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 .
Election/Restrictions
Applicant’s election without traverse of “at least 10 genes” as a species of number of genes included in the first plurality of genes associated with tumor cells(claims 14-19) and "CD274," "EPCAM," "BRCA1," "MAGEA3," "ERBB3," "STMN1," "ICAM1," "ERBB2," "CDK4," and "MAGEA10" as species of genes (claims 16-19) in the reply filed on 06/22/2026 is acknowledged.
Claims 14-15 and 17-19 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 06/22/2026.
Claim Status
Claims 1-29 are pending.
Claims 14-15 and 17-19 are withdrawn, pursuant to an Election/Restriction Requirement, discussed above.
Claims 1-13, 16 and 20-29 are under examination herein.
Claims 1-13, 16 and 20-29 are rejected.
Priority
The instant application claims priority from Provisional Application 63239895 , filed 09/01/2021
and Provisional Application 63181365 , filed 04/29/2021. As such, the effective filing date assigned to each of claims 1-13, 16 and 20-29 is 04/29/2021.
Information Disclosure Statement
The Information Disclosure Statements filed 09/22/2022, 05/15/2023, 04/23/2024, 07/29/2024, 03/30/026 and 08/04/2026 are in compliance with the provisions of 37 CFR 1.97 and have therefore been considered. A signed copy of the IDS is included with this Office Action.
Drawings
The drawings filed 04/29/2022 are accepted by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13, 16 and 20-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea/law of nature/natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1 and 28-29 recite determining the tumor expression levels of the first plurality of genes in the tumor cells using a plurality of machine learning models, the plurality of machine learning models comprising a respective machine learning model for each gene in the first plurality of genes including a first machine learning model for a first gene in the first plurality of genes, the tumor expression levels including a first tumor expression level for the first gene in the tumor cells, the determining comprising: generating a first set of features for the first gene, the generating including: obtaining, using the expression data, an initial expression level estimate of the first gene in the tumor cells of the biological sample and including the initial expression level estimate of the first gene in the first set of features; including at least some of the first total expression levels in the first set of features; and including at least some of the second total expression levels in the first set of features; providing the first set of features as input to the first machine learning model to obtain an output indicative of a TME expression level estimate of the first gene in the TME cells; and determining the first tumor expression level for the first gene in the tumor cells using the output of the first machine learning model and a total expression level, in the first total expression levels, for the first gene.
Claim 1 also recites obtaining expression data for a set of genes, the set of genes comprising a first plurality of genes associated with the tumor cells and a second plurality of genes associated with the tumor microenvironment cells, the expression data comprising first total expression levels for genes in the first plurality of genes and second total expression levels for genes in the second plurality of genes; outputting the tumor expression levels of the first plurality of genes in the tumor cells.
These recitations equate to steps of collecting information, analyzing data and making observations, evaluations and judgements that can be carried out in the human mind. Specifically, the steps for obtaining expression data, generating a first set of features, obtaining initial expression estimates for genes, including expressions as part of the features, providing the features as input to a machine learning model to obtain an output, determining the expression level for a gene using the output, and outputting expression levels can be practically performing the human mind as claimed, since the mind can read data and make mathematical estimations/comparisons as claimed and are similar to the concepts of collecting and comparing known information in Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011) and collecting information, analyzing it, and reporting certain results of the collection and analysis in Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) that the courts have identified as concepts that can be practically performed in the human mind. The instant specification discloses in para 0116 that the machine learning model “a non- linear regression model (e.g., a logistic regression model), a neural network model, a support vector machine, a Gaussian mixture model, a random forest model, a decision tree model, or any other suitable type of machine learning model, as aspects of the technology described herein are not limited in this respect”. Therefore, each of the above recited limitations fall under the “Mental Processes” grouping of abstract ideas. Furthermore, the steps of determining expression levels based on gathered data, and using and applying the generically recited machine learning model equates to organizing information and manipulating information through mathematical correlations and reciting a mathematical equation, similar to the concepts of taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form in Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). Therefore, these limitations fall under the “mathematical concepts” grouping of abstract ideas. Claims 2-13, 16, 20-24 and 27 either further recite or further qualify the judicial exceptions. As such, claims 1-13, 16 and 20-29recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology, applies or uses the recited judicial exception to affect a particular treatment for a condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Rather, the instant claims recite additional elements that amount to mere data gathering and outputting and mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements:
Claim 25 recites administering the at least one anti-cancer therapy.
Claim 25 recites wherein the at least one anti-cancer therapy is selected from the group of therapies for the first gene listed in Table 3.
Claims 28 and 29 recites at least one processor; at least one non-transitory computer-readable storage medium storing processor; obtaining expression data for a set of genes, the set of genes comprising a first plurality of genes associated with the tumor cells and a second plurality of genes associated with the TME cells, the expression data comprising first total expression levels for genes in the first plurality of genes and second total expression levels for genes in the second plurality of genes; and outputting the tumor expression levels of the first plurality of genes in the tumor cells.
Claim 25-26 amount to mere instructions to "apply" the exception in a generic way, as the claims do not require treatment with a particular therapy. For example, some therapies listed in table 3 are not therapies, (e.g. therapy listed for MDM2 gene). Thus, the administration step does not integrate the mental analysis step into a practical application. Claims 28-29 merely recites using a generic computing systems and computer program products to carry out instructions to implement an abstract idea on a computer. The computer system and computer program product as claimed fails to recite details of how a solution to a problem is accomplished and only recites the idea of a solution or outcome. There are no limitations that indicate that the claimed steps require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Furthermore, the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). There is no indication that any of these additional elements provide a practical application of the recited judicial exception outside of the judicial exception itself. As such, claims 1-13, 16 and 20-29 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). Further analyzing the additional elements under step 2B, the additional elements as described above do not rise to the level of significantly more than the judicial exception. As set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims under the 2B analysis, the prior art to van ‘t Veer and Bernards (Nature 2008, 452, 564–570) reviews personalized cancer medicine through analysis of gene-expression patterns and discloses cancer therapy has slowly but steadily begun to shift from a 'one size fits all' approach to a more personalized approach, in which each patient is treated according to the specific genetic defects in the tumor (title; abstract; p 564, col 1, para 1- col 2, para 3). And therefore, administering the at least one anti-cancer therapy based on gene expression is well-known, routine, and conventional in the art. Furthermore, the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Therefore, the additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception, and the claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-13, 16 and 20-29 are not patent eligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-13, 21-23, and 28-29 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Igartua et al. (US20200210852A; 09/22/2022 IDS, US Patent Document 3; hereafter referred to as Igartua).
With respect to claims 1-4, 28, and 29, Igartua discloses a platform for transcriptome deconvolution of metastatic tissue sample that may be used in assessing metastatic cancer samples, wherein the deconvolution model uses machine learning algorithms (title; abstract; para 0068).
Igartua discloses obtaining and analyzing mixed sample gene expression data, and that the mixed sample may contain metastatic cancer tissue, immune cells, and background tissue from the biopsy collection site (i.e. tumor cells and TME) (para 0006-0008; para 0064; para 0081).
Igartua further discloses the deconvolution model uses machine learning algorithms (MLA) such as unsupervised or supervised clustering techniques to examine gene expression data to quantify the level of tumor versus normal cell populations present in the data, and that RNA expression data examined by the deconvoluted RNA expression model may be used to determine which genes, or networks of related genes, have expression levels that differ between tumor and normal tissue, and that training may include identifying common expression characteristics shared across RNA gene expressions in tissue normal samples, primary samples, and metastatic samples, such that the MLA may predict the ratio of a metastases tumor from the background tissue and identify which portion of an input RNA expression set may be attributed to the tumor and which portion may be attributed to the background tissue (para 0064-0076; fig 2; fig 3)
Igartua further discloses the MLA result may identify a percentage of membership in each cluster and post processing of the grade of membership output may include a multivariate regression which will accommodate for the influence of each cluster, that the RNA expression data may be configured in a N×G matrix, where N is the number of samples and G is the number of genes, and that an expression level value associated with a gene may represent the combined amount of all transcripts that can be a product of that gene (for example, splice variants and/or isoforms), or an expression level may be a single transcript or subset of transcripts associated with that gene, and that a linear regression based on the expression levels of one gene in all of the training samples that had membership in one of the respective clusters may, for each gene, be used to calculate a regressed gene expression level (i.e. different machine learning models for each gene) (para 0072; para 0087).
Igartua further discloses the methods may be computer implemented (para 0133).
With respect to claims 5-8, 11, and 21-23, Igartua discloses determine which cell type is associated with each cluster by analyzing the genes expression (para 0077). Igartua further discloses training may include identifying common expression characteristics shared across RNA gene expressions in tissue normal samples, primary samples, and metastatic samples, such that the MLA may predict the ratio of a metastases tumor from the background tissue and identify which portion of an input RNA expression set may be attributed to the tumor and which portion may be attributed to the background tissue (i.e. RNA percentage), and discloses using this data determine which cell types the clusters represent, and for further cluster optimization, which can be used to develop a deconvolution regression model of RNA expression data (para 0076-para 0080). Igartua also discloses that the deconvoluted RNA matrix may be validated, which may perform an in silico validation by using in silico mixtures of cancers and background RNA expression data, which can be used to update parameters of the models (para 0082-0084).
With respect to claim 9, Igartua discloses the mixture may comprise immune cells, including lymphocytes and macrophages (0008).
With respect to claims 10 and 12, Igartua discloses the expression level of each gene associated with that cluster may be scaled to increase or decrease the gene expression level measured in the mixture sample based on where the gene's expression falls in relation to the average at that membership percentage on the linear regression plot and by calculating each cluster's effect on the expression levels of all genes associated with the cluster, these factors may be regressed out (i.e., by summing the initial RNA gene expression level measured in the mixture sample with the additive inverse of each cluster's effect) and the resulting deconvoluted RNA expression data may be evaluated for biomarkers or other biological indications, and therefore the output from the MLA processing may identify a percentage of membership within each cluster as well as a prediction of the type of sample, and further that for each gene a linear model is generated to correlate the proportion of background tissue present in a sample with the expression level of the gene associated with that sample (para 0072-0089).
With respect to claim 13, Igartua discloses receiving RNA expression information of a sample of tumor tissue comprises sequencing the sample of tumor to generate RNA expression information (claim 26).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Igartua et al. (US20200210852A; 09/22/2022 IDS, US Patent Document 3; hereafter referred to as Igartua) as applied to claim 1 above, and further in view of Skog et al. (US20140045915A1; hereafter referred to as Skog).
With respect to claims 16, Igartua discloses the steps of claim 1, as applied above.
However, while Igartua discloses associating mutations with gene expression patterns, and associating tumor gene expression profiles with a list of cancer treatments that may predict response for a patient with that profile, Igartua does not disclose the at least 10 genes of table 1.
However, with respect to claim 16, the prior art to Skog, in the same field of endeavor, discloses "CD274," "EPCAM," "BRCA1," "MAGEA3," "ERBB3," "STMN1," "ICAM1," "ERBB2," "CDK4," and "MAGEA10" are genes associated with cancer-related somatic mutations (para 0112; table 3).
Skog further discloses cancer-related changes include specific mutations in gene sequences and that increasing knowledge of the genetic and epigenetic changes occurring in cancer cells provides an opportunity to detect, characterize, and monitor tumors by analyzing tumor-related nucleic acid sequences and profiles (para 0004).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps for transcriptome deconvolution of metastatic tissue samples as disclosed by Igartua with the use of the 10 specific genes associated with cancer-related somatic mutations as disclosed by Skog, because cancer-related changes include specific mutations in gene sequences and that increasing knowledge of the genetic and epigenetic changes occurring in cancer cells provides an opportunity to detect, characterize, and monitor tumors by analyzing tumor-related nucleic acid sequences and profiles. There would be a reasonable expectation of success because Igartua discloses associating mutations with gene expression patterns, and associating tumor gene expression profiles with a list of cancer treatments that may predict response for a patient with that profile, and therefore using known genes associated with cancer related somatic mutations would not impede the steps of Igartua. Therefore, the invention is prima facie obvious.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Igartua et al. (US20200210852A; 09/22/2022 IDS, US Patent Document 3; hereafter referred to as Igartua) as applied to claim 1 above, and further in view of Zaytcev et al. (Cancer Research 2020, 80(16_Supplement), 853-853; hereafter referred to as Zaytcev).
With respect to claims 20, Igartua discloses the steps of claim 1, as applied above.
With respect to claim 20, while Igartua discloses numerous types of machine learning models can be used, including decision tree learning, Igartua does not disclose using a gradient boosted model (para 0068).
However, the prior art to Zaytcev, in the same field of endeavor, discloses a method for tumor cellular deconvolution based on a machine learning-based algorithm using a three-stage hierarchical learning procedure for LightGBM model that included training on artificial RNAseq mixtures of different purified cell types, including major immune (T, B, NK, Macrophage) and stromal (CAF, endothelial) cell populations, and discloses the model was then trained to reconstruct proportions of T cell subtypes such as Th, CTL, Treg, as well as M1 and M2 macrophages and other cell types (para 2).
Zaytcev further discloses that their presented approach yielded higher performance compared with other cell deconvolution technologies including EPIC, quanTIseq, Cibersort, xCell and MCPcounter (para 3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps for transcriptome deconvolution of metastatic tissue samples as disclosed by Igartua with the use of the LightGBM model used by Zaytcev, because this presented approach yielded higher performance compared with other cell deconvolution technologies including EPIC, quanTIseq, Cibersort, xCell and MCPcounter, as disclosed by Zaytcev. There would be a reasonable expectation of success because use of a LightGBM model would not impede the steps of Igartua, as Igartua discloses numerous kinds of machine learning models can be used, including decision trees learning models. Therefore, the invention is prima facie obvious.
Claims 24-27 are rejected under 35 U.S.C. 103 as being unpatentable over Igartua et al. (US20200210852A; 09/22/2022 IDS, US Patent Document 3; hereafter referred to as Igartua) as applied to claim 1 above, and further in view of Sahu et al. (South Asian J Cancer. 2013 Apr-Jun;2(2):91–97; hereafter referred to as Sahu).
With respect to claims 24-27, Igartua discloses the steps of claim 1, as applied above.
Igartua also discloses comparing tumor expression levels with normal tissue levels permits biomarker discovery, by determining which genes or gene networks have a higher or lower expression level in tumor tissue than normal tissue that may be adjusted or targeted by treatment, and that such a comparison permits predicting the type of cancer or the origin of the cancer, associating mutations with gene expression patterns, and associating tumor gene expression profiles with a list of cancer treatments that may predict response for a patient with that profile, and further discloses the cancers could include non-small cell lung cancer (NSCLC) (para 0070).
However, Igartua does not appear to disclose identifying or administering at least one-antitherapy based on the gene expression level.
However, the prior art to Sahu, in the same field of endeavor, discloses that Anaplastic lymphoma kinase (ALK) gene rearrangements are present in a small subset of non-small-cell lung cancers (NSCLC), and ALK-positivity confers sensitivity to small-molecule ALK kinase inhibitors, such as crizotinib, and further that phase I and II studies of crizotinib in ALK-positive lung cancer have demonstrated significant activity and impressive clinical benefit (abstract).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps for transcriptome deconvolution of metastatic tissue samples as disclosed by Igartua with the use of ALK as a first gene and applying the therapy of crizotinib to ALK-positive NSCLC as disclosed by Sahu, because phase I and II studies of crizotinib in ALK-positive lung cancer have demonstrated significant activity and impressive clinical benefit. There would be a reasonable expectation of success because Igartua discloses associating mutations with gene expression patterns, and associating tumor gene expression profiles with a list of cancer treatments that may predict response for a patient with that profile, including cancers such as NSCLC and therefore administering a known therapy for NSCLC based on the gene expression levels of a gene associated with that cancer would not impede the steps of Igartua. Therefore, the invention is prima facie obvious.
Conclusion
No claims allowed.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NIDHI DHARITHREESAN whose telephone number is (571)272-5486. The examiner can normally be reached Monday - Friday 9:00 - 5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs II can be reached at (571) 270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.D./Examiner, Art Unit 1686
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
