DETAILED ACTION
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 08/26/2025 is acknowledged. Claim 1 has been amended. Claims 49-67 have been added. Claims 2-48 have been cancelled. Claims 1 and 49-67 are pending in the instant application and the subject of this non-final office action.
Election/Restrictions
Applicant’s election without traverse of the following species in the reply filed on 08/26/2025 is acknowledged.
The particular combination of CpG sites of Fig. 8A (reading on claims 1 and 49-67)
The particular disease classification of NASH (reading on claims 1 and 49-67)
The parameter of “stage of fibrosis” (reading on claims 57-67)
The particular type of data analysis of mutual information analysis (reading on claims 49-67)
The election requirement for a particular disease classification and type of data analysis is withdrawn upon further consideration.
Claim Interpretation
In evaluating the patentability of the claims presented in this application, claim terms have been given their broadest reasonable interpretation (BRI) consistent with the specification, as understood by one of ordinary skill in the art, as outlined in MPEP 2111.
Regarding claim 1 and 49, the specification recites the following limits for NAFLD, NASH and cirrhosis in para [0003]: “NAFLD often progresses to nonalcoholic steatohepatitis (NASH), which can progress to cirrhosis, and eventually progress to liver cancer”. However, in the art, NAFLD is most commonly used as an umbrella term: “The broader term of nonalcoholic fatty liver disease (NAFLD) started to be used in 2002, and encompassed the full spectrum of fatty liver disease from isolated hepatic steatosis or NAFL to NASH and NASH cirrhosis” (Cotter TG, Rinella M. Nonalcoholic Fatty Liver Disease 2020: The State of the Disease. Gastroenterology. 2020 May;158(7):1851-1864; as cited in the IDS dated 08/26/2025: pg. 1851, para 2).
It is therefore interpreted that NALFD, as used in the instant application, encompasses “NAFL” and any other NAFLD-spectrum condition(s) with lower severity than NASH and cirrhosis, but excludes NASH and cirrhosis.
Likewise, as these conditions/diseases exist on a spectrum, it is likewise interpreted that by determining a particular “severity” with stated thresholds, a model would inherently be placing a patient/sample into a given “bin” or “class” for a particular disease state (e.g., NAFL, NASH, or NASH cirrhosis).
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 and 57-67 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 and 57-67, claim 1 recites “the methylation pattern distinguishes between…NAFLD…NASH…and cirrhosis”. Claim 57 recites “the methylation pattern further distinguishes between F0-F3 fibrosis and F4 fibrosis” and claim 58 recites that “the methylation pattern further distinguishes between F0 fibrosis, F1 fibrosis, … , and F4 fibrosis”.
The instant disclosure teaches pairwise methylation patterns for distinguishing between (i) instant “NALFD” and cirrhosis (Fig. 6); (ii) NASH and cirrhosis (Fig. 7); and (iii) instant “NAFLD” and NASH (Fig. 8).
It is noted that while the instant disclosure teaches distinguishing between fibrosis grade 3/4 and fibrosis grade 0 (Fig. 11), no methylation pattern is provided.
No species in the genus of a classifier capable of distinguishing three or more states using a methylation pattern.
For this reason, the described species of pairwise classifiers are not sufficient to represent the claimed species of the identification of a particular methylation pattern and assignment of a liver disease state based on the methylation pattern capable of distinguishing between three or more states. Thus, claims 1 and 57-67 lack written description support sufficient to convey to a skilled artisan that the Applicant had possession of the full breadth of the claimed invention at the time of filing.
Claim Rejections - 35 USC § 112(d)
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], 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.
Claims 49-57 and 60-67 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, 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. 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.
Regarding claims 49-57, claim 49 recites “(b) is performed using a model that is trained to identify the methylation pattern…such that the methylation pattern distinguishes between NAFLD and NASH” (emphasis added). In contrast, claim 1 recites “(b) identifying a methylation pattern…wherein the methylation pattern distinguishes between…NAFLD…NASH…and cirrhosis”.
In claim 1, the methylation pattern is capable of distinguishing between three conditions in the NALFD spectrum, whereas in claim 49 and dependents, “the methylation pattern” is selected such that it distinguishing between (only) NALF [instant “NAFLD”] and NASH.
For this reason, claims 49 and dependent claims fail to incorporate all limitations of the claim upon which they depend.
Regarding claims 60-67, claim 60 recites “(b) is performed using a model that is trained to identify the methylation pattern…such that the methylation pattern distinguishes between F0 fibrosis…and F4 fibrosis” (emphasis added). In contrast, claim 1 recites “(b) identifying a methylation pattern…wherein the methylation pattern distinguishes between…NAFLD…NASH…and cirrhosis” and claim 57 recites “the methylation pattern further distinguishes between F0 fibrosis…and F4 fibrosis”.
In claim 57, the methylation pattern is capable of distinguishing between NAFLD, NASH, cirrhosis and distinguishing between each of F0-F4 fibrosis, whereas in claim 60 “the methylation pattern” is selected such that it distinguishes between (only) each of F0-F4 fibrosis.
For this reason, claims 60 and dependent claims fail to incorporate all limitations of the claim upon which they depend.
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 and 49-67 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claim(s) recite(s) abstract ideas and natural phenomena. This judicial exception is not integrated into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The following three inquiries are used to determine whether a claim is drawn to patent-eligible subject matter:
Step 1. Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claims are directed to process.
Step 2A, prong 1. Does the claim recite a law of nature, a natural phenomenon, or an abstract idea (recognized judicial exceptions)?
The claims are directed to classifying a liver disease of a subject based on a methylation pattern comprising assigning a liver disease state to the subject based on the methylation pattern, as recited in claim 1. Assigning encompasses the abstract idea of a mental process (i.e., evaluation of data or information to reach a conclusion or make a judgement).
Additionally, the assertion of assigning a liver disease state classification based on a methylation pattern also recites a natural phenomenon of the correlation between a liver disease and methylation statuses of a set of CpG sites of target sequence.
Step 2A, prong 2. Is the judicial exception(s) integrated into a practical application?
Regarding claims 1, the claim recites obtaining a set of CpG methylation status at a set of CpG sites of target sequences from a … sample of the subject. This limitation encompasses merely looking up the information in a table (i.e., an abstract idea of a mental process). Further, it is noted that MPEP 2106.04(d) notes that adding insignificant extra-solution activity to a judicial exception—such as mere data gathering of to obtain data input for an equation (see MPEP 2106.05(g))—is not sufficient to integrate a judicial exception into a practical application.
Regarding claims 57-59, the claims recite that the methylation pattern further distinguishes between grades of fibrosis. The step of identification in claim 1, step (b) encompasses an abstract idea of a mental process so performing to identify additional categories fails to integrate the claims into a practical application.
Regarding claims 49 and 60, the claims recite first that the identifying is performed using a model and that places limitations directed to a choice of a subset of for the training data used. It is noted that models encompass calculations such as simple machine learning algorithms that may be performed by hand, i.e., an abstract idea.
In addition, choice of data, even should the data be novel, does not represent integration into practical application. See MPEP 2106.05(g) and e.g., SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018); Elec. Power, 830 F.3d at 1353 (“[W]e have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas.” (citing Internet Pats. Corp. v. Active Network, Inc., 790 F.3d 1343, 1349 (Fed. Cir. 2015); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Content Extraction, 776 F.3d at 1347; Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., Case: 23-2437 Document: 51 Page: 14 Filed: 04/18/2025 RECENTIVE ANALYTICS, INC. v. FOX CORP. 15 758 F.3d 1344, 1351 (Fed. Cir. 2014); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011))).”
Regarding claims 50 and 61, the claims recite administering a therapy selected to treat a disease corresponding to the liver disease state. MPEP 2106.04(d) require that a treatment be particular for such a limitation to integrate the claim into a practical application. The limitation of “a therapy selected to treat a disease” is not particular and encompasses treatments that have no more than a nominal or insignificant relationship to the disease corresponding to the liver disease state.
Regarding claims 51 and 62, the claims recite that the methylation pattern comprises particular modifications at sites of the target sequences. As with claims 57-59, such limitations further limit the calculations that encompass those performed by hand and thus fail to integrate the claims into a practical limitation.
Regarding claims 52-54 and 63-65, the claims limit the assignment step, such that it remains directed to an abstract idea, encompassing a mental process of forming a judgment (claims 52 and 63) or performing a calculation that could be performed by hand (claims 53-54 and 64-65), and thus also fail to integrate the claims into a practical limitation.
Regarding claims 55-56 and 66-67, the claims recite generic classes of well-known machine learning algorithms that are capable of being performed—at least on a small scale—by hand. See Pandey (Pandey D, et al. Machine learning algorithms: a review [Internet]. Mach Learn. 2019 [cited 2025 Sep 26]. Available from: https://d1wqtxts1xzle7.cloudfront.net/59817925/IRJET-V6I217620190621-73452-h5pwu7-libre.pdf); Artley (Artley B. Training a neural network by hand [Internet]. 2022 [cited 2025 Sept 26]. Available from: https://towardsdatascience.com/training-a-neural-network-by-hand-1bcac4d82a6e/); and Breiman (Breiman L. Random Forests [Internet]. University of California, Berkeley; 2001 Jan [cited 2025 Sep 26]. Available from: https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf). For this reason, they too fail to integrate the claims into a practical application.
Step 2B. Does the claim amount to significantly more?
The claims are directed to abstract ideas. Further limitations themselves encompass abstract ideas, are directed to aspects of the selections of data/simple calculations, or are directed to treatment or machine learning techniques are recited at a high level, which are therefore equivalent to mere directions to apply the judicial exception. Therefore, no claim amounts to significantly more.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1 and 49-56 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Olsen (WO 2020/150258 A1; published 07/23/2020; cited on the IDS dated 03/13/2024).
Regarding claim 1, Olsen teaches a method of classifying a liver disease of a subject (entire document, e.g., Fig. 1) comprising:
obtaining a set of CpG methylation status at a set of CpG sites of target sequences from a cfDNA sample (para [0017-18]; [0099]; [0194]; [0204]: “A cell-free biological sample is obtained…from a subject…processed and assayed for nucleic acid molecules (e.g., nucleic acid molecules derived from the cell-free biological sample)”; [0201]: “Methylation values are calculated for 10,508 analyzed CpGs”; Fig. 1)
identifying a methylation pattern based on the set of CpG methylation statuses, wherein the methylation pattern distinguishes between NAFL [instant “NALFD”], NASH, and cirrhosis (para [0194]: “Group A (…NAFL/Fibrosis F0-1), Group B (NASH/F0-1), and Group C (NASH/F3-4)”; [0193]: “a panel of cfDNA methylation markers that predict the presence of…NASH is identified”; para [0178]: “based at least in part on a data set obtained using the methods…identifying the presence or absence of cirrhosis”; [0093]: “identifying liver disease by processing biological samples…subjects may include patients with NAFLD who are at increased risk of having SH and/or fibrosis” [i.e., those with NAFL]; [0202]; Table 3; [0154])
assigning a liver disease state to the subject based on the methylation pattern, wherein the liver disease state comprises one of: NAFL [instant “NALFD”], NASH, and cirrhosis (Fig. 1; para [0040]; “the presence or the severity of the liver disease is identified according to a Non-Alcoholic Fatty Liver Disease Activity Score [NAS] composite comprising steatosis, lobular inflammation, and hepatocellular ballooning”; [0154]: “an NAS value of >5 including both steatosis and hepatocyte ballooning is considered indicative of NASH”; [0178]: “identifying the presence or absence of cirrhosis”; [0072]; [0197]; claim 10)
Regarding claim 49, Olsen teaches processing a data set comprising a methylation profile of one or more genomic regions with a machine learning algorithm to identify a presence or severity of a liver disease in the subject, where the liver disease may be at least NASH or NAFLD (para [0204]) and the regions may be a subset of a plurality of CpG methylation statuses (para [0201]).
See also para [0193] (“a panel of cfDNA methylation markers that predict…NASH”) and [0163]: “identification of the severity of the disease may be obtained based on…one or more of: number of differentially methylated regions in the data set…and a predicted Non-Alcoholic Fatty Liver Disease (NAFLD) Activity Score identified from markers in the data set”, wherein the cfDNA methylation sites are referred to as “markers” (see, e.g., para [0193] and Table 1).
As above, Olsen teaches that the subject may be at risk of [i.e., not have] NASH but have NALFD, i.e., have NAFL [instant “NAFLD”], thus teaching that methylation pattern distinguishes between instant “NALFD” and NASH in a training subset of data. It is likewise noted that Olsen proves a NAS score threshold for distinguishing NASH and lower severity NAFLD, i.e., NALF.
Regarding claim 50, Olsen teaches that the method may also be used as a part of assessing a therapeutic regimen comprising proving a therapeutic regimen to a subject based on the identification of the presence and/or severity of the liver disease (para [0109-110]).
Regarding claim 51, Olsen teaches that the methylation pattern may be derived from whole genome bisulfite sequencing (para [0194] and [0201]) and that a bisulfite conversion process converts unmethylated cytosines and optionally 5hmC to uracils while preserving at least 5mC (para [0101]). Olsen teaches that the absolute or relative amount of methylation within nucleic molecules may be determined by sequencing and processed to identify hypo and/or hypermethylated regions of the one or more genomic regions (para [0102]).
Thus, Olsen teaches that the methylation pattern comprises at least 5mC at individual sites of the target sequences.
Regarding claim 52, Olsen teaches that continuous output values that may indicate a presence and/or severity or a liver disease such as the NAS score of a subject may comprise a probability value (para [0126]). As the NAS score is linked to a threshold that indicates NASH or NAFL, as discussed above, Olsen teaches that the liver disease state may be a probability.
Regarding claim 53, Olsen teaches that a machine learning algorithm may be configured to identify a disease and/or a severity of the disease with a sensitivity of at least about 5-99%, including at least about 75% (para [0145] and [0056]).
Regarding claim 54, Olsen teaches that a machine learning algorithm may be configured to identify a disease and/or a severity of the disease with a specificity of at least about 5-99%, including at least about 75% (para [0146] and [0056]).
Regarding claim 55, Olsen teaches that the trained algorithm may comprise a random forest (para [0058] and [0201]).
Regarding claim 56, Olsen teaches that the trained algorithm may comprise a neural network (para [0058]).
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.
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.
Claim(s) 1, 49-67 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olsen (WO 2020/150258 A1; published 07/23/2020; cited on the IDS dated 03/13/2024).
Regarding claim 1, Olsen teaches a method of classifying a liver disease of a subject (entire document, e.g., Fig. 1) comprising:
obtaining a set of CpG methylation status at a set of CpG sites of target sequences from a cfDNA sample (para [0017-18]; [0099]; [0194]; [0204]: “A cell-free biological sample is obtained…from a subject…processed and assayed for nucleic acid molecules (e.g., nucleic acid molecules derived from the cell-free biological sample)”; [0201]: “Methylation values are calculated for 10,508 analyzed CpGs”; Fig. 1)
identifying a methylation pattern based on the set of CpG methylation statuses, wherein the methylation pattern distinguishes between NAFL [instant “NALFD”], NASH, and cirrhosis (para [0194]: “Group A (…NAFL/Fibrosis F0-1), Group B (NASH/F0-1), and Group C (NASH/F3-4)”; [0193]: “a panel of cfDNA methylation markers that predict the presence of…NASH is identified”; para [0178]: “based at least in part on a data set obtained using the methods…identifying the presence or absence of cirrhosis”; [0093]: “identifying liver disease by processing biological samples…subjects may include patients with NAFLD who are at increased risk of having SH and/or fibrosis” [i.e., those with NAFL]; [0202]; Table 3; [0154])
assigning a liver disease state to the subject based on the methylation pattern, wherein the liver disease state comprises one of: NAFL [instant “NALFD”], NASH, and cirrhosis (Fig. 1; para [0040]; “the presence or the severity of the liver disease is identified according to a Non-Alcoholic Fatty Liver Disease Activity Score [NAS] composite comprising steatosis, lobular inflammation, and hepatocellular ballooning”; [0154]: “an NAS value of >5 including both steatosis and hepatocyte ballooning is considered indicative of NASH”; [0178]: “identifying the presence or absence of cirrhosis”; [0072]; [0197]; claim 10)
Regarding claim 49, Olsen teaches processing a data set comprising a methylation profile of one or more genomic regions with a machine learning algorithm to identify a presence or severity of a liver disease in the subject, wherein the liver disease may be at least NASH or NAFLD (para [0204]) and the regions may be a subset of a plurality of CpG methylation statuses (para [0201]), and that the subset may be selected based on a feature subset search [i.e., feature selection] (para [0122-123]; [0151]; [0201]).
See also para [0193] (“a panel of cfDNA methylation markers that predict…NASH”) and [0163]: “identification of the severity of the disease may be obtained based on…one or more of: number of differentially methylated regions in the data set…and a predicted Non-Alcoholic Fatty Liver Disease (NAFLD) Activity Score identified from markers in the data set”, wherein the cfDNA methylation sites are referred to as “markers” (see, e.g., para [0193] and Table 1).
As above, Olsen teaches that the subject may be at risk of [i.e., not have] NASH but have NALFD, i.e., have NAFL [instant “NAFLD”], thus teaching that methylation pattern distinguishes between instant “NALFD” and NASH in a training subset of data. It is likewise noted that Olsen proves a NAS score threshold for distinguishing NASH and lower severity NAFLD, i.e., NALF.
Regarding claim 57, Olsen does not explicitly teach identifying a methylation pattern distinguishes between F0-F3 and F4 fibrosis. It is noted that the disease state of cirrhosis comprises F4 (Table 2).
Olsen rectifies this by teaching that identifying the presence or severity of the liver disease comprises determining a presence or absence of fibrosis or significant fibrosis in the subject (para [0002]), where significant fibrosis may be defined according to the Non-Alcoholic Fatty Liver Disease Score criteria (para [0002]).
Olsen teaches identifying a presence or absence of cirrhosis in the subject (para [0025]) and that F4 on the Non-Alcoholic Fatty Liver Disease Score criteria corresponds to “cirrhosis” (Table 2).
Olsen teaches that the machine learning algorithm may comprise a binary output (para [0125]) and identifying [methylation] markers associated with a fibrosis severity of disease (para [0120]).
Olsen also teaches setting a threshold at F2 for significant fibrosis (para [0008]). Olsen teaches adjusting the parameters including cutoff values used to classify a sample to improve performance or other characteristics (para [0150]).
Olsen teaches that the present of liver fibrosis has become increasingly recognized as reliable predictive marker of NAFLD outcomes (para [0002]) and that there is a need for non-invasive diagnostic tests that can effectively detect and discriminate different severities of liver disease (para [0003]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the threshold of F4 for the threshold of F2 in the method of Olsen to identify markers that discriminate between cirrhosis and no cirrhosis on the NAS scoring system (i.e., F0-3 and F4), motivated by the desire to create a non-invasive diagnostic test for NASH cirrhosis in view of the ability to reliably predict NAFLD outcomes. There would have been a strong expectation of success as Olsen teaches performing and optimizing relevant machine learning operations on relevant data.
Alternatively, choosing the F4 threshold in the binary model of significant fibrosis would have been obvious to try as there was a demonstrated need in the market to discriminate different severities of liver disease, a finite number of identified thresholds for fibrosis where fibrosis could predictably predict outcomes, and the artisan had a reasonable expectation of success given the teachings of Olsen in performing and optimizing relevant machine learning operations on relevant data. The artisan would have been so motivated by the desire to further improve/tune the performance of the model.
It is further noted that a choice of threshold for “significant fibrosis” may be also be considered a matter of routine optimization. See MPEP 2144.05(II).
Regarding claims 58-59, Olsen does not explicitly teach identifying a methylation pattern that further distinguishes between F0, F1, F2, F3, and F4. It is noted that the disease state of cirrhosis comprises F4 (Table 2; instant claim 59).
Olsen rectifies this by teaching that the output values may comprise continuous values that may indicate severity of a liver disease of a subject and may comprise Non-Alcoholic Fatty Liver Disease Activity Score of the subject (para [0126]).
Olsen teaches that the Non-Alcoholic Fatty Liver Disease Activity Score values comprise F0, F1, F2, F3, and F4 (Table 2; para [0194]). Olsen teaches adjusting the parameters including cutoff values used to classify a sample to improve performance or other characteristics (para [0150]).
Olsen teaches that the present of liver fibrosis has become increasingly recognized as reliable predictive marker of NAFLD outcomes (para [0002]); that challenges may be encountered in using imaging and current blood-based tests to monitor modest changes in fibrosis progression (para [0003]; and that there is a need for non-invasive diagnostic tests that can effectively detect and discriminate different severities of liver disease (para [0003]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to try using the “continuous” fibrosis scores from the NAS rather than a binary system with a threshold, in view of the recognized need for diagnostic tests that can differentiate between different severities of liver disease and the reliability of fibrosis as a predictable marker of NAFLD outcomes, wherein there are is a finite number of fibrosis categories in the NAS system and ways to choose a number of classes (i.e., 5, corresponding to F0, F1, F2, F3, and F4) within that. The artisan would have been so motivated by the desire to further improve/tune the performance of the model.
It is noted that the choice of the number of classes within the fibrosis classification is also, therefore, held to be a matter of routine optimization. See MPEP 2144.05(II).
Regarding claim 60, Olsen teaches processing a data set comprising a methylation profile of one or more genomic regions [i.e., methylation pattern] with a machine learning algorithm to identify a presence or severity of a liver disease in the subject (para [0204]) and the regions may be a subset of a plurality of CpG methylation statuses (para [0201]), and that the subset may be selected based on a feature subset search [i.e., feature selection] (para [0122-123]; [0151]; [0201]).
Olsen teaches a subset of a plurality of input values may be identified indicating the genomic regions whose methylation profiles are most influential or most important for making high quality classifications, such as identification of severity of a liver disease (para [0151]).
Olsen teaches adjusting the parameters of a trained machine learning algorithm including cutoff values used to classify a sample to improve/tune performance, accuracy, sensitivity, specificity, or other characteristics of identifying a presence and/or severity of the disease (para [0150]).
Thus, as discussed above in claim 58, it follows that this method would be applied to the F0, F1, F2, F3, and F4 classes such by adjusting parameters including cutoff values to classify a sample and choosing output classes such as the NAS values corresponding to the fibrosis values, Olsen would have identified methylation pattern distinguishes among them using a training dataset comprising a plurality of CpG methylation statuses based on feature selection, according to the same motivations as claim 58 as well as obtaining a high quality classification.
It is further noted that routine optimization would be applied to enable such subset to be capable of distinguishing between all of them. See MPEP 2144.05(II).
Regarding claims 50 and 61, Olsen teaches that the methods may also be used as a part of assessing a therapeutic regimen comprising proving a therapeutic regimen to a subject based on the identification of the presence and/or severity of the liver disease (para [0109-110]).
Regarding claims 51 and 62, Olsen teaches that the methylation pattern may be derived from whole genome bisulfite sequencing (para [0194] and [0201]) and that a bisulfite conversion process converts unmethylated cytosines and optionally 5hmC to uracils while preserving at least 5mC (para [0101]). Olsen teaches that the absolute or relative amount of methylation within nucleic molecules may be determined by sequencing and processed to identify hypo and/or hypermethylated regions of the one or more genomic regions (para [0102]).
Thus, Olsen teaches that the methylation pattern comprises at least 5mC at individual sites of the target sequences.
Regarding claim 52 and 63, Olsen teaches that continuous output values that may indicate a presence and/or severity or a liver disease such as the NAS score of a subject may comprise a probability value (para [0126]). As the NAS score is linked to a threshold that indicates NASH or NAFL, as discussed above, Olsen teaches that the liver disease state may be a probability (instant claim 52).
It also follows that the fibrosis NAS score, with chosen thresholds as discussed above, may also be probability (instant claim 63).
Regarding claims 53 and 64, Olsen teaches that a machine learning algorithm may be configured to identify a disease and/or a severity of the disease with a sensitivity of at least about 5-99%, including at least about 75% (para [0145] and [0056]).
Olsen further teaches that the algorithm may be adjusted or tune to improve performance, sensitivity, specificity, or AUC of identifying a presence and/or severity of the disease (para [0150]), wherein the algorithm may be adjusted or tune by configuring parameters or during or after the training process (para [0150]).
Olsen likewise teaches ROC curves comprising TPR [sensitivity] vs. FPR [specify] and an area under the curve (AUC) [Fig. 3-4].
Thus, while Olsen does not explicitly show the assignment of the liver disease state with sensitivity in the embodiments where the liver disease state is i) NASH or NALFD or ii) one of F0-F4, the choice of optimizing the model until a sensitivity for assignment of 75% is reached would have been obvious to one of ordinary skill in the art before the effective filing date, motivated by the desire to improve the performance of the model, as taught by Olsen. There would have been a strong expectation of success as Olsen teaches means of optimizing machine learning models to improve sensitivity.
Further, in view of the relationship between sensitivity and specificity demonstrated by the ROC curve and the methods of optimizing each detailed by Olsen, picking a sensitivity threshold for the model for assigning a disease state is likewise held to be matter of routine optimization. See MPEP 2144.05(II).
Regarding claims 54 and 65, Olsen teaches that a machine learning algorithm may be configured to identify a disease and/or a severity of the disease with a specificity of at least about 5-99%, including at least about 75% (para [0146] and [0056]).
Olsen further teaches that the algorithm may be adjusted or tune to improve performance, sensitivity, specificity, or AUC of identifying a presence and/or severity of the disease (para [0150]), wherein the algorithm may be adjusted or tune by configuring parameters or during or after the training process (para [0150]).
Olsen likewise teaches ROC curves comprising TPR [sensitivity] vs. FPR [specify] and an area under the curve (AUC) [Fig. 3-4].
Thus, while Olsen does not explicitly show the assignment of the liver disease state with a sensitivity and specificity in the embodiments where the liver disease state is i) NASH or NALFD or ii) one of F0-F4, the choice of optimizing the model until a sensitivity and specificity for assignment of 75% is reached would have been obvious to one of ordinary skill in the art before the effective filing date, motivated by the desire to improve the performance of the model, as taught by Olsen. There would have been a strong expectation of success as Olsen teaches means of optimizing machine learning models to improve sensitivity and specificity.
Further, in view of the relationship between sensitivity and specificity demonstrated by the ROC curve and the methods of optimizing each detailed by Olsen, picking a sensitivity and specificity threshold for the model for assigning a disease state is likewise held to be matter of routine optimization. See MPEP 2144.05(II).
Regarding claims 55-56 and 66-67, Olsen teaches that the trained algorithm may comprise a random forest (para [0058] and [0201]) or a neural network (para [0058]; instant claim 67).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1 are 49-67 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14, 20-27, 31-35, and 38 of copending Application No. 18/344,616 in view of Olsen (WO 2020/150258 A1; published 07/23/2020; cited on the IDS dated 03/13/2024). This is a provisional nonstatutory double patenting rejection.
Both sets of claims are directed to a method of classifying a liver disease of a subject comprising obtaining a set of CpG methylation status in a cfDNA sample of a subject; identifying a methylation pattern based on the set of CpG methylation statuses, wherein the methylation pattern distinguishes between states that comprise NAFLD, NASH, and cirrhosis.
Given that claim 1 of ‘616 recites that the states may comprise “NAFLD”, NASH, and cirrhosis, it anticipates a selection of two. Claim 1 of ‘616 recites identifying a methylation pattern by selection a set of CpG sites based on feature selection such that the methylation is predictive of NAFLD, NASH, or cirrhosis. Thus, it teaches a pattern that distinguishes NAFLD and NASH.
Claim 1 of ‘616 further teaches administering a therapy to treat the disease states. The claims of ‘616 further teach at least determining the presence of 5mc/5hmC; a probability belonging to the states; and further classifying a stage of fibrosis and a probability of F0, F1, F2, F3, or F4 fibrosis.
The ‘616 application fails to teach a sensitivity or specificity and that the model comprises a random forest or neural network. ‘616 fails to explicitly teach that the pattern distinguishes F0-F3 and F4 or each of F0-F4.
Any additional limitations of the ‘616 claims are encompassed by the open claim language “comprising” found in the instant claims.
Olsen rectifies this by teaching assigning a liver disease state with at least about 75% sensitivity and specificity, as discussed and cited in the rejection of claims 53-54 and 64-65 above, with the noted routine optimization. Olsen teaches that the model may comprise a random forest or neural network, as cited in claims 55-56 and 66-67 above.
Olsen further teaches assigning a threshold in a binary classifier for fibrosis and that the cutoff may be optimized, as cited and discussed in claim 57. Olsen teaches classifying based on “continuous” values such as NAS scores and the importance of monitoring small changes in fibrosis, as cited and discussed in claims 58-59.
Olsen teaches that machine learning algorithms may be adjusted or tuned to improve the performance or other characteristics (para [0150]) and that a subset of inputs is identified as most important to be included for making high quality classifications (para [0151]).
Therefore, it would have been obvious before the effective filing date to combine ‘616 with the method of Olsen, motivated by the desire to improve the performance of the method to make high quality classifications. It further would have been obvious, for the same reasons as cited in claims 57-59 to modify the method of ‘616 in view of Olsen to identify a methylation pattern that distinguishes F0-F3 from F4 and each of F0-F4, for the same motivations as above. There would have been a strong expectation of success as both are directed to machine learning methods using methylation datasets on diseases on the NAFLD spectrum.
Conclusion
No claims are allowed.
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/EMMA R HOPPE/ Examiner, Art Unit 1683
/ANNE M. GUSSOW/ Supervisory Patent Examiner, Art Unit 1683