DETAILED ACTION
Applicant’s response, filed 01/23/2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Claim Status
Claims 1-8 and 10-13 are pending.
Claims 9 and 14 are canceled.
Claims 1-6 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected species, as described above.
Claims 1-8 and 10-13 are under examination.
Claims 1-8 and 10-13 are rejected.
Priority
Applicant's claim for the benefit of a prior-filed application, PCT/KR2020/006305, filed 05/13/2020.
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to Republic of Korea App. No.10-2019-0056105, filed 05/14/2019 and Republic of Korea App. No.10-2020-0055087, filed 05/08/2020. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Accordingly, each of claims 7-13 are afforded the effective filing date of 05/14/2019.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 11/03/2021 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Drawings
The Drawings submitted 11/03/2021 are accepted.
Claim Rejections- 35 USC § 112
The outstanding rejections to the claims are withdrawn in view of the amendments submitted herein.
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.
For the following rejections, underlined text indicates newly recited portions necessitated by claim amendment.
Claims 1-8 and 10-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. Any newly recited portions are necessitated by claim amendment.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to method, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claim 7:
deriving, by the analysis device, at least one expression profile including expression levels of first genes and second genes derived from the gene expression data of the patient; and
deriving, by the analysis device, a disease state or an effective treatment for the patient based on the at least one expression profile, wherein the first genes are genes that are present in a first kernel module of first genomic module network constructed from normal tissues, and absent from a second kernel module of second genomic module network constructed from tumor tissues, and wherein the second genes are genes that are present in the first kernel module and also present in the second kernel module, excluding the first genes, wherein the first kernel module is one module having a lower entropy than that of other modules by at least a reference value in the first genomic module network, and wherein the second kernel module is one module having a lower entropy than other modules by at least a reference value in the second genomic module network, wherein the first genomic module network is constructed using a gene expression dataset of the normal tissues based on entropies of genes of the normal tissues, and wherein the second genomic module network is constructed using a gene expression dataset of the tumor tissues based on entropies of genes of the tumor tissues, wherein the entropies represent relations among a plurality of genes based on probabilities of transcriptional states for the plurality of genes, and wherein the normal tissues include normal tissues derived from different tissue origins, and the tumor tissues include tumor tissues derived from different tissue origins
Dependent claim 8:
dividing a plurality of genes in the gene expression dataset into a plurality of gene sets;
removing genes from each of the plurality of gene sets such that an entropy of each gene set of the plurality of gene sets is smaller than a threshold value
adding, to each of the plurality of gene sets, genes that do not belong to any one of the plurality of gene sets, when the entropy of the each gene set equal to or smaller than the threshold value.
Independent claim 13:
construct a first genomic module network and a second genomic module network using the program
analyze the sample on the basis of first gene group and second gene group in the sample, wherein the first gene group and the second gene group are determined by a first kernel module in a first genomic module network and a second kernel module in the second genomic module network, wherein the first genomic module network is constructed using a gene expression data set of normal tissues based on entropies of genes of the normal tissues, and wherein the second genomic module network is constructed using a gene expression data set of tumor tissues based on entropies of genes of the tumor tissues, wherein the reference first kernel module in the first genomic module network comprises a module having a lower entropy than that of other modules in the first genomic module network by at least a reference value, and the second kernel module in the second genomic module network comprises a module having a lower entropy than that of other modules in the second genomic module network by at least a reference value, wherein the first gene group includes genes that are present in the first kernel module in the first genomic module network, and absent from the second kernel module of the second genomic module network, and wherein the second gene group includes genes that are present in the first kernel module and also present in the second kernel module, excluding the first gene group, and wherein the entropy represents relations among of a plurality of genes on the basis of probabilities of transcriptional states of the plurality of genes.
Dependent claims 10-12 recite further steps that limit the judicial exceptions in independent claim 7 and, as such, also are directed to those abstract ideas. For example, claims 10-12 further limits the expression profile of claim 7.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually deriving, dividing, removing, adding, construct, and analyze. Without further detail as to the methodology involved in “deriving, …, at least one expression profile”, “deriving, …, a disease state or an effective treatment “, “dividing a plurality of genes”, “removing genes from each of the plurality of gene sets“, “adding, to each of the plurality of gene sets“, “construct a first genomic module network and a second genomic module network“, and “analyze the sample“, under the BRI, one may simply, for example, use pen and paper to diagnosis a tumor using gene expression data.
Therefore, claims 7 and 13 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claim 7:
receiving, by an analysis device, gene expression data of a patient
an analysis device
Dependent claim 8:
acquiring a gene expression dataset of normal tissue; dividing a plurality of genes in the gene expression dataset into a plurality of gene sets
Independent claim 13:
an input device
a storage device
store a data analysis program for analyzing data using a kernel module in a genomic module network
a computing device
The claims also include non-abstract computing elements. For example, independent claims 7 and 13 includes an analysis device, an input device, a storage device, and a computing device.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “receive” and “acquire” and to data outputting, such as “store”, perform functions of collecting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of “a computing system, in input device, and a storage device” do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)).
As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 1 and 13 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0566-0568 and 0573]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Response to Applicant Arguments
Applicant submits practical biomarkers for disease diagnosis and treatment selection transforms the natural phenomenon into a patent eligible practical application with significantly more than a judicial exception [p. 11, par. 6].
It is respectfully found not persuasive. The practical application must be found among the additional elements of the claims; i.e. elements that are not judicial exceptions (MPEP 2106.04(d)). Mathematical calculations and mental processes are judicial exceptions, so no matter how useful or beneficial those methods are, they are not a practical application.
Applicant submits the present claims are directed to a specific technological solution – using computationally identified kernel module genes (CG and CGX) to derive actionable medical information. The method integrates multiple unconventional steps that amount to significantly more than merely observing a natural correlation [p. 12, par. 1].
It is respectfully found not persuasive. Analyzing DNA to provide sequence information or detect allelic variants is considered to be well-understood, routine, conventional activity in the life science arts when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Claim Rejections - 35 USC § 102
The outstanding 102 rejection has been withdrawn in view of the amendments. Anastassiou does not discloses wherein the first genes are genes that are present in a first kernel module of first genomic module network constructed from normal tissues, and absent from a second kernel module of second genomic module network constructed from tumor tissues, and wherein the second genes are genes that are present in the first kernel module and also present in the second kernel module, excluding the first genes.
Claim Rejections - 35 USC § 103
The previous 103 rejection is withdrawn.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
A. Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anastassiou et al (US 2008/0300799 AI, published 12/04/2008, previously cited), in view of Han et al. (Han, Jinil et al. “The Effects of Variations in Genomic Modules on Breast Cancer Phenotype.” Animal cells and systems 18.5 (2014): 296–303. Web, cited on IDS dated 11/03/2021.
The instant rejection is new and any newly recited portions are necessitated by claim amendment.
Claim 7 is directed to a sample analysis method based on a kernel module in a genomic module network.
Anastassiou discloses an approach that identifies modules of genes that are jointly associated with disease from gene expression data [0005]. Anastassiou further discloses the smallest module of genes whose joint expression levels can predict the presence of disease can be identified [0007] which reads on a kernel module.
receiving, by an analysis device, gene expression data of a patient, and deriving, by the analysis device, at least one expression profile including expression levels of first genes and second genes derived from the gene expression data of the patient; and
Anastassiou discloses Entropy Minimization ... is used to identify modules of genes [0007]. Anastassiou further discloses prostate cancer microarray expression data contains gene expression profiles ... of which 52 are cancerous and 50 are healthy [0048] which reads on sample gene expression data.
deriving, by the analysis device, a disease state or an effective treatment for the patient based on the at least one expression profile
Anastassiou discloses wherein the two or more factors comprise two or more genes, the data includes gene expression data comprising expression levels for each of the two or more, and the one or more outcomes includes presence or absence of a disease [claim 5].
wherein the first genes are genes that are present in a first kernel module of first genomic module network constructed from normal tissues, and absent from a second kernel module of second genomic module network constructed from tumor tissues, and wherein the second genes are genes that are present in the first kernel module and also present in the second kernel module, excluding the first genes,
Anastassiou discloses a normal and tumor gene expression data where the first prostate cancer microarray expression data contains gene expression profiles for 102 prostate tissues, of which 52 are cancerous and 50 are healthy [0048], but is silent on wherein the first gene group consists of genes that are present in a kernel module of a first genomic module network but are not present in a kernel module of a second genomic module network, wherein the second gene group consists of the remaining genes except the first gene group in the kernel module in the first genomic module network.
However, Han discloses defining ‘DP-exclusive genes’ as noncommon genes included in the DP genomic module but not in the corresponding DN genomic module [p. 297, col. 2, par. 4].
wherein the first kernel module is one module having a lower entropy than that of other modules by at least a reference value in the first genomic module network, and wherein the second kernel module is one module having a lower entropy than other modules by at least a reference value in the second genomic module network,
Anastassiou discloses the smallest module of genes whose joint expression levels can predict the presence of disease can be identified [0007]. Anastassiou further discloses entropy minimization can be directed to identifying the gene set with the minimum conditional entropy [0034].
wherein the first genomic module network is constructed using a gene expression dataset of the normal tissues based on entropies of genes of the normal tissues, and wherein the second genomic module network is constructed using a gene expression dataset of the tumor tissues based on entropies of genes of the tumor tissues,
Anastassiou discloses a normal and tumor gene expression data where the first prostate cancer microarray expression data contains gene expression profiles for 102 prostate tissues, of which 52 are cancerous and 50 are healthy [0048].
wherein the entropies represent relations among a plurality of genes based on probabilities of transcriptional states for the plurality of genes, and
Anastassiou discloses 2 possible gene expression states. For each state S the number NO(S) of times that the state appears in a healthy tissue ... and the number of times N1 (S) that it appears in a diseased tissue can also be counted [0025]. Anastassiou further discloses entropy minimization because the uncertainty can be quantified with the information theoretic measure known as conditional entropy. A probabilistic model can be created in which probabilities are equal to relative frequencies derived from the counts NO(S) and N1 (S) [0027].
wherein the normal tissues include normal tissues derived from different tissue origins, and the tumor tissues include tumor tissues derived from different tissue origins
Anastassiou discloses it can also be assumed that there are two types of tissues, either healthy ones or tissues suffering from a particular disease [0024]. Anastassiou further discloses the latter assumption can also be generalized to include more than two types of tissues, or modified to be used for classification among several types of cancer [0024].
Claim 8 is directed to the sample analysis method according to claim 7, wherein one genomic module network among the first genomic module networks includes a plurality of genomic modules, and wherein generating of the plurality of genomic modules comprises:
acquiring a gene expression dataset of normal tissue;
Anastassiou discloses Entropy Minimization ... is used to identify modules of genes [0007]. Anastassiou further discloses prostate cancer microarray expression data contains gene expression profiles ... of which 52 are cancerous and 50 are healthy [0048] which reads on sample gene expression data.
dividing a plurality of genes in the gene expression dataset into a plurality of gene sets removing genes from each of the plurality of gene sets such that an and adjusting the entropy of each gene set of the plurality of gene sets is smaller than a threshold value, adding, to each of the plurality of gene sets, genes that do not belong to any one of the plurality of gene sets, when the entropy of the each gene set is equal to or smaller than the threshold value.
Anastassiou discloses the smallest module of genes can be identified [0035], starting from a randomly chosen gene set of size n and if the conditional entropy of the new gene set is lower than that of the current gene set, then the new gene set replaces the current gene set; [0049], gene modules of size n=1, 2, 3 and 4 were considered [0050], and the thresholds for which the minimum entropy values were below 0.20 [Fig. 1]. Anastassiou is silent on removing genes.
However, Han discloses the effects of variations in genomic modules on breast cancer phenotype [title]. Han further discloses given a genomic module candidate formed by k-means clustering, the gene set is iteratively reduced or extended to increase the dominance of the first eigenvector of expressions of the genomic module candidate, until the dominance is over the threshold level (>97%) [p. 297, col. 1, par. 1].
Claim 10 is directed to the sample analysis method according to claim 7, wherein the at least one expression profile comprises at least one of relative entropy between the first genes and the second genes and a degree of malignant transformation of at least one of the first genes and the second genes in the sample.
Anastassiou discloses 2 possible gene expression states. For each state S the number NO(S) of times that the state appears in a healthy tissue ... and the number of times N1 (S) that it appears in a diseased tissue can also be counted [0025]. Anastassiou further discloses entropy minimization because the uncertainty can be quantified with the information theoretic measure known as conditional entropy. A probabilistic model can be created in which probabilities are equal to relative frequencies derived from the counts NO(S) and N1 (S) [0027].
Claim 11 is directed to the sample analysis method according to claim 7, wherein the at least one expression profile comprises contribution level of one genes in the first genes to the first kernel modules.
Anastassiou disclose a notable feature of the EMBP method of the disclosed subject matter is that it can be systems-based, in the sense that it considers the synergistic contributions of sets of genes, rather than individual genes [0071]. Anastassiou further discloses as a result, the optimal gene module may not be a subset of the optimal gene module [0071].
Claim 12 is directed to the sample analysis method according to claim 7, wherein the at least one expression profile comprises connectivity between a kernel module in a genomic module network among the first genomic module networks and the other modules in the genomic module network.
Anastassiou discloses the method includes identifying factors jointly associated with an outcome from a data set for a plurality of factors, and analyzing each of the plurality of modules to determine a structure of interactions among the factors with respect to the outcome and the data set can be a set of measurements that includes values of the factors and the outcome [0020].
Claim 13 is directed to an analysis device to perform the methods of claim 7. The art is applied as above in claim 7 to claim 13.
In regards to claim(s) 7-8 and 10-13, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Anastassiou with the methods of Han, because both references disclose methods for analyzing gene expression data. The motivation would have been to modify the genomic model network of Anastassiou with the genomic module of Han to analyze novel traits to improve gene expression as a tool for breast cancer prognosis [p. 296, col. 1, par. 1]. One could have therefore combined the elements as claimed by the known methods of Anastassiou, and Han, and that in combination, each element merely would have performed the same function as it did separately for the predictable result of comparing genomic modules genes between different sample types.
Conclusion
No claims are allowed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/D.M.B./Examiner, Art Unit 1685 /Soren Harward/Primary Examiner, TC 1600