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 .
DETAILED ACTION
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office Action.
Status of Claims
Claims 1-20 are currently pending. Claims 1, 2, 4 and 5 have been amended by Applicants’ amendment filed 04-16-2026. No claims have been added or canceled by Applicants’ amendment filed 04-16-2026.
Applicant's election of Group I, claims 1-6, directed to a method of determining cell members in a tissue of interest; and Applicant’s election of Species as follows:
Species (A): wherein the cell types comprise a unique surface marker or a unique combination of cell surface markers (claim 4); and
Species (B): wherein said tissue is not hepatic tissue (claim 5), in the reply filed December 23, 2025 was previously acknowledged.
Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election of invention has been treated as an election without traverse (MPEP
§ 818.03(a)).
Claims 7-20 were previously withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected invention, there being no allowable generic or linking claim.
Claims 3 and 6 were previously withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected species, there being no allowable generic or linking claim.
The restriction requirement is still deemed proper and is therefore made FINAL.
The claims will be examined insofar as they read on the elected species.
A complete reply to the final rejection must include cancellation of nonelected claims or other appropriate action (37 CFR 1.144) See MPEP § 821.01.
Therefore, claims 1, 2, 4 and 5 are under consideration to which the following grounds of rejection are applicable.
Priority
The present application filed August 10, 2022, is a CON of the 35 U.S.C. 371 national stage filing of International Application PCT/IL2021/050161, filed February 10, 2021, which claims the benefit of Isreal patent IL272586, filed February 10, 2020.
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Isreal on February 10, 2020. It is noted, however, that applicant has not filed a certified copy of Isreal Patent Application No. 272586 as required by 37 CFR 1.55.
Withdrawn Objections/Rejections
Applicants’ amendment and arguments filed April 16, 2026 are acknowledged and have been fully considered. The Examiner has re-weighed all the evidence of record. Any rejection and/or objection not specifically addressed below are herein withdrawn.
Claim Rejections - 35 USC § 112(b)
The rejection of claims 1, 2, 4 and 5 is withdrawn under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention due to Applicant’s amendment of the claims, in the reply filed 04-16-2026.
In view of the withdrawn rejection, Applicant’s arguments are rendered moot.
Maintained Objections/Rejections
Claim Objections
Claim 1 is objected to because of the following informalities: Claim 1 recites a mixture of pronouns including “the” and “said” within the claim (e.g., “said library”), such that for consistency, a single pronoun reciting either “the” or “said” should be used.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
The rejection of claims 1, 2, 4 and 5 is maintained under 35 U.S.C. 102(a1)/102(a2) as being anticipated by Kumar et al. (hereinafter “Kumar”) (Cell Reports, 2018, 25, 1458-1468 and e1-e4).
Regarding claims 1, 2, 4 and 5, Kumar teaches the development of an approach to characterize cell-cell communication mediated by ligand-receptor interactions across all cell types in a microenvironment using scRNA-seq data (interpreted as receiving a transcriptome), such that after assigning cell types based on the scRNA-seq data using a decision tree classifier, our approach quantifies potential ligand-receptor interactions between all pairs of cell types based on their gene expression profiles, wherein the approach can assess similarities and differences in cell-cell communications between six syngeneic mouse tumor models (interpreted as cell members that physically interact with one another; interpreting scRNA-Seq data as receiving a transcriptome; cell clusters; cell types; and interacting via receptor ligand interaction, claims 1 and 2) (pg. 1459, col 2, last partial paragraph; and pg. 1460, col 1; first partial paragraph, lines 1-2). Kumar teaches quantifying ligand-receptor interactions between T cell subsets and their relation to immune infiltration using a publically available human melanoma dataset (interpreted as accessing a computer readable medium storing a library having a plurality of entries including a predicted transcriptome of a cell cluster, and a set of identities of known cell types forming the cluster, claim 1b) (Abstract, lines 17-20). Kumar teaches a Key Resource Table including a list of reagents, assays, deposited data, experimental cell lines, experimental models, and software and algorithms (MATLAB) that were used (pg. e1)). Kumar teaches that scRNA-seq was performed on tumors from six treatment-naive syngeneic mouse tumor models including B16-F10 melanoma, EMT6 breast mammary carcinoma, LL2 Lewis lung carcinoma, CT26 colon carcinoma, MC-38 colon carcinoma, and Sa1N fibrosarcoma; two samples per tumor model (interpreted as a tissue of interest, claim 1) (pg. 1460, col 1, last partial paragraph). Kumar teaches that for scRNA-seq of mouse syngeneic tumor models, two mice for each syngeneic model were implanted, resulting in a total of 12 samples, and each mouse tumor was harvested when the tumor size reached 100 – 200 mm, wherein each sample was minced and digested with reagents from Mouse Tumor Dissociation Kit, cells were resuspended at 2x105 cells/mL in PBS-0.04% BSA, and each sample was processed individually and run in technical duplicates (interpreted as cell clusters, claim 1) (pg. e2, Method Details). Kuma teaches t-distributed scholastic neighbor embedding (t-SNE) plots of cells from six syngeneic tumor models show distinct clusters predominantly determined by cell type (interpreted as cell cluster; cell members; and cell type, claim 1) (pg. 1460, Figure 1). Kumar teaches that to check that the scRNA-seq measurements reflect protein abundances (predicted transcriptome), and the single-cell suspensions from the same tumors were stained in parallel with antibodies and analyzed expression of protein marker genes by flow cytometry (Figure 1A; Table S2), wherein comparison of the frequencies of single cells positive for cell surface markers between the scRNA-seq data and flow cytometry results showed significant correlation between markers measured using the two approaches (R2 = 0.74, p = 2.3 x 10-28) (receiving a transcriptome; and accessing a computer having predicted transcriptome matching received transcriptome); and the similarity of frequencies of five immune cell populations was evaluated, each defined by two or three markers, and again found significant correlation between scRNA-seq and flow cytometry data (R2 = 0.48, p = 3.4 x 10-3; Table S2; Figure S1B) (searching a predicted transcriptome and the received transcriptome), such that the data indicated that scRNA-seq measurements recapitulate both cell type abundances and marker expression measured by flow cytometry (extracting corresponding set of identities, claim 1a-d) (pg. 1459, col 2, second full paragraph). Kumar teaches scoring cell-cell interactions using known ligand-receptor interactions (interpreted as accessing a computer readable medium storing a library or a plurality of entries having a predicted transcriptome and a set of identities of known cell type; physically interacting; and ligand-receptor interactions, claims 1 and 2) (pg. 1461, col 2; second full paragraph, Title). Kumar teaches that, having defined cell types, the potential cell-cell interactions were quantified between all cell types present in the tumor micro-environment using a reference list of approximately 1,800 known, literature-supported interactions containing receptor ligand interactions from the chemokine, cytokine, receptor tyrosine kinase (RTK), and tumor necrosis factor (TNF) families and extracellular matrix (ECM)-integrin interactions (Ramilowski et al., 2015), wherein known B7 family member interactions were manually added (Southan et al., 2016) because of their relevance to cancer immunology, such that to identify potential cell-cell interactions that are conserved across the six syngeneic tumor models, each tumor model was screened for cases where both members of a given ligand-receptor interaction are expressed by cell types present within the tumor microenvironment (Figure 2), wherein interactions were scored by calculating the product of average receptor expression and average ligand expression in the respective cell types under examination; and after computing scores for each tumor, the interaction score was averaged across the tumor models to identify conserved interactions (Figure 2), such that approximately 1,500 ligand-receptor pairs were screened after converting to mouse homologs [Human to Mouse Homolog Conversion] and 64 pairwise combinations of cell types; as well as, assessing the statistical significance of each interaction score using a one-sided Wilcoxon rank-sum test and performed Benjamini-Hochberg multiple hypothesis correction, such that all interactions for all identified cell types were computed; and all interactions involving tumor cells were examined, wherein many of the highest-scoring interactions were chemokine interactions, often involving the same receptors including Ccr1, Ccr2, Ccr5, and their shared ligands including Ccl2, Ccl4, and Ccl12 (interpreted as accessing a set of identities of known cell types forming said cell cluster; known cell types; searching the library for an entry having a predicted transcriptome matching; extracting from the entry, a set of identities; and interpreting Ccr1, Ccr2, Ccr5 as cell surface markers, claims 1, 2, 4 and 5) (pg. 1461, col 2, last full paragraph; and last partial paragraph; pg. 1463, col 1, first partial paragraph; and first full paragraph, lines 1-4). Kumar teaches that the approach presented can be used to compare the interaction strengths observed in the tumor with those in control tissue from the same donor, such as nearby tissue of the same type or peripheral blood, such that tumor-specific cell-cell interactions can be identified (interpreted as extracting an entry, and determining the cell members of the cell cluster in the tissue, claim 1) (pg. 1467, col 2, first partial paragraph). Kumar teaches that the approach to quantify ligand-receptor interaction was extended to human metastatic melanoma samples, wherein the association of individual cell-cell interactions with pathophysiological characteristics of the tumor micro-environment were examined (interpreted as not hepatic tissue, claim 5) (pg. 1460, col 1, first partial paragraph).
Kumar meets all the limitations of the claims and, therefore, anticipates the claimed invention.
Response to Arguments
Applicant’s arguments filed April 16, 2026 have been fully considered but they are not persuasive. Applicants essentially assert that: (a) Kumar does not teach: a composite transcriptome of a physically interacting cell cluster (Applicant Remarks, pg. 7, second full paragraph, lines 7-8); (b) the SNE clusters of Kumar are not physically interacting multi-cell clusters having a shared composite transcriptome (Applicant Remarks, pg. 7, second full paragraph, lines 11-12); (c) Kuman does not contain predicted composite transcriptomes of cell clusters, does not associate any predicted transcriptome with a defined combination of cell identities, and does not disclose searching such predicted transcriptome entries to determine cluster membership (Applicant Remarks, pg. 7, second full paragraph, lines 16-19); and (d) Kumar performs no transcriptome prediction, no composite transcriptome matching, and no extraction of cluster member identities from a predicted transcriptome entry (Applicant Remarks, pg. 7, second full paragraph, lines 24-26);
Regarding (a)-(d), as an initial matter, based on Applicant’s reply, the Examiner would like to offer to use a different term than “interpreted” when indicating what portion of a cited reference reads on a limitation within a claim. If Applicant would prefer that the Examiner replace this term with another that the Applicant prefers (e.g., corresponds to, reads on, correlates to, referring to, etc.), the Examiner would be happy to do so.
It is noted that 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, 26USPQ2d 1057 (Fed. Cir. 1993). Moreover, MPEP 2112.01(I),
where the claimed and prior art products are identical or substantially identical in structure or composition, or are produced by identical or substantially identical processes, a prima facie case of either anticipation or obviousness has been established. In re Best, 562 F.2d 1252, 1255, 195 USPQ 430, 433 (CCPA 1977). "When the PTO shows a sound basis for believing that the products of the applicant and the prior art are the same, the applicant has the burden of showing that they are not." In re Spada, 911 F.2d 705, 709, 15 USPQ2d 1655, 1658 (Fed. Cir. 1990).
Additionally, examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are: (A) "adapted to" or "adapted for" clauses; (B) "wherein" clauses; and (C) "whereby" clauses. As noted in MPEP 2111.04(I),
the determination of whether each of these clauses is a limitation in a claim depends on the specific facts of the case. See, e.g., Griffin v. Bertina, 285 F.3d 1029, 1034, 62 USPQ2d 1431 (Fed. Cir. 2002) (finding that a "wherein" clause limited a process claim where the clause gave "meaning and purpose to the manipulative steps"). In In re Giannelli, 739 F.3d 1375, 1378, 109 USPQ2d 1333, 1336 (Fed. Cir. 2014), the court found that an "adapted to" clause limited a machine claim where "the written description makes clear that 'adapted to,' as used in the [patent] application, has a narrower meaning, viz., that the claimed machine is designed or constructed to be used as a rowing machine whereby a pulling force is exerted on the handles." In Hoffer v. Microsoft Corp., 405 F.3d 1326, 1329, 74 USPQ2d 1481, 1483 (Fed. Cir. 2005), the court held that when a "‘whereby’ clause states a condition that is material to patentability, it cannot be ignored in order to change the substance of the invention." Id. However, the court noted that a "‘whereby clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited.’" Id. (quoting Minton v. Nat’l Ass’n of Securities Dealers, Inc., 336 F.3d 1373, 1381, 67 USPQ2d 1614, 1620 (Fed. Cir. 2003)).
Applicant’s assertions that:
Kumar does not teach a composite transcriptome of a physically interacting cell cluster;
(b) The SNE clusters of Kumar are not physically interacting multi-cell clusters having a shared composite transcriptome;
Kuman does not contain predicted composite transcriptomes of cell clusters, does not associate any predicted transcriptome with a defined combination of cell identities, and does not disclose searching such predicted transcriptome entries to determine cluster membership; and
Kumar performs no transcriptome prediction, no composite transcriptome matching, and no extraction of cluster member identities from a predicted transcriptome entry;
are not found persuasive.
Instant claim 1 is very broadly recited, such that no specific method of receiving, method of accessing, method of searching, method of extracting, cell cluster, transcriptome, entries, method of matching, method of determining, cell members, physical interactions, etc. are recited. Moreover, claim 1 does not recite features that Applicant argues are not taught by Kumar including, for example, a composite transcriptome, multi-cell clusters having a shared composite transcriptome, associating any predicted transcriptome with a defined combination of cell identities, performing transcriptome prediction, and/or composite transcriptome matching.
Additionally with regard to step (d), in light of MPEP 2111.04(I) the “thereby” clause of claim 1 was not given patentable weight because it simply expresses the intended result of a process step positively recited. Thus, the step of “extracting” a set of identities, “thereby” determines the cell members of the cell cluster in the tissue of interest. Moreover, the Examiner contends that Kumar teaches all of the limitations of the claims. To that end -
Kumar teaches:
Characterizing cell-cell communication mediated by ligand-receptor interactions across all cell types in a microenvironment using scRNA-seq data, such that after assigning cell types based on the scRNA-seq data using a decision tree classifier, potential ligand-receptor interactions between all pairs of cell types were quantified based on their gene expression profile to assess cell-cell communication between six syngeneic mouse tumor models (interpreting scRNA-seq data and gene expression profiles as receiving a transcriptome of the cell cluster, claim 1a) (pg.1459, col 2, last partial paragraph; and pg. 1460, col 1, first partial paragraph).
Quantifying ligand-receptor interactions between T cell subsets and their relation to immune infiltration using a publically available human melanoma dataset (interpreted as accessing a computer readable medium storing a library having a plurality of entries including a predicted transcriptome of a cell cluster, and a set of identities of known cell types forming the cluster, claim 1b) (Abstract, lines 17-20).
Having defined cell types, the potential cell-cell interactions were quantified between all cell types present in the tumor micro-environment using a reference list of approximately 1,800 known, literature-supported interactions containing receptor ligand interactions from the chemokine, cytokine, receptor tyrosine kinase (RTK), and tumor necrosis factor (TNF) families and extracellular matrix (ECM)-integrin interactions (Ramilowski et al., 2015) (interpreting literature-supported interactions as receiving a transcriptome of the cell cluster, and accessing a computer-readable medium; interpreting potential cell-cell interactions as predicted transcriptome of a cell cluster and a set of identities, claim 1b).
To check that the scRNA-seq measurements reflect protein abundances (predicted transcriptome), and the single-cell suspensions from the same tumors were stained in parallel with antibodies and analyzed expression of protein marker genes by flow cytometry (Figure 1A; Table S2), wherein comparison of the frequencies of single cells positive for cell surface markers between the scRNA-seq data and flow cytometry results showed significant correlation between markers measured using the two approaches (R2 = 0.74, p = 2.3 x 10-28) (receiving a transcriptome; and accessing a computer having predicted transcriptome matching received transcriptome); and the similarity of frequencies of five immune cell populations was evaluated, each defined by two or three markers, and again found significant correlation between scRNA-seq and flow cytometry data (R2 = 0.48, p = 3.4 x 10-3; Table S2; Figure S1B) (searching a predicted transcriptome and the received transcriptome), such that the data indicated that scRNA-seq measurements recapitulate both cell type abundances and marker expression measured by flow cytometry (extracting corresponding set of identities; and determining cell members, claim 1a-d) (pg. 1459, col 2, second full paragraph).
In quantifying interactions in human metastatic melanoma, we applied the classification approach to identify cell types and quantify cell type percentages using markers identified by Tirosh (determining cell members, claim 1d) (pg. 1463, col 1, first partial paragraph).
Kumar teaches all of the limitations of the claim. Thus, the claims remain rejected.
The rejection of claims 1, 2, 4 and 5 is maintained under 35 U.S.C. 102(a1)/102(a2) as being anticipated by Boisset et al. (hereinafter “Boisset”) (Nature Methods, 2018, 15, 547-553).
Regarding claims 1 and 5, Boisset teaches that to create a cellular interaction network, 727 small interacting bone marrow (BM) structures were manually dissected for a total of 1,728 cells across 18 independent experiments (Methods), and inferring the cell types present in the micro-dissected units by scRNA-seq (Fig. 1a) (interpreted as clusters of cell; receiving a transcriptome; and not hepatic tissue, claims 1 and 5) (pg. 547, col 1; last partial paragraph, lines 7-10). Boisset teaches creating a network of physical interactions in mouse BM allowed us to find two new preferential interactions: promyelocyte/myeloblast–plasma cell interactions and megakaryocyte–mature neutrophil interactions; and building a network of cell interactions in small intestinal crypts using a modified approach that does not require extensive microdissection (interpreted as clusters of cell; receiving a transcriptome; cell members that physically interact; and not hepatic tissue, claims 1 and 5) (pg. 547, col 1; last partial paragraph, lines 15-20). Boisset teaches that the frequency of cells per RaceID2 clusters were compared to label permutation simulations, wherein the sorted cell population contained fewer erythroblasts and eosinophils, whereas handpicked cells contained fewer neutrophils, progenitors, and putative macrophage progenitors (expressing S100a4) (Supplementary Fig. 4c); however, despite these few differences in frequency, the cell types present in the two datasets were similar (interpreted as accessing a computer readable medium; searching the library; and extracting from the entry a corresponding set of identities, claim 1) (pg. 548, col 1, first full paragraph). Boisset teaches preparing cells by isolating BM from femurs and tibias, where small interacting structures were selected by visual inspection under a dissection stereomicroscope, such that the structures can be combinations of cells such as doublets or triplets, or slightly bigger units composed of around 10-20 cells (interpreted as cell clusters; and not hepatic tissue, claims 1 and 5) (pg. 554, col 1; first full paragraph). Boisset teaches a CEL-Seq library preparation, clustering, and differential gene expression, wherein total RNA was prepared according to the manufacturer's instructions; wherein a CAST/EiJ transcriptome reference was created, where all CAST/EiJ single nucleotide polymorphisms were introduced into the C57BL/6 RefSeq transcriptome gene model (interpreted as receiving a transcriptome of the cell cluster; accessing a computer readable medium storing a library having a plurality of entries, each entry having a predicted transcriptome of a cell cluster and a set of identities of known cell types forming said cell cluster, claim 1b) (pg. 554, col 1, sixth and seventh full paragraphs). Boisset teaches that transcripts were mapped to both C57BL/6 and CAST/EiJ transcriptomes, where transcripts that mapped uniquely to only one of the transcriptomes were kept; and then the strain identity was determined by calculating the ratio of the total number of CAST/EiJ transcripts to that of C57BL/6 transcripts (interpreted as search the library for an entry having a predicted transcriptome matching the received transcriptome; and extracting from said entry a corresponding set of identities, claim 1c-d) (pg. 554, col 1, seventh full paragraph).
Regarding claim 2, Boisset teaches that compared with the random model, enrichment for specific interaction between macrophages and a subtype of adult erythroblasts was observed (interpreted as interacting via receptor-ligand interaction, claim 2) (pg. 548, col 2, first full paragraph).
Regarding claim 4, Boisset teaches in Figure 2, the identification of enriched and depleted interactions in the BM: (a) a, t-SNE map of transcriptome similarities with enriched and depleted interactions, wherein nodes represent cluster centers, edges represent inter-cluster interactions, and solid nodes represent intra-cluster interactions (b–d), t-SNE map of transcriptome similarities, with color-coded representation of transcript counts for Beta-s (b), Elane (c), and Retnlg (d), such that Pink edges represent all interactions stemming from cluster 10 (macrophages; b), cluster 16 (plasma cells; c), and cluster 2 (megakaryocytes; d) (interpreted as cell types comprise a unique surface marker, claim 4) (pg. 549, Figure 2).
Boisset meets all the limitations of the claims and, therefore, anticipates the claimed invention.
Response to Arguments
Applicant’s arguments filed April 16, 2026 have been fully considered but they are not persuasive. Applicants essentially assert that: (a) Boisset does not receive or analyze a composite transcriptome of a cell cluster as required by the claims (Applicant Remarks, pg. 8, last partial paragraph, lines 5-7); (b) Boisset does not disclose a library having a plurality of entries, each comprising a predicted transcriptome of a cell cluster associated with a predefined set of cell-type identities, and does not disclose searching such predicted entries to determine cluster membership (Applicant Remarks, pg. 8, last partial paragraph, lines 10-14); and (c) Boisset does not disclose generating predicted transcriptomes of cell clusters, storing such predicted transcriptomes in entries associated with defined cell-type combinations, or searching such entries to determine the members of a cell cluster (Applicant Remarks, pg. 9, first partial paragraph).
Regarding (a)-(c), please see the discussion supra regarding the Examiner’s response to Applicant’s arguments including the broadness of instant claim 1. Applicant’s assertion that Boisset does not teach:
Boisset does not receive or analyze a composite transcriptome of a cell cluster as required by the claims;
Boisset does not disclose a library having a plurality of entries, each comprising a predicted transcriptome of a cell cluster associated with a predefined set of cell-type identities, and does not disclose searching such predicted entries to determine cluster membership; and
Boisset does not disclose generating predicted transcriptomes of cell clusters, storing such predicted transcriptomes in entries associated with defined cell-type combinations, or searching such entries to determine the members of a cell cluster;
are not found persuasive.
It is noted that instant claim 1 does not recite, for example, a composite transcriptome, a predefined set of cell-type identities, generating predicted transcriptomes of cell clusters and/or storing predicted transcriptomes in entries associated with defined cell-type combinations.
Regarding step (d), please see the discussion supra regarding that the “thereby” clause was not given patentable weight. The Examiner contends that Boisset teaches all of the limitations of the claims. To that end -
Boisset teaches:
CEL-Seq library preparation, clustering, and differential gene expression, wherein total RNA was prepared (interpreted as receiving a transcriptome of the cell cluster, claim 1a) (pg. 554, col 1, sixth full paragraph, lines 1-2).
CEL-Seq library preparation, clustering, and differential gene expression, wherein total RNA was prepared according to the manufacturer's instructions; a CAST/EiJ transcriptome reference was created, where all CAST/EiJ single nucleotide polymorphisms were introduced into the C57BL/6 RefSeq transcriptome gene model (accessing a computer readable medium storing a library having a plurality of entries, each entry having a predicted transcriptome of a cell cluster and a set of identities of known cell types forming said cell cluster, claim 1b) (pg. 554, col 1, sixth and seventh paragraphs).
Transcripts were mapped to both C57BL/6 and CAST/EiJ transcriptomes, where transcripts that mapped uniquely to only one of the transcriptomes were kept; and then determining the strain identity by calculating the ratio of the total number of CAST/EiJ transcripts to that of C57BL/6 transcripts (interpreted as search the library for an entry having a predicted transcriptome matching the received transcriptome; extracting from said entry a corresponding set of identities; and identifying cell members that physically interact, claim 1c-d) (pg. 554, col 1, seventh paragraph).
Boisset teaches all of the limitations of the claim. Thus, the claims remain rejected.
Conclusion
Claims 1, 2, 4 and 5 remain rejected.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY M BUNKER whose telephone number is (313) 446-4833. The examiner can normally be reached on Monday-Friday (6am-2:30pm).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Heather Calamita can be reached on (571) 272-2876. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/AMY M BUNKER/Primary Examiner, Art Unit 1684
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