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
Claims 1, 4-6, 9-11, 13-14 have been amended changing the scope and contents of the claim.
Claim 15 has been newly added.
Claims 2-3 and 7-8 have been cancelled.
Applicant’s amendment filed November 14, 2025 overcomes the following objection/rejection(s) from the last Office Action of October 14, 2025:
Rejections to the claims under 35 USC § 112(b)
Rejections to the claims under 35 USC § 102
Response to Arguments
Applicant's arguments filed November 14, 2025 have been fully considered but they are not persuasive.
Regarding claims 6 and 15, Applicant argues, “WO '374, Haaga, Carmi and Stransky-Heilkron, either individually or in combination, fail to disclose or suggest at least the aforementioned features recited in amended independent claim 6 and new claim 15 (Remarks, page 10).” Specifically, Applicant’s argument relies on, “Applicant respectfully submits that Stransky-Heilkron fails to disclose or suggest at least the aforementioned features recited in amended independent claim 6 and new claim 15 (Remarks, page 11).”
The examiner respectfully disagrees. As cited below, the examiner cites Stransky-Heilkron disclosing tumor type includes a primary focus and a metastasis focus at the abstract, “a method for discriminating primary tumour and/or metastases from brown and/or beige adipose tissue.” Further, within Stransky-Heilkron, the discussion is focused on “ a method for discriminating primary tumour and/or metastases from brown and/or beige adipose tissue, by PET-CT imaging, in a human cancer patient that has undergone a prior administration of 18FDG PET tracer (paragraph 0425).” Thus, Stransky-Heilkron is specifically relied upon for determination of primary or metastasis focuses within tumors based upon injected compounds. Though not explicitly stated multiple tracers can be injected, Stransky-Heilkron does note that there are a variety of tracers that can be used at paragraphs 0402-0404. Stransky-Heilkron is relied upon for the teaching that classification of tumor focuses can be made based on uptake of tracers in tumors that have been medically imaged.
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.
Claim(s) 6, 9-10 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2022008374 (hereinafter WO ‘374), and further in view of U.S. Publication No. 2015/0221082 to Carmi (hereinafter Carmi), [ 4 U.S. Publication No. 2023/0284994 to Stransky-Heilkron et al. (hereinafter Stransky-Heilkron).
Regarding independent claim 6, WO ‘374 discloses a medical image diagnosis system (abstract, “Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images.”) comprising:
an imaging apparatus configured to apply one-time or multiple-time medical imaging to a subject to which a plurality of drugs having a tissue accumulation property, which corresponds to a tissue characteristic, are administered successively or simultaneously (paragraph 0015, “In certain embodiments, the machine learning module receives, as input, at least a portion of the 3D functional image and automatically detects the one or more hotspots based at least in part on intensities of voxels of the received portion of the 3D functional image;” paragraph 0009, “In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) techniques to detect regions of 3D nuclear medicine images that represent potential cancerous lesions in the subject. In certain embodiments, these regions correspond to localized regions of elevated intensity relative to their surroundings - hotspots - due to increased uptake of radiopharmaceutical within lesions;” paragraph 0031, “In certain embodiments, the 3D functional image comprises a PET or SPECT image obtained following administration of an agent (e.g., a radiopharmaceutical; e.g., an imaging agent) to the subject;” paragraph 0200, “Nuclear medicine images (e.g., PET scans; e.g., SPECT scans; e.g., whole-body bone scans; e.g. composite PET-CT images; e.g., composite SPECT-CT images) detect radiation emitted from the radionuclides of radiopharmaceuticals to form an image;” NOTE: radiopharmaceuticals is plural, indicating multiple agents administered),
wherein the drugs have mutually different tissue type sensitivities (paragraph 0200, "Different radiopharmaceuticals may be designed to take advantage of different biological mechanisms and/or particular specific enzymatic or receptor binding interactions and thus, when administered to a patient, selectively concentrate within particular types of tissue and/or regions within the patient. "),
and processing circuitry configured to:
reconstruct one or a plurality of medical images, based on one or a plurality of set of raw data acquired by the one-time or multiple-time medical imaging (paragraph 0015, "In certain embodiments, the machine learning module receives, as input, at least a portion of the 3D functional image and automatically detects the one or more hotspots based at least in part on intensities of voxels of the received portion of the 3D functional image;" reconstruction is read as happening already to generate the 3D functional image) estimate a tissue characteristic of a target region included in the subject, based on a spatial distribution of the drugs rendered on the one or the plurality of medical images (paragraph 0009, " In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) techniques to detect regions of 3D nuclear medicine images that represent potential cancerous lesions in the subject. In certain embodiments, these regions correspond to localized regions of elevated intensity relative to their surroundings - hotspots - due to increased uptake of radiopharmaceutical within lesions;" paragraph 0010, "For example, once image hotspots representing lesions are detected, segmented, and classified, lesion index values can be computed to provide a measure of radiopharmaceutical uptake within and/or a size (e.g., volume) of the underlying lesion. The computed lesion index values can, in turn, be aggregated to provide an overall estimate of tumor burden, disease severity, metastasis risk, and the like, for the subject. ");
estimate a tissue type of the target region as the tissue characteristic (paragraph 0200, " Different radiopharmaceuticals may be designed to take advantage of different biological mechanisms and/or particular specific enzymatic or receptor binding interactions and thus, when administered to a patient, selectively concentrate within particular types of tissue and/or regions within the patient. Greater amounts of radiation are emitted from regions within the patient that have higher concentrations of radiopharmaceutical than other regions, such that these regions appear brighter in nuclear medicine images. Accordingly, intensity variations within a nuclear medicine image can be used to map the distribution of radiopharmaceutical within the patient. This mapped distribution of radiopharmaceutical within the patient can be used to, for example, infer the presence of cancerous tissue within various regions of the patient's body;" being that certain radiopharmaceuticals apply to specific tissue types, it is known that the hotspot is of a potential lesion in that specific tissue type ),
display the estimated tissue characteristic on a display device (paragraph 0012, In certain embodiments, detected hotspots and associated information, such as computed lesion index values and anatomical labeling, are displayed with an interactive graphical user interface (GUI) so as to allow for review by a medical professional, such as a physician, radiologist, technician, etc."),
wherein the target region is a tumor, or a tissue that is suspected to be a tumor (abstract, “detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject”) ,
the tissue characteristic is a tumor type that means a progress degree or a type of a tumor (paragraph 0014, “ In one aspect, the invention is directed to a method for automatically processing 3D images of a subject to identify and/or characterize (e.g., grade) cancerous lesions within the subject”),
the tissue accumulation property is a capability of accumulation of a drug in a tumor (paragraph 0010, “or example, once image hotspots representing lesions are detected, segmented, and classified, lesion index values can be computed to provide a measure of radiopharmaceutical uptake within and/or a size (e.g., volume) of the underlying lesion.”).
WO ‘374 fails to explicitly disclose as further recited. However, Carmi discloses
a first drug among the drugs accumulates in tissues of a plurality of types (Figure 8, element 802), a second drug among the drugs accumulates in a tissue of a specific type among the types (Figure 8, element 806):
estimate the tissue type as the tissue characteristic, based on a difference between a spatial distribution of the first drug rendered on a first medical image at a time when the first drug is administered to the subject, and a spatial distribution of the second drug rendered on a second medical image at a time when the second drug is administered to the subject (Figure 8, element 810; paragraph 0076-0078, “At 810, the data is reconstructed, generating volumetric temporal imaging data for each of the two different contrast materials. At 812, a time enhancement curve is generated for each of the contrast materials based on the corresponding volumetric temporal imaging data. At 814, a permeability of the vascular tissue of interest is determined based on the two time enhancement curves, and a signal indicative thereof is generated.”)
WO ‘374 is directed toward “systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images (abstract).” Carmi is directed toward “A method includes determining a permeability metric of vascular tissue of interest (abstract)” and “Tumor vascular permeability, in which blood penetrates from the capillaries into the interstitial space, is caused by tumor blood vessels which have defective and leaky endothelium. (paragraph 0002).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, WO ‘374 and Carmi are directed toward similar methods of endeavor of analysis of abnormal tissues. Further, it is well known in the art at the time of filing the claimed invention that blood movement and interaction with the lesion itself can aid in classifying and categorizing lesions (see Carmi, paragraph 0002-0003). Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Carmi to analyze patient perfusion characteristics, in order to perform analysis of abnormal tissues.
WO ‘374 and Carmi in the combination as a whole fail to explicitly disclose as further recited. However, Stransky-Heilkron discloses
and the tumor type includes a primary focus and a metastasis focus (abstract, “a method for discriminating primary tumour and/or metastases from brown and/or beige adipose tissue”).
As noted above, WO ‘374 and Carmi are directed toward analysis of abnormal tissues. Further, WO ‘374 is directed toward “systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images (abstract).” Stransky-Heilkron is directed toward “a method to differentiate tumour tissue from brown adipose tissue on a PET-CT scan. In particular, the invention concerns a method for discriminating primary tumour and/or metastases from brown and/or beige adipose tissue, by PET-CT imaging, in a human cancer patient (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, WO ‘374, Carmi and Stransky-Heilkron are directed toward similar methods of endeavor of analysis of abnormal tissues in medical images. Further, it is well known in the art at the time of filing the claimed invention that there is more to tumor classification beyond just benign/malignant, such as primary tumor and/or metastases that may also be important to diagnosis. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Stransky-Heilkron to provide more accurate classification data, for ideally more accurate treatment.
Regarding dependent claim 9, the rejection of claim 6 is incorporated herein. Additionally, WO ‘374 in the combination further discloses wherein
the first drug is a contrast agent used in PET imaging that contrast-enhances the tissues of the plurality of types (paragraph 0004, “Choice of a particular imaging modality depends on and/or dictates the particular radiopharmaceutical used. For example, technetium 99m (99mTc) labeled compounds are compatible with bone scan imaging and SPECT imaging, while PET imaging often uses fluorinated compounds labeled with 18F;” paragraph 0198, “In some embodiments, a radionuclide is one used in positron emission tomography (PET). In some embodiments, a radionuclide is one used in single-photon emission computed tomography (SPECT).”).
WO ‘374 and Carmi in the combination as a whole fail to explicitly disclose as further recite. However, WO ‘374 discloses the use of radiopharmaceuticals that are specific to the modality in question (paragraph 0004, “Choice of a particular imaging modality depends on and/or dictates the particular radiopharmaceutical used. For example, technetium 99m (99mTc) labeled compounds are compatible with bone scan imaging and SPECT imaging, while PET imaging often uses fluorinated compounds labeled with 18F;” paragraph 0198, “In some embodiments, a radionuclide is one used in positron emission tomography (PET). In some embodiments, a radionuclide is one used in single-photon emission computed tomography (SPECT).”). Thus, WO ‘374 does disclose the use of specific radiopharmaceuticals in correlation to the selected image modality. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to select PCCT as an imaging modality for its known benefits, and further selecting a radiopharmaceutical that is compatible with that imaging modality.
Regarding dependent claim 10, the rejection of claim 6 is incorporated herein. Additionally, WO ‘374 in the combination further discloses wherein
the target region is a tumor, or a tissue that is suspected to be a tumor (abstract, “detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject”),
the tissue characteristic is a tumor type that means a progress degree or a type of a tumor (paragraph 0014, “ In one aspect, the invention is directed to a method for automatically processing 3D images of a subject to identify and/or characterize (e.g., grade) cancerous lesions within the subject”),
the tissue accumulation property is a capability of accumulation of a drug in a tumor (paragraph 0010, “or example, once image hotspots representing lesions are detected, segmented, and classified, lesion index values can be computed to provide a measure of radiopharmaceutical uptake within and/or a size (e.g., volume) of the underlying lesion.”),
the first drug includes an iodine preparation that contrast-enhances tumors of the plurality of types (paragraph 0198, “In some embodiments, a non-limiting list of radionuclides includes 99nTc, 111in, 64Cu, 67Ga, 68Ga, 186Re, 188Re, 153Sm, 177Lu, 67Cu, 123I, 124I, 125I, 126I, 131I, 11C, 13N, 150, 18F, 153Sm, 166Ho, 177Lu, 149Pm, 90Y, 213Bi, 103Pd, 109Pd, 159Gd, 140La, 198AU, 199 Au, 169Yb, 175Yb, 165Dy, 166Dy, 105Rh, 11 'Ag, 89Zr, 225Ac, 82Rb, 75Br, 76Br, 77Br, 80Br, 80mBr, 82Br, 83Br, 211At and 192Ir.”), and
However, WO ‘374 and Carmi in the combination as a whole fails to explicitly disclose as further recited. Stransky-Heilkron discloses the second drug includes an anticancer drug for a tumor of the specific type (paragraph 0328, “4) Administering the anti-cancer treatment one or more times”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Stransky-Heilkron to provide more options for drug tracking, such as for anti-cancer drugs.
Regarding independent claim 15, the rejection of claim 6 applies directly. Additionally, WO ‘374 discloses a medical image diagnosis method (abstract, “Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images.”) comprising:
applying one-time or multiple-time medical imaging to a subject to which a plurality of drugs having a tissue accumulation property, which corresponds to a tissue characteristic, are administered successively or simultaneously (paragraph 0015, “In certain embodiments, the machine learning module receives, as input, at least a portion of the 3D functional image and automatically detects the one or more hotspots based at least in part on intensities of voxels of the received portion of the 3D functional image;” paragraph 0009, “In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) techniques to detect regions of 3D nuclear medicine images that represent potential cancerous lesions in the subject. In certain embodiments, these regions correspond to localized regions of elevated intensity relative to their surroundings - hotspots - due to increased uptake of radiopharmaceutical within lesions;” paragraph 0031, “In certain embodiments, the 3D functional image comprises a PET or SPECT image obtained following administration of an agent (e.g., a radiopharmaceutical; e.g., an imaging agent) to the subject;” paragraph 0200, “Nuclear medicine images (e.g., PET scans; e.g., SPECT scans; e.g., whole-body bone scans; e.g. composite PET-CT images; e.g., composite SPECT-CT images) detect radiation emitted from the radionuclides of radiopharmaceuticals to form an image;” NOTE: radiopharmaceuticals is plural, indicating multiple agents administered),
wherein the drugs have mutually different tissue type sensitivities (paragraph 0200, "Different radiopharmaceuticals may be designed to take advantage of different biological mechanisms and/or particular specific enzymatic or receptor binding interactions and thus, when administered to a patient, selectively concentrate within particular types of tissue and/or regions within the patient. "),
reconstructing one or a plurality of medical images, based on one or a plurality of set of raw data acquired by the one-time or multiple-time medical imaging (paragraph 0015, "In certain embodiments, the machine learning module receives, as input, at least a portion of the 3D functional image and automatically detects the one or more hotspots based at least in part on intensities of voxels of the received portion of the 3D functional image;" reconstruction is read as happening already to generate the 3D functional image) estimating a tissue characteristic of a target region included in the subject, based on a spatial distribution of the drugs rendered on the one or the plurality of medical images (paragraph 0009, " In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) techniques to detect regions of 3D nuclear medicine images that represent potential cancerous lesions in the subject. In certain embodiments, these regions correspond to localized regions of elevated intensity relative to their surroundings - hotspots - due to increased uptake of radiopharmaceutical within lesions;" paragraph 0010, "For example, once image hotspots representing lesions are detected, segmented, and classified, lesion index values can be computed to provide a measure of radiopharmaceutical uptake within and/or a size (e.g., volume) of the underlying lesion. The computed lesion index values can, in turn, be aggregated to provide an overall estimate of tumor burden, disease severity, metastasis risk, and the like, for the subject. ");
estimate a tissue type of the target region as the tissue characteristic (paragraph 0200, " Different radiopharmaceuticals may be designed to take advantage of different biological mechanisms and/or particular specific enzymatic or receptor binding interactions and thus, when administered to a patient, selectively concentrate within particular types of tissue and/or regions within the patient. Greater amounts of radiation are emitted from regions within the patient that have higher concentrations of radiopharmaceutical than other regions, such that these regions appear brighter in nuclear medicine images. Accordingly, intensity variations within a nuclear medicine image can be used to map the distribution of radiopharmaceutical within the patient. This mapped distribution of radiopharmaceutical within the patient can be used to, for example, infer the presence of cancerous tissue within various regions of the patient's body;" being that certain radiopharmaceuticals apply to specific tissue types, it is known that the hotspot is of a potential lesion in that specific tissue type ),
displaying the estimated tissue characteristic on a display device (paragraph 0012, In certain embodiments, detected hotspots and associated information, such as computed lesion index values and anatomical labeling, are displayed with an interactive graphical user interface (GUI) so as to allow for review by a medical professional, such as a physician, radiologist, technician, etc."),
wherein the target region is a tumor, or a tissue that is suspected to be a tumor (abstract, “detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject”) ,
the tissue characteristic is a tumor type that means a progress degree or a type of a tumor (paragraph 0014, “ In one aspect, the invention is directed to a method for automatically processing 3D images of a subject to identify and/or characterize (e.g., grade) cancerous lesions within the subject”),
the tissue accumulation property is a capability of accumulation of a drug in a tumor (paragraph 0010, “or example, once image hotspots representing lesions are detected, segmented, and classified, lesion index values can be computed to provide a measure of radiopharmaceutical uptake within and/or a size (e.g., volume) of the underlying lesion.”).
WO ‘374 fails to explicitly disclose as further recited. However, Carmi discloses
a first drug among the drugs accumulates in tissues of a plurality of types (Figure 8, element 802), a second drug among the drugs accumulates in a tissue of a specific type among the types (Figure 8, element 806):
estimate the tissue type as the tissue characteristic, based on a difference between a spatial distribution of the first drug rendered on a first medical image at a time when the first drug is administered to the subject, and a spatial distribution of the second drug rendered on a second medical image at a time when the second drug is administered to the subject (Figure 8, element 810; paragraph 0076-0078, “At 810, the data is reconstructed, generating volumetric temporal imaging data for each of the two different contrast materials. At 812, a time enhancement curve is generated for each of the contrast materials based on the corresponding volumetric temporal imaging data. At 814, a permeability of the vascular tissue of interest is determined based on the two time enhancement curves, and a signal indicative thereof is generated.”)
WO ‘374 is directed toward “systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images (abstract).” Carmi is directed toward “A method includes determining a permeability metric of vascular tissue of interest (abstract)” and “Tumor vascular permeability, in which blood penetrates from the capillaries into the interstitial space, is caused by tumor blood vessels which have defective and leaky endothelium. (paragraph 0002).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, WO ‘374 and Carmi are directed toward similar methods of endeavor of analysis of abnormal tissues. Further, it is well known in the art at the time of filing the claimed invention that blood movement and interaction with the lesion itself can aid in classifying and categorizing lesions (see Carmi, paragraph 0002-0003). Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Carmi to analyze patient perfusion characteristics, in order to perform analysis of abnormal tissues.
WO ‘374 and Carmi in the combination as a whole fail to explicitly disclose as further recited. However, Stransky-Heilkron discloses
and the tumor type includes a primary focus and a metastasis focus (abstract, “a method for discriminating primary tumour and/or metastases from brown and/or beige adipose tissue”).
As noted above, WO ‘374 and Carmi are directed toward analysis of abnormal tissues. Further, WO ‘374 is directed toward “systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images (abstract).” Stransky-Heilkron is directed toward “a method to differentiate tumour tissue from brown adipose tissue on a PET-CT scan. In particular, the invention concerns a method for discriminating primary tumour and/or metastases from brown and/or beige adipose tissue, by PET-CT imaging, in a human cancer patient (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, WO ‘374, Carmi and Stransky-Heilkron are directed toward similar methods of endeavor of analysis of abnormal tissues in medical images. Further, it is well known in the art at the time of filing the claimed invention that there is more to tumor classification beyond just benign/malignant, such as primary tumor and/or metastases that may also be important to diagnosis. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Stransky-Heilkron to provide more accurate classification data, for ideally more accurate treatment.
Allowable Subject Matter
Claims 1, 4-5 and 11-14 are allowed.
Claims 1, 4-5, 11-14:
The following is an examiner’s statement of reasons for allowance: the closest prior arts of record teach methods of analyzing drug accumulations in tissues to perform tissue analysis as related to various diseases. However, none of them alone or in any combination teaches determining the tissue progress degree based on a first drug of a first particle size, in a specific region, and estimating the progress degree being more advanced when the second drug is of a larger particle size than the first, and is distributed in a region. The closest prior art being WO ‘374 discloses “systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images (abstract).” WO ‘374 further allows for analysis of multiple drugs administered to a patient (paragraph 0003, “ Radiopharmaceuticals are administered to patients and accumulate in various regions in the body in manner that depends on, and is therefore indicative of, biophysical and/or biochemical properties of tissue therein, such as those influenced by presence and/or state of disease, such as cancer.”). However, WO ‘374 fails to disclose determining the tissue progress degree based on a first drug of a first particle size, in a specific region, and estimating the progress degree being more advanced when the second drug is of a larger particle size than the first, and is distributed in a region
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Courtney J. Nelson whose telephone number is (571)272-3956. The examiner can normally be reached Monday - Friday 8:00 - 4:00.
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/COURTNEY JOAN NELSON/Primary Examiner, Art Unit 2661