCR cycle to obtain a measured portion of a qPCR curve of intensity values; estimate, after cycling the predetermined plurality of qPCR cycles, a remainder of the qPCR curve using the measured intensity values and a data-based trainable qPCR model; select one of a plurality of steps of the method based on the remainder of the plot of the qPCR curve; and conduct the selected one of the plurality of steps of the method.
Relevant to claim 13, Woo et al. teaches “Furthermore, in various embodiments, the methodologies described herein may be advantageously integrated into software applications and/or computer hardware so as to perform the baseline determination in a substantially automated manner without the requirement of user intervention. This inventive feature may therefore improve the performance of PCR-based quantitation and provide more rapid identification of initial target concentrations as compared to other less efficient conventional analysis methodologies” (column 10, lines 25-33).
This teaching reads on claim 13 a computer program configured to execute the qPCR method.
Relevant to claim 14, Woo et al. teaches “A data processing module 840, according to various embodiments, receives selected data from the data storage module 830 or alternatively from the data collection module 820 and performs the operations associated with noise determination and threshold selection. These analytical methods may be implemented using one or more computer program or modules which comprise functions designed to manipulate the data and generate requested information including: baseline noise level determination, exponential region identification, threshold selection and combination, quantitative analysis, and other related analytical methods. In one aspect, the data processing module 840 is designed to operate in a user-independent manner where all of the calculations and analytical tasks are performed without the need for the user to manually assess or interpret the data” (column 18, lines 43-57).
This teaching reads on claim 14 a non-volatile electronic storage medium on which the computer program is stored.
Woo et al. does not teach a specific embodiment having all the claimed elements. That being said, however, it must be remembered that "[w]hen a patent simply arranges old elements with each performing the same function it had been known to perform and yields no more than one would expect from such an arrangement, the combination is obvious." KSR v. Teleflex, 127 S.Ct. 1727, 1740 (2007) (quoting Sakraida v. AG. Pro, 425 U.S. 273, 282 (1976)). "[W]hen the question is whether a patent claiming the combination of elements of prior art is obvious," the relevant question is "whether the improvement is more than the predictable use of prior art elements according to their established functions." (Id.). Addressing the issue of obviousness, the Supreme Court noted that the analysis under 35 USC 103 "need not seek out precise teachings directed to the specific subject matter of the challenged claim, for a court can take account of the inferences and creative steps that a person of ordinary skill in the art would employ." KSR at 1741. The Court emphasized that "[a] person of ordinary skill is... a person of ordinary creativity, not an automaton." Id. At 1742.
Consistent with this reasoning, it would have been prima facie obvious to have
selected various combinations of various disclosed elements — including recursive determination, ct value determination, and devices — for a method of analyzing a biological sample, to arrive at compositions "yielding no more than one would expect from such an arrangement."
Applicant’s Arguments
Applicant argues that “Ultimately, Woo requires the entire qPCR curve to perform the analysis disclosed in that reference. Woo does not disclose or contemplate applying a trainable model to a limited number of qPCR cycles to determine the remainder of the qPCR curve. The obviousness rejections of claims 1 and 12 are incorrect” (Remarks 9/9/25, page 7, paragraph 1).
Applicant further argues that “It is further noted that the obviousness rejections of some of the dependent claims are also in error… Woo discloses using all 40 cycles to generate the PCR curve that is subsequently subjected to polynomial curve fitting. Woo does not disclose measuring only 5-15 cycles” (Remarks 9/9/25, page 7, paragraph 2).
Applicant further argues that “Woo does not use a data-base trainable model to determine the qPCR curve, as required by claim 4. With respect to claim 6, the rejection is based on incorrectly asserting that Woo discloses determining a ct value from an ‘estimated remainder of the qPCR curve’, as required by the claim. As discussed above, Woo does not estimate any part of the PCR curve being analyzed, so the rejection of claim 6 is in error” (Remarks 9/9/25, page 7, paragraph 3).
Response to Applicant’s Arguments
Applicant is reminded that the cited prior art reference must be read in its entirety, not merely selective portions. As set forth in MPEP 2141.02, “Ascertaining the differences between the prior art and the claims at issue requires interpreting the claim language, and considering both the invention and the prior art references as a whole… A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention… However, ‘the prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed….’ In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004)” (emphasis added).
To that end, Woo et al. does teach a trainable model to a limited number of qPCR cycles to determine the remainder of the qPCR curve. Woo et al. teaches “In still another embodiment, the invention comprises a method for quantitating at least one nucleic acid target of unknown concentration. The method further comprising the steps of: (a) performing PCR-based amplification of each target using a detectable reporter construct; (b) acquiring detection information generated by the detectable reporter construct indicative of a change in the concentration of each target over the course of the amplification; (c) assembling a data set comprising at least a portion of the detection information to model amplification reaction characteristics; (d) identifying an exponential region for each target of the data set from the modeled amplification reaction characteristics; (e) identifying a baseline component based, in part, on the exponential region; (f) normalizing the data set using the baseline component; (g) determining a threshold based upon a comparison of the exponential regions for the targets of the data set; (h) identifying a polynomial equation whose root is identified using the threshold and wherein the root is assigned as a threshold cycle; and (i) quantifying each target using the threshold cycle” (column 2, lines 20-39).
This Woo et al. teaching provides for a trainable model based on “at least a portion of the detection information”. Taken with the below teaching that earlier cycles provide for approximate doubling (compared to later cycles that may impact amplification efficiency, reaction kinetics, and modeling), the skilled artisan would find it obvious that Woo et al. reads on the instant trainable model that does not require performing the entire qPCR curve.
Woo et al. teaches that “during the earlier cycles of a PCR reaction there may be an approximate doubling of the nucleotide strands with each cycle (exponential amplification). In the later cycles of the reaction, however, the efficiency of the amplification process may be diminished resulting in non-exponential amplification. Some of the factors that may affect the amplification efficiency include limiting quantities or depletion of reagents and competition for reaction products. The aforementioned changes in reaction kinetics may result in difficulties in determining the initial target concentration without performing detailed analysis of the reaction profile. In one aspect, it is desirable to monitor the reaction at various time or cycle intervals and acquire data which quantifies the emitted fluorescence of the reaction at these intervals” (column 4, lines 39-53).
These teachings, and Woo et al. as a whole, obviate that Woo et al. does “use a data-base trainable model to determine the qPCR curve” and estimation of “any part of the PCR curve being analyzed”.
Claims 3, 5, 7-11, and 15 remain/are rejected under 35 U.S.C. 103 as being unpatentable over Woo et al. (2018; US 10,002,229 B2; USPat citation B in PTO-892 filed 6/10/25) as applied to claims 1-2, 4, 6, and 12-14 above, and further in view of Clarkson et al. (2002; US 6,493,640 B1; USPat citation A in PTO-892 filed 6/10/25).
The teachings of Woo et al. are applied to instantly rejected claims 3, 5, 7-11, and 15 as they were previously applied to claims 1-2, 4, 6, and 12-14 as rendering obvious a method and device for quantitative polymerase chain reaction (qPCR).
(i) Woo et al. teaches limitations relevant to claims 11 and 15.
Relevant to claim 11, Woo et al. teaches “In the later cycles of the reaction, however, the efficiency of the amplification process may be diminished resulting in non-exponential amplification. Some of the factors that may affect the amplification efficiency include limiting quantities or depletion of reagents and competition for reaction products. The aforementioned changes in reaction kinetics may result in difficulties in determining the initial target concentration without performing detailed analysis of the reaction profile. In one aspect, it is desirable to monitor the reaction at various time or cycle intervals and acquire data which quantifies the emitted fluorescence of the reaction at these intervals. Using this information, data analysis methods may be used to assess the acquired fluorescence measurements and determine the initial concentration of target present in the reaction” (column 4, lines 41-56).
This teaching reads on claim 11 the error is determined depending on a specified reaction efficiency.
Relevant to claim 15, Woo et al. teaches “The data collection module 820, according to various embodiments, can transmit the fluorescence data to a data storage module 830 responsible for archiving the fluorescence results for each reaction over the specified time course. The data storage module 830 may store the data in numerous different forms and configurations including tables, charts, arrays, spreadsheets, databases, and the like. In one aspect, the data storage module 830 receives the results from many different experiments and presents the data to other modules responsible for the subsequent comparison and analysis of the data. Furthermore, the data storage module 830 stores the results of the quantitation analysis which may be output as needed or requested” (column 18, lines 30-42).
The Woo et al. data storage module fulfills a long short-term memory (LSTM) function, and thus reads on claim 15 the neural network is in the form of an LSTM.
(ii) Woo et al. is silent to limitations relevant to claims 3, 5, and 7-10. However, these limitations were known in the prior art and taught by Clarkson et al.
Clarkson et al. teaches “A system for optimising the cycling conditions used to control a polymerase chain reaction assigns membership values for denaturation, annealing and extension events in order to determine the relative contribution of each event during the reaction, and using genetic algorithms to determine the optimum times required to complete each event” (Abstract).
Relevant to claim 3, Clarkson et al. teaches “The preferred embodiment provides a process which allows intelligent control of the PCR. This is achieved by modelling and predicting levels of amplification through a novel combination of membership function assignment (association of reaction events with temperature), genetic algorithms and artificial neural networks. Here, the membership component infers and provides a crisp definition for the various reaction parameters that determines the degree of amplification for a specific reaction. Genetic algorithms are used to determine the optimum times for each step of temperature cycle. The neural network component is then used to enhance the membership rules and membership functions. After an initial training, the neural network can be used to update the membership functions as it learns more from its input signals. This process may be used to accurately predict optimum reaction conditions (FIG. 1)” (column 3, lines 15-30).
This teaching reads on claim 3 the data-based trainable qPCR model comprises a neural network configured to estimate, using a plurality of consecutive intensity values, at least one subsequent intensity value.
Relevant to claim 5, Clarkson et al. Figure 7 and associated drawing description (column 3, lines 57-59) teaches that “FIG. 7 is a schematic representation of a mode in an artificial neural network and a three layer artificial neural network”.
These teachings read on claim 5 the neural network is in the form of a deep neural network.
Relevant to claims 7-10, Clarkson et al. teaches “During an initial training procedure, a series of input patterns with their corresponding expected output are presented to the network in an iterative fashion while the weights are adjusted. This is continued until the desired level of perception between expected observed outputs has been achieved. Different learning algorithms can be used, however back-propagation is currently the algorithm of preference. The error in the expected output is back propagated through the network using the generalised delta rule to determine the adjustment to the weights…” (column 9, lines 34-46). Clarkson et al. also teaches “The terms n and n-1 refer to the present iteration and the previous iteration, respectively. The presentation of the entire set of p [observation] training observations is repeated when the number of iterations, n, exceeds p. A similar method is used to adjust the weights connecting the hidden layers of nodes to the next hidden layer, and between the final hidden layer and the output layer. All weights are initially given random values prior to training. The neural net algorithms are used to associate membership functions with time profiles that define each amplification stage. After training, this procedure can be used to predict the level of amplification with a high degree of accuracy. Comparison of predicted amplification levels with real-time monitoring could be used to optimise reactions further” (column 10, lines 3-18). Clarkson et al. further teaches “The amount of amplification obtained from each of these reactions was scored based on 0 to 5 system. This data was used to train a neural network using backpropagation and sigmoidal transfer functions…” (column 10, lines 64-67).
These teachings read on claim 7 estimating the remainder of the qPCR curve includes: estimating at least one intensity value; determining for the estimated at least one intensity value, a measure of uncertainty, which measure of uncertainty indicates a measure of the reliability of the prediction of the estimated at least one intensity value, wherein the uncertainty value is provided by the data-based trainable qPCR model or by an uncertainty model; claim 8 the method includes determining a ct value from the remainder of the qPCR curve; and the method is terminated when the ct value is determined on the basis of a qPCR curve having, for the ct value, a measure of uncertainty below a specified uncertainty threshold; claim 9 the data-based qPCR model is trained using completely measured qPCR curves; and an error of a model prediction and the corresponding intensity value actually measured are used for the training of the qPCR model; and claim 10 the data-based qPCR model is trained using at least one completely measured qPCR curve; a training qPCR curve is estimated using the data-based qPCR model and a portion of the at least one completely measured qPCR curve for the training of the data-based qPCR model; and an error between the at least one completely measured qPCR curve and the training qPCR curve is used to train parameters of the data-based qPCR model for the training of the data-based qPCR model.
(iii) Although Woo et al. does not include the Clarkson et al. neural network and data training methodologies, it would have been prima facie obvious to the skilled artisan to include the Clarkson et al. teachings within the method and device rendered obvious by Woo et al.
Woo et al. and Clarkson et al. are analogous disclosures within polymerase chain reaction methodologies. The skilled artisan would be motivated to include the Clarkson et al. neural network and data training methodologies within the Woo et al. method and device because Clarkson et al. teaches that their “approach provides the basis of a PCR specific control software that can be applied to standardisation thermal cycler control and is the first description of an intelligent control process for PCR optimization” (column 4, lines 43-47). Clarkson et al. provides further motivation through the teaching of “Reactions optimised by our process have inherently increased robustness. This process can therefore be used as the basis of a standardised optimisation procedure. Robustness to variables such as the variation in the performance of the cycling instrument, etc., will enhance specificity and sensitivity” (column 13, lines 64-67 continued through column 14, lines 1-2).
The skilled artisan would be motivated to include the Clarkson et al. teachings within the Woo et al. method and device in order to better optimize the reactions and improve specificity and sensitivity. The skilled artisan would have a reasonable expectation of success based on the disclosures of Woo et al. and further in view of Clarkson et al.
Applicant’s Arguments
Applicant argues that “Clarkson does not disclose a neural network that is used to determine intensity values based on a limited number of PCR cycles, as required by dependent claims 3-5, 7-11 and 15. The obviousness rejections of these claims are in error. Moreover, here is no legitimate basis to modify the polynomial curve fitting feature of Woo to incorporate the neural network feature of Clarkson, since Clarkson is concerned with optimizing thermal cycling conditions by intelligent control of the cycling conditions, and not directed to evaluating a polynomial root to determine if it is a real root, as in Woo. In other words, the neural network feature of Clarkson is unrelated to the polynomial curve-fitting method of Woo” (Remarks 9/9/25, last paragraph of page 7 continued to first paragraph of page 8).
Response to Applicant’s Arguments
As discussed above for the rejections of claims 1-2, 4, 6, and 12-14, Woo et al. obviates “a data-base trainable model to determine the qPCR curve” and estimation of “any part of the PCR curve being analyzed”. Clarkson et al. “optimizing thermal cycling conditions by intelligent control of the cycling conditions” is related to the Woo et al. methodology.
In response to applicant's argument that Clarkson et al. is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Clarkson et al. is from the same field of endeavor as both Woo et al. and the inventor’s endeavor, and Clarkson et al. is reasonably pertinent to the particular problem with which the inventor was concerned.
Both references relate to PCR methodology. Woo et al. addresses determining intensity values from limited PCR cycles and Clarkson et al. addresses optimization of thermal cycling conditions. Optimization of PCR conditions is directly relevant to the reliability and outcome of PCR intensity determinations. Thus, the skilled artisan would have reasonably consulted the Clarkson et al. when seeking to understand or improve the PCR conditions described within Woo et al. Accordingly, the references are analogous and the combination is proper.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 remains/is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 17/904,301 (reference application; claim reproduced below). Although the claims at issue are not identical, they are not patentably distinct from each other because both claims are directed to a method for conducting a quantitative polymerase chain reaction (qPCR).
This rejection is necessitated by claim amendments filed 9/9/25. The copending application has been amended (9/3/25), with the updated claim reproduced below.
Both claims include cyclically executing… qPCR cycles; measuring an intensity value… to obtain a measured portion of a qPCR curve of intensity values. Although the instant claim 1 recites estimating, after cycling the predetermined plurality of qPCR cycles, a remainder of the qPCR curve using the measured intensity values and a data-based trainable qPCR model and the copending claim recites “evaluating a shape of the qPCR curve with a data-based classification model which has been trained to provide a classification result depending on the shape of the qPCR curve”, these claims fulfill the same function. The instant data-trainable qPCR model is equivalent to the copending “data-based classification model”.
Similarly, the instant selecting one of a plurality of steps of the method based on the remainder of the plot of the qPCR curve; and conducting the selected one of the plurality of steps of the method is equivalent to the copending “conducting the qPCR process depending on the classification result from the evaluation of the shape of the qPCR curve” because both claims require conducting a portion of the qPCR reaction based on the qPCR curve.
Although the copending claim 1 additionally recites “wherein the data-based classification model has been trained to provide, depending on the shape of the qPCR curve, the classification result which indicates one of a presence and a nonpresence of a DNA strand segment to be detected”, this limitation would be embraced through the instant claim 1 method for conducting a quantitative polymerase chain reaction (qPCR), as the skilled artisan would recognize that the method would obviously conclude in determination of a presence or nonpresence result, as those are the two possible outcomes of qPCR.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Reproduced claim 1 of copending Application No. 17/904, 301:
“A method, which is computer implemented, for conducting a quantitative polymerase chain reaction (qPCR) process, the method comprising: cyclically executing qPCR cycles; measuring an intensity value of a fluorescence at each qPCR cycle to obtain a qPCR curve composed of intensity values; evaluating a shape of the qPCR curve with a data-based classification model which has been trained to provide a classification result depending on the shape of the qPCR curve; and conducting the qPCR process depending on the classification result from the evaluation of the shape of the qPCR curve, wherein the data-based classification model has been trained to provide, depending on the shape of the qPCR curve, the classification result which indicates one of a presence and a nonpresence of a DNA strand segment to be detected.”
Applicant’s Arguments
Applicant argues that “The provisional non-statutory double patenting rejection in view of Applicant’s pending application 17/904,301 is noted. However, since no claims have been allowed in either application, and since the claims of the ‘301 Application have been amended, the rejection is premature” (Remarks 9/9/25, page 8, second paragraph).
Response to Applicant’s Arguments
The nonstatutory double patenting rejection has been updated to reflect the copending ‘301 application amendments.
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 Sarah J Kennedy whose telephone number is (571)272-1816. The examiner can normally be reached Monday - Friday 8a - 5p.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Winston Shen can be reached at 571-272-3157. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SARAH JANE KENNEDY/Examiner, Art Unit 1682
/WU CHENG W SHEN/Supervisory Patent Examiner, Art Unit 1682