DETAILED ACTION
Applicant's response, filed 12/17/2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied in view of instant application amendments. They constitute the complete set presently being applied to the instant application. Herein, "the previous Office action" refers to the Non-Final rejection of 07/01/2025.
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 .
Claim Status
Claims 1-4, 6-14, and 16-29 are pending.
Claims 1-7, 9, 12, and 14-16 were elected without traverse in the reply filed on 03/14/2025.
Claims 8, 10-11, 13, and 17-27 were previously withdrawn.
Claims 5 and 15 are herein cancelled.
Claims 28-29 are newly added
Claims 1-4, 6-7, 9, 12, 14, 16, and 28-29 are under exam.
Claims 1-4, 6-7, 9, 12, 14, 16, and 28-29 are rejected.
The instant Application was examined under Track I status.
Withdrawn Rejections/Objections
Rejections and/or objections not reiterated from previous office actions are hereby withdrawn in view of the 12/17/2025 amendments and Applicant’s remarks. Upon further consideration, newly applied rejections/portions are necessitated by the instant amendments are discussed below.
All rejections of claims 5 and 15 are moot in view of their cancellation.
The rejection under 35 U.S.C. §112b is hereby withdrawn, regarding:
-the term “significant” in claims 1 and 2.
-the term “the prior subjects” in claim 12.
Information Disclosure Statement
The Information Disclosure Statement, filed on 12/10/2025, are in compliance with the provisions of 37 CFR 1.97 and have been considered. Signed copies of the IDS are included with this Office Action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-4, 6-7, 9, 12, 14, 16, and 28-29 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. The dependent claims are also rejected because they depend on and/or do not remedy the deficiencies inherited by their parent claims.
Claim 1, and those claims dependent therefrom, recite “based at least in part on using a trained machine learning (ML) classifier to select the therapy by determining a disease gene expression signature that predicts responsiveness of the subject to the therapy.” It is unclear if the claim requires actually carrying out the analysis with the trained machine learning classifier or if this limitation merely recites a process by which the subject was identified for treatment that was previously performed, i.e. a product-by-process limitation. For examination purposes, it is interpreted that these limitations are not required to be performed in the metes and bounds of the claimed invention and merely recite product-by-process limitations. The instant claim amendments do not alter the product-by-process characterization, and the “administering” step remains the only active step. If the Applicant intended the trained ML steps to be performed and to further limit the administering limitation (which would narrow the field of prior art which can be applied), these limitations should be amended to be active steps which lead to administering to the subject…
The term “trained machine learning” (ML) is indefinite as to whether the training of said therapy selecting machine learning occurred within the metes and bounds of the instant application, as there are no active steps by which machine learning is trained to perform the task (e.g. the initial training dataset, what the ML does with the training dataset, what features or parameters were considered). Further, this claim fails to point out and particularly claim any aspect of ML structure, ML parameters, training data, and training process and further fails to particularly point out how the trained ML acts on the data received to achieve the desired goal.” The minimally sufficient steps and limitations to provide the instant trained machine learning and its asserted function are not particularly pointed out or distinctly claimed, but are necessary, in order to show the instant ML is distinguishable from any generic NN. It would appear that, without positive active method steps reciting details of the ML, and the training of the ML, that any “trained ML” would be applicable. Limitations equivalent to those in claim 16 (which adds training step criteria of negative predictive value of therapy responsiveness), or other structural criteria, particular parameters, weights, relationships rooted in the instant specification for how instant ML produces distinct outputs, could improve the analysis under this statute.
Claim 1, and those claims dependent therefrom, recite “wherein therapy is determined at least in part by using ….” It is unclear if the claim requires actually carrying out the method steps for determining a disease gene expression signature or if this limitation merely recites a process by which the disease gene expression signature was previously performed, i.e. a product-by-process limitation, as the basis for therapy selection. For examination purposes, it is interpreted that these limitations are not required to be performed in the metes and bounds of the claimed invention and merely recite product-by-process features of the therapy and the disease gene expression signature determinations. It is unclear that the minimally sufficient set of limitations required to achieve the invention is present in the independent claim. It is noted that dependent claims recite further limitations on the process for determining the disease gene expression signature which may address this issue.
Claim 1, and those claims dependent therefrom, recite “determined at least in part by using a trained machine learning ….” The claim fails to clarify what part is determined by the ML, and what part is not. It fails to point out how any other non-ML aspect is to be determined. In other words, it is unclear whether these claims inform one of ordinary skill in the art about the scope and structure of the administered therapy selection or how to decide whether to administer the therapy to a subject. Also, the claim fails to particularly point out and distinctly claim steps of identification of the disease or disorder or condition suffered by the patient. No patient samples are provided or tested. No gene expression data of the patient is provided or analyzed. It is entirely unclear how to use the trained ML, without patient-specific data with respect to the disease/ disorder/ condition and gene expression data, to select a patient-specific treatment.
Claim 1, and those claims dependent therefrom, recite “the at least one therapy causes reversal of the disease gene expression signature.” As above, it is unclear how one of ordinary skill in the art would administer a partially ML determined therapy without the disease gene expression signature of said subject or any of the compared cohorts. There is no link between the generic steps of administering and the specific subject(s), to apply “reversal” when one of ordinary skill in the art did not determine said subject(s) has said reversible disease gene signature, and if so, how was the at least one therapy specific therapy choice finally matched to an administered subject.
Claim 1, and those claims dependent therefrom, recite “the at least one therapy causes reversal of the disease gene expression signature.” It is unclear if “reversal” is intended to imply that a gene expression signature returns to “normal” or whether this is intended to encompass any change of the gene expression dataset as a whole, or in part. It is unclear if the “reversal” indicates that ALL gene expression levels are “reversed” or whether it is a subset of gene expression levels Does reversal apply to disease gene signatures characterized by overexpression as well as underexpression, and/or complete gene(s) signatures, every possible gene in the disease gene expression signature, or can partial gene expression signatures count as well? As the claim fails to provide gene expression information of the patient prior to, and subsequent to treatment, it is entirely unclear how the “reversal” is to be determined.
Claim 1, and those claims dependent therefrom, recite “receiving small molecule compound data” in order to generate “a biological network comprising at least (i) the first set of genes, (ii) the second set of genes, and (iii) the one or more proteins” is indefinite. No protein data is received, only mentioned as a part of protein-modulating therapies related to the receive[d] small molecule data and gene expression data. Further, there are not active steps to inform one of ordinary skill in the art for biological network generation nor, how the aforementioned components are used in said network. Claim 1 fails to particularly point out and distinctly claim the minimally sufficient steps, data elements and actions to generat[e] said biological network including proteins.
Claim 4 recites “significant sub-network”, a relative term which renders the claim indefinite. The term “significant” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree of adjacency/relatedness, nor how to assess a sub-network for significance. Further, it is unclear when a sub-network approaches the required level of “significance” for one of ordinary skill in the art to carry out the claimed limitations of the invention.
Therefore, said limitations are indefinite as claimed. Clarification is requested through clearer claim language.
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.
(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.
Note on formatting: quotes from the instant application are italicized in the following section.
A. Claims 1-2, 7, 9, 12, 14, 16, and 28-29 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tao et al. (2021: Tao W al. (2021) Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis; Vol. 73, No. 2, February 2021, pp 212–222 DOI 10.1002/art; PTO 892 cited, herein Tao). Although the claims recite numerous product-by-process limitations, how those limitations are taught by the prior art are addressed below in the interest of compact prosecution. The prior art of Tao remains applicable as it anticipates the only active step, and the ML steps for selecting the therapy remain indefinite as limitations, discussed in above 112b.
Regarding instant claim 1, instant application recites:
A method of treating a subject suffering from a disease, disorder, or condition, the method comprising:
administering to the subject a therapeutically effective amount of a therapy, based at least in part on using a trained machine learning (ML) classifier to select the therapy by determining
wherein the therapy
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) the first set of genes, (ii) the second set of genes, and (iii) the one or more proteins; determining a proximity of each protein of the one or more proteins to each gene of the first set of genes on a biological network, wherein the determining comprises using an average shortest path; determining a selectivity of each protein of the one or more proteins to each therapy of the small molecule compound data on the biological network, wherein the determining comprises using a diffusion state distance: compiling a target ranking of each therapy, wherein the target ranking is a function of each proximity and each selectivity; and selecting at least one therapy of the small molecule compound data, based at least on the target ranking, to treat the subject suffering from the disease, disorder, or condition, wherein treating the subject with the at least one therapy causes reversal of the disease gene expression signature.
The prior art to Tao teaches:
a method to predict response to anti–tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis/RA (subject suffering from a disease), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment [Abstract] (administering to a test subject a therapeutically effective amount of (i) a therapy).
gene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied [Abstract] (receiving gene expression data from a cohort of subjects suffering from the disease, disorder, or condition (administering to a test subject a therapeutically effective amount of (i) a therapy, based at least in part on a trained machine learning classifier analyzing a disease gene expression signature to predict responsiveness of the test subject to the therapy, or (ii) a second therapy different from the therapy, based at least in part on the trained machine learning classifier analyzing the disease gene expression signature to predict non-responsiveness of the test subject to the therapy, wherein the disease gene expression signature is determined).
using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further
validated by a follow-up study [Abstract] (based at least in part on a trained machine learning classifier analyzing a disease gene expression signature to predict responsiveness of the test subject to the therapy, or (ii) a second therapy different from the therapy, based at least in part on the trained machine learning classifier analyzing the disease gene expression signature to predict non-responsiveness of the test subject to the therapy, wherein the disease gene expression signature is determined).
After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response-associated transcription and epigenetic signatures [Abstract] (stratifying the cohort of subjects into two or more groups based at least in part on the gene expression data; calculating differences in gene expression between the two or more groups of subjects and a group of non-diseased subjects; selecting one or more genes having significant differences in gene expression between the two or more groups of subjects and the group of non-diseased subjects ("disease candidate genes"))
Regarding instant claim 2, instant application recites:
wherein the disease gene expression signature is determined at least in part by further mapping the first set of genes and the one or more proteins onto the biological network to determine each proximity and each selectivity.
The prior art to Tao teaches:
Genome-wide DNA methylation analysis of PBMCs from the same patients identified 16,141 and 17,026 differentially methylated positions (DMPs) of CpG sites (nominal P < 0.05) associated with response to ADA and ETN, respectively (Figures 3A and B and Supplementary Figure 2, [p216 Col 2].
Regarding instant claim 7, instant application recites:
wherein the disease, disorder, or condition comprises ulcerative colitis (UC), Crohn's disease (CD), rheumatoid arthritis (RA), juvenile arthritis, psoriatic arthritis, plaque psoriasis, ankylosing spondylitis, Guillain-Barre syndrome, Sjogren's syndrome, scleroderma, vitiligo, bipolar disorder, Graves' disease, schizophrenia, Alzheimer's disease, multiple sclerosis, Parkinson's disease, or a combination thereof.
The prior art to Tao teaches:
a method to predict response to anti–tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis/RA (subject suffering from a disease), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment [Abstract].
Regarding instant claim 9, instant application recites:
wherein the disease, disorder, or condition comprises rheumatoid arthritis (RA).
The prior art to Tao teaches:
a method to predict response to anti–tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis/RA (subject suffering from a disease), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment [Abstract].
Regarding instant claim 12, instant application recites:
wherein the stratifying the cohort of subjects into two or more groups is random or based at least in part on whether the prior subjects do or do not respond to the therapy. wherein the gene expression data from the cohort of subjects is stratified into two or more groups, wherein the two or more groups comprise responders (R), nonresponders (NR), responders before and after treatment (RBA), or nonresponders before and after treatment (NRBA).
The prior art to Tao teaches:
using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further validated by a follow-up study [Abstract]
Regarding instant claim 14, instant application recites:
wherein the therapy comprises an anti-TNF therapy or a therapy different than the anti-TNF therapy.
The prior art to Tao teaches:
using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further validated by a follow-up study [Abstract]
Regarding instant claim 16, instant application recites:
wherein the trained machine learning classifier is configured to predict responsiveness or non-responsiveness of the subject to the therapy with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.
The prior art to Tao teaches:
a method to predict response to anti–tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis/RA (subject suffering from a disease), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment [Abstract].
Genome-wide DNA methylation analysis of PBMCs from the same patients identified 16,141 and 17,026 differentially methylated positions (DMPs) of CpG sites (nominal P < 0.05) associated with response to ADA and ETN, respectively (Figures 3A and B and Supplementary Figure 2, [p216 Col 2].
The model based on differentially expressed genes/DEGs of PBMC cellular type (“PBMC RNA” ADA model) showed the highest overall accuracy of predicting response to ADA among other models (ADA models). More specifically, these ADA models reached overall accuracy of 80.3%, 72.7%, 85.9%, and 84.7% using DEGs on monocytes, CD4+ T cells, PBMCs, and DMPs of PBMCs, respectively. The true-positive rates of these models ranged from 76.0% to 90.0%, and the true-negative rates ranged from 70.0% to 89.0% [Figure 5A. p218 Col 2] (responsiveness or non-responsiveness of the test subject with a negative predictive value of…).
Thus, we combined the gene expression/methylation signatures identified using nominal P values to build machine learning models to improve the prediction of response. We achieved high accuracy using the RNA models and/or DNA model to predict response to ADA and ETN. These models were further validated by a follow-up study, which shows a reliable application prospect to guide clinical decision-making [p220 Col 1] (wherein the trained machine learning classifier is configured to predict responsiveness or non-responsiveness of the test subject with a negative predictive value of at least…).
Regarding claims 28-29, these limitations further correspond to product-by-process limitations that are not required to be performed within the metes and bounds of the claim. Since Tao teaches treating a subject that is an equivalent subject to the claimed subject, these claims do not make a contribution over the prior art.
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 § 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.
Note: quotes from the instant application are italicized in the following section.
A. Claims 3-4, and 6 are rejected under 35 U.S.C. 103 as being anticipated by Tao et al. (2021: Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis, Arthritis & Rheumatology 73(2): 212–222, DOI 10.1002/art; PTO 892 cited, herein Tao), as applied above to claims 1 and 2, in view of Köhler S et al. (2008: Walking the interactome for prioritization of candidate disease genes. The American Journal of Human Genetics 82.4: 949-958; PTO 892 cited, herein Kohler). This rejection is applied in the interest of compact prosecution.
Regarding claims 3-4, and 6, these limitations correspond to product-by-process limitations that are not required to be performed within the metes and bounds of the claim. Since Tao teaches treating a subject that is an equivalent subject to the claimed subject, these claims do not make a contribution over the prior art.
Regarding instant claim 3, instant application recites:
wherein the biological network comprises a human interactome.
The prior art to Tao teaches machine learning models based on molecular signatures (differential gene or methylation expression) accurately predict response before anti-TNF treatments (two types: ADA and ETN), for personalized anti-TNF treatment [Abstract]. However, Tao does not teach wherein the biological network comprises a human interactome (Kohler teaches candidate disease gene prioritization with global network-similarity measures captured relationships between disease proteins, and constructed an interaction network (biological network) based verified protein-protein interactions/human interactome [Introduction, Col 2]).
Regarding instant claim 4, instant application recites:
wherein the adjacent genes form a significant sub-network with each other or to the disease candidate genes wherein the second set of genes and the one or more proteins form a significant sub-network with the first set of genes, and wherein the significant sub-network is determined at least by a largest connected component (LCC)..
The prior art to Tao teaches:
machine learning models based on molecular signatures (differential gene or methylation expression) accurately predict response before anti-TNF treatments (two types: ADA and ETN), for personalized anti-TNF treatment [Abstract].
Genome-wide DNA methylation analysis of PBMCs from the same patients identified 16,141 and 17,026 differentially methylated positions (DMPs) of CpG sites (nominal P < 0.05) associated with response to ADA and ETN, respectively (Figures 3A and B and Supplementary Figure 2, [p216 Col 2].
However, Tao does not wherein the adjacent genes form a significant sub-network with each other or to the disease candidate genes. Kohler teaches “genes related to a specific or similar disease phenotype tend to be located in a specific neighborhood (adjacent genes, subnetwork) in the protein-protein interaction network (interactome). …exploring biological networks have been applied to the problem of candidate-gene prioritization, including the search for direct neighbors of other disease genes and the calculation of the shortest path between candidates and known disease proteins” [Introduction, Col 1].
Regarding instant claim 6, instant application recites:
wherein the diffusion state distance comprises a random walk.
The prior art to Tao teaches using these disease gene signatures and building machine learning models by random forest algorithm to predict response prior to anti-TNF treatment, and were further validated by a follow-up study [Abstract]. However, Tao does not teach adjacent genes are identified via a machine- learning algorithm. Kohler teaches candidate-gene prioritization by local distance methods (adjacency) and machine learning algorithms (random walk and the related diffusion-kernel method) to capture global relationships within an interaction network [Introduction Col 2].
Therefore, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Tao’s machine learning models based gene signatures for anti-TNF treatment response prediction with Kohler’s candidate-gene prioritization by local distance methods (adjacency) and machine learning algorithms/random walk. Combining these prior art elements would have been obvious because Kohler’s candidate gene modelling captures global relationships within an interaction network [Introduction Col 2]. One of ordinary skill in the art would predict combining Tao with Kohler’s teachings with a reasonable expectation of success because said prior art are analogously applicable to optimizing machine learning for disease gene and related genes discovery. Therefore, the invention is prima facie obvious.
Response to Remarks: 102/103
Applicant's remarks (p.8-11), filed 12/17/2025, have been fully considered in view of claim amendments. Applicant asserts:
Tao fails to disclose every amended limitation of independent claim 1, including “(i) a biological network, (ii) a proximity and selectivity, (iii) a target ranking of therapies, and (iv) a therapy for administering to a patient…“Kohler does not disclose (i) a biological network, (ii) a proximity and selectivity, (iii) a target ranking of therapies, and (iv) a therapy for administering to a patient…silent on predicting response to a therapy AND identifying novel targets for therapy…“Further, like Tao, Kohler is merely concerned with "identification of genes associated with hereditary disorders ... [based on] prioritization of candidate genes." Kohler at Abstract. Kohler is entirely silent on predicting response to a therapy AND identifying novel targets for therapy. As mentioned, the present Application provides a technical solution for, not only predicting response to a therapy, but also using "a network-based framework that simultaneously captures the relation between disease formation and successful treatment as a method to identify novel potential targets." Id. at 170 (emphasis added). …”
”
However, it is respectfully submitted that Applicant’s assertion is not persuasive. In the prior Office Action for compact prosecution, the product-by-process limitations claims were examined for prior art, as above. The additional limitations directed to how the therapy was selected were both indefinite (discussed in above 112b) and occurred at some previous point in time outside the scope of the administering step as claimed. As such, they have no limiting effect on the function of the administering step and they do not further limit the subject receiving therapy (see MPEP 2113 and 2173.05(p)). Therefore, the claim recites the sole positive, active method step of administering a therapy to a patient, which is clearly met by Tao, as set forth.
The prior art to Tao teaches:
a method to predict response to anti–tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis/RA (subject suffering from a disease), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment [Abstract] (administering to a test subject a therapeutically effective amount of (i) a therapy) based on gene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied [Abstract].
using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further
validated by a follow-up study [Abstract] (based at least in part on a trained machine learning classifier analyzing a disease gene expression signature to predict responsiveness of the test subject to the therapy, or (ii) a second therapy different from the therapy, based at least in part on the trained machine learning classifier analyzing the disease gene expression signature to predict non-responsiveness of the test subject to the therapy, wherein the disease gene expression signature is determined).
After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response-associated transcription and epigenetic signatures [Abstract].
While the dependent claims were not further limiting, in the interest of addressing Applicant remarks regarding Tao being silent to “(i) a biological network, (ii) a proximity and selectivity, (iii) a target ranking of therapies,” Applicant is reminded the combination of Tao and Kohler was relied on to teach these additional claims. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., Inc., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Where a rejection of a claim is based on two or more references, a reply that is limited to what a subset of the applied references teaches or fails to teach, or that fails to address the combined teaching of the applied references may be considered to be an argument that attacks the reference(s) individually. Where an applicant’s reply establishes that each of the applied references fails to teach a limitation and addresses the combined teachings and/or suggestions of the applied prior art, the reply as a whole does not attack the references individually as the phrase is used in Keller and reliance on Keller would not be appropriate. This is because "[T]he test for obviousness is what the combined teachings of the references would have suggested to [a PHOSITA]." In re Mouttet, 686 F.3d 1322, 1333, 103 USPQ2d 1219, 1226 (Fed. Cir. 2012). Tao “simultaneously captures the relation between disease formation” (“on gene expression and/or DNA methylation profiling”) and successful treatment (anti-TNF treatment, either adalimumab (ADA) or etanercept (ETN) therapy), and in combination with Kohler to identify novel potential targets (“differential expression and methylation analyses were performed to identify the response-associated transcription and epigenetic signatures” of Tao with Kohler’s “search for direct neighbors of other disease genes and the calculation of the shortest path between candidates and known disease proteins”) as set forth below.
The prior art to Kohler teaches:
candidate disease gene prioritization with global network-similarity measures captured relationships between disease proteins, and constructed an interaction network (biological network) based verified protein-protein interactions/human interactome [Introduction, Col 2]).
“genes related to a specific or similar disease phenotype tend to be located in a specific neighborhood (adjacent genes, subnetwork) in the protein-protein interaction network (interactome). …exploring biological networks have been applied to the problem of candidate-gene prioritization, including the search for direct neighbors of other disease genes and the calculation of the shortest path between candidates and known disease proteins” [Introduction, Col 1].
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) 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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission.
Any newly applied rejection/portion is necessitated by instant application amendment.
Note: quotes from the instant application are italicized in the following Double Patent section.
A. Instant claims 1, 5, 7, 9, and 14-16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 6-8, 11, and 13 of US Patent No. 12062415. The reference claims of ‘415 are obvious variants (RA, administering TNF therapy, machine learning classifier, negative predictive value.) of the claims of the instant application. ‘415 recites all the limitations of those required by instant claim 1, administering therapy, plus additional limitations (e.g. for selecting a gene expression signature). Therefore, instant claim(s) 1 is anticipated by the narrower claims (i.e. species anticipates the genus). This is a nonstatutory double patenting rejection.
B. Instant claims 1-5, and 14-16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 7-8, and 10-11 of US Patent No. 11198727. The reference claims of ‘727 are obvious variants (RA, administering TNF therapy, machine learning classifier, interactome) of the claims of the instant application. ‘727 recites all the limitations of those required by instant claim 1, administering therapy, plus additional limitations (e.g. for selecting a gene expression signature). Therefore, instant claim(s) 1 is anticipated by the narrower claims (i.e. species anticipates the genus). This is a nonstatutory double patenting rejection.
C. Instant claims 1, 7-9, and 14-16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 6-7, 9, 11, and 13 of US Patent No. 11198727. The reference claims of ‘913 are obvious variants (RA, administering TNF therapy, machine learning classifier) of the claims of the instant application. ‘727 recites all the limitations of those required by instant claim 1, administering therapy, plus additional limitations (e.g. for selecting a gene expression signature). Therefore, instant claim(s) 1 is anticipated by the narrower claims (i.e. species anticipates the genus). This is a nonstatutory double patenting rejection.
D. Instant claims 1, 7-8, and 14-16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 48-50, 51-54, and 57-59 of US Patent No. 11783913. The reference claims of ‘913 are obvious variants (UC, administering TNF therapy, machine learning classifier) of the claims of the instant application. ‘913 recites all the limitations of those required by instant claim 1, administering therapy, plus additional limitations (e.g. for selecting a gene expression signature). Therefore, instant claim(s) 1 is anticipated by the narrower claims (i.e. species anticipates the genus). This is a nonstatutory double patenting rejection.
E. Instant claims 1-4, 6-7, 9, 12, 14, 16, and 28-29 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9, and 12, of US Patent No. 12046338. The reference claims of ‘367 are obvious variants (RA, administering therapy in claim 8, trained machine learning classifier) of the claims of the instant application. ‘338 recites all the limitations of those required by instant claim 1, administering therapy, plus additional limitations (e.g. for selecting a gene expression signature). Therefore, instant claim(s) 1 is anticipated by the narrower claims (i.e. species anticipates the genus). This is a nonstatutory double patenting rejection.
F. Instant claims 1-4, 6-7, 9, 12, 14, 16, and 28-29 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 29-48, of US Application No. 19/066,911. The reference claims of ‘911 are obvious variants (RA, administering therapy, biological network, trained machine learning classifier, gene expression data) of the claims of the instant application. ‘911 recites all the limitations of those required by instant claim 1, administering therapy, plus additional limitations (e.g. for selecting a gene expression signature). Therefore, instant claim(s) 1 is anticipated by the narrower claims (i.e. species anticipates the genus. This is a provisional obviousness-type double patenting rejection because the conflicting claims have not in fact been patented.
Response to Remarks: DP
Applicant's remarks (p.11-12), filed 12/17/2025, have been fully considered in view of claim amendments. Applicant asserts instant claim amendments are patentably distinct over the cited claims of the '415 patent, the '727 patent, the '913 patent, and the '338 patent. However, it is respectfully submitted that Applicant’s assertion is not persuasive as each patent/application recites all the limitations of those required by instant claim 1, administering therapy, plus additional limitations (e.g. for selecting a gene expression signature). Therefore, instant claim 1 is anticipated by the narrower claims (i.e. species anticipates the genus). As discussed above, the double patenting rejection is maintained.
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.
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/VR/
Examiner
Art Unit 1685
/MARY K ZEMAN/Primary Examiner, Art Unit 1686