DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Amendments
2. The amendment filed Dec. 12, 2025 has been entered. Claims 1, 37-38, 40 and 42 are amended. Claims 3-14, 18-35 and 43-44 have been canceled. Claims 1-2, 15-17, 37-42, and 45 are under consideration in this Office Action.
Withdrawal of Claim Rejections
3. The following rejections have been withdrawn in view of applicant’s amendments and cancellation of claims:
a) The rejection of claims 1-2, 15-17, 36-42, and 44-45 under 35 U.S.C. 101;
b) The rejection of claims 1-2, 15-17, 36-42, and 44-45 under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph; and
c) The rejection of claims 1-2, 15-17, 36-42, and 44-45 under 35 U.S.C. 103 as being unpatentable over Cutliffe et al., in view of Gordon et al.
New Grounds of Rejection Necessitated By Applicants Amendments
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
4. Claims 1-2, 15-17, 37-42, and 45 are rejected under 35 U.S.C. 103 as being unpatentable over Cutliffe et al., (US Pat Pub. 20150259728 published Sept, 2015 priority to July 2014) in view of Budding et al., (US 20170159108 published 2017-05-14; priority to 2015-05-06).
The claims are drawn to a method of treating a method of treating diarrhea in an individual suspected of having Clostridioides difficile infection (CDI), comprising
(a) administering fecal microbiota transplantation (FMT) and/or one or more antibiotics suitable for treating CDI to the individual when the individual is determined to have a CDI signature generated using a microbiome-based risk classifier by comparing measured feature levels from sequencing 16S rDNA from a gastrointestinal sample and/or amplification of the level of 16S rRNA, and mass spectrometry-based metaproteomics, wherein said comparing is to a threshold to yield a prediction score;
wherein the CDI signature is selected from:
(i) a microbiome signature comprising feature levels from sequencing 16S
rDNA from one or more Bacteroides; Eubacterium rectale; Ruminococcus;
Faecalibacterium; Enterococcus; Streptococcus; Klebsiella; Enterobacteriaceae;
Roseburia; Coprococcus; Dorea; Lachnoclostridium; Clostridium XIVa;
Erysipelatoclostridium: Alistipes; Fusicatenibacter; Odoribacter; Lactobacillus;
Anaerostipes; Adlercreutzia; Gemmiger, Collinsella; Clostridiales; Prevotella;
Veillonella; Dialister; Barnesiella; Phascolarctobacterium; Eggerthella;
Oxalobacter; Colidextribacter, Agathobaculum; Lachnospiraceae; Fusobacterium
or any combination thereof; and
(ii) a metaproteome signature comprising one or more microbiome and/or
host proteins features and wherein the prediction score for the individual is greater to indicate CDI or reducing the administration of one or more antibiotics to an individual having a prediction score of less than 0.50 indicating a non-infectious enteric disease or antibiotic-associated diarrhea.
Cutliffe et al., disclose a method of treating a disease in a subject, comprising: measuring a microbiome profile in a biological sample obtained from the subject, wherein the microbiome profile comprises at least one microbe; detecting a presence or absence of the disease in the subject based upon said measuring; and treating the disease in the subject based upon said detecting [para 14]. The method of diagnosing a subject a disease in a subject, comprising: measuring a microbe panel in a biological sample obtained from the subject, wherein the microbiome panel comprises at least one microbe; detecting a presence or absence of a disease state in said subject based upon said measuring; and; recommending to the subject at least one microbial-based therapeutic or cosmetic for treatment of said disease based on the detecting [para 17]. Thereby teaching claim 1. Cutliffe et al., disclose generating a microbiome profile of said microbiome panel based upon the measurement data; comparing said microbiome profile of said microbiome panel to a reference profile [Para 12]. Cutliffe et al., disclose comparing said microbiome profile of said microbiome panel to a threshold level of a reference; and determining a likelihood of a disease status in said subject based on said comparing of at least one threshold level of a reference of said microbiome panel [para 16]. Example 5 discloses healthy control subjects; thus teaching claim 1.
Cutliffe et al., disclose a method of determining metabolic pathways that are indicative of a health status in a subject, comprising: obtaining RNA sequences from a biological sample from a subject, such that the entire transcript is contained within a single read length; analyzing said transcripts by a sequencing method; comparing the sequenced transcripts to a reference; and determining the metabolic pathways that are indicative of a health status [para 13]. FIG. 1 depicts an exemplary computer system for implementing a method described herein. This includes a continually enlarging database of full rRNA operons as the methods described herein allow this to be expanded in a cost-effective manner that hasn't been previously available. Microarray chips are generally used for DNA and RNA gene expression detection [para 46]. Cutliffe et al., describe the identification, classification or quantification of at least one microbiome comprising comprehensive analysis of at least one of the 16S ribosomal RNA (rRNA) subunits or intergenic regions [para 50]. The analysis of the 16S ribosomal RNA gene is one approach that can be used to understand microbial diversity [para 120]. Any of the methods provided herein can include embodiments wherein the biological sample is taken from a microbiome is the intestinal microbiome, stomach microbiome, gut microbiome, and/or gastrointestinal tract microbiome [para 22]. Thus teaching claims 1-2.
The tested bacteria genera include Coprococcus [para 169-170]. Any diagnostic microbiome profile, a subject-specific microbiome profile, or a therapeutic/cosmetic described herein can include one or more, but are not limited to the following microbes Actinomyces, Adlercreutzia, Anaerostipes, Clostridioides, Collinsella, Dorea, Enterococcus, Eubacterium, Faecalibacterium, Fusicatenibacter, Gemmiger,
Klebsiella, Odoribacter, Prevotella, Roseburia, Ruminococcus, Streptococcus, Dialister, Barnesiella, Bacteroides, Phascolarctobacterium, Escherichia, Veillonella, Odoribacter, Oxalobacter, Lachnobacterium, and Lachnospiracea, [para 144]. Thus teaching the bacteria of claims 1 and 37-38. It is noted that Lachnoclostridium is an obsolete genus of Lachnospiracea which Cutliffe et al., clearly teach. While Agathobaculum has been classified or reclassified as a Butyricicoccus, a Butyricicoccus bacterium, and/or a Eubacterium. Thus teaching the bacteria of claims 1, 37-38, 40, 42 and 45.
Cutliffe et al., clearly teach nucleic acids sample that can be used with the present disclosure include all types of DNA and RNA [para 96]. Other sequencing systems and approaches that can be used with the present disclosure include but are not limited to long read length Sanger sequencing, long read ensemble sequencing approaches [para 102]. In some applications the entire genome to the microbe will be analyzed to determine a subject's microbiome profile. In other applications, the variable regions of the microbe's genome will be analyzed to determine a subject's microbiome profile. In some applications, the variable regions of the 16S or 23S ribosomal subunit to the microbe will be analyzed to determine a subject's microbiome profile [para 72]. Cutliffe et al., provide for a database that has additional or more accurate microbe information such as the composition of particular microbiomes in a particular cohort, or microbiome reference profiles of a particular cohort. Such database can include additional or more accurate sequences comprising the 16S subunit of ribosome for a given microbe strain, additional or more accurate information of the sequence comprising the intergenic region between the 16S subunit and 23S subunit of ribosome, additional or more accurate information of the sequence comprising variable regions in the 16S ribosome for a particular strain, additional or more accurate information of the sequence comprising variable regions with a high accuracy in strain resolution at the genus level, additional or more accurate information of the sequence comprising variable regions with a high accuracy in strain resolution at the species level, or additional or more accurate information of the sequence comprising variable regions with a high accuracy in strain resolution at the sub-type level [para 165].
16S sequencing can be used to detect the presence or absence of specific candidate bacteria that are biomarkers for health or a particular disease state [para 214]. Measuring, or determining a profile of a microbiome can include the use of a database as provided herein [para 175]. Such profiling method can have at least an accuracy greater than 70% based on measurement of 19 or fewer microbes where the microbiome profile can be used in part or solely to calculate a quantitative score [para 51-52]. A decrease or increase in one or more microbes' threshold values in a subject's microbiome profile indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management [para 54]. Computer systems disclosed herein may comprise computer-executable code for performing at least one of: generating a cohort-generalized microbiome profile or a subject-specific microbiome profile based upon the measurement data from a biological sample from the subject, comparing the cohort-generalized microbiome profile or subject-specific microbiome profile to at least one reference and determining the health status of a subject [para 176]. Thus teaching the instant claims. The present disclosure provides for machine learning algorithms for building a diagnostic microbiome profile of a subject. Depending on the application a diagnostic microbiome profile can a generate score from a microbiome profile, can be a comparison to a reference microbiome profile, can be the level of a microbiome profile above a defined threshold or a combination thereof [para 132]. In the case of amplicon sequencing (16S, 23S, and other marker genes) the raw data produced from this platform is first filtered for proper primer orientation, pairing, and completeness. The resulting molecules are then filtered based on quality (with quality thresholds of greater than 0.95, 0.99, 0.999, etc. being possible). These molecules then form the basis set of reads to be used to establish the de novo clusters, and can directly be compared to the known reference databases [para 134]. The resulting molecules are then filtered based on quality (with quality thresholds of greater than 0.95, 0.99, 0.999, etc. being possible). All scores above 0.5, just as required by the instant claims. These molecules then form the basis set of reads to be used to establish the de novo clusters, and can directly be compared to the known reference databases [par 134]. Examples of machine learning algorithms that can be used include, but are not limited to: elastic networks, random forests, support vector machines, and logistic regression. The algorithms provided herein can aid in selection of important microbes and transform the underlying measurements into a score or probability relating to, for example, disease risk, disease likelihood, presence or absence of disease, treatment response, and/or classification of disease status [para 136]. An increase in a score indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some embodiments, a decrease in the quantitative score indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management [para 138]. Thus teaching using a microbiome-based risk classifier by comparing measured feature levels from sequencing 16S rDNA from a gastrointestinal sample wherein said comparing is to a threshold to yield a prediction score; wherein the prediction score for the individual is greater to indicate CDI or producing a lower prediction score of less than 0.50 indicating a non-infectious enteric disease or antibiotic-associated diarrhea.
Cutliffe et al., teach combinations of microbes provided can be used to develop appropriate compositions for treating a subject suffering from a condition [para 55]. Thus teaching claim 39. The biological sample can be stool [para. 58]. Thereby teaching instant claim 2. Bacteremia or septicemia refers to the presence of bacteria in the blood. A diagnosis of bacteremia is usually confirmed by a blood culture. Treatment usually requires hospitalization and intravenous antibiotics. Without prompt treatment, bacteremia can quickly progress to severe sepsis. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for bacteremia, which can include the antibiotic susceptibilities of the infection [para 204]. Thus teaching claim 1. The gastrointestinal disease, disorder or condition is diarrhea caused by C. difficile including recurrent C. difficile infection, ulcerative colitis, colitis, Crohn's disease, or irritable bowel disease [para 207]. Clostridium difficile, often called “C. difficile” or “C. diff”, is a bacterium that can cause symptoms ranging from diarrhea to life-threatening inflammation of the colon. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for infections such as C. difficile. The present disclosure also provides therapeutic or cosmetic formulations for treatment of Clostridium difficile infections [para 208]. Thereby teaching claims 16-17.
The mammalian subject is a human subject who has one or more symptoms of a dysbiosis [para 205]. When a mammalian subject is suffering from a disease, disorder or condition characterized by an aberrant microbiota, the bacterial compositions described herein are suitable for treatment [para 206]. Thus teachings claims 39 and 41. The subjects are those who are at risk for infection with or who may be carriers of these pathogens, including subjects who will have an invasive medical procedure (such as surgery), who will be hospitalized, who live in a long-term care or rehabilitation facility, who are exposed to pathogens by virtue of their profession (livestock and animal processing workers), or who could be carriers of pathogens (including hospital workers such as physicians, nurses, and other health care professionals) [para 207]. Thereby teaching claims 15-17. Qualitative assessments can be accomplished using 16S profiling of the microbial community in the feces of normal mice. Cutliffe et al., teach the mice can receive an antibiotic treatment to mimic the condition of dysbiosis [para 223].
Therefore Cutliffe et al., teach a method of treating a subject with diarrhea, comprising detecting a level of one or more microbe compared to a control, wherein the one or more microbes comprise Clostridioides, Gemmiger, Adlercreutzia, Roseburia, Faecalibacterium, Anaerostipes, Collinsella, Lachnoclostridium, Actinomyces, Streptococcus, Enterococcus, Prevotella, Klebsiella, Odoribacter, and Dorea in a gut sample from the subject by amplification and sequencing of 16S rDNA from the sample and calculating a quantitative score that can be used to predict disease status to a therapeutic in a subject. The computer system can further comprise computer-executable code for providing a report communicating the detecting, measuring, or determining a profile of a microbiome from a subject [para 175].
Budding et al., teach microbial population analysis. Budding et al., teach testing fecal samples of Clostridium Difficile Associated Diarrhea patients in Example 20. Budding et al., teach a method to identify Clostridium difficile in fecal samples up to the strain level and at the same time measure the diversity of the fecal microbiota (FIG. 46). Budding et al., teach analyzing the composition of the population of microorganisms in said microbiome based on taxonomic variation in the DNA sequence of the microbial 16S rRNA in the genomic DNA of said microorganisms [abstract].
By combining this information, Budding et al., can provide an accurate diagnosis, providing doctors with information on the presence of Clostridium difficile, the strain type and the status of the microbiota, which taken together determines the diagnosis and treatment modality: when microbiota is severely depleted, a choice could be made for microbial supplementation therapy (e.g. fecal transplantation), when microbiota is less depleted, a conventional antibiotics therapy could be given, while no therapy should be given if Clostridium difficile is not found. Finally, the effect of therapy can be monitored: whether bacterial suppletion (e.g. FMT) therapy is successful, whether antibiotic therapy is not depleting the microbiota and other effects [para 411]. Budding et al., teach providing a sample of genomic DNA from the microorganisms in a microbiome; performing a PCR amplification reaction on said sample of genomic DNA using at least one set of PCR amplification primers directed to said flanking conserved DNA regions to thereby amplify and provide amplification products of said ITS regions comprised in said genomic DNA sample; analyzing said amplification products based on length differences in said amplification products to thereby provide a test signature of the composition of the population of microorganisms in said microbiome; and comparing said test signature with at least one reference signature of a desirable microbiome and/or with at least one reference signature of an undesirable microbiome, preferably by clustering of ITS profiles, and classifying the test signature as a signature of a desirable microbiome or as a signature of an undesirable microbiome [para 16]. The method of typing the intestinal flora of a subject for having a signature, an increased diversity in the phylum Proteobacteria is indicated by an increase in the presence of at least one of the species selected from the group consisting of Escherichia coli, Klebsiella pneumoniae, Enterobacter aerogenes, Serratia marcescens, Klebsiella variicola, Providencia stuartii, Desulfovibrio spp., Stenotrophomonas spp. (Xanthomonas spp.), Pseudomonas aeruginosa, Burkholderia spp. and Aggregatibacter actinomycetemcomitans [para 116].
Diagnosis, monitoring or typing can be performed by assessing the absence or presence of specific peaks, the abundance thereof, or the peak volume, or by applying machine learning algorithms on at least one, preferably at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 signatures or profiles, preferably having been typed previously as desirable (e.g. healthy) and/or undesirable (e.g. diseased) signatures or profiles. Suitable machine learning algorithms include Support Vector Machines, Random Forest or partial least squares discriminant analysis. The skilled person is aware of these and other machine learning programs and knows how to apply them on generated data sets in order provide a tool for diagnosis, monitoring or typing in methods as described [para 242]. Partial Least Square-Discriminant Analysis (PLS-DA) provides a quantitative estimate of the discriminatory power of each descriptor by means of VIP (variable importance for the projection) parameters. VIP values rank the descriptors by their ability to discriminate different groups and are therefore considered an appropriate quantitative statistical parameter. The VIP criterion can be used to rank the different OTUs based on their contribution to the response variable (for example, clinical status such as: present or absent) and PLS components. Only the OTUs with the highest contribution (VIP score>1.2) are considered [para 245]. We used the VIP criterion to rank the different OTUs based on their contribution to the response variable (clinical status, i.e: yes or no) and PLS components [para 318].
FIG. 46 shows the result of diagnosis of Clostridium difficile infection in the intestinal tract of infected and diseased patients, asymptomatic carriers and healthy (no C. difficile) subjects having diarrhoea. Displayed are the IS-pro profiles, the Phylum distribution, and the Diagnosis. Clearly visible is the low population diversity as indicated by a low Shannon index in diarrhoea patients suffering from infection with a pathogenic C. difficile strain, while asymptomatic carriers of a low pathogenic C. difficile strain and healthy subjects maintain Shannon index diversities above 3. Hence, subject having been diagnosed with a low overall population diversity in combination with C. difficile infection may benefit from fecal microbiota transplantation, rather than antibiotic treatment alone, while diarrhoea patients having a high overall population diversity in combination with C. difficile infection may benefit from antibiotic treatment [para 165]. Shannon diversity index was calculated for all samples and showed significant differences between healthy and diseased samples (FIG. 16). Difference in diversity was most outspoken with a mean Shannon diversity index of 2.38 for disease samples (IQR 0.56) and 0.69 for healthy samples (IQR 1.09) [para 378].
Therefore, it would have been prima facie obvious at the time of applicants’ invention to apply Buddings et al’s binary (yes or no) diagnosis analysis to Cutliffe et al’s method of treating CDI in order to provide a method to identify Clostridium difficile in fecal samples which provides an accurate diagnosis and treatment plan. One of ordinary skill in the art would have a reasonable expectation of success by incorporating Cutliffe et al., and Budding et al., in order to provide a choice/guidance for microbial fecal transplantation supplementation therapy, conventional antibiotics therapy or no therapy should be given if CDI is not found. Finally, Budding et al., teach an increase or decrease in a score provides a microbiome-based risk classifier by comparing measured feature levels from sequencing 16S rDNA from a fecal sample wherein said comparing is to a threshold to yield a prediction score.
Additionally, KSR International Co. v. Teleflex Inc., 127 S. Ct. 1727, 1741 (2007), discloses combining prior art elements according to known methods to yield predictable results, thus the combination is obvious unless its application is beyond that person's skill. KSR International Co. v. Teleflex Inc., 127 S. Ct. 1727, 1741 (2007) also discloses that "The combination of familiar element according to known methods is likely to be obvious when it does no more than yield predictable results". It is well known to take a method of treating a subject with diarrhea where there is no change in the respective function of the amplification and sequencing procedure using methods well known in the art , thus the combination would have yielded a reasonable expectation of success along with predictable results to one of ordinary skill in the art at the time of the invention. Therefore, it would have been obvious to a person of ordinary skill in the art to combine prior art elements according to known methods that is ready for improvement to yield predictable results. The claimed invention is prima facie obvious in view of the teachings of the prior art, absent any convincing evidence to the contrary.
Response to Arguments
5. Applicant’s arguments with respect to claims 1-2, 15-17, 37-42, and 45 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicants argue that Cutliffe et al., fails to teach a CDI-specific risk classier that compares measured features to a threshold indicating CDI. Contrary to Applicants argument, Cutliffe et al., disclose a method of treating a disease in a subject, comprising: measuring a microbiome profile in a biological sample obtained from the subject, wherein the microbiome profile comprises at least one microbe; detecting a presence or absence of the disease in the subject based upon said measuring; and treating the disease in the subject based upon said detection. The gastrointestinal disease, disorder or condition is diarrhea caused by C. difficile including recurrent C. difficile infection. Cutliffe et al., disclose generating a microbiome profile of said microbiome panel based upon the measurement data levels of the instantly recited bacteria; comparing said microbiome profile of said microbiome panel to a reference profile. Similarly, Budding analyzes the fecal composition for the population of microorganisms in said microbiome based on taxonomic variation in the DNA sequence of the microbial 16S rRNA genomic DNA of said microorganisms. Therefore, the prior art references a CDI-specific risk classier that compares measured features to a threshold indicating CDI. More specifically, Budding et al., teach testing fecal samples for Clostridium Difficile Associated Diarrhea in patients. Therefore, the prior art references clearly teach a CDI-specific risk classier that compares measured features to a threshold indicating CDI.
Applicants argue that Cutliffe et al., does not teach administering fecal microbiota transplantation (FMT) and/or one or more antibiotics suitable for treating CDI based upon comparing is to a threshold to yield a prediction score. However, Cutliffe et al., teach therapeutic treatment of a C. diff infection. Budding et al., provide an accurate diagnosis, providing doctors with information on the presence of Clostridium difficile and the status of the microbiota, which taken together determines the diagnosis and treatment modality: when microbiota is severely depleted, a choice could be made for microbial supplementation therapy (e.g. fecal transplantation), when microbiota is less depleted, a conventional antibiotics therapy could be given. Therefore, the prior art references clearly teach the instantly recited claimed limitations.
None of Applicants arguments are found persuasive.
Pertinent Art
6. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Schubert et al., (ASM Journals mBio. Vol. 5. No.3) teach Microbiome Data Distinguish Patients with Clostridium difficile Infection and Non-C. difficile-Associated Diarrhea from Healthy Controls.
WO 2013070962 teach methods for generating a metabolite profile of a stool sample and methods of assessing the status of a subject using the metabolic profile derived from a stool sample.
US20170058430 teach methods of identifying infections, such as methods of identifying bacterial infections which utilize whole metagenome sequence analysis to sequence the entire wound microbiome of clinical samples. The disclosed methods use fast k-mer based sequence analysis, predictive modeling, and Bayesian network analysis, to analyze bacterial metagenomic sequence compositions in conjunction with clinical factors to stratify communities of bacteria into healing versus non-healing clusters. The abundance value for k-mers found only infrequently in a comparison sample is set to 0 as if the k-mer was not found at all. This step keeps the mode at zero for reads that are derived from sequencing error, technical errors in library preparation (e.g., chimeras), etc.
Conclusion
7. No claims allowed.
8. 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.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JA-NA A HINES whose telephone number is (571)272-0859. The examiner can normally be reached Monday thru Thursday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Dan Kolker, can be reached on 571-272-3181. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JANA A HINES/Primary Examiner, Art Unit 1645