DETAILED ACTION
Applicant’s response filed 08/19/2026 has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied.
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 .
Status of the Claims
Claims 1-20 are pending and under consideration in this action.
Priority
The instant application claims domestic benefit to U.S. Provisional Application No. 63/363,618, filed 04/26/2022, as reflected in the filing receipt mailed 02/22/2024. The claim for domestic benefit for claims 1-20 is acknowledged. As such, the effective filing date of claims 1-20 is 04/26/2022.
Specification
The objection to the abstract is withdrawn in view of Applicant’s amendment to the abstract filed 08/19/2026.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 18 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
This rejection is newly recited and necessitated by claim amendment.
Claim 18 recites the limitation “wherein the probe-classification-machine-learning model comprises a neural network or one or more decision trees”. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. As amended, claim 17 recites “one or more convolutional layers” and “an output layer” of the probe-classification-machine-learning model, clearly indicating that the machine learning model is a neural network. It is therefore unclear what steps and/or layer of the neural network would use the decision trees recited in claim 18. This rejection can be overcome by amendment of claim 18 to clarify what steps or parameters are required to use the decision trees.
Claim Rejections - 35 USC § 101
Maintained Rejections
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Any newly recited portion is necessitated by claim amendment.
Framework with which to evaluate Subject Matter Eligibility as outlined in MPEP § 2106:
Step 1: Are the claims directed to a process, machine, manufacture or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework as it pertains to the instant claims:
Step 1:
In the instant application, claims 1-9 are directed towards a method, claims 10-16 are directed towards a system, and claims 17-20 are directed towards a manufacture, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claims 1 and 10 recite a mental process (i.e., an evaluation of candidate probes) in “identifying candidate oligonucleotide probes for hybridizing with target oligonucleotides”; a mental process (i.e., evaluating the probes to determine a sequence) in “determining a nucleotide sequence of an oligonucleotide probe from the candidate oligonucleotide probes”; a mathematical concept (i.e., using a convolutional layer to determine feature values performs a convolution/cross-correlation using a kernel) in “determining, by one or more convolutional layers of the probe-classification-machine-learning model processing the dataset, feature values representing one or more nucleotide sequence patterns within the nucleotide sequence associated with hybridization to the oligonucleotide probe, wherein the one or more convolutional layers are trained to detect motifs or other nucleotide-sequence patterns that correlate with favorable or unfavorable probe accuracy based on genotyping metrics”; and a mental process (i.e., an evaluation of the output of the model to determine a probe accuracy classification) in “determining, by utilizing an output layer of the probe-classification-machine-learning model, a probe accuracy classification for the oligonucleotide probe based on the feature values”.
Claim 2 recites a mental process (i.e., determination of a favorable or unfavorable class based on the probability) in “determining, for the oligonucleotide probe, a favorable genotyping accuracy class indicating a probability that the oligonucleotide probe yields an accurate genotype call or an unfavorable genotyping accuracy class indicating a probability that the oligonucleotide probe yields an inaccurate genotype call”.
Claim 3 recites a mental process (i.e., determination of a favorable or unfavorable class based on the probability for binding) in “determining, for the oligonucleotide probe, a favorable binding accuracy class indicating a probability that the oligonucleotide probe accurately binds to a target oligonucleotide for genotyping or an unfavorable binding accuracy class indicating a probability that the oligonucleotide probe inaccurately binds to the target oligonucleotide for genotyping”.
Claim 4 recites a mathematical concept (i.e., determination of a score) in “determining a score indicating a genotyping probability that the oligonucleotide probe yields an accurate genotype call or a binding probability that the oligonucleotide probe accurately binds to a target oligonucleotide for genotyping”.
Claim 5 recites a mental process (i.e., evaluation of the classification for probe selection) in “selecting the oligonucleotide probe for use in a microarray based on the probe accuracy classification”.
Claim 6 recites a mental process (i.e., an evaluation / analysis of the hybridization data to determine a variant call) in “determining a variant call for the genomic sample based on one or more copies of the oligonucleotide probe hybridizing with one or more copies of the target oligonucleotide”.
Claim 7 recites a mental process (i.e., an evaluation of the pattern between the probe and target) in “determining, by the probe-classification-machine-learning model, the feature values representing a pattern of the one or more nucleotide sequence patterns within the nucleotide sequence of the oligonucleotide probe corresponding to complementary nucleobase bonds between the oligonucleotide probe and a target oligonucleotide”; and a mental process (i.e., an evaluation of the model output) in “determining, by the probe-classification-machine-learning model, the probe accuracy classification for the oligonucleotide probe based on the feature values representing the pattern”.
Claim 9 recites a mental process (i.e., an evaluation of the output of the model) in “determining the probe accuracy classification for the oligonucleotide probe based on the feature values corresponding to the nucleobases of one or more nucleobase classes”.
Claim 11 recites a mental process (i.e., an evaluation of the probe to determine a sequence) in “determining the probe accuracy classification for the oligonucleotide probe based on the feature values corresponding to the nucleobases of one or more nucleobase classes”; and a mental process (i.e., an evaluation of the output of the model to determine a probe accuracy classification) in “determine, by utilizing the probe-classification-machine-learning model to recognize one or more nucleotide-sequence patterns of the different nucleotide sequence, a different probe accuracy classification for the additional oligonucleotide”.
Claim 12 recites a mental process (i.e., an evaluation of genotyping metrics to determine threshold ranges) in “identify threshold ranges for the genotyping metrics indicating accurate probes and inaccurate probes for genotyping”; and a mental process (i.e., an evaluation of probes for categorization) in “categorize, based on the threshold ranges for genotyping metrics, the candidate oligonucleotide probes into a favorable probe-accuracy-training class for training the probe-classification-machine-learning model and an unfavorable probe-accuracy-training class for training the probe-classification-machine-learning model”.
Claim 13 recites a mental process (i.e., an evaluation of training data to determine a ground-truth probe) in “identify, from among the favorable probe-accuracy-training class or the unfavorable probe-accuracy-training class, a ground-truth oligonucleotide probe corresponding to the oligonucleotide probe”; and a mental process (i.e., a comparison of two classification accuracies) in “determine a value difference between a ground-truth probe accuracy classification for the ground-truth oligonucleotide probe and the probe accuracy classification for the oligonucleotide probe”.
Claim 14 recites a mental process (i.e., determination of a favorable or unfavorable class based on the probability) in “determining, for the oligonucleotide probe and based on the dataset representing the nucleotide sequence of the oligonucleotide probe, a favorable genotyping accuracy class indicating a probability that the oligonucleotide probe yields an accurate genotype call or an unfavorable genotyping accuracy class indicating a probability that the oligonucleotide probe yields an inaccurate genotype call”.
Claim 15 recites a mental process (i.e., determination of a favorable or unfavorable class based on the probability for binding) in “determine the probe accuracy classification by determining, for the oligonucleotide probe and based on the dataset representing the nucleotide sequence of the oligonucleotide probe, a favorable binding accuracy class indicating a probability that the oligonucleotide probe accurately binds to a target oligonucleotide for genotyping or an unfavorable binding accuracy class indicating a probability that the oligonucleotide probe inaccurately binds to the target oligonucleotide for genotyping”.
Claim 16 recite a mental process (i.e., an evaluation / analysis of the hybridization data to determine a variant call) in “determine a variant call for the one or more genomic coordinates of the genomic sample based on one or more copies of the oligonucleotide probe hybridizing with one or more copies of the target oligonucleotide”.
Claim 17 recites a mental process (i.e., an evaluation of candidate probes) in “identify candidate oligonucleotide probes for hybridizing with target oligonucleotides”; a mental process (i.e., evaluating the probes to determine a sequence) in “determine a first nucleotide sequence of a first oligonucleotide probe from the candidate oligonucleotide probes and a second nucleotide sequence of a second oligonucleotide probe from the candidate oligonucleotide probes”; mathematical concepts (i.e., using a convolutional layer to determine feature values performs a convolution/cross-correlation using a kernel) in “determine, by one or more convolutional layers of the probe-classification-machine-learning model processing the first dataset, a first set of feature values representing one or more nucleotide sequence patterns within the first nucleotide sequence associated with hybridization to the first oligonucleotide probe, wherein the one or more convolutional layers are trained to detect motifs or other nucleotide-sequence patterns that correlate with favorable or unfavorable probe accuracy based on genotyping metrics” and “determine, by the one or more convolutional layers of the probe-classification-machine-learning model processing the second dataset, a second set of feature values representing one or more nucleotide sequence patterns within the second nucleotide sequence associated with hybridization to the second oligonucleotide probe”; and mental processes (i.e., an evaluation of the output of the model to determine a probe accuracy classification) in “determine, by utilizing an output layer of the probe-classification-machine-learning model, a favorable probe accuracy class for the first oligonucleotide probe based on the first set of feature values” and “determine, by utilizing the output layer of the probe-classification-machine-learning model, an unfavorable probe accuracy class for the second oligonucleotide probe based on the second set of feature values”.
Claim 19 recites a mental process (i.e., an evaluation of a score to determine class) in “determine the favorable probe accuracy class or the unfavorable probe accuracy class by determining a score indicating a genotyping probability that the first oligonucleotide probe or the second oligonucleotide probe yields an accurate genotype call or a binding probability that the first oligonucleotide probe or the second oligonucleotide probe accurately binds to a target oligonucleotide for genotyping”.
Claim 20 recites a mental process (i.e., evaluation of the accuracy class for probe selection) in “select the first oligonucleotide probe for use in a microarray based on the favorable probe accuracy class for the first oligonucleotide probe”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Specifically, claims 1 and 10 involve nothing more than identifying candidate probes, determining a sequence for the nucleotide probes, determining feature values using convolutional layers, and determining a probe classification accuracy based on the output of a machine learning model. Claim 17 involves nothing more than identifying candidate probes, determining a first and second nucleotide sequence for the candidate probes, determining first and second sets of feature values using convolutional layers, and determining a favorable and unfavorable probe accuracy based on the output of the machine learning model. Since there are no specifics in the methodology, the identification of candidate probes, the determination of (one or more) sequences for the nucleotide probes, and the determination of the probe classification accuracy (favorable or unfavorable) based on analyzing the output of the machine learning model, are something that under BRI, one could perform mentally. Additionally, the steps reciting determining (first and second) feature values using convolutional layers are, under the BRI, performed using mathematical operations. The convolutional layers perform a convolution/cross-correlation using a kernel (with a size of 3 in the instant case, see Specification Para. [0095]) to generate the output feature values. Therefore, the claimed steps are not further defined beyond something that reads on merely looking at data and making a determination, and performing calculations using a computer as a tool. As such, said steps are directed to judicial exceptions. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following independent claims recite limitations that equate to additional elements:
Claim 1 recites “providing a dataset representing the nucleotide sequence of the oligonucleotide probe to a probe-classification-machine-learning model”.
Claim 10 recites “at least one processor”; “a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor”; and “provide a dataset representing the nucleotide sequence of the oligonucleotide probe to a probe-classification-machine-learning model”.
Claim 17 recites “a non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause a computing device to…”; and “provide a first dataset representing a first nucleotide sequence of the first oligonucleotide probe and a second dataset representing a second nucleotide sequence of the second oligonucleotide probe to a probe-classification-machine-learning model”.
Regarding the above cited limitations in claims 10 and 17 of (i) at least one processor; and (ii) a non-transitory computer readable medium comprising instructions that, when executed by at least one processor. These limitations require only a generic computer component, which does not improve computer technology. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Regarding the above cited limitations in claims 1, 10, and 17 of (iii) provide a dataset representing the nucleotide sequence of the oligonucleotide probe to a probe-classification-machine-learning (claims 1 and 10); and (iv) provide a first dataset representing a first nucleotide sequence of the first oligonucleotide probe and a second dataset representing a second nucleotide sequence of the second oligonucleotide probe to a probe-classification-machine-learning model (claim 17). These limitations equate to insignificant, extra-solution activity of mere data gathering because these limitations gather data to input into the model before the recited judicial exceptions of determining a (favorable or unfavorable) probe classification accuracy based on the output of a machine learning model (see MPEP § 2106.04(d)).
Additionally, none of the recited dependent claims recite additional elements which would integrate the judicial exception into a practical application. Specifically, claims 6 and 16 recite an extra-solution step of hybridizing the probe; claims 8 and 9 further limit the kernel size and channels in the machine learning model; claim 11 recites a data gathering step of inputting data into the model analogous to claims 1, 10, and 17 above; claims 13 and 18 further limit the machine learning model; and claim 20 recites a generic extra-solution step of displaying data on a graphical user interface. As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The instant independent claims recite the same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitations in claims 10 and 17 of (i) at least one processor; and (ii) a non-transitory computer readable medium comprising instructions that, when executed by at least one processor. These limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept (see MPEP § 2106.05(d) and MPEP § 2106.05(f)).
Regarding the above cited limitations in claims 1, 10, and 17 of (iii) provide a dataset representing the nucleotide sequence of the oligonucleotide probe to a probe-classification-machine-learning (claims 1 and 10); and (iv) provide a first dataset representing a first nucleotide sequence of the first oligonucleotide probe and a second dataset representing a second nucleotide sequence of the second oligonucleotide probe to a probe-classification-machine-learning model (claim 17). These limitations when viewed individually and in combination, are WURC limitations as taught by Buterez (Scaling up DNA digital data storage by efficiently predicting DNA hybridization using deep learning. Sci Rep. 11: 20517, 12 pages (2021); previously cited). Buterez discloses a comprehensive study of machine learning methods applied to the task of predicting DNA hybridization. They introduce an in silico generated hybridization dataset to enable the use of deep learning methods (Abstract). Buterez et al. further discloses the use of a convolutional neural network for hybridization prediction. They designed an architecture, which first perform 2D convolutions with large filters of size 4×9, followed by 1D convolutions of size 9, 3, 3 and finally 1. The large filters in the first two layers ensure that spatial information is captured from a wide area, while the following layers, decreasing in size, can learn more specialized features (limitations (iii) and (iv)) (Pg. 9, Para. 5).
These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-20 are not patent eligible.
Response to Arguments under 35 U.S.C. 101
Applicant’s arguments filed 08/19/2026 have been fully considered but they are not persuasive.
1. Applicant argues that under Step 2A, the amended claims are not directed to an abstract idea but constitute “claims that improve [other] technology.” The amended claims recite patent-eligible subject matter based on Ex Parte Wu, Appeal No. 2025-002027 (PTAB Dec. 2025). Like Wu, amended Claim 1 Recites Patent-Eligible Subject Matter that Uses One or More Trained Convolutional Layers to Generate Feature Values Representing Nucleotide Sequence Patterns that Improve Oligonucleotide-Probe Accuracy and DNA Sequencing Technology. Analogous to Wu's holding, the specification for the present U.S. Patent Application No. 18/307,482 (filed April 26, 2023) (hereinafter, Specification) demonstrates how the currently amended claims improve a machine-learning-based approach to predicting probe accuracy and, consequently, downstream computing efficiency and reagent-wash conservation of a DNA sequencing device running microarrays. By (a) "determining, by one or more convolutional layers of the probe-classification-machine-learning model processing the dataset, feature values representing one or more nucleotide sequence patterns within the nucleotide sequence associated with hybridization to the oligonucleotide probe, wherein the one or more convolutional layers are trained to detect motifs or other nucleotide-sequence patterns that correlate with favorable or unfavorable probe accuracy based on genotyping metrics," and (b) "determining, by utilizing an output layer of the probe-classification-machine-learning model, a probe accuracy classification for the oligonucleotide probe based on the feature values," as amended claim 1 recites, the claimed technology improves probe-hybridization or genotyping accuracy and microarray computing efficiency. See Specification [0026]. Amended independent claims 10 and 17 include similar limitations to (a) and (b). First, as indicated above, the currently amended independent claims improve a machine-learning-based approach to predicting the accuracy of oligonucleotide-probe hybridization. To address such probe-based genotyping inaccuracies, the claimed probe-classification-machine-learning model generates a probe accuracy classification for an oligonucleotide probe that "improves the accuracy with which selected oligonucleotide probes hybridize with target nucleotides as part of a genotyping microarray." Id [0026]. "As discovered by the inventors of this disclosure, ... when a trained machine-learning model scores or otherwise classifies a probe accuracy of a candidate oligonucleotide probe-based on a nucleotide sequence of the candidate oligonucleotide probe-the probe design system can identify oligonucleotide probes exhibiting superior hybridization accuracy than probes designed or selected by existing microarray systems." Id Indeed, "[i]n a first-of-its-kind machine-learning model, the disclosed probe-classification-machine-learning model can use layers trained to identify nucleotide-sequence patterns to identify more accurate probes before performing a microarray." Id Here, in amended limitations (a) and (b) quoted above, the claimed technology leverages this very type of trained layers delivering improved performance-that is, "one or more convolutional layers ... trained to detect motifs or other nucleotide-sequence patterns that correlate with favorable or unfavorable probe accuracy based on genotyping metrics," as amended claim 1 recites (Applicant’s Remarks, Pg. 14-18).
It is respectfully submitted that this is not persuasive for the following reasons:
The analysis of the instant claims under Step 2A, Prong Two is different than the analysis in Wu. Quoting Wu (Pg. 12-13), “Indeed, the only input of claim 1’s method is raw amino acid sequence(s), upon which amino acid character embeddings are computed, which in turn provides materials for the refining step and the predicting step. Ultimately, the protein structure simulation is based on the predicted dihedral angles, without any template, structural biological knowledge, or co-evolutional information. Thus, we agree with Appellant that claim 1 is directed to a specific improvement to the technical field of protein design, simulation, and synthesis as used in “bioengineering, medicine and materials science” applications.” It is important to note that the limitation of “computing…utilizing a multi-scale neighborhood-based neural network (MNNN) model” recites a mathematical concept (Wu, Pg. 8-9), and the limitation of “simulating a protein structure based on the predicted one or more dihedral angles” recites an additional element (Wu, Pg. 9-10). When considered as a whole, the additional elements, and how limitations interact and impact each other when are evaluated when considering whether the exception is integrated into a practical application. In Wu, the additional elements of obtaining at least one raw amino acid sequence and simulating a protein structure, when considered in combination with their interaction with the judicial exceptions, as quoted above, recite a specific improvement.
The analysis in Wu is different than the instant claims for the following reasons. Claim 1 (and similar limitations in claims 10 and 17) recites an additional element of “providing a dataset representing the nucleotide sequence of the oligonucleotide probe to a probe-classification-machine-learning model”, and judicial exceptions (mental processes or mathematical concepts as described in Step 2A, Prong One above) in the following limitations: “identifying candidate probes…”, “determining a nucleotide sequence”, “determining, by one or more convolutional layers of the probe-classification-machine-learning model…” (termed limitation (a) by Applicant), and “determining, by utilizing an output layer of the probe-classification-machine learning model…” (termed limitation (b) by Applicant). The only additional element is a step of providing data to the machine learning algorithms, which recite judicial exceptions. Even when the additional element is considered in combination with its interaction with the judicial exception (i.e., inputting data), this element is not sufficient to recite the improvement in the specification (i.e., improve a machine-learning-based approach to predicting probe accuracy and downstream computing efficiency and reagent-wash conservation of a DNA sequencing device running microarrays).
MPEP § 2106.04(d)(II) recites:
The analysis under Step 2A Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon (including products of nature). Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).
Additionally, the integration of a judicial exception into a practical application can only be achieved by additional elements, not by a limitation that recites a judicial exception. Thus, limitations (a) and (b), as indicated by Applicant, are not considered as an improvement in the approach to predicting the accuracy of oligonucleotide-probe hybridization. This argument is thus not persuasive.
2. Applicant also argues that the currently amended independent claims improve computing efficiency, processing time, and reagents consumed by specialized sequencing devices running microarrays. Id [0028]. As noted in the Specification, "some existing systems re-run microarrays on multiple copies of DNA fragments from a genomic sample or run different types of microarrays to determine more reliable genotyping calls." Id "Rather than perform redundant or time-intensive processing on specialized sequencing devices, the probe design system can apply a probe-classification-machine-learning model to nucleotide sequences of candidate oligonucleotide probes for a microarray and identify oligonucleotide probes with nucleotide sequences compatible with accurate genotyping and/or accurate hybridization-thereby obviating microarray re-runs or diversified microarray types." Id Relatedly, because the claimed technology reduces the number of microarray re-runs, the currently amended claims likewise conserves the amount of reagents and resources used during laborious and computationally intensive microarrays, id [0006],[0028] – avoiding the reagent waste of "a washing solution rins[ing] away the target oligonucleotide and potentially interfer[ing] with correctly determining a genomic sample's genotype," id [0004]. Again, here, in amended limitations (a) and (b) quoted above, the claimed technology delivers such downstream improvements to specialized sequencing devices running microarrays by using a machine-learning model with a specially trained layer-that is, "one or more convolutional layers ... trained to detect motifs or other nucleotide-sequence patterns that correlate with favorable or unfavorable probe accuracy based on genotyping metrics," as amended claim 1 recites (Applicant’s Remarks, Pg. 18-19).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in argument (1) above, limitations (a) and (b) indicated by Applicant recite judicial exceptions and are not considered as an improvement in the approach to predicting the accuracy of oligonucleotide-probe hybridization (see MPEP § 2106.04(d)(II)).
MPEP 2106.05(a) recites:
After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification. However, the claim itself does not need to explicitly recite the improvement described in the specification (e.g., thereby increasing the bandwidth of the channel"). The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology (e.g., the improvement described in the specification). In making this determination, it is critical that examiners look at the claim "as a whole," in other words, the claim should be evaluated "as an ordered combination, without ignoring the requirements of the individual steps." When performing this evaluation, examiners should be "careful to avoid oversimplifying the claims" by looking at them generally and failing to account for the specific requirements of the claims. McRO, 837 F.3d at 1313, 120 USPQ2d at 1100.
Additionally, the alleged improvements indicated by Applicant are not commensurate in scope with the claimed invention. Applicant appears to assert that the claimed features may be used reduce reagents consumed by specialized sequencing devices running microarrays (see Applicant’s Remarks, Pg. 19 and Specification Para. [0028]). However, the claim does not require the use of the determined probe in microarrays, as for example, in dependent claims 6 and 16. Therefore, it appears the alleged improvements are not commensurate in scope with the claimed invention. This argument is thus not persuasive.
3. Applicant also argues that analogous to Wu's Eligible Claim, the Claimed Trained Convolutional Layers and Output Layer Deliver the Technological Improvements. Like Wu, the currently amended independent claims here are patent eligible for reciting an improved technology. Intricate and more detailed machine-learning architecture is not required by the law or the USPTO to recite patent-eligible subject matter. In Wu, for example, the only specific type of machine-learning architecture mentioned in representative claim 1 is the type of the model-that is "a multi-scale neighborhood-based neural network (MNNN) model." Here, the claimed technology goes further than Wu in claiming a specific type of layer with details concerning its training as well as an output layer-that is, "the one or more convolutional layers are trained to detect motifs or other nucleotide-sequence patterns that correlate with favorable or unfavorable probe accuracy based on genotyping metrics" and "an output layer of the probe-classification-machine-learning model" used for determining "a probe accuracy classification." As in Wu, the claimed machine-learning architecture is not recited merely as a tool for performing an abstract idea, but implements the claimed technological improvement in identifying oligonucleotide probes exhibiting improved hybridization and/or genotyping accuracy. Moreover, the currently amended independent claims specify the relevant machine-learning architecture and its operations in at least as much (if not more) detail as the MNNN architecture considered in Wu, while tying that architecture to the technological improvement described in the Specification, as set forth above (Applicant’s Remarks, Pg. 19).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in argument (1) above, the multi-scale neighborhood-based neural network (MNNN) model of Wu recites a mathematical concept. Analogously, the convolutional layers used to determine feature values in limitation (a) also recite a mathematical concept. Additionally, it is the additional element of simulating a protein structure (in Wu) combination with the interaction with the judicial exception (the MNNN model) recites the technical improvement. This is different from the instant claims where the only additional element is inputting data into the judicial exception. As described in argument (1), even when the additional element is considered with its interaction with the judicial exceptions, it is not sufficient to recite the technological improvement in identifying oligonucleotide probes exhibiting improved hybridization and/or genotyping accuracy. This argument is not persuasive.
4. Applicant also argues that the Currently Amended Independent Claims Recite a Patent-Eligible Practical Application. As set forth in Parts A and B above, like the Wu claims, any abstract idea recited in the currently amended independent claims is not claimed in isolation but rather integrated into a practical application. The claimed technology improves probe accuracy and downstream microarray efficiency by proactively determining a likelihood that the oligonucleotide probe will yield an accurate or inaccurate genotype call or will hybridize with a target oligonucleotide. See Specification [0027], [0035]. Indeed, by predicting the accuracy and/or hybridization of the oligonucleotide probe before running the microarray, the currently amended independent claims identify an oligonucleotide probe that, when used in a microarray, does not need require a device to perform several runs or re-runs to find oligonucleotide probes that bind to target oligonucleotides for genotyping. Id [0028], [0036]. The claimed method "improv[es] the accuracy of probe hybridization or genotyping calls in microarrays and improv[es] microarray computing efficiency." Id [0026]. Consistent with Wu, amended claims 1, 10, and 17 therefore integrate any abstract idea into a practical application in a technical field and are not directed to an abstract idea under § 101 (Applicant’s Remarks, Pg. 20).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in argument (2) above, while the claims identify a probe with a probe accuracy, the claims do not require the probe to be used in a microarray. In fact, claims 1 and 10 only require “a probe accuracy classification for the oligonucleotide probe based on the feature values” to be determined. While claims 1 and 10 are trained based on favorable or unfavorable probe accuracy (from genotyping metrics), claims 1 and 10 only require a probe accuracy classification to be determined, not specifically a favorable probe accuracy, which would be required to improve the downstream microarray efficiency. Therefore, limitations (a) and (b) do not integrate the abstract ideas into a practical application and this argument is not persuasive.
Claim Rejections - 35 USC § 103
The rejection of claims 1, 5-12, and 16-18 under 35 U.S.C. 103 as being unpatentable over Shchegrova et al. in view of Buterez is withdrawn in view of Applicant’s amendments to the claims filed 08/19/2026 (Applicant’s Remarks, Pg. 20-25). Specifically, Shchegrova et al. nor Buterez discloses the determination of feature values representing one or more nucleotide sequence patterns within the nucleotide sequence associated with hybridization to the oligonucleotide probe, as disclosed in claims 1, 10, and 17.
The rejection of claims 2-4, 14-15, and 19-20 under 35 U.S.C. 103 as being unpatentable over Shchegrova et al. in view of Buterez and Balac Sipes et al. is withdrawn in view of Applicant’s amendments to the claims filed 08/19/2026 (Applicant’s Remarks, Pg. 20-25). Specifically, Balac Sipes et al. also does not disclose the determination of feature values representing one or more nucleotide sequence patterns within the nucleotide sequence associated with hybridization to the oligonucleotide probe.
Conclusion
No claims allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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.
Claims 1-20 appear to be free from the prior art because the prior art does not fairly suggest or teach the determination of feature values representing one or more nucleotide sequence patterns within the nucleotide sequence associated with hybridization to the oligonucleotide probe. The closest prior art is Buterez (Scaling up DNA digital data storage by efficiently predicting DNA hybridization using deep learning. Sci Rep. 11: 20517, 12 pages (2021); previously cited). Buterez discloses machine learning methods applied to the task of predicting DNA hybridization (Abstract). Buterez further discloses the use of convolutional layers to predict a hybridization yield (Pg. 3, Fig. 1). The model is trained on features alignment and GC content to associate sequences that will form stable duplexes to provide a low or high yield (i.e., trained to correlate with probe accuracy) (Pg. 2, Pg. 8-9; and Pg. 3, Fig. 1). While the convolutional layers inherently output feature values, Buterez does not teach that the feature values represent one or more nucleotide sequence patterns within the nucleotide sequence associated with hybridization to the oligonucleotide probe, as disclosed in instant claims 1, 10, and 17. Claims 2-9, 11-16, and 18-20 appear to be free from the prior art due to their dependency on claims 1, 10, and 17
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/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687
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