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 1-20 were previously pending and subject to a non-final Office Action having a notification date of January 15, 2026 (“non-final Office Action”). Following the non-final Office Action, Applicant filed an amendment on April 2, 2026 (the “Amendment”), amending claims 1, 9, and 17.
The present Final Office Action addresses pending claims 1-20 in the Amendment.
Response to Arguments
Response to Applicant’s Statements Regarding Subject-Matter Eligibility Under 35 USC 101
On page 11 of the Amendment, Applicant asserts "the Office Action's reasons for patent eligibility should not be attributed to Applicants as an indication of the basis for Applicants' believe that the claims are patent eligible." As Applicant is thus not agreeing with the Examiner's reasons for patent-eligibility set forth in the non-final Office Action (by virtue of Applicant explicitly taking no position regarding the Examiner's reasons and declaring that the Examiner's reasons are not to be attributed to Applicant), then Applicant is respectfully requested to explain any inaccuracies in the Examiner's reasons for patent-eligibility in the next response, upon which the Examiner will reconsider patent-eligibility of the present claims under 35 USC 101.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §102/103
On page 9 of the Amendment, Applicant takes the position that Nicula does not disclose or suggest removing "inter-experimental" technical noise across the plurality of datasets as recited in the independent claims because the disclosed filtering steps are allegedly performed within a single dataset and make reference only to properties of fragments within that same dataset. The Examiner disagrees that the filtering steps are performed only within a single dataset at least because [0158] discloses how the filtering is performed for each of the training subjects where the various training data (e.g., 124-130 in Figure 1 and [0114]-[0115]) for each subject is a different respective dataset such that there are a plurality of datasets on which the filtering is performed. Furthermore, and in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., technical "variation" across "groups of measurements with qualitatively different behaviors across different experimental design and execution") are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant then takes the position on pages 9-10 of the Amendment that Nicula does not disclose or suggest aggregating the plurality of denoised datasets to generate the preprocessed data as recited in the independent claims because the training data from the plurality of subjects allegedly originates from a single experimental cohort which is not the same as aggregating independent, separate datasets. The Examiner disagrees because the training dataset associated with each particular training subject is distinct/separate from those of the other training subjects (i.e., they are associated with different respective training subjects) such that after filtering/denoising, the resulting denoised datasets of the training subjects collectively represent an aggregated plurality of denoised datasets (preprocessed dataset). Furthermore, Applicant's assertions regarding the transfer learning techniques of [0105] are moot as the Examiner is not relying on such techniques in the first place.
The 35 USC 102 and 103 rejections is maintained.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4, 6-10, 12, 14-18, and 20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by U.S. Patent App. Pub. No. 2021/0358626 to Nicula et al. ("Nicula"):
Regarding claim 1, Nicula discloses a computer implemented method for processing an unlabeled omics feature vector ([0345]-[0346] discloses using a trained autoencoder to determine whether methylation patterns/stages in CpG sites (omics features which are vectors per [0094]-[0095], [0380], [0384]; also, [0088]-[0098] discloses various "omics" data) of a test subject correspond to a cancer state; furthermore, the omics features of the test subject are "unlabeled" as [0074], [0196] disclose the test samples are "unknown," hence the reason for using the autoencoder to determine the cancer state of the test subject in the first place), comprising:
receiving, by one or more processors, a plurality of datasets containing omics feature vectors ([0339] discloses obtaining a training dataset including methylation patterns/fragments and corresponding nucleic acid sequences including methylation states of CpG sites (omics feature vectors) per [0094]-[0095], [0380], [0384]; for instance, Figure 1 and [0114]-[0115] illustrate/disclose various training data 124-130 for each of a plurality of training subjects 122; therefore, the various training data for each subject is a "dataset containing omics feature vectors" such that there are "a plurality of datasets containing omics feature vectors"; furthermore, processing core 102 in Figure 1 and [0053] disclose processor(s));
preprocessing, by the one or more processors, the plurality of datasets to produce a preprocessed dataset ([0158], [0194], [0198], [0201] discuss performing preprocessing/filtering the training datasets of each of the subjects (which would result in a "preprocessed dataset") before training of the autoencoder), wherein the preprocessing comprises:
removing inter-experimental technical noise across the plurality of datasets using one or more normalization techniques to produce a corresponding plurality of denoised datasets ([0158], [0167]-[0179] discloses how the filtering (preprocessing as noted above) can include analyzing and removing certain one of the methylation/nucleic acid fragments in the training datasets using various techniques ("normalization techniques") such as when they have less than a threshold sequencing depth, number of CpG sites, number of residues; are duplicates; also, [0194]-[0195] discloses how the preprocessing can include filtering by size, splitting, truncating, padding, etc. (all of which is removing "technical noise"); also, [0181] discloses removing noisy fragments); removal of the "technical noise" would result in "denoised datasets"; still further, as the above-noted filtering/preprocessing is performed on the data across all of the training subjects (where each training subject is associated with a corresponding "experiment" made up of obtaining biological samples, performing sequencing reads, determining nucleic acid sequences and methylation patterns, etc.), then the removed "technical noise" is "inter-experimental" technical noise); and
aggregating the plurality of denoised datasets to generate the preprocessed dataset ([0198] discloses using the preprocessed/filtered fragments 124 ("denoised datasets") to train the autoencoder, where the "denoised datasets" of the subjects collectively represent an aggregated "preprocessed dataset");
training, by the one or more processors, a machine learning model using a subset of preprocessed omics feature vectors labeled as non-anomalous to perform anomaly detection ([0338]-[0340] discloses training an autoencoder using the training dataset (which can previously be preprocessed/filtered as noted above per [0167]-[0181], [0194]-[0195] resulting in the preprocessed dataset as noted above) to detect a cancer state (perform anomaly detection), where the preprocessed training dataset includes methylation patterns/fragments and corresponding nucleic acid sequences including methylation states of CpG sites (omics features) which are vectors per [0094]-[0095], [0380], [0384]; furthermore, as the end of [0339] notes how the training subjects have the cancer state which can be the absence of cancer per [0350], then some subset of the preprocessed omics feature vectors are labeled not having cancer (non-anomalous);
providing, by the one or more processors, an unlabeled omics feature vector to the trained machine learning model ([0346] discloses applying the trained autoencoder to a test dataset including methylation/nucleic acid fragments including methylation states of CpG sites (omics features) which are vectors per [0094]-[0095], [0380], [0384]; furthermore, the omics feature vectors of the test subject are "unlabeled" as [0074], [0196] disclose how the test samples are "unknown," hence the reason for using the autoencoder to determine the cancer state of the test subject in the first place);
generating, using the trained machine learning model, a low-dimensional latent space representation of the unlabeled omics feature vector ([0206] discloses how the autoencoder encodes a compressed (low-dimensional) latent representation of the test input (the unlabeled omics feature vector));
reconstructing, using the trained machine learning model, the unlabeled omics feature vector from the low-dimensional latent space representation to produce a reconstructed feature vector ([0206], [0346] disclose using the compressed latent representation to reconstruct the unknown test input (reconstructing the unlabeled omics feature vector to produce a "reconstructed feature vector"));
evaluating a reconstruction error between the unlabeled omics feature vector and the reconstructed feature vector against an anomaly threshold to obtain a reconstruction error evaluation ([0346] discloses computing a score based on the reconstruction while [0050] discloses how the score is a loss/error between the reconstructed and inputted fragments (feature vectors) and [0281]-[0284] discloses how the score/loss/error is evaluated against a threshold);
generating a feature label indicating whether the unlabeled omics feature vector is anomalous or non-anomalous based on the reconstruction error evaluation ([0281]-[0283], [0291], [0346] disclose how the fragment is indicative of the cancer state (which can be presence (anomalous) or absence (non-anomalous) of cancer per [0080], [0085], [0342], [0349]-[0350]) based on the evaluation of the score in view of the threshold; furthermore, the output determination of the cancer state by the trained autoencoder (e.g., see end of claim 135) is a generated feature label); and
assigning the feature label to the unlabeled omics feature vector as metadata ([0326]-[0327] discloses how the cancer state determination can be repeated over time for continual cancer monitoring/assessments; accordingly, the feature label would be assigned to the omics feature vector of the test subject at each respective time to allow for such monitoring/assessments (e.g., to allow medical professionals to assess any similarities/differences in the omics feature vectors leading to the cancer state determinations over time); still further, the feature label would be assigned as "metadata" because the feature label is data about data (the later data being the omics feature vectors)).
Regarding claim 2, Nicula discloses the computer implemented method of claim 1, further including wherein the preprocessing further comprises:
labeling the omics feature vectors in each of the plurality of datasets as anomalous or non-anomalous ([0114] discloses how the first training subjects (represented by omics feature vectors per [0339], [0094]-[0095], [0380], [0384] as noted previously) have the first cancer state (e.g., healthy/non-cancer/non-anomalous per [0128]) and second training subjects have the second cancer state (e.g., cancer/anomalous per [0128]); in order to distinguish the first and second training subjects, they would necessarily be labeled to indicate the respective first/second cancer state); and
separating the preprocessed dataset into the subset of preprocessed omics feature vectors labeled as non-anomalous ([0158], [0194], [0198], [0201] discloses how the first training dataset (labeled as healthy/non-cancer/non-anomalous as noted above) can be filtered/preprocessed which results in a subset of preprocessed omics feature vectors labeled as non-anomalous) and a subset of preprocessed omics feature vectors labeled as anomalous ([0238]-[0239] discloses how the second training dataset (labeled as cancer/anomalous as noted above) can be filtered/preprocessed which results in a separate subset of preprocessed omics feature vectors labeled as non-anomalous).
Regarding claim 4, Nicula discloses the computer implemented method of claim 1, further including wherein the subset of preprocessed omics feature vectors labeled as non-anomalous comprises the set of omics feature vectors across each of the plurality of denoised datasets that are identified to not be associated with a disease, an illness, or adverse health symptoms ([0114] discloses how the first training subjects (represented by omics feature vectors per [0339], [0094]-[0095], [0380], [0384] as noted previously) and which are denoised per [0158], [0167]-[0179] as discussed previously have the first cancer state (e.g., healthy/non-cancer per [0128]) (not associated with disease/illness/adverse health symptoms).
Regarding claim 6, Nicula discloses the computer implemented method of claim 1, further including wherein the omics feature vectors comprise gene expression levels or methylation statuses of mRNA, miRNA, methylated DNA, or microbiomes ([0339] discloses obtaining a training dataset including methylation fragments/patterns and corresponding nucleic acid sequences including methylation states/statuses of CpG sites (omics features) which are vectors per [0094]-[0095], [0380], [0384]; also, [0045] discloses targeted DNA methylation sequencing, [0093] discloses methylated DNA fragments, and [0096] discloses DNA methylation).
Regarding claim 7, Nicula discloses the computer implemented method of claim 1, further including wherein the machine learning model is an autoencoder or a convolutional neural network ([0338]-[0340] discloses training an autoencoder).
Regarding claim 8, Nicula discloses the computer implemented method of claim 1, further including wherein the plurality of datasets are public repository datasets or generated datasets ([0137]-[0154] discloses performing one or more types of sequencing of biological samples to obtain the methylation/nucleic acid patterns/fragments (the datasets)).
Regarding claim 9, Nicula discloses a system (system 100 in Figure 1), comprising:
one or more memories (memory 111 in Figure 1);
at least one processor (processing core 102 in Figure 1) each coupled to at least one of the memories and configured to perform operations.
The remaining limitations of claim 9 are disclosed by Nicula as discussed above in relation to claim 1.
Claims 10, 12, and 14-16 are rejected in view of Nicula as respectively discussed above in relation to claims 2, 4, and 6-8.
Claims 17, 18, and 20 are rejected in view of Nicula as respectively discussed above in relation to claims 9, 10, and 12.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2021/0358626 to Nicula et al. ("Nicula") in view of U.S. Patent App. Pub. No. 2021/0257047 to Zhu et al. ("Zhu"):
Regarding claim 3, Nicula discloses the computer implemented method of claim 1, further including wherein the preprocessing further comprises:
…using principal component analysis (the end of [0105] discloses obtaining principal components of the training dataset which would necessarily involve use of PCA).
However, Nicula appears to be silent regarding use of such PCA confirms that the technical noise across the plurality of datasets has been removed.
Nevertheless, Zhu teaches ([0421]-[0423]) that it was known in the healthcare informatics art to utilize PCA to remove noise from patient samples (including omics data per [0024]) to effectively improve normalization thereby increasing the accuracy of downstream analyses.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the PCA of Nicula to confirm that the technical noise across the datasets has been removed similar to as taught by Zhu to effectively improve normalization thereby increasing the accuracy of downstream analyses. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claims 11 and 19 are rejected in view of the Nicula/Zhu combination as discussed above in relation to claim 3.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2021/0358626 to Nicula et al. ("Nicula") in view of U.S. Patent App. Pub. No. 2025/0022542 to Poole et al. ("Poole"):
Regarding claim 5, Nicula discloses the computer implemented method of claim 1, but appears to be silent regarding wherein the one or more normalization techniques comprise quantile normalization, surrogate variable estimation, or z-score normalization, or combinations thereof.
Nevertheless, Poole teaches ([0074]) that it was known in the healthcare informatics art to Z-score normalize omics data to facilitate determination of overexpression and underexpression of biomolecules thereby improving downstream analyses such as colon cancer screening and the like ([0007]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the one or more normalization techniques of Nicula to include z-score normalization similar to as taught by Poole to advantageously facilitate determination of overexpression and underexpression of biomolecules thereby improving downstream analyses such as colon cancer screening and the like. person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim 13 is rejected in view of the Nicula/Poole combination as discussed above in relation to claim 5.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHON A. SZUMNY whose telephone number is (303) 297-4376. The examiner can normally be reached Monday-Friday 7-5.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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