DETAILED ACTION
Applicant’s response, filed 28 Sept. 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 Claims
Claims 4-5, 7-8, 10, 14, 16-17, 19-21, 24, 26-27, 29, 31-32, 34, 36, and 38 are cancelled.
Claims 1-3, 6, 9, 11-13, 15, 18, 22-23, 25, 28, 30, 33, 35, and 37 are pending.
Claims 1-3, 6, 9, 11-13, 15, 18, 22-23, 25, 28, 30, 33, 35, and 37 are rejected.
Claims 1, 35, and 37 are objected to.
Priority
The effective filing date of the claimed invention is 30 Aug. 2019.
Drawings
The drawings filed 11 Nov. 2020 are objected to for the following reasons. This objection is previously stated.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
#310-1 and 310-2 in FIG. 3
The drawings fail to comply with 37 CFR 1.84(u)(1), which states View numbers must be preceded by the abbreviation "FIG.". Therefore each figure label of “Figure 1”, “Figure 2A”, etc. should be relabeled “FIG. 1”, “FIG. 2A”, etc.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), and/or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Response to Arguments
Applicant's arguments filed 25 Feb. 2026 regarding the drawings have been fully considered but they are not persuasive because they do not present any arguments.
Claim Objections
The previous objection to claims 1, 35, and 37 in the Office action mailed 30 April 2026 has been withdrawn in view of claim amendments received 28 Sept. 2026.
Claims 1, 35, and 37 are objected to because of the following informalities. This objection is newly recited and necessitated by claim amendment:
Claims 1, 35, and 37 recite “encoding, as elements of the feature tensor, the discounted value… and the low bag depth flag for the respective base position corresponding to each base read”. To increase clarity, the claims should be amended to recite “… and the low bag depth flag of each base read corresponding to the respective base position”, given the features are for each base read.
Appropriate correction is required.
Claim Interpretation
Claims 1, 35, and 37 recite “…causing sequencing of a biological sample of a subject, the sequencing being the parallel sequencing…”. Claims 35 and 37 recite “A non-transitory computer readable storage medium storing instructions…, when executed, causing the computer to perform steps…” and “A computer system, comprising: one or more processors; memory…causing the one or more processors to perform steps…”. Thus the claims encompass a genetic computer capable of performing the “causing sequencing….”. Therefore, the claims are interpreted to only require causing sequencing (e.g. transmitting data with instructions to sequence to a sequencer), rather than requiring the method comprises sequencing or the computer performs the step of sequencing, given this is within what the metes and bounds of what a computer can perform.
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.
Claims 1-3, 6, 9, 11-13, 15, 18, 22-23, 25, 28, 30, 33, 35, and 37 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Any newly recited portion is necessitated by claim amendment.
Claims 1, 35, and 37, and claims dependent therefrom, are indefinite for recitation of “assigning each base read a discounted value of a feature… such that the feature is weighted according to a discount function”. It is unclear if the phrase “such that the feature is weighted according to a discount function” is simply reciting an intended use or result of assigning each read a discounted value of a feature, if the claims intend to require weighting the feature according to a discount function (e.g. after assigning each base read a discounted value), or if the assigned discounted value of a feature is intended to be the same value as the feature weighted according to a discount function. Clarification is requested. For purpose of examination, “such that the feature is weighted according to a discount function” is interpreted as an intended result of assigning a base read a discounted value of a feature. If Applicant intends for the discounted value to be determined by a discount function, the claims should be amended accordingly.
Claims 1, 35, and 37 are indefinite for recitation of “b) transforming the sequencing dataset of 1,000 or more sequence reads into a feature tensor that represents…;inputting training feature vectors having the feature representation generated according to step (b)”. A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claims 1, 35, and 37 recites the broad recitation of a feature tensor, and the claims then later recite using “feature vectors having the feature representation generated according to step (b)” which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims. As a result, it is unclear if “training feature vectors having the feature representation generated according to step (b)” is requiring the feature tensor generated in step (b) is actually a feature vector, or if the feature tensor can be of any dimension. For purpose of examination, the training data is interpreted to be training feature tensors, consistent with step (b), given the tensors comprise a plurality of dimensions. Clarification is requested via claim amendment.
Response to Arguments
Applicant's arguments filed 28 Sept. 2026 regarding 35 U.S.C. 112(b) have been fully considered but they are not persuasive.
Applicant remarks the claims are amended to address the various 112 rejections raised in the Office action (Applicant’s remarks at pg. 19, para. 4-5).
This argument is not persuasive. The rejection regarding the phrase “assigning each base read a discounted value of a feature… such that the feature is weighted according to a discount function” is maintained, and Applicant’s amendments to not serve to clarify the limitation. Furthermore, the rejection regarding the feature vectors having the feature representation of (b) is newly recited and necessitated by claim amendment.
Claim Rejections - 35 USC § 112(d)
The rejection of claim 18 under 35 U.S.C. 112(d) in the Office action mailed 30 April 2026 has been withdrawn in view of claim amendments received 28 Sept. 2026.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 3 is rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. This rejection is previously cited.
Claim 3 recites “…wherein the plurality of base positions is at least 1000 base positions…or at least 1 million base positions”. However, claim 1, from which claim 3 ultimately depends, already recites there are “1,000 or more base positions” (i.e. at least 1000 base positions). Therefore, claim 3 fails to further limit the subject matter of claims 1-2, from which it depends.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Response to Arguments
Applicant's arguments filed 28 Sept. 2026 regarding 35 U.S.C. 112(d) have been fully considered but they are not persuasive.
Applicant remarks the claims are amended to address the various 112 rejections raised in the Office action (Applicant’s remarks at pg. 19, para. 4-5).
This argument is not persuasive because Applicant did remove the limitation of “at least 1000 base positions” in claim 3, which was italicized as contributing to the 112(d) issue in the previous Office action. Therefore, claim 3 still fails to further limit claims 1-2 since claim 1 already requires there are at least 1000 base positions.
Claim Rejections - 35 USC § 101
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 1-3, 6, 9, 11-13, 15, 18, 22-23, 25, 28, 30, 33, 35, and 37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Any newly recited portion herein is necessitated by claim amendment.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claims 1, 35, and 37 being representative) is directed to a method, product, and system. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1, 35, and 37 recite the following steps which fall under the mathematical concepts and/or mental processes groupings of abstract ideas:
applying a machine-learning based (ML-based) base calling processing to determine consensus base calls for 1,000 or more base positions, wherein the ML-based base calling process comprises:
b) transforming the sequencing dataset of 1,000 or more sequence reads into a feature tensor that represents a distribution of the plurality of features for each respective base read in the sequencing dataset, wherein the feature tensor comprises a plurality of dimensions each corresponding to a feature of the plurality of features, wherein transforming the sequencing dataset of 1,000 or more sequence reads into a feature tensor comprises:
assigning each base read a discounted value of a feature based on a quality score rank of the base read, such that the feature is weighted according to a discount function,
encoding, as elements of the feature tensor, the discounted value, the bag depth and the low bag depth flag of each base read for the respective base position.
c) training a neural network for determining consensus base calls in the sequencing dataset of 1,000 or more sequence reads, wherein training the neural network comprises:
initiating weighting factors of the neural network;
inputting training feature vectors having the feature representation generated according to step (b)…to generate predicted base calls;
comparing the predicted base calls to respective reference calls to identify misclassified training feature vectors;
analyzing a distribution of the misclassified training feature vectors in a principal component analysis feature space;
selecting, based on the distribution of the misclassified training feature vectors in the principal component analysis feature space, features of the training feature vectors having discriminative power between nucleotide base classes;
transforming the training feature vectors based on the selected features to generate reduced training feature vectors;
inputting the training feature vectors with selected features into the neural network;
adjusting, in backpropagation based on differences between base calls predicted from the reduced training feature vectors and respective reference base calls, the weighting factors of the machine learning model;
d) assessing….the feature tensor that is transformed from the sequencing dataset of 1,000 or more sequence reads to determine a consensus base call for the respective base position, wherein the consensus base call comprises a predicted nucleotide base; and
generating, based on the ML-based calling process, a sequence of the biological sample as an end result of the sequencing.
The identified claim limitations falls into one of the groups of abstract ideas of mathematical concepts, mental processes, and/or certain methods of organizing human activity for the following reasons. First, the machine-learning based calling process to determine consensus base calls recites a mental process and mathematical concept as applied below regarding each of the various steps in this process. Next, the step of transforming the sequencing dataset into a feature tensor representing a distribution of a plurality of features, with a plurality of feature dimension each corresponding to a feature in the recited list of features can be practically performed in the mind by organizing the feature data in the sequencing data set into a tensor data structure (e.g. a two-dimensional matrix) with rows and columns representing two features. With respect to the sub-steps of applying a discounted value, the step of assigning each base read a discounted value according to a quality score rank can be practically performed in the mind by ordering numerical quality scores in order of rank, and an associated a score corresponding to a rank to a base read, and then multiplying a given score by some value from a discount function.
Furthermore, the steps pertaining to training the neural network can be practically performed in the mind as follows. Initiating weighting factors of the model involves assigning a number to each input variable of the model. Generating training feature vectors from training data comprising base reads involves organizing information from the training data into vectors corresponding to features of a respective given read of the training data. Generated a predicted base call from inputted training feature vectors encompasses a mere analysis of the read features of the vectors to identify a likely base at each position, which is a mental process. Comparing a predicted base call to a reference base call to identify misclassified training vectors simply requires comparing two values, which is a mental process. Performing a principal component analysis on a distribution of values to transform a feature space to a reduced feature space can be practically performed in the mind aided by pen and paper by carrying out linear algebra techniques, including calculating eigenvalues and eigenvectors. Furthermore, transforming the feature vectors to generated reduced training features involves removing certain features from the original feature vectors to generated reduced training features, which can be practically performed in the mind aided with and paper. Therefore, the above steps of training the neural network can be practically performed in the mind aided by pen and paper. Assessing the feature tensor to determine the consensus base call for the respective base position amounts to a mere analysis of data, which can be practically performed in the mind. Last, generating a sequence of the biological sample involves analyzing the base calls of the 1,000 base positions and arranging them into a sequence, which is a mental process. That is, other than reciting the steps are carried out by a processor, nothing in the claims precludes the steps from being practically performed in the mind. As such, these limitations recite a mental process. See MPEP 2106.04(a)(2).
Furthermore the limitations of weighting base reads for a given score according to a discount function, c) training a neural network by initiating weighting factors, analyzing a distribution of the misclassified training feature vectors in a principal component analysis feature space, inputting training feature vectors to an initialized neural network, transforming the training feature vectors to reduce features, adjusting the weighting factors of the model in backpropagation further recite a mathematical concept. A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. See MPEP 2106.04(a)(2) C. In the instant case, weighting a score according to a discount function amounts to a textual equivalent of multiplying the score by a weight corresponding to a discount function. The limitations of c) training the neural network using backpropagation to determine consensus base calls amounts to a textual equivalent to performing mathematical calculations (i.e. backpropagation is a mathematical algorithm, as explained in Example 47 of the SME examples 47-49 issued in 2024). Furthermore, performing a principal component analysis to on features to transform features into a transformed feature space is a mathematical dimensionality reduction technique that amounts to a textual equivalent of performing mathematical calculations. Therefore, these limitations recite a mathematical concept. See MPEP 2106.04(a)(2) I.
Dependent claims 2, 6, 9, 11-13, 15, 18, 28, 30, and 33 further recite an abstract idea. Dependent claim 2 further recites the mental process of repeating the above identified abstract idea of claim 2. Dependent claim 6 further recites the mental process and mathematical concept of training and using the neural network to determine a model quality score for the consensus base call. Dependent claim 9 further recites the mental process of identifying a quality bin obtained during training of the neural network, and the mental process and mathematical concept of interpolating the model quality score based on an actual error rate to generate a recalibrated model quality score. Dependent claim 11 further recites the mental process of generating a feature tensor representing a quantified distribution of the plurality of features for each respective base read. Dependent claim 12, further recites the mental process of generating a feature tensor representing a quantified distribution of the plurality of features for each base read, the respective plurality of features comprising a respective nucleotide base and a respective read quality score, and the quantified distribution comprising a discounted distribution. Dependent claim 13 further recites the mental process and mathematical concept of computing a plurality of class probabilities based on the feature tensor, wherein the plurality of class probabilities correspond to a set of at least four classes, and determining the consensus base call based on a highest class probability. Dependent claim 15 further recites the mental process and mathematical concept of using a penalized logistic regression model to derive a plurality of model coefficients for training. Dependent claim 18 further limits the abstract idea of transforming to represent values associated with the additional features appended to the tensor. Dependent claim 28 further recites the mental process of removing each base position that fails to satisfy a selection criterion of a measure of central tendency. Dependent claim 30 further recites the mental process removing, from the plurality of sequence reads, each base position in the plurality of base positions that satisfy a filtering criterion, wherein the filtering criterion is satisfied when the plurality of base reads comprises a threshold number of alternative nucleotides. Dependent claim 33 further recites the mental process of determining the sequence reads provide a coverage of 20x. Therefore, claims 1-3, 6, 9, 11-13, 15, 18, 22-23, 25, 28, 30, 33, 35, and 37 a recite an abstract idea. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
Dependent claims 2, 6, 9, 11-13, 15, 28, 30, and 33 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements of claims 1, 35, and 37 include:
a computer system comprising at least one processor and a memory (claim 37);
a non-transitory computer readable storage medium (claim 35);
causing sequencing of a biological sample of sample of a subject, the sequencing being the parallel sequencing that results in varying sequencing depths at various base positions (i.e. transmitting data, as discussed above in claim interpretation);
a) obtaining a respective sequencing dataset of 1,000 or more sequence reads in a biological sample of a subject, the respective sequencing dataset corresponding to a plurality of base reads for a respective base position, in electronic form, wherein each base read of the plurality of base reads is captured in a respective sequence read among a plurality of sequence reads, wherein the respective sequencing dataset includes a respective plurality of features for each respective base read of the plurality of base reads for the respective base position, and wherein the respective plurality of features for each respective base read comprises at least two features selected from a feature group, the feature group comprising: a nucleotide base of the respective base read, a read quality score associated with the respective base read, a strand identifier indicating whether the respective sequence read of the respective base read corresponds to a forward or a reverse strand, a trinucleotide context based on three consecutive nucleotide bases including the respective base read, a confidence score associated with the trinucleotide context, a quality score for each baes read, a bag depth count denoting a total number of base reads at the respective base position, and a low bag depth flag indicating when the total number of base reads at a respective base position is below a minimum threshold (i.e. receiving data) (claims 1, 35, and 37);
inputting…into the neural network…. (claims 1, 35, and 37);
assessing, with the neural network (claims 1, 35, and 37).
The additional element of claim 3 includes:
wherein the plurality of base positions is at least 1000 base positions…(i.e. receiving data) (claim 3)
The additional element of claim 18 includes:
wherein the sequencing dataset further comprises one or more additional features selected from the group consisting of…(including the list of features) (i.e. receiving data).
The additional element of claim 22 includes:
…wherein the biological sample includes a cell-free nucleic acid molecule (i.e. receiving data).
The additional element of claim 23 includes:
…wherein the sequencing dataset is obtained from a methylation sequencing or a targeted sequencing (i.e. receiving data).
The additional element of claim 25 includes:
prior to obtaining (a) the sequencing dataset, obtaining a mapping string for each sequence read in a plurality of sequence reads, thereby obtaining a plurality of mapping strings, wherein each mapping string (i) is determined…and (ii) comprises….(including the full limitations) (i.e. receiving data).
The additional elements of at least one processor, memory, a non-transitory computer readable medium, receiving data, transmitting data, and using a neural network are generic computer components and/or merely recite the abstract idea as being performed on a computer. Claims 3, 18, and 22-23 only serve to further limit the data being received by claim 1 and thus are part of the step of receiving data. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Furthermore, reciting that the generating a prediction and assessing are performed with a neural network provide nothing more than mere instructions to implement an abstract idea (i.e. assessing) on a generic computer (see MPEP 2106.05(f)), and furthermore, merely indicates a field of use or technological environment (i.e. neural networks) in which the judicial exception is performed
Last, the step of transmitting data to cause sequencing and obtaining/receiving the sequencing dataset only serve to collect information for use by the abstract idea, which amounts to insignificant extra-solution activity that does not integrate the recited judicial exception into a practical application. See MPEP 2106.05(g).
Therefore, the additionally recited elements merely invoke computers as a tool, link the judicial exception to a particular technological environment, and/or amount to insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application. Thus, claims 1-3, 6, 9, 11-13, 15, 18, 22-23, 25, 28, 30, 33, 35, and 37 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception for the following reasons. Dependent claims 2, 6, 9, 11-15, 28, 30, and 33 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements of claims 1, 3, 18, 22-23, 25, 35, and 37 are outlined above.
The additional elements of at least one processor, memory, a non-transitory computer readable medium, receiving data, transmitting data, and using a neural network are conventional computer components and/or merely recite the abstract idea as being performed on a computer. Claims 3, 18, and 22-23 only serve to further limit the data being received by claim 1 and thus are part of the step of receiving data. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Therefore, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Response to Arguments
Applicant's arguments filed 28 Sept. 2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant remarks the specific ways of training a machine learning model and improvements to computer component or system performance based upon adjustments of a machine learning model are not judicial exceptions, and the PTO now recognized examples of improvement in computer functionality that amounts to a practical application under Step 2A, Prong 2 (Applicant’s remarks at pg. 19, para. 7 to pg. 20, para. 1). Applicant remarks the claims are the type of improvement that involves a specific way to train models and includes adjustments to parameters of machine learning models which is a technical and laboratory process (Applicant’s remarks at pg. 20, para. 2). Applicant remarks that the claim does not use a generic neural network but instead defines (i) a structured feature set uniquely tailored to base calling in sequencing environments, (ii) a transformation of 1,000 or more sequence reads into a multi-dimensional feature tensor representing a distribution of these features, and (iii) a discounted weighting mechanism that assigns each base read a value based on its quality score (Applicant’s remarks at 20, para. 2). Applicant further remarks the claims recite a specific training workflow including misclassification based feature selection, PCA in a feature space, dimensionality reduction, and backpropagation-based adjustment of weighting factors, which recite concrete adjustments to machine learning parameters and model inputs that are engineered to address uneven read depth and base position variability in sequencing, and under the Desjardins guidance, such recitations are directed to improving computer functionality (Applicant’s remarks at pg. 20, para. 2-3).
This argument is not persuasive. First, MPEP 2106.05(a) explains an indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements, alone or in combination with the judicial exception.
Furthermore, in Ex Parte Desjardins, the claims recited a specific way of training a machine learning model to learn knew tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting”. “Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation (see pg. 2, second bullet point of the Advance notice of change to the MPEP in light of Ex Parte Desjardins published 05 Dec. 2025).
In the instant case, the only details with how the neural network itself is actually trained (i.e. how parameters of the model are adjusted) recites “adjusting, in backpropagation based on differences between base calls predicted from the reduced training feature vectors and respective reference base calls, the weighting factors of the neural network”. Example 47 of the 2024 AI-related SME examples discusses that backpropagation is a mathematical calculation conventionally used for supervised learning of neural networks (see pg. 3, para. 2 and pg. 6, para. 2 of the AI SME examples). Unlike the claims in Ex Parte Desjardins, the instant claims do not describe a specific way of training a machine learning model that overcomes a particular technological problem, such as catastrophic forgetting. Instead, the instant claims merely use a conventional “backpropagation” algorithm for training the neural network, which does not reflect any improved way of training a neural network that solves a technical problem. Furthermore, applying a standard principal component analysis to perform feature selection prior to inputting features into a neural network is part of the abstract idea, discussed above, and furthermore, does not specify details on how the neural network itself actually operates.
Regarding (i)-(iii) discussed by Applicant above, the creation of a structured feature set containing the desired features and discounted values are part of the abstract idea, and furthermore, only prepare input data for the neural network.
Overall, the claims do not recite additional elements that integrate the recited judicial exception into a practical application, including an improvement to technology.
Conclusion
No claims are allowed.
Claims 1-3, 6, 9, 11-13, 15, 18, 22-23, 25, 28, 30, 33, 35, and 37 are free of the prior art for the reasons discussed in the Office action mailed 30 April 2026.
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.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAITLYN L MINCHELLA whose telephone number is (571)272-6485. The examiner can normally be reached 7:00 - 4:00 M-Th.
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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
