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 .
Response to Amendment
The Reply filed 06 August 2026 included
Replacement figures that overcome the drawing objections;
An amended title that overcomes the title objection;
Amendments to claims 11 and 12 adding a non-transitory computer readable medium that overcomes the 35 USC 101 rejections;
Amendments to claim 12 that avoid the 112(f) claim interpretations;
Claim amendments that overcome the 112(b) rejections;
Amendments to independent claim 1 to include the limitations of claims 2 and 3 which are insufficient to distinguish over the applied art of record as argued below and which have necessitated new grounds of rejection for claim 1; and
Amendments to independent claim 9 reciting “hyperspectral test spectrum” instead of “spectrum” and not substantive amendments to independent claim 11 thus also failing to distinguish over the applied art of record.
Response to Arguments
Applicant's arguments filed 06 August 2026 have been fully considered but they are not persuasive.
Applicant argues that Lencastre’s databases “are clinical and epidemiological surveillance registries, that contain textural, statistical, but no spectral data”. This argument is quite revealing in both its lack of appreciation of the claim breadth and as an individual attack against the applied combination of references. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Significantly, the claimed database is recited as “comprising, for each reference microbial class, the susceptibility to the microbial agent of the reference microbial class”. The claimed database does NOT include spectral data. Instead, the claimed database merely corresponds to highly standard and widely published clinical and epidemiological surveys acting as a lookup table that returns susceptibility to antimicrobial agent when fed with the classification/identity of the microbial class.
Guillemot performs the initial classification/identification of microbial class using hyperspectral data and, once this microbial class is obtained, it is matter of simple database lookup inputting microbial class and receiving susceptibility to an antimicrobial agent. Applicant’s admission that “Guillemot compares a measured reflectance spectrum with a reference spectrum to identify a colorimetric class of strain” is appreciated and serves to strengthen the rejection.
Applicant also argues that both Ragan and Witkowska are directed to Surface-Enhanced-Raman Scattering (SERS) which is a different technology that “cannot be combined with the standard agar plate process of Guillemot”. This argument does not appreciate the claim breadth and disconnected nature of the claimed invention. As illustrated in instant Fig. 4, the invention performs classification of a microbial class the results of which is the class. This class is then used in a wholly separate process using a database lookup procedure matching class and susceptibility. Further as to combinability, the rejection is well-supported and motivated by several by a line of reasoning that has not been directly challenged by Applicant. See rejection below which includes this same line of reasoning.
Moreover, the claimed strain identification is wholly independent of susceptibility determination; indeed the claimed hyperspectral image is obtained from a colony of the strain that is “devoid of an antimicrobial agent”. As such, the strain is not being subjected to an antimicrobial agent and the identification thereof is wholly independent of the susceptibility determination thus further contributing to the combinability of the references and further rebutting Applicant’s arguments to the contrary.
Still further, susceptibility is determined from a dataset that is known and highly conventional. Namely, susceptibility is determined merely by looking-up the known susceptibility data in one of many conventional databases that record strain-by-strain susceptibilities. Lencastre describes several examples of many such publicly available databases including CEM-NET and RESIST referred to therein which are international database recording surveillance of bacterial strains such as 4,500 MRSA isolates (strains) and their resistance to methicillin. For example, on pg. 625, a particular microbial strain (MRSA clone sequence type 5) is listed in the database and documents the wide-spread dominance of this strain and its resistance/susceptibility to methicillin. Lencastre demonstrates the conventional nature of a database (storing, e.g., a collection of 4,500 MRSA strains and their susceptibility data) and the routine nature of using this database to determine the susceptibility of identified microbial strains in order to document the spread of specific strains. Such database usage to determine susceptibility of identified strains is not only highly conventional but strongly motivated by providing important epidemiological evidence such as shown in Figs. 4-7 that help international experts track and combat the spread of antibiotic-resistant strains on a strain-by-strain basis.
Claim Objections
Claim 1 is objected to because of the following informalities: Amended claim 1 includes a typo, namely “phrase of training”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 4-6 and 8 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Guillemot {Guillemot, Mathilde, et al. "Hyperspectral imaging for presumptive identification of bacterial colonies on solid chromogenic culture media." Biophotonics: Photonic Solutions for Better Health Care V. Vol. 9887. SPIE, 2016} ; Lencastre {De Lencastre, Herminia, and Alexander Tomasz. "The CEM-NET initiative: molecular biology and epidemiology in alliance–tracking antibiotic-resistant staphylococci and pneumococci in hospitals and in the community." International Journal of Medical Microbiology 301.8 (2011): 623-629}, Ragan (US 20230223113 A1) and Witkowska {Witkowska, Evelin, et al. "Sources of variability in SERS spectra of bacteria: comprehensive analysis of interactions between selected bacteria and plasmonic nanostructures." Analytical and bioanalytical chemistry 411.10 (2019): 2001-2017}.
Claim 1
In regards to claim 1, Guillemot discloses a method for predicting the
implementing, by a data processor of a client device {see section 2.2 hardware and software including an HSI system including software for data acquisition, preprocessing and classification. See also Section 3 Results and Discussion showing output results from the processor implemented client device}, the following steps of:
(a) obtaining a hyperspectral image between 390 nm and 900 nm
representing at least one colony of the strain in a sample devoid of an
antimicrobial agent {section 2.2 Imaging Hardware & software including hyperspectral camera scanning a culture plate colonized by a cultured bacterial strain and devoid of antimicrobial agents as per section 2.1 Sample preparation};
(b) determining a spectrum, called "test spectrum" hereafter, of the colony
from the pixels of the hyperspectral image corresponding to the colony {section 2.3 Hyperspectral acquisition protocol and 2.4 Data preprocessing to determine reflectance spectra of the test sample};
(c) comparing the test spectrum with microbial classes, called
"reference microbial class" hereafter, of a predetermined database, with the
classes corresponding to a taxonomic level lower than the species and being learnt on
at least one hyperspectral spectrum of a microbial strain,
{Section 2.5 Classification which compares the reflectance spectra spectrum of the colony with microbial classes to identify the strain (which is a lower taxonomic level than species) and being learnt on hyperspectral spectra of microbial strains using a training set constituted of 20 EMS spectra representative of 19 biological strains. See also section 3 Results and Discussion}; and
(d)
Initially, it is noted that claim 1 essentially infers/”predicts” susceptibility to an antimicrobial agent by two separate processes: first identifying/classifying the microbial strain using hyperspectral spectra and then using such information to compare the hyperspectral spectrum “test spectrum” of the sample colony against known spectra in a “database” as per steps (a), (b) and the portion of step (c) not stuck through. These steps are highly conventional and Guillemot is but one example that refines the class-level taxonomic determination of Lallemand (WO 2019/122732 A1) or the gram-type determination of Lallemand (US 2021/0079441 A1) to a strain-level taxonomic determination all of which make these classification determinations using comparisons of hyperspectral spectra.
It is noted with particular emphasis that the claimed strain identification/classification is wholly independent of susceptibility determination; indeed the claimed hyperspectral image is obtained from a colony of the strain that is “devoid of an antimicrobial agent”. As such the strain is not being subjected to an antimicrobial agent and the identification thereof is wholly independent of the susceptibility determination.
Furthermore, susceptibility is determined from a dataset that is known and highly conventional. Namely, susceptibility is determined merely by looking up the susceptibility data in one of many conventional databases that record strain-by-strain susceptibilities. Lencastre describes several examples of many such publicly available databases including CEM-NET and RESIST referred to therein which are international database recording surveillance of bacterial strains such as 4,500 MRSA isolates (strains) and their resistance to methicillin. For example, on pg. 625, a particular microbial strain (MRSA clone sequence type 5) is listed in the database and documents the wide-spread dominance of this strain and its resistance/susceptibility to methicillin. Lencastre further demonstrates the conventional nature of a database (storing, e.g., a collection of 4,500 MRSA strains and their susceptibility data) and the routine nature of using this database to determine the susceptibility of identified microbial strains in order to document the spread of specific strains. Such database usage to determine susceptibility of identified strains is not only highly conventional but strongly motivated by providing important epidemiological evidence such as shown in Figs. 4-7 that help international experts track and combat the spread of antibiotic-resistant strains on a strain-by-strain basis.
It 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 to have modified Guillemot which performs a method that includes (a) obtaining a hyperspectral image between 390 nm and 900 nm representing at least one colony of the strain in a sample devoid of an antimicrobial agent; (b) determining a spectrum, called "test spectrum" hereafter, of the colony from the pixels of the hyperspectral image corresponding to the colony; and (c) comparing the test spectrum with microbial classes, called "reference microbial class" hereafter, of a predetermined database, with the classes corresponding to a taxonomic level lower than the species and being learnt on at least one hyperspectral spectrum of a microbial strain such that the method then accesses a conventional database , the database comprising, for each reference microbial class, the susceptibility to the antimicrobial agent of the reference microbial class; and (d) determining the susceptibility of the microbial strain to the microbial agent
as being that associated with the reference microbial class closest to the hyperspectral
test spectrum as taught by Lencastre because using this database to determine the susceptibility of identified microbial strains is motivated by documenting the spread of specific strains and further motivated by providing important epidemiological evidence such as shown in Figs. 4-7 of Lencastre that help international experts track and combat the spread of antibiotic-resistant strains on a strain-by-strain basis; because there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Guillemot is not relied upon to discloses the convolutional neural network or it’s training as recited in amended claim 1 and indicated above in strike-through font.
Ragan is analogous art from the same field of predicting the susceptibility of a microbial strain to an antimicrobial agent {see title, abstract, [0018] and cites below}, the method comprising implementing, by a data processor of a client device, the following steps of:
(a) obtaining a hyperspectral image between 390 nm and 900 nm
representing at least one colony of the strain in a sample {Fig. 1, copied below, obtains SERS (surface enhanced Raman scattering) spectra, [0007], [0049], [0054], [0061]-[0062]};
(b) determining a spectrum, called "test spectrum" hereafter, of the colony
from the pixels of the hyperspectral image corresponding to the colony {Fig. 1, step 102 determines SERS spectra, [0055] including smoothing the spectra during pre-processing};
(c) comparing the test spectrum with microbial classes, called
"reference microbial class" hereafter, of a predetermined database, with the
classes corresponding to a taxonomic level lower than the species and being learnt on
at least one hyperspectral spectrum of a microbial strain, the database comprising, for
each reference microbial class, the susceptibility to the antimicrobial agent of the
reference microbial class {Fig. 1, step 104, classify spectra which involves comparing the test spectra for a microbial class including a database comprising, for
each reference microbial class, the susceptibility to the antimicrobial agent of the
reference microbial class using machine learning analysis. Note that the output datasets of antimicrobial susceptibility, step 105, may be also be considered the database as it collectively associates, for each strain, the antimicrobial susceptibility profile as per [0056]-[0060]}; and
(d) determining the susceptibility of the microbial strain to the microbial agent
as being that associated with the reference microbial class closest to the hyperspectral
test spectrum {Fig. 4, including machine learning process 103 that determines susceptibility based on the SERS spectra, [0056]-[0060]}.
Ragan also teaches wherein the steps of comparing and of determining are carried out by means of a predictor based on a monitored classification having the identity of the microbial strains of the database as reference microbial classes {see above cites including machine learning model which is a predictor as claimed}, with the phase of training the classification comprising:
(c1) acquiring hyperspectral spectra of various colonies for each microbial
strain of the database; and (c2) training the classification on the hyperspectral spectra of various colonies {see the training process in [0057]-[0060] that learns relationships between acquired SERS spectra of various colonies for each strain and trains the classification of susceptibility on the hyperspectral SERS spectra of various colonies including (claim 3) wherein the predictor is a convolutional artificial neural network, [0056]}.
It 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 to have modified Guillemot which already performs a method that includes (a) obtaining a hyperspectral image between 390 nm and 900 nm representing at least one colony of the strain in a sample devoid of an antimicrobial agent; (b) determining a spectrum, called "test spectrum" hereafter, of the colony from the pixels of the hyperspectral image corresponding to the colony; and (c) comparing the test spectrum with microbial classes, called "reference microbial class" hereafter, of a predetermined database, with the classes corresponding to a taxonomic level lower than the species and being learnt on at least one hyperspectral spectrum of a microbial strain such that the manual/semi-automated database lookup for susceptibility based on identified strain is replaced by a machine learning method that learns the susceptibility based on the spectra of the hyperspectral image including wherein the steps of comparing and of determining are carried out by means of a predictor based on a monitored classification having the identity of the microbial strains of the database as reference microbial classes {see above cites including machine learning model which is a predictor as claimed}, with the phase of training the classification comprising: (c1) acquiring hyperspectral spectra of various colonies for each microbial strain of the database; and (c2) training the classification on the hyperspectral spectra of various colonies as taught by Ragan because the machine learning model improves the accuracy of the susceptibility and granularity of the susceptibility including the antibiotic resistance metabolite profile, temporal response and antibiotic dosage response as motivated by Ragan in [0059]; because using this machine learning model to determine the susceptibility of identified microbial strains improves the accuracy and granularity of the documentation efforts regarding the spread of specific strains and their susceptibility profiles; and further motivated by providing important epidemiological evidence such as shown in Figs. 4-7 of Lencastre that help international experts track and combat the spread of antibiotic-resistant strains on a strain-by-strain and susceptibility basis; because there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Although Ragan’s SERS spectra are a hyperspectral image between 390 nm and 900 nm, Ragan does not specifically mention the wavelengths being captured in this image. Witkowska is cited to demonstrated that SERS spectra are a hyperspectral image between 390 nm and 900 nm. See pg. 2003, Instrumentation in which the sensor employs different “diffraction gratings (1200 grooves/mm [for the 785-nm line], 1800 grooves/mm [for the 633-nm and 532-nm lines]” to spectrally separate the light for hyperspectral image detection. As such the detected spectra include 785nm (NIR), 633nm (bright red), and 532nm (green) which are values between 390nm and 900nm and thus anticipate the claimed range}.
It 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 to have realized that Ragan’s SERS spectra are a hyperspectral image between 390 nm and 900 nm as taught by Witkowska because the BRI of this phrase encompasses plural spectral bands within the broad range of 390nm to 900nm, because the SERS spectra are useful for determining susceptibility of microbial strains thus further contributing to a reasonable expectation of success yielding predictable and useful results.
Claim 4
In regards to claim 4, Guillemot discloses wherein step (b) comprises segmenting the hyperspectral image SO as to detect the colony in the sample {see section 2.4 Data Preprocessing which includes colony-of-interest selection}.
Claim 5
In regards to claim 5, Guillemot discloses wherein step (b) comprises smoothing and/or normalizing said the spectrum of the colony {see section 2.4 Data Preprocessing which includes smoothing and normalization}.
Claim 6
In regards to claim 6, Guillemot discloses wherein step (a) comprises acquiring the hyperspectral image by an observation device connected to the client device {see section 2.2 hardware and software including an HSI system (client) connected to a hyperspectral camera (observation device)}.
Claim 8
In regards to claim 8, Guillemot is not relied upon to disclose but Lencastre teaches wherein the microbial strain is a Staphylococcus aureus strain and the antimicrobial agent is methicillin {Lencastre describes several examples of many such publicly available databases including CEM-NET and RESIST are international database recording surveillance of bacterial strains such as 4500 MRSA isolates (strains) and their resistance to methicillin. For example, on pg. 625, a particular microbial strain (MRSA clone sequence type 5 that is listed in the database and documents the wide-spread dominance of this strain and its resistance/susceptibility to methicillin)}.
It 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 to have modified Guillemot which performs a method that includes (a) obtaining a hyperspectral image between 390 nm and 900 nm representing at least one colony of the strain in a sample devoid of an antimicrobial agent; (b) determining a spectrum, called "test spectrum" hereafter, of the colony from the pixels of the hyperspectral image corresponding to the colony; and (c) comparing the test spectrum with microbial classes, called "reference microbial class" hereafter, of a predetermined database, with the classes corresponding to a taxonomic level lower than the species and being learnt on at least one hyperspectral spectrum of a microbial strain such that the method then accesses a conventional database, the database comprising, for each reference microbial class, the susceptibility to the antimicrobial agent of the reference microbial class; and (d) determining the susceptibility of the microbial strain to the microbial agent
as being that associated with the reference microbial class closest to the hyperspectral
test spectrum as taught by Lencastre and wherein the microbial strain is a Staphylococcus aureus strain and the antimicrobial agent is methicillin as also taught by Lencastre because using this database to determine the susceptibility of identified microbial strains is motivated by documenting the spread of specific strains and further motivated by providing important epidemiological evidence such as shown in Figs. 4-7 of Lencastre that help international experts track and combat the spread of antibiotic-resistant strains on a strain-by-strain basis; because there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claims 12-14
As as to the computer program product comprising code instructions limitations of claim 12 see the section 2.2 hardware and software. As to the normalizing and smoothing of the spectrum limitations of claims 13-14 see the mapping of claim 5 above.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Guillemot, Lencastre, Ragan, and Witkowska as applied to claim 1 above, and further in view of, and Shamsheyeva (US 9677109 B2).
Claim 7
In regards to claim 7, Guillemot is not relied upon to disclose but Ragan teaches comprising a step (a0) of learning, by the data processor already classified spectra of colonies {see the training process in [0057]-[0060] that learns parameters of an automated classification model from a training database of SERS spectra of already classified spectra of colonies}. Further as to hyperspectral image, see the application of Witkowska above which is hereby incorporated by reference.
Shamsheyeva is analogous art from the same field of determining microbial susceptibility to antibiotics. See abstract, Figs. 2-3 and cites below. Shamsheyeva also teaches the conventional nature of running models and other resource-intensive applications on a server as per column 56, lines 57-column 61, line 15.
It 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 to have modified the base combination including implementing Ragan’s automatic classification model in a server as taught by Shamsheyeva because servers can more efficiently and quickly process such resource intensive applications due to the greater processing power of servers, because there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Guillemot and Lencastre.
Claim 11
In regards to claim 1, Guillemot discloses a non-transitory computer-readable medium storing code instructions determining the
(a) obtaining a hyperspectral image between 390 nm and 900 nm
representing at least one colony of the strain in a sample devoid of an
antimicrobial agent {section 2.2 Imaging Hardware & software including hyperspectral camera scanning a culture plate colonized by a cultured bacterial strain and devoid of antimicrobial agents as per section 2.1 Sample preparation};
(b) determining a test spectrum of the colony from the pixels of the hyperspectral image corresponding to the colony {section 2.3 Hyperspectral acquisition protocol and 2.4 Data preprocessing to determine reflectance spectra of the test sample};
(c) comparing the test spectrum with microbial classes, called
"reference microbial class" hereafter, of a predetermined database, with the
classes corresponding to a taxonomic level lower than the species and being learnt on
at least one hyperspectral spectrum of a microbial strain,
{Section 2.5 Classification which compares the reflectance spectra spectrum of the colony with microbial classes to identify the strain (which is a lower taxonomic level than species) and being learnt on hyperspectral spectra of microbial strains using a training set constituted of 20 EMS spectra representative of 19 biological strains. See also section 3 Results and Discussion}; and
(d)
Initially, it is noted that claim 11 essentially infers/”predicts” susceptibility to an antimicrobial agent by two separate processes: first identifying/classifying the microbial strain using hyperspectral spectra and then using such information to compare the hyperspectral spectrum “test spectrum” of the sample colony against known spectra in a “database” as per steps (a), (b) and the portion of step (c) not stuck through. These steps are highly conventional and Guillemot is but one example that refines the class-level taxonomic determination of Lallemand (WO 2019/122732 A1) or the gram-type determination of Lallemand (US 2021/0079441 A1) to a strain-level taxonomic determination all of which make these classification determinations using comparisons of hyperspectral spectra.
It is noted with particular emphasis that the claimed strain identification/classification is wholly independent of susceptibility determination; indeed the claimed hyperspectral image is obtained from a colony of the strain that is “devoid of an antimicrobial agent”. As such the strain is not being subjected to an antimicrobial agent and the identification thereof is wholly independent of the susceptibility determination.
Furthermore, susceptibility is determined from a dataset that is known and highly conventional. Namely, susceptibility is determined merely by looking up the susceptibility data in one of many conventional databases that record strain-by-strain susceptibilities. Lencastre describes several examples of many such publicly available databases including CEM-NET and RESIST referred to therein which are international database recording surveillance of bacterial strains such as 4,500 MRSA isolates (strains) and their resistance to methicillin. For example, on pg. 625, a particular microbial strain (MRSA clone sequence type 5) is listed in the database and documents the wide-spread dominance of this strain and its resistance/susceptibility to methicillin. Lencastre further demonstrates the conventional nature of a database (storing, e.g., a collection of 4,500 MRSA strains and their susceptibility data) and the routine nature of using this database to determine the susceptibility of identified microbial strains in order to document the spread of specific strains. Such database usage to determine susceptibility of identified strains is not only highly conventional but strongly motivated by providing important epidemiological evidence such as shown in Figs. 4-7 that help international experts track and combat the spread of antibiotic-resistant strains on a strain-by-strain basis.
It 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 to have modified Guillemot which performs a method that includes (a) obtaining a hyperspectral image between 390 nm and 900 nm representing at least one colony of the strain in a sample devoid of an antimicrobial agent; (b) determining a spectrum, called "test spectrum" hereafter, of the colony from the pixels of the hyperspectral image corresponding to the colony; and (c) comparing the test spectrum with microbial classes, called "reference microbial class" hereafter, of a predetermined database, with the classes corresponding to a taxonomic level lower than the species and being learnt on at least one hyperspectral spectrum of a microbial strain such that the method then accesses a conventional database , the database comprising, for each reference microbial class, the susceptibility to the antimicrobial agent of the reference microbial class; and (d) determining the susceptibility of the microbial strain to the microbial agent
as being that associated with the reference microbial class closest to the hyperspectral
test spectrum as taught by Lencastre because using this database to determine the susceptibility of identified microbial strains is motivated by documenting the spread of specific strains and further motivated by providing important epidemiological evidence such as shown in Figs. 4-7 of Lencastre that help international experts track and combat the spread of antibiotic-resistant strains on a strain-by-strain basis; because there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claims 9 and 10
The rejection of CRM claim 1 above applies mutatis mutandis to the corresponding limitations of system claim 9 while noting that the rejection above cites to both device and method disclosures including mappings to Guillemot for the hyperspectral imager for acquiring the hyperspectral image of claim 10.
Conclusion
Dacosta US 2017 /0236281 A1 discloses a method of comparing hyperspectral to determine bacterial strain. See [0141], Fig. 30, 32, [0047]-[0057], [0101]-[0104].
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 Michael R Cammarata whose telephone number is (571)272-0113. The examiner can normally be reached M-Th 7am-5pm EST.
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/MICHAEL ROBERT CAMMARATA/ Primary Examiner, Art Unit 2667
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