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
Applicant's amendment filed 10/15/2025 is acknowledged. Claims 1, 19-20, and 23-24 have been amended. Claims 28-45 have been added. Claims 2-3, 9-12, 16, 18, 21-22, and 25-26 have been cancelled. Claims 1, 4-6, 13-14, 17, 19-20, 23-24, and 27-45 are pending in the instant application and claims 1, 4-5, 13-14, 17, 19-20, 23-24, and 27-33, 35-45 are the subject of this final office action.
All of the amendments and arguments have been reviewed and considered. Any rejections or objections not reiterated herein have been withdrawn in light of amendments to the claims or as discussed in this office action.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Previous Rejection
Status of Prior Rejections/Objections:
The substantial duplicates warning has been removed in view of the cancellation of claims 2 and 18.
The 112(b) rejections to claims 1, 19 and 22 are withdrawn in view of the amendments to claims 1 and 19 and the cancellation of claim 22.
The rejection over 35 USC 101 are withdrawn in view of the amendments and arguments.
The prior art rejection(s) under 35 USC 102 directed to claim(s) 1, 11-12, 17, and 22 as being anticipated by Sidow are withdrawn in view of the amendments and/or cancellation of the claims.
The prior art rejection(s) under 35 USC 103 directed to claims 2-3, 13-14, 18-19, 21, and 27 as being unpatentable over Sidow, as evidenced by UCSC – NBEA; claim 20 over Sidow in view of Fialoke; claims 4-5 and 9-10 over Sidow in view of El Messaoudi; and claims 23-26 over Sidow in view of Raza are withdrawn in view of the amendments and/or cancellation of the claims.
New Ground(s) of Rejections
The new ground(s) of rejections were necessitated by applicant’s amendment of the claims.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4-5, 13-14, 17, 19-20, 23-24, and 27-33, 35-45 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1, 4-5, 13-14, 17, 19-20, 23-24, and 27-33, 35-45, claims 1, 24, 31, and 41 recite either “an apoptosis signaling kinase 1 (ASK1) inhibitor” (claims 1 and 31) or “the ASK1 inhibitor” (claims 24 and 41).
The terms “ASK1” and “apoptosis” do not appear within the specification.
The non-patent literature of Sumida (cited in the remarks) was incorporated by reference in para [0189] of the instant specification. However, Sumida is not a US patent or US patent application publication and provides support for claimed features. Therefore, this incorporation by reference is of “essential material,” which must be amended into the instant specification. See 35 CFR 1.57(d).
Therefore, the disclosure, in its current state, is insufficient to support the entire breath of the claimed invention under the written description requirement.
Claim Rejections - 35 USC § 112(b)
Claims 28 and 43 are 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.
Regarding claims 28 and 43, the claim recites “the mapped genomic location represents a first CpG site of fragments of the methylation pattern or the methylation level”.
First, the nexus between the DNA molecules of claims 1 and 31 (b) and the fragments is unclear. That is to say, are the fragments intended to be physical fragments (i.e., all or a subset of the DNA molecules of the cfDNA sample)? Or are they intended to be a fragment (i.e., portion) of the methylation pattern/level in genomic “space” (e.g., a representation of a particular enriched region, i.e., a “bin”)?
Second, it is not clear if the “first” of the “first CpG site” is intended to be a positional marker (i.e., counting from a 5’) or an ordinal label merely to distinguish between future designated CpG sites. It is noted that instant para [0252-253] discuss the “cfDNA fragments mapped to specific genomic locations” and “the CpG location of its first CpG”…”was added to the feature[s] of each fragment” but, lacking a limiting definition and clear nexus within this section in the specification, it has been interpreted broadly for the purposes of applying art.
Claim Rejections - 35 USC § 103
Claim(s) 1, 4-5, 13-14, 17, 19, 23-24, and 27-33, 35-38, and 40-45 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sidow (WO 2022/119847 A1; published 06/09/2022; as cited in the IDS dated 05/14/2025) in view of Lo (WO 2023/093782 A1; filed 11/24/2022 with the US designated) and Raza (Raza S, et al. Current treatment paradigms and emerging therapies for NAFLD/NASH. Front Biosci (Landmark Ed). 2021 Jan 1;26(2):206-237), evidenced by UCSC - NBEA (Human HG38 CHR19:40,682,749-40,682,760 UCSC genome browser V483 [online]. UCSC; 2025 [retrieved 2025 Jul 8]. Available from: https://genome.ucsc.edu/cgi-bin/hgTracks?db=hg38&lastVirtModeType=default&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&position=chr19%3A40682749-40682760&hgsid=2713308444_0VwPbz1arXFLA8ilH0nZFxTtsiEq).
Regarding claims 1, 23-24, 31, and 40-41, Sidow teaches a method comprising:
obtaining a DNA sample, wherein the DNA comprises cfDNA;
determining CpG methylation status at CpG sites of DNA molecules of the DNA sample and identifying a methylation pattern based on the CpG methylation status of the DNA molecules;
assigning to the sample a liver disease classification based on the methylation pattern (claim 1).
Sidow teaches that the assigning the sample a liver disease comprises classifying the sample as having a probability of NALFD (instant claim 31) and/or NASH (instant claim 1) (claim 10).
Sidow further teaches that a methylation level used to indicate a probability that the sample belongs to a particular liver disease classification (claim 7) is established by fitting a model based on the methylation patterns and one or more “CpG features” (claim 21), wherein the model is fitted using data from sampled in a training set (claim 22) and wherein the samples [of the training set] comprise subjects with and without liver disease (claim 23).
Sidow teaches administering to the subject a therapy selected to treat a disease corresponding to the disease classification, i.e., NAFLD and/or NASH (claim 48).
It is noted that while Sidow uses the term “NAFLD” to refer to the art-recognized “NAFL” (i.e., para [0003]: “NAFLD often progresses to nonalcoholic steatohepatitis (NASH)”) both NAFL and NASH are conditions within the art-recognized spectrum term “NALFD”/MASLD of the instant claim 31 (see also instant specification para [0045] and [0081-82]).
Sidow teaches obtaining features through rounds of feature selection (para [0109], [0112], [0121], [0124], and [0127]), i.e., optimization.
Sidow teaches that the set of CpG feature are a set of coordinates, i.e., mapped genomic locations (e.g., Fig. 3A, 4A, 6A, 7A, and 8A).
Sidow fails to teach that the set of features also comprises one or more of a fragment frequency, an inverse sample frequence, and the product of the fragment frequency and the inverse sample frequency.
Sidow fails to teach a specific therapeutic intervention.
Lo rectifies this in part by teaching a method comprising determining a classification of a disease in a biological sample based on methylation-aware sequencing of cfDNA, wherein for each read a methylation pattern is compared to a reference methylation pattern and a tissue classification is determined (claim 1) and wherein the tissue may be a diseased tissue type (claim 8) and the classification of the disease identifies a severity of the disease (claim 15).
Lo teaches that the classification of the disease may be based on a first amount for sequence reads [corresponding to fragments] (claim 2) by applying a machine-learning model to the first amount (claim 5). Lo teaches that sequencing is performed to a particular depth (para [0114]) and that performance depends on sequencing depth (para [0345]).
Lo teaches training a machine learning model based on a set of features including genomic coordinates (para [0394]; Fig. 74) used in the classification of reads (para [0396-397]; see also claims 107-112 and 117).
It would be understood by the artisan that “a machine learning algorithm” could comprise multiple (i.e. ensemble) classifiers (e.g., para [0342]: “deep learning model comprising CNN and LSTM”). Thus, by teaching a set of features comprising a [mapped] genomic location to classify reads and the classification of the amount of said reads to classify disease, Lo teaches a machine learning algorithm that processes those two sets of features to determine NASH (instant claim 1) and/or NAFLD (instant claim 31).
Lo further teaches determining a relative frequency for occurrence of one or more sequence motifs wherein the relative frequency may be a percentage of the DNA molecules with a particular sequence motif (para [0242]. Lo teaches that pattern recognition analysis for methylation haplotypes improves performance of determining tissue of origin for each long plasma DNA molecule, which may then be used to determine a disease classification (para [0251]).
Lo teaches that [its method of] utilizing longer cfDNA molecules can be more specific than short molecules by utilizing higher numbers of CpG sites, wherein permutations in the order of methylated/unmethylated sites would be greater allowing for improved identification of DNA molecules originating from any particular tissue (para [0146]; see also [0196] and [0346])).
Lo and Sidow fail to teach specific treatments for MASH/NASH/MASLD/NAFLD. Raza further rectifies this by teaching treatments of NASH and that NASH is that progressive form of NAFLD (Abstract). Raza teaches that that the pharmacological inhibition of ACC is an attractive approach to control fatty acid synthesis in lipogenic tissues, wherein a trial of an inhibitor of ACC (acetyl-CoA carboxylase) in NASH patients showed reduced hepatic steatosis and improvement in markers of liver injury (4.3. Lipotoxicity based targets, para 1) (instant claims 24 and 41).
Raza also teaches that bariatric surgery may be opted to achieve weight loss in NAFLD/NASH patients, wherein it helps to improve the metabolic functioning of lipid metabolism and inflammatory pathways associated with pathophysiology of NAFLD (5. Bariatric Surgery, para 1) (instant claim 23 and 40).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to combine the method of classifying liver diseases including NASH and NAFLD using methylation patterns in cfDNA with the method of classifying a disease of Lo, wherein the disease is classified using features comprising genomic location and sequence/fragment amount using Lo’s trained ML models (i.e., as an ensemble algorithm), motivated by the desire to improve the performance of the disease classification by utilizing a method suited for reads with additional CpG sites, as taught by Lo. Alternatively, and/or additionally, it would have been obvious to utilize the relative frequency of the sequence motifs as a feature in the algorithm, motivated by the desire to improve performance in determining the tissue of origin, as taught by Lo. There would have been a strong expectation of success as both are directed to cfDNA methylation methods for classifying diseases, including liver disease, and the method represents the application of a known technique applied to a known method.
It further would have been obvious to utilize ACC inhibitors and/or bariatric surgery in NASH and NAFLD patients, motivated by the desire to reduce hepatic steatosis/improve markers of liver injury and/or improve lipid metabolism, as taught by Raza. There would have been a strong expectation of success as both are directed to the treatment of NAFLD/NASH and such represents the application of known techniques (therapies) to a known method.
It would be understood by the artisan that an amount may be substituted for a normalized “frequency” of the reads as part of routine optimization (e.g., para [0102]) given that sequencing depth (i.e., a total number of reads) is discussed as impactful for performance. See MPEP 2144.05(II).
Regarding claims 4-5 and 32-33, in the method of Sidow in view of Lo and Raza, Sidow teaches the DNA sample comprises DNA fragments from cfDNA and/or blood cell DNA (claim 1).
Lo teaches that the biological sample can be blood, plasma, and serum (para [0092]).
Regarding claims 13-14, 17, and 35-37, in the method of Sidow in view of Lo and Raza, Sidow teaches, in the step of determining, binding the DNA molecules to a DNA array and enriching the sample (instant claims 13 and 35) using probes from the targeted panel (claim 44; see also para [0053] and para [0084]). Sidow teaches that the panel corresponds to genomic regions (claim 36; instant claims 14 and 36).
Sidow teaches that the methods of the invention may include capturing DNA molecules from the subject’s sample with a targeted panel using the targeted panel probes (instant claims 14 and 36) and sequencing the targeted panel from the sample (para [0084]; instant claims 17 and 37).
Sidow also teaches preparing a sample with a set of primers selected to amplify a set of CpG markers (para [0107]).
It is further noted that the order of steps is held to be an obvious variant (see MPEP 2144.04(IV)(V)) and/or a matter of nomenclature whether the step of enriching is performed in a step of “providing” or in the step of “assaying” as one of ordinary skill in the art would understand that such as step would need to be performed after the acquisition of the sample and prior to the acquisition of the methylation pattern/level data (i.e., in essence, between the recited steps (a) and (b) of Sidow claim 1) so that the sample may be enriched, as taught by Sidow, regardless if it is formally assigned to step (a) or step (b).
Regarding claims 19 and 38, in the method of Sidow in view of Lo and Raza, Sidow teaches enriching genomic regions of a sample with a targeted panel established by a method comprising an L1 regression based on samples from subjects with and without liver disease (para [0018]), and identifying a set of features using an L1 regression (para [0130]; see also claim 36), wherein the subset of features [i.e., genomic regions] comprises cg26933384 (Fig. 9A), which corresponds to a region in NBEA, as evidenced by UCSC – NBEA (pg. 21 of instant claim 19 and pg. 48 of instant claim 38). Sidow teaches that this marker distinguishes between NAFLD and NASH (Fig. 8B).
Sidow further teaches that there is a need in the art for a robust means for diagnosing and staging liver diseases without requiring liver biopsy (para [0003]).
Therefore, as Sidow teaches all the claim limitations, the combination of the elements of the claim is held to be obvious, motivated by the need for developing such a robust means for diagnosing liver disease without liver biopsy, as taught by Sidow, and further enhancing the method of Sidow through the enrichment taught by Sidow. There would be a strong expectation of success as this is applying a known technique to a known method.
It is noted that Sidow further teaches analyzing the features of a targeted panel to distinguish between states of additional markers noted in para [0087-94] and Fig. 2-10.
Regarding claims 27 and 42, in the method of Sidow in view of Lo and Raza, Sidow teaches using a logistic regression (claim 28), random forest (claim 31), neural network (claim 32), support vector machine (claim 33), and support vector machine (claim 33) (also para [0017] and [0063-65]).
Lo also teaches that the model may include learning models including neural networks (e.g., CNNs, wherein CNNs may be part of a deep learning model, see para [0342]), SVMs, random forests, linear or logistic regression (para [0166-167] and [0395]).
Regarding claims 28 and 43, in the method of Sidow in view of Lo and Raza, Sidow teaches the genomic positions of CpGs (Fig. 3A, 4A, 6A, 7A, and 8A), that the methylation pattern may comprise a set of CpG sites located on the same DNA fragment or that the features may include a single CpG (para [0064]).
Sidow teaches that the set of genomic regions is selection from subjects with and without liver disease using mutation information or L1 logistic region [i.e., “feature” selection] (claim 36; see also, e.g., para [0115]) on sequencing data [i.e., that would require mapping to a genome as part of processing] (e.g., para [0114]).
Thus, Sidow teaches utilizing mapped genomic regions representing CpG sites of fragments of a methylation pattern as features.
Lo also teaches that the mapped genomic position is the end of the read mapped to a reference genome (para [0402]). Lo teaches that in vitro techniques may alter the true in vivo physical ends of cfDNA molecules, such that each detectable end may represent a true end or one or more nucleotides inward (para [0098]). Lo teaches that the end position may refer to a position on a cfDNA molecule that is read by target-specific probes or amplification (para [0098]).
Regarding claims 29 and 44, in the method of Sidow in view of Lo and Raza, Lo teaches additional calculations for the determination of tissue-of-origin of plasma DNA molecules comprising a methylation index for each CpG site in the genome (para [0264]), wherein CpG sites with a methylation index above a threshold were scored to determine a likelihood of a DNA molecule originating from a particular tissue based on a comparison between the observed methylation pattern in that molecule and reference profiles [i.e., probability of observing a fragment, e.g., given a particular tissue] (para [0264]; see also para [0336-337]). Lo teaches determining a severity of disease from said tissue-of-origin analysis, wherein the methylation haplotype-based analysis can be effectively used to guide treatment modality selection (para [0270]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize a fragment frequency indicative of a probability of observing a fragment, as taught by Lo, in the method of Sidow, Lo, and Raza, motivated by the desire to use such tissue-of-origin haplotype-based analysis to guide treatment more effectively, as taught by Lo. There would have been a strong expectation of success as such represents that application of a known technique to a known method.
Regarding claims 30 and 45, in the method of Sidow in view of Lo and Raza, the set of features taught comprises a mapped genomic location and a fragment frequency.
Claims 20 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Sidow (WO 2022/119847 A1; published 06/09/2022; as cited in the IDS dated 05/14/2025), Lo (WO 2023/093782 A1; filed 11/24/2022 with the US designated) and Raza (Raza S, et al. Current treatment paradigms and emerging therapies for NAFLD/NASH. Front Biosci (Landmark Ed). 2021 Jan 1;26(2):206-237) as applied to claims 1 and 31 above, and further in view of Fialoke (Fialoke S, et al. Application of Machine Learning Methods to Predict Non-Alcoholic Steatohepatitis (NASH) in Non-Alcoholic Fatty Liver (NAFL) Patients. AMIA Annu Symp Proc. 2018 Dec 5;2018:430-439.)
Regarding claim 20 and 39, in the method of Sidow in view of Lo and Raza, Sidow teaches a method of analyzing samples comprising probabilities of having no fibrosis (para [0010]), no hepatitis (para [0011]), no inflammation (para [0011]), but does not explicitly teach that the subject from which the sample is derived is asymptomatic.
Fialoke rectifies this by teaching a method of identifying patients diagnosed with benign steatosis and NASH and predicting NASH disease status based on a trained machine learning classifier (Abstract), wherein NASH is taught as being under-diagnosed due to its asymptomatic nature (Abstract) and further extending the method to apply the mode to a cohort of benign fatty liver (NAFL) patients (Conclusion, para 1; Performance of Supervised Machine Learning Models, para 1).
Based on the instant specification (see, e.g., instant para [0045] and [0082]) and the teaching of Fialoke (Abstract) that NAFL is an equivalent or overlapping term to steatosis, and that the instant specification defines simple steatosis as the earliest state of NAFLD and that it often remains asymptomatic (instant para [0083]), Fialoke teaches performing machine learning on subjects, wherein the subjects would be expected to comprise asymptomatic subjects, to generate an output indicative of whether the subject has the liver disease.
Fialoke further teaches that identifying patients likely to have NASH allows clinicians to choose to have more frequent interactions with these patients, confirm diagnosis via biopsy, and, when appropriate, raise awareness on upcoming treatments (Conclusion, para 1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of Sidow, Lo, and Raza with the method of extending a trained ML model to predict and treat NASH/NAFLD in subjects who are asymptomatic in view of Fialoke, motivated by the desire to allow clinicians to reach additional patients who are currently under-diagnosed due to the asymptomatic nature of the early stages of the disease, as taught by Fialoke. There would be a strong expectation of success as this is applying a known technique to a known method and both are directed to machine learning methods of NASH/NAFLD.
Response to Arguments
Applicant’s arguments with respect to the rejection(s) of claim(s) 1, 11-12, 17, and 22 under 102 regarding newly amended requirement for at least two of the set of features have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Sidow, Lo, and Raza.
Conclusion
No claims are 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMMA R HOPPE whose telephone number is (703)756-5550. The examiner can normally be reached Mon - Fri 11:00 am - 7:00 pm.
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/EMMA R HOPPE/ Examiner, Art Unit 1683
/ANNE M. GUSSOW/ Supervisory Patent Examiner, Art Unit 1683