DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
In the reply filed on 20 February 2026 the following changes have been made: amendments to claims 1 and 10-11. Claim 25 remains withdrawn.
Claims 1-7, 9-13, 16-17, 19-22, and 24 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7, 9-13, 16-17, 19-22, and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process, (claims 1-7, 9, 20-22, and 24), machine (claim 10), and article of manufacture (claim 11-13, 16-17, and 19).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
receiving, by a computing device, a first dataset, the first dataset comprising (i) genotype data for a subject having one or more non-Mendelian gene variants of interest (g) and (ii) genotype data and phenotype data for one or more blood relatives of the subject that have one or more of the non-Mendelian gene variants of interest and a phenotype of interest,
receiving, by the computing device, a second dataset, the second dataset comprising genotype population data and phenotype population data, wherein the population comprises one or more sets of two or more blood relatives that have the one or more non-Mendelian gene variants of interest and have or do not have and the phenotype of interest,
modeling, by the computing device, a probability P(D) of the subject developing the phenotype of interest D from each of the one or more non-Mendelian gene variants of interest, the modeled probability P(D) being represented by
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and based on at least one or more indicator variables X1-XG for each of the one or more non-Mendelian gene variants of interest, the genotype population data, the phenotype population data, and whether the one or more sets of two or more blood relatives have the phenotype of interest Xr;
training, by the computing device, at least one classification model based on the first dataset, the second dataset and the modeled probability, wherein the training is based on a sum of each of one or more effect sizes bg,r associated with the one or more blood relatives, where
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and where P(D|XrXg = 1) and P(D|XrXg = 0) are computed from the first dataset,
each of the one or more effect sizes bg,r being based on the phenotype of interest D being observed in the one or more blood relatives that have the one or more non-Mendelian gene variants of interest (g);
generating, by the computing device using the at least one classification model, a phenotypic risk score for the one or more non-Mendelian gene variants of interest for the subject, and
outputting, by the computing device and based on the phenotypic risk score, the subject as having or being at risk of the phenotype of interest.
Claims 10-11 and 23 recite similar limitations as claim 1 but for the recitation of generic computers such as a processor and memory.
These steps directed to determining genetic risk for non-mendelian phenotypes, as drafted, under the broadest reasonable interpretation, performance of the limitations in the mind. That is, nothing in the claim element precludes the italicized portions from practically being performed in the mind through incorporating family disease history to determine whether a subject is at risk for a non-Mendelian phenotype. This could be analogized to collecting information, analyzing it, and displaying certain results of the collection and analysis. The italicized portions containing the recitations of modeling and training at a high level of generality have now been treated as part of the abstract idea, specifically as mathematical calculations which falls within the abstract idea of mathematical concepts, in light of the new 2024 USPTO AI Guidance. Furthermore, the italicized portions containing the recitation of modeling probability (i.e., conditional probability) and recitation of generating a classification model (i.e., based on log likelihood) have been treated as part of the abstract idea, specifically as mathematical formulas or equations which falls within the abstract idea of mathematical concepts. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations in the mind and mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Process” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements non-italicized portions identified above for claims 1, 10-11, and 23 do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as by a computing device; a processor; a memory coupled to the processor to store instructions which, when executed by the processor, cause the processor to perform operations; and a non-transitory machine-readable medium having instructions stored therein which, when executed by a processor, cause the processor to perform operations amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of receiving, […] a first dataset; receiving, […] a second dataset; and, outputting, […] based on the phenotypic risk score, the subject as having or being at risk of the phenotype of interest amounts to mere data gathering and output since it does not add meaningful limitations to the receiving and outputting actions performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity, and also add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as by a computing device; a processor; a memory coupled to the processor to store instructions which, when executed by the processor, cause the processor to perform operations; and a non-transitory machine-readable medium having instructions stored therein which, when executed by a processor, cause the processor to perform operations, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f);
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of receiving, […] a first dataset; receiving, […] a second dataset; and, outputting, […] based on the phenotypic risk score, the subject as having or being at risk of the phenotype of interest, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-7, 9, 12-13, 16-17, 19-22, and 24 reciting particular aspects of determining genetic risk for non-mendelian phenotypes such as
[Claims 2 & 12] wherein the second dataset comprises genotype population data and phenotype population data for more than one set of two or more blood relatives;
[Claims 3 & 13] wherein the one or more blood relatives in the first dataset comprise one or more of the subject's mother, father, brother, sister, son, daughter, grandfather, grandmother, aunt, uncle, niece, nephew, and first cousin; and wherein the second dataset comprises two or more blood relatives having a same blood relationship as does the subject and one of the subject's one or more blood relatives in the first dataset;
[Claim 4] wherein one or more of the blood relatives is a male relative;
[Claim 5] wherein one or more of the blood relatives is a female relative;
[Claims 6 & 16] wherein the first dataset comprises genotype data and phenotype data for more than one blood relative of the subject;
[Claims 7 & 17] wherein one or more of the blood relatives is a male relative and one or more of the blood relatives is a female relative;
[Claims 9 & 19] wherein the first dataset and second dataset each comprise data associated with an age of onset of a phenotype;
[Claim 20] wherein the one or more non-Mendelian gene variants of interest comprises at least two genes, the risk in the subject is associated with two or more of the non-Mendelian gene variants of interest, and the phenotypic risk score is a polygenic risk score;
[Claim 21] training a model on the first and second datasets to predict how the risk in the subject is modified by one or more non-Mendelian gene variants of interest, relative to the risk in the subject given the phenotype data of the blood relatives; and,
[Claim 22] treating the subject based on the phenotypic risk score
[Claim 24] wherein the PRS is computed by PRS =
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these italicized portions covers performance of the limitations in the mind but for recitation of generic computer components since they merely describe types of data and determinations that can be performed by humans. Additionally, the italicized portions containing the recitation of training at a high level of generality have been treated as part of the abstract idea, specifically as mathematical calculations which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance. The italicized portion containing the recitation of computing the PRS has been treated as as mathematical formulas or equations which falls within the abstract idea of mathematical concepts).
Step 2A Prong 2
Dependent claims do not recite additional subject matter which integrate the abstract idea into a practical application.
Step 2B
The dependent claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Response to Arguments
The arguments filed on 20 February 2026 have been considered, but are not fully persuasive.
Regarding the election by original presentation, applicant compares the language of claim 1 and claim 25 to assert that the both claim 1 and claim 25 re directed to modeling the probability of the phenotype and generating a risk score
Applicant also asserts that the equation presented in Example 8 recited in claim 25 has a variable that uses the effect size in Example 7; also recited in previous claim 1. Applicant requests reconsideration of the election by original presentation.
Examiner disagrees with the applicant’s argument. Applicant’s argument is unpersuasive as it tries to justify how mathematically distinct approaches for modeling genetic risk are related. Examiner did not make the argument that the output of the log-odds in claim 1 does not apply to claim 25. Rather, it is evident to one of ordinary skill in the art that the modeling for the probability P(D) performed in claim 1 uses a multiplicative adjustment based on the Bayes Rule with respect to a set of relatives who have the phenotype. The Bayesian logic is used to justify the weights to then sum those weights into a linear score. Example 7 in the specification uses the Bayesian ratio to manually calculate what an effect size should be. On the other hand, one of ordinary skill in the art would see that the modeling for the probability P(D) performed in claim 25 uses logistic regression as an alternative way rather than using direct multiplicative ratios disclosed in claim 1. Example 8 relies on modeling the fed genotype/phenotype data to “learn” the best effect sizes (versus the manual computation performed in Example 7 reflected in claim 1) that minimizes prediction error. Therefore, the examiner’s decision of election by original presentation is final and claim 25 will remain withdrawn.
Regarding the USC 112(d) rejection, applicant has amended claim 1 to
remove the phrase "treating the subject based on the outputting," thus claim 22 further limits the subject matter of claim 1. Therefore, the 112(d) rejection has been withdrawn.
Regarding the USC 101 rejection, applicant argues on pages 11 to 14 that amended claims 1, 10, and 11 recite are directed to subject matter that the USPTO’s examination guidance demonstrates is eligible for patenting. Applicant cites claim 3 of Example 47 from the 2024 USPTO AI Guidance and how it was patent eligible. Applicant points to the datasets/data recited and claim 1 and cites [0042] & Example 7 to assert that the data volumes are well beyond human capacity. Where because the rejected limitations (model construction, model training, dataset integration, effect-size summations from relatives, etc.) cannot be carried out mentally in any practical sense, they do not recite a mental process. Regarding Step 2A Prong 2, the applicant cites the 2024 USPTO AI Guidance and asserts that the claims as a whole integrate the judicial exception into a practical application. Applicant cites [0077] and [0078] of the specification to show the math solution being reflected in the claims; also, applicant lists several limitations of claim 1 to show how these limitations are similar to claim 3 of Example 47 and reflect an improvement of determining genetic risk of non-Mendelian phenotypes. Applicant disagrees with the examiner’s previous assertion that the claims are similar to claim 2 of Example 47, and points out that compared to claim 2 of Example 47 the present claims do place limits on how the trained model functions (i.e., modeled probability and training is based on a sum of each of one or more effect sizes). Applicant asserts that claim 1 recites improvements to how the classification model is trained. Regarding Step 2B, citing the examiner’s justification for withdrawing the prior art, applicant asserts that even if the claimed invention were to recite an abstract idea, under Step 2B the trained model adds inventive concept. Applicant recites the limitations of claim 1 and asserts that the limitation of “determining…based on the phenotypic risk score…” uses the result of the unconventional step (i.e., "training, based on a sum of each of the one or more effect sizes) to improve a conventional system for determining whether a subject is at risk for a non-Mendelian phenotype. Applicant states that claims 10 and 11 recite similar limitations as claim 1. Applicant requests withdrawal of the USC 101 rejection.
Examiner disagrees with the applicant’s arguments. Examiner asserts that the present amendment still doesn’t do much to help overcome the USC 101 rejection. There is no nexus between what is argued/in the specification [0042] and what is actually claimed. Specifically, there is zero quantitative indication in claim 1 that hints at “one-million-subject” or any large volume of data. Examiner also points out that since the applicant’s claim construction places no limits on the amount of time for their invention to process the data, a human could take as long as they want or as many seconds, minutes, hours, etc. needed to mentally perform the claimed invention with (or without) the use of a computer (or pen and paper) as a tool; since the claim also recite math. Claiming derivations of well-known mathematical equations and calculations that reflect the established mathematical concepts i.e., conditional probability & log likelihood do not automatically overcome the abstract idea. Examiner points to the USPTO October 2019 Guidance (also incorporated in MPEP 2106) which states that “claims can recite a mental process even if they are claimed as being performed on a computer.” The USPTO October 2019 Guidance is clear in that the courts have found claims requiring a generic computer or nominally reciting a generic
computer may still recite a mental process even though the claim
limitations are not performed entirely in the human mind. The present
claims are still rejected as falling under mental process. The claimed steps
are analogous to "collecting information, analyzing it, and displaying
certain results of the collection and analysis," where the data analysis
steps are recited at a high level of generality such that they could
practically be performed in the human mind, Electric Power Group v.
Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed.
Cir. 2016). While the limitation such as the outputting step has been further evaluated under Step 2A Prong 2, the training step and use of the classification model (i.e., based on log-likelihood) has been treated as a part of the abstract idea. Applicant’s claim still follows the fact pattern of ineligible claim 2 of Example 47 where that training step was also treated as a part of the abstract idea. Despite the applicant’s assertions, examiner asserts that the claim puts no limit on how the computer actually performs the argued limitations (i.e., computer-focused operations) such they cannot be considered an abstract idea. Specifically, the claim limitations, especially the training limitation pointed to by the applicant, are very outcome-focused and do not detail how each of the outcomes are exactly reached. Examiner also points out that unlike claim 3 of Example 47, it is unclear what algorithm is even being used for the training step. Hence, due to lack of details on the algorithm, the claim is performing a black box training with no clarity on the actual computer processing or how the computer is programmed to achieve the results in a non-abstract way different from how humans analyze/process data. Examiner asserts that the August 4, 2025 Memo from the USPTO still does not apply to the present invention as that memo was in regard to inventions that demonstrated improvements to AI technology and thus were AI-focused inventions; this is clearly not the case for the present invention as the applicant is improving upon the abstract idea. One of ordinary skill in the art would understand that applicant’s invention is directed to judicial exception, as also confirmed by multiple subject matter experts at the USPTO. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to ‘implement[ing] the abstract idea of intermediated settlement on a generic computer’, it cannot save OIP's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). Even if the claims nominally recites computer components that are rooted in technology, there is no recitation of how the computer components are specifically programmed to distinguish from generic computer processes. Thus, the present claim(s) are still not eligible under Step 2A Prong 1.
With respect to Step 2A Prong 2, examiner asserts the present specification provides a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art. The MPEP provides that improvements to the functioning of a computer or to any other technology or technical field can signal eligibility, see MPEP 2106.05(a), and provides examples of improvements to computer functionality, MPEP 2106.05(a)(I), and improvements to any other technology of technical field, MPEP 2106.05(a)(I). “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool”. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification' s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general-purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691. Unlike Enfish, the instant claimed invention appears to improve upon a judicial exception rather than a problem in the software arts or computer technology. Rather than improving a computer's algorithm (i.e., solving a technically based problem), the claimed invention purports to solve the non-technological problems of the effects of multiple genes in non-Mendelian phenotypes and the inaccuracy of polygenic models ([0003] of the specification) through training a generic model to output a non-Mendelian phenotypic risk score ([0004] of the specification). The problems outlined by the specification do not point to any issue with the functionality of comparable computer/software-based technologies for phenotypic risk. In other words, one of the main/glaring issues with the present invention is that the problem solved by the applicant is not a technological problem. Applicant directs to approaches such Example 7 in the specification allowing or more accuracy and precision, but examiner points out that is simply describing a mathematical derivation which is an improvement to the abstract idea and not patent eligible subject matter. With respect to Example 47, examiner asserts that the present claims are still not similar in scope to claim 3 of Example 47. In claim 3 of Example 47 provided a technology solution to a technology problem where after the detection of the anomaly is performed in steps (a)-(c), steps (d)-(f) actively block future traffic from the source address thus integrating the judicial exception into a practical application. On the other hand, applicant’s claims determines whether a subject is at risk for a non-Mendelian phenotype, but unlike claim 3 of Example 47 applicant’s claims are lacking critical steps on using the phenotype risk determination to address/treat the patient; instead, the claim just ends with an outputting step which does not make the claim eligible. Applicant’s current amendment isn’t meaningful and still does not help integrate the judicial exception into a practical application. In regards to the modeling, all the applicant is still doing is applying an existing/generic model for training in a new data environment and calling it an improvement. The applicant’s own specification does not support the assertion that the improvement is of a technological nature. The examiner asserts the following facts:
1) the invention does NOT involve a novel algorithm or data structure that significantly improves the computer's functionality,
2) the invention does NOT involve a new hardware component or configuration that works with the computer to achieve a specific technical benefit, and
3) the computer is NOT used in a completely new way demonstrating a significant technical advancement. Improvement to the abstract idea is not an improvement to computer technology.
Thus, examiner does not see how the present claims improve the functioning of a computer or provide improvements to any other technology or technical field. The claimed invention appears similar to the example of improvements that are insufficient to show an improvement in computer-functionality such as arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). See MPEP 2106.05(a)(I)(viii). Examiner points out that the claimed limitations have no indication in the specification that the operations recited invoke any inventive programming, require any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is implemented using other than generic computer components to perform generic computer functions. See DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (fed Cir. 2014) (“[A]fter Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.”). Most importantly, in DDR Holdings & unlike the present claims, the claims at issue specified how interactions with the Internet were manipulated to yield a desired result—a result that overrode the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink. 773 F.3d at 1258; 113 USPQ2d at 1106. The examiner also points out that there is no indication in the specification that the claimed invention affects a transformation or reduction of a particular article to a different state or thing. Examiner points to the recitation of a classification model for training as generic. "[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice Corp. v. CLS Banklnt'l, 573 U.S. 208 223 (2014). Applicant does not and cannot contend they invented the concept of machine learning, nor does the specification disclose any new machine learning technique. The alleged improvement of using a classification model lies in the abstract idea itself, not to any technological improvement nor to any improvement to the functioning of a computer. See BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88 (Fed. Cir. 2018). This supports the examiner’s assertion that the present invention does not integrate the abstract idea into a practical application. To show an involvement of a computer assists in improving technology, the claims must recite details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (MPEP 2106.05(a)(II)). In Finjan, Inc. v. Blue Coat Systems the courts found that the claims were “directed to a non-abstract improvement in computer functionality…” (MPEP 2106.04(d)). The present invention does not meet the condition set forth by the courts and thus does not integrate the judicial exception into a practical application.
With respect to Step 2B, examiner first points out that the MPEP makes it clear that the search for a 101 inventive concept is thus distinct from demonstrating 102 novelty.”). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016). Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with
respect to the prior art is neither required for, nor a guarantee of, patent eligibility
under 35 U.S.C. 101. In comparison to Bascom, examiner points out that Bascom is not similar to the present application because Bascom claimed a technical improvement in the art i.e., a technology-based solution to filter content on the internet while the present application is not presenting an improvement to computer technology (as indicated above). The additional elements have been treated under the “well-understood, routine, and conventional” consideration with citation of court case(s) in addition to the “apply it” consideration under Step 2B. The use of a computer or other machinery in its ordinary capacity for economic or other tasks or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Applicant’s claims do not recite unconventional steps that improve a conventional system. Thus, "significantly more" standard has not been satisfied. The applicant has not demonstrated that their invention is inventive and thus the present invention is still not patent-eligible under USC 101. Therefore, the USC 101 rejection is strongly maintained.
Prior Art Cited but Not Relied Upon
Russell, R. K., Drummond, H. E., Nimmo, E. E., Anderson, N., Smith, L., Wilson, D. C., ... & Satsangi, J. (2005). Genotype-phenotype analysis in childhood-onset Crohn's disease: NOD2/CARD15 variants consistently predict phenotypic characteristics of severe disease. Inflammatory bowel diseases, 11(11), 955-964.
This reference is relevant because it investigates the contribution of these variants to disease susceptibility and phenotype in the Scottish early-onset IBD population.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WINSTON FURTADO whose telephone number is (571)272-5349. The examiner can normally be reached Monday-Friday 8:00 AM to 4:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WINSTON R FURTADO/Examiner, Art Unit 3687