DETAILED ACTION
Response to Arguments
The amendment filed 2/23/2026 have been entered and made of record.
The application has pending claim(s) 1-19 and 36.
In response to the amendments filed on 2/23/2026:
The objections to the claims have been entered and therefore the Examiner withdraws the objections to the claims.
The claim rejections under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph have been entered and therefore the Examiner withdraws the rejections under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Applicant's arguments filed 2/23/2026 have been fully considered but they are not persuasive.
The Applicant alleges, “Rejections of Claims under 35 U.S.C. 103 …” in pages 9-13, and states respectively that Praljak does not cure the deficiencies of Svekolkin because Praljak does not teach the claimed hierarchical type-to-subtype classification architecture, conditional hierarchical inference, a taxonomy that is subsequently refined, nor that the resulting model is configured to receive a previously determined cell type as an input and then conditionally classify a subtype within that type. Firstly, in response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., hierarchical type-to-subtype classification architecture, conditional hierarchical inference, a taxonomy that is subsequently refined) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Secondly, the Examiner disagrees because the references in combination do indeed disclose the broadest reasonable claim language interpretation of such limitations. More specifically Svekolkin provides motivation in [0144] “The information can further include information 210 about cell types and an analysis of sub-population(s) of cell” and Praljak similarly like Svekolkin discloses a trained machine learning model to segment followed by a trained machine learning model to classify types (see Praljak, Fig. 1, [0043]-[0048], [0050]-[0051], [0063], [0065]) and further and more particularly Praljak further discloses that the trained machine learning model that classifies types is followed by a trained machine learning model to classify subtypes for the type detected by the trained machine learning model that classifies types (see Praljak, Fig. 1, claim 2, [0043]-[0048], [0050]-[0051], [0063], [0065], and more specifically Praljak’s claim 2). Further discussions are addressed in the prior art rejection section below. Therefore claims 1-19 and 36 are still not in condition for allowance because they are still not patentably distinguishable over the prior art reference(s).
Claim Rejections - 35 USC § 103
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) 1-2, 5-9, 12-13, 16-18, and 36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Svekolkin et al (US 2021/0279866 A1, as applied in previous Office Action) in view of Praljak et al (US 2023/0221239 A1, as applied in previous Office Action).
Re Claim 1: Svekolkin discloses a method for identifying and classifying cells of a sample, the method comprising: at a first processor configured to execute a first trained algorithm, wherein the first trained algorithm is trained on a first set of training images each comprising a plurality of training regions identified as a cell region, a non-cell region, or a background (see Svekolkin, [0123], [0125], [0145]-[0146], [0179]-[0181], trained neural network to generate cell segmentation data for identifying cell locations and boundaries, [0277], cell segmentation - processor implemented), and wherein the first trained algorithm comprises: receiving an image of a biological sample (see Svekolkin, [0145]-[0146], receive image(s) of the tissue sample); and dividing the image into a plurality of regions (see Svekolkin, [0115], [0145]-[0146], background, noise, and artifact removal followed by segmenting the image); identifying a location of at least one cell region comprising one or more cells located in at least one of the plurality of regions (see Svekolkin, [0115], [0145]-[0146], segmenting the image to identify cell location); outputting the location of the at least one cell region to a second processor for classifying (see Svekolkin, [0145]-[0146], the cell segmentation component outputting the cell location information to the cell typing component for performing cell typing, [0277], cell segmentation processor implementation outputs to the cell typing processor implementation); at the second processor configured to execute a second trained algorithm, wherein the second trained algorithm is trained on a second set of training images comprising a plurality of cell types in a plurality of cell regions (see Svekolkin, [0137], [0145]-[0146], [0179]-[0181], trained neural network to identify the cell type(s) for each cell group, [0277], cell typing - processor implemented), and wherein the second trained algorithm comprises: receiving the image of the biological sample comprising the location of the at least one cell region (see Svekolkin, [0145]-[0146], the cell typing component receiving the cell location information); classifying a cell type for each of the at least one cell region (see Svekolkin, [0145]-[0146], perform cell typing to determine the cell type(s) for each cell group); and outputting the cell type for each of the at least one cell region (see Svekolkin, [0145]-[0146], outputting cell typing to the characteristic determination component); wherein each of: the first trained algorithm, and the second trained algorithm are independently trained (see Svekolkin, [0123], [0125], [0145]-[0146], [0179]-[0181], separately trained neural network to generate cell segmentation data for identifying cell locations and boundaries, [0137], [0145]-[0146], [0179]-[0181], separately trained neural network to identify the cell type(s) for each cell group).
However Svekolkin fails to explicitly disclose where Praljak discloses at a third processor configured to execute a third trained algorithm, wherein the third trained algorithm is trained on a third set of training images comprising the plurality of cell types and a plurality of cell subtypes (see Praljak, Fig. 1, claim 2, [0043]-[0048], [0063], [0065], trained machine learning model to classify subtypes, [0086], processor implemented), and wherein the third trained algorithm comprises: receiving the cell type for each of the at least one cell region (see Praljak, Fig. 1, claim 2, [0043]-[0048], trained machine learning model 110 receives the type detected by the previous machine learning model 106); classifying a cell subtype for each cell type (see Praljak, Fig. 1, claim 2, [0043]-[0048], trained machine learning model 110 to classify subtypes for the type detected and received from the previous machine learning model); and outputting the cell subtype associated with the cell type for each region of the at least one cell region (see Praljak, Fig. 1, claim 2, [0043]-[0048], output the determined classification types and/or subtypes), wherein each of: the first trained algorithm, the second trained algorithm, and the third trained algorithm are independently trained (see Praljak, [0050]-[0051], the one or more neural network models trained for segmentation, detection, and classification are built and separately trained machine learning models).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Svekolkin’s method using Praljak’s teachings by including the subtype classification [after the type is detected] after Svekolkin’s cell typing process in order to reliably improve the end-user’s experience with further sub-population / sub-type information (see Svekolkin, [0144]) (see Praljak, Fig. 1, claim 2, [0043]-[0048], [0050]-[0051]).
Re Claim 2: Svekolkin as modified by Praljak further discloses at the first processor: masking the non-cell region and the background of the image (see Svekolkin, [0115], [0145]-[0146], [0168]-[0169], background, noise, and artifact removal followed by segmenting the image, [0277], cell segmentation - processor implemented) (Praljak, [0045], multi-class segmentation mask including sickle red blood cells (sRBCs), other, and background). See claim 1 for obviousness and motivation statements.
Re Claim 5: Svekolkin further discloses wherein the image is a highly multiplexed fluorescence immunostained image of the biological sample (see Svekolkin, [0145]-[0146], and more specifically [0075] and [0151], stained multiplexed immunofluorescence images).
Re Claim 6: Svekolkin discloses a system for identifying and classifying cells in a sample (see Svekolkin, [0145]-[0146], generate cell segmentation data and perform cell typing, [0277], processor implemented), the system comprising: a first processor configured to execute a first machine learning layer comprising a first trained algorithm (see Svekolkin, [0123], [0125], [0145]-[0146], [0179]-[0181], trained neural network to generate cell segmentation data for identifying cell locations and boundaries, [0277], cell segmentation - processor implemented) configured to: receive an image of a biological sample (see Svekolkin, [0145]-[0146], receive image(s) of the tissue sample), divide the image into a plurality of regions (see Svekolkin, [0115], [0145]-[0146], background, noise, and artifact removal followed by segmenting the image), and identify a location in a subset of the plurality of regions having one or more of cells and one or more of non-cells (see Svekolkin, [0115], [0145]-[0146], [0168]-[0169], segmenting the image to identify cell location along with e.g. an indicator of present or not present), and a second processor configured to execute a second machine learning layer comprising a second trained algorithm (see Svekolkin, [0137], [0145]-[0146], [0179]-[0181], trained neural network to identify the cell type(s) for each cell group, [0277], cell typing - processor implemented) configured to: receive the image of the biological sample and the location of the subset of the plurality of regions having cells (see Svekolkin, [0145]-[0146], the cell typing component receiving the cell location information), and classify the subset of the plurality of regions having cells based on a cell type (see Svekolkin, [0145]-[0146], perform cell typing to determine the cell type(s) for each cell group), wherein each of: the first machine learning layer, and the second machine learning layer are independently trained (see Svekolkin, [0123], [0125], [0145]-[0146], [0179]-[0181], separately trained neural network to generate cell segmentation data for identifying cell locations and boundaries, [0137], [0145]-[0146], [0179]-[0181], separately trained neural network to identify the cell type(s) for each cell group).
However Svekolkin fails to explicitly disclose where Praljak discloses a third processor configured to execute a third machine learning layer comprising a third trained algorithm (see Praljak, Fig. 1, claim 2, [0043]-[0048], [0063], [0065], trained machine learning model to classify subtypes, [0086], processor implemented) configured to: receive the image of the biological sample and the classified cell type in the subset of the plurality of regions (see Praljak, Fig. 1, claim 2, [0043]-[0048], trained machine learning model 110 receives the image and the type detected by the previous machine learning model 106), and further classify the cell type of the subset of the plurality of regions based on a cell subtype (see Praljak, Fig. 1, claim 2, [0043]-[0048], trained machine learning model 110 to classify subtypes for the type detected and received from the previous machine learning model), wherein each of: the first machine learning layer, the second machine learning layer, and the third machine learning layer are independently trained (see Praljak, [0050]-[0051], the one or more neural network models trained for segmentation, detection, and classification are built and separately trained machine learning models).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Svekolkin’s system using Praljak’s teachings by including the subtype classification [after the type is detected] after Svekolkin’s cell typing process in order to reliably improve the end-user’s experience with further sub-population / sub-type information (see Svekolkin, [0144]) (see Praljak, Fig. 1, claim 2, [0043]-[0048], [0050]-[0051]).
Re Claim 7: Svekolkin further discloses wherein identifying the location performed by the first trained algorithm (see Svekolkin, [0123], [0125], [0145]-[0146], [0179]-[0181], trained neural network to generate cell segmentation data for identifying cell locations and boundaries, [0277], cell segmentation - processor implemented) further comprises identifying one or more features in one or more of the plurality of regions (see Svekolkin, [0115], [0128], [0145]-[0146], [0168]-[0169], identify cell location and boundaries along with e.g. an indicators of present, not present, size, shape, etc.).
Re Claim 8: Svekolkin as modified by Praljak further discloses wherein the second trained algorithm classifies regions based on the cell type (see Svekolkin, [0137], [0145]-[0146], [0179]-[0181], trained neural network perform cell typing to determine the cell type(s) for each cell group, [0277], cell typing - processor implemented); and wherein the third trained algorithm classifies objects based on cell subtypes (see Praljak, Fig. 1, claim 2, [0043]-[0048], [0063], [0065], trained machine learning model 110 to classify subtypes for the type detected and received from the previous machine learning model, [0086], processor implemented). See claim 6 for obviousness and motivation statements.
Re Claim 9: Svekolkin further discloses wherein the one or more features comprise one or more of: a nuclear region or nuclear marker, a cytoplasmic region or cytoplasmic marker, a membrane region or membrane marker, a cellular region or cellular marker, a fluorescence intensity, a region shape or area, or a combination thereof (see Svekolkin, [0115], [0128], [0145]-[0146], [0168]-[0169], identify cell location and boundaries along with e.g. an indicators of present, not present, size, shape, etc.).
Re Claim 12: Svekolkin discloses a method for identifying and classifying a cell in a sample, the method comprising: receiving, at a machine learning system comprising a plurality of trained algorithms (see Svekolkin, [0123], [0125], [0145]-[0146], [0179]-[0181], separately trained neural network to generate cell segmentation data for identifying cell locations and boundaries, [0137], [0145]-[0146], [0179]-[0181], separately trained neural network to identify the cell type(s) for each cell group, [0277], processor implemented), an image of an unknown biological sample (see Svekolkin, [0088], [0145]-[0146], [0156], receive image(s) of a tissue sample in order to classify the new data and determine a likelihood of whether or not this new data corresponds to a known class typing); segmenting the image into regions (see Svekolkin, [0115], [0145]-[0146], background, noise, and artifact removal followed by segmenting the image); identifying a location of a region of cells having one or more features in one or more of the regions according to a first trained algorithm (see Svekolkin, [0115], [0123], [0125], [0128], [0145]-[0146], [0168]-[0169], [0179]-[0181], segmenting the image [using the trained neural network to generate cell segmentation data] to identify cell locations and boundaries along with e.g. an indicators of present, not present, size, shape, etc.); classifying the region of cells at the identified location based on a cell type according to a second trained algorithm (see Svekolkin, [0137], [0145]-[0146], [0179]-[0181], the cell typing component receiving the cell location information to perform cell typing [using the trained neural network to identify the cell type(s)] to determine the cell type(s) for each cell group); wherein each of: the first trained algorithm, and the second trained algorithm are independently trained (see Svekolkin, [0123], [0125], [0145]-[0146], [0179]-[0181], separately trained neural network to generate cell segmentation data for identifying cell locations and boundaries [0137], [0145]-[0146], [0179]-[0181], separately trained neural network to identify the cell type(s) for each cell group).
However Svekolkin fails to explicitly disclose where Praljak discloses classifying the region of cells at the identified location having the cell type based on a cell subtype according to a third trained algorithm (see Praljak, Fig. 1, claim 2, [0043]-[0048], [0063], [0065], trained machine learning model 110 to classify subtypes for the type detected and received from the previous machine learning model, [0086], processor implemented), wherein each of: the first trained algorithm, the second trained algorithm, and the third trained algorithm are independently trained (see Praljak, [0050]-[0051], the one or more neural network models trained for segmentation, detection, and classification are built and separately trained machine learning models).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Svekolkin’s method using Praljak’s teachings by including the subtype classification [after the type is detected] after Svekolkin’s cell typing process in order to reliably improve the end-user’s experience with further sub-population / sub-type information (see Svekolkin, [0144]) (see Praljak, Fig. 1, claim 2, [0043]-[0048], [0050]-[0051]).
Re Claim 13: Svekolkin further discloses wherein the one or more features comprise one or more of: a nuclear region or nuclear marker, a cytoplasmic region or cytoplasmic marker, a membrane region or membrane marker, a cellular region or cellular marker, a fluorescence intensity, a region shape or area, or a combination thereof (see Svekolkin, [0115], [0128], [0145]-[0146], [0168]-[0169], identify cell location and boundaries along with e.g. an indicators of present, not present, size, shape, etc.).
Re Claim 16: Svekolkin as modified by Praljak further discloses training the machine learning system, wherein training comprises: acquiring a plurality of training images of known biological samples, wherein locations of the regions of cells, cell types, and cell subtypes of the known biological samples are identified (see Svekolkin, [0145]-[0146], [0156], [0208], annotated training dataset wherein the images are annotated with the cell location data and cell type classes) (see Praljak, Fig. 1, claim 2, [0043]-[0048], [0063], [0065], labeled trained dataset wherein the images are labeled with types / classes and subtypes / subclasses); training a first machine learning layer with the plurality of training images and developing the first trained algorithm by using the identified location of the region of cells having the one or more features to distinguish between regions of cells, non-cells, and a background of the image (see Svekolkin, [0115], [0123], [0125], [0128], [0145]-[0146], [0168]-[0169], [0179]-[0181], training the neural network using the annotation / labeled training dataset to generate cell segmentation data for identifying cell locations and boundaries along with e.g. an indicators of present, not present, size, shape, etc.) (Praljak, [0045], multi-class segmentation mask including sickle red blood cells (sRBCs), other, and background); training a second machine learning layer with the plurality of training images and developing the second trained algorithm by using the identified location of the region of cells and the cell types (see Svekolkin, [0137], [0145]-[0146], [0179]-[0181], training the neural network using the annotation / labeled training dataset to identify the cell type(s) for each cell group); and training a third machine learning layer with the plurality of training images and developing the third trained algorithm by using the identified location of the region of cells, the cell types, and the cell subtypes (see Praljak, Fig. 1, claim 2, [0043]-[0048], [0063], [0065], training the machine learning model using the annotation / labeled training dataset to classify subtypes, [0086], processor implemented). See claim 12 for obviousness and motivation statements.
Re Claim 17: Svekolkin further discloses wherein the plurality of training images is highly multiplexed fluorescence immunostained images of a plurality of known biological samples (see Svekolkin, [0145]-[0146], and more specifically [0051], [0075], [0151], [0156], [0180], training images including annotated (DAPI) stained multiplexed immunofluorescence images).
Re Claim 18: Svekolkin as modified by Praljak further discloses wherein the machine learning system is one of: a supervised, unsupervised, reinforcement, semi-supervised, self-supervised, multi-instance, inductive, deductive inference, transductive, multi-task, active, online, transfer, ensemble, neural networks, convolutional neural networks, recurrent neural networks, modular neural networks, long short-term memory, Classification Trees, Discriminant Analysis, k-Nearest Neighbors, Naive Bayes, Support Vector Machines, deep learning, or a combination thereof (see Svekolkin, [0123], [0125], [0145]-[0146], [0179]-[0181], separately trained neural network to generate cell segmentation data for identifying cell locations and boundaries [0137], [0145]-[0146], [0179]-[0181], separately trained neural network to identify the cell type(s) for each cell group) (see Praljak, [0050]-[0051], the one or more neural network models trained for segmentation, detection, and classification are built and separately trained machine learning models). See claim 12 for obviousness and motivation statements.
Re Claim 36: Praljak further discloses wherein one or more of: the first machine learning layer, the second machine learning layer, or the third machine learning layer is configured to be debugged or replaced without affecting the other layers (see Praljak, [0050]-[0051], the one or more neural network models trained for segmentation, detection, and classification are built and separately trained machine learning models, [0072], wherein the network is tailored for transfer learning so that a layer is able to be replaced by another layer).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Svekolkin’s system using Praljak’s teachings by including the transfer learning framework to Svekolkin’s [as modified by Praljak] neural network framework in order to reliably improve the training framework with a higher starting accuracy and faster convergence (see Praljak, [0050]-[0051], [0072]).
Claim(s) 3, 11, 15, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Svekolkin as modified by Praljak, and further in view of Ulmann et al (US 2022/0004737 A1, as applied in previous Office Action). The teachings of Svekolkin as modified by Praljak have been discussed above.
Re Claim 3: Svekolkin as modified by Praljak fails to explicitly disclose where Ulmann discloses wherein the cell subtype is an object including a circulating tumor cell or a cancer cell (see Ulmann, [0061], cancer cell type and sub-type).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Svekolkin’s method, as modified by Praljak’s, using Ulmann’s teachings by including the cancer subtype classification to Svekolkin’s [as modified by Praljak] cell type and subtype processing in order to improve the classification of cells (see Ulmann, [0061]).
Re Claim 11: Svekolkin as modified by Praljak fails to explicitly disclose where Ulmann discloses wherein each of the plurality of cell subtypes is an object comprising one of: an isomorphic tumor cell, a polymorphic non-white blood cell, a polymorphic tumor cell, an atypical white blood cell, a megakaryocyte, a megakaryoblast, an activated mononuclear cell, a neutrophil, an eosinophil, a basophil, a T cell, a B cell, a granular lymphocyte, a lymphocyte, a plasma cell, a monocyte, an hematopoietic stem cell, a circulating tumor cell, a cancer cell, or a combination thereof (see Ulmann, [0061], cancer cell type and sub-type). See claim 3 for obviousness and motivation statements.
Re Claim 15: Svekolkin as modified by Praljak fails to explicitly disclose where Ulmann discloses wherein the cell subtype is an object comprising: an isomorphic tumor cell, a polymorphic non-white blood cell, a polymorphic tumor cell, an atypical white blood cell, a megakaryocyte, a megakaryoblast, an activated mononuclear cell, a neutrophil, an eosinophil, a basophil, a T cell, a B cell, a granular lymphocyte, a lymphocyte, a plasma cell, a monocyte, an hematopoietic stem cell, a circulating tumor cell, a cancer cell, or a combination thereof (see Ulmann, [0061], cancer cell type and sub-type). See claim 3 for obviousness and motivation statements.
Re Claim 19: Svekolkin as modified by Praljak fails to explicitly disclose where Ulmann discloses wherein the at least one of the cell subtypes is an object including a circulating tumor cell or a cancer cell (see Ulmann, [0061], cancer cell type and sub-type). See claim 3 for obviousness and motivation statements.
Claim(s) 4, 10, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Svekolkin as modified by Praljak, and further in view of Karny et al (US 2023/0384205 A1, as applied in previous Office Action). The teachings of Svekolkin as modified by Praljak have been discussed above.
Re Claim 4: Svekolkin as modified by Praljak fails to explicitly disclose where Karny discloses wherein the cell type for each of the at least one cell region comprises a polymorphonuclear cell, an atypical polymorphonuclear cell, an atypical cell, a mononuclear cell, an atypical mononuclear cell, a megakaryocyte cell, or a combination thereof (see Karny, [0049], [0055], [0079], classified to determine cell type / cellular group comprising e.g. megakaryocyte cellular area); and wherein the cell subtype is an object comprising an isomorphic tumor cell, a polymorphic non-white blood cell, a polymorphic tumor cell, an atypical white blood cell, a megakaryocyte, a megakaryoblast, an activated mononuclear cell, a neutrophil, an eosinophil, a basophil, a T cell, a B cell, a granular lymphocyte, a lymphocyte, a plasma cell, a monocyte, a hematopoietic stem cell, a circulating tumor cell, a cancer cell, or a combination thereof (see Karny, [0049], [0055], [0079], classified to determine cell type / group and further classified into subgroups / cellular subgroup comprising e.g. a circulating tumor cell).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Svekolkin’s method, as modified by Praljak’s, using Karny’s teachings by including the megakaryocyte cell type and circulating tumor cell subgroup classification to Svekolkin’s [as modified by Praljak] cell type and subtype processing in order to improve the classification of cells (see Karny, [0049], [0055], [0079]).
Re Claim 10: Svekolkin as modified by Praljak fails to explicitly disclose where Karny discloses wherein each cell type is one of: a polymorphonuclear cell, an atypical polymorphonuclear cell, an atypical cell, a mononuclear cell, an atypical mononuclear cell, a megakaryocyte cell, or a combination thereof (see Karny, [0049], [0055], [0079], classified to determine cell type / cellular group comprising e.g. megakaryocyte cellular area). See claim 4 for obviousness and motivation statements.
Re Claim 14: Svekolkin as modified by Praljak fails to explicitly disclose where Karny discloses wherein the cell type comprises one of: a polymorphonuclear cell, an atypical polymorphonuclear cell, an atypical cell, a mononuclear cell, an atypical mononuclear cell, a megakaryocyte cell, or a combination thereof (see Karny, [0049], [0055], [0079], classified to determine cell type / cellular group comprising e.g. megakaryocyte cellular area). See claim 4 for obviousness and motivation statements.
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 extension fee 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 date of this final action.
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/Bernard Krasnic/Primary Examiner, Art Unit 2671 May 22, 2026
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