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 .
Claim Status
Claims 1-12 are currently pending and under exam herein.
Claims 1-12 are rejected.
Claims 1 and 11 are objected to.
Priority
The instant application claims benefit to WIPO application PCT/KR2020/019017 filed 23 December 2020, and foreign application KR 10-2020-0035816 filed 24 March 2020. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. At this point in examination, the effective filing date of claims 1-12 is 24 March 2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 23 February 2024, 3 September 2024, and 17 September 2025 comply(s) with 37 CFR 1.98. Accordingly, all references listed have been considered by the examiner.
Drawings
The drawings filed on 7 September 2022 have been received and are accepted.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
Claim 1 recites “the first to third prediction value.” To make clear that all three values are used, this limitation should read “the first to third prediction values.”
Claim 11 recites “and the immune tolerance between the peptide sequence and the HLA allele sequence.” This limitation should read “and the immune tolerance between the peptide sequence and the HLA allele sequence as an output.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1-3, 8, and 10-11 recite the element “HLA allele sequence” without previously defining the acronym “HLA” within the claims. Moreover, “HLA” is not defined in the specification, which renders the metes and bounds of the claim unclear. Claims 4-7 and 9 are also indefinite due to their dependency upon claim 1. Claim 12 is similarly indefinite because it is a product-by-process claim that purports to claim the computer-readable medium for executing the method of claim 1. This rejection can be overcome by (1) amending the claims to spell out the acronym upon first appearance in the claims (although applicant is reminded that no new matter may be added to the application), or (2) removing the recitation of “HLA” from the claims.
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.
Claim 12 is rejected under 35 U.S.C. 101 because it does not fall within one of the four enumerated categories of statutory subject matter. Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract ideas) without significantly more. Under MPEP § 2106, subject matter is patent eligible when the claimed invention is to one of the four statutory categories of invention [Step 1], and the claim is not directed to a judicial exception [Step 2A] unless the claim as a whole includes additional limitations amounting to significantly more than the exception [Step 2B].
Step 1
Claims 1-11 describe inventions that are to one of the statutory categories. In Step 1, a claim must fall within one of the four enumerated categories of statutory subject matter (process, machine, manufacture, or composition of matter); a claim falling outside these categories is ineligible without further analysis. See MPEP § 2106.03. Claims 1-11 are properly to one of the four statutory categories because the claimed invention is a method, which falls into the process category [Step 1: Yes].
Claim 12 recites “a computer program stored in a computer-readable storage medium for executing the method of claim 1 by using a computer.” The specification does not limit the computer program to non-transitory computer-readable mediums, but recites at para. [0092] that “[t]he software and/or data may be permanently or temporarily embodied as any kind of … transmitted signal wave.” Transitory forms of signal transmission are not directed to any of the statutory categories. See MPEP § 2106.03(I). A claim whose broadest reasonable interpretation covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. See MPEP § 2106.03(II). Therefore, claim 12 is not eligible for patent protection because it is directed to non-statutory subject matter when the broadest reasonable interpretation embraces transitory forms of signal transmission, which is not directed to any of the statutory categories.
Step 2A
Under Step 2A, a claim is directed to a judicial exception if, under the broadest reasonable interpretation, it recites an abstract idea, law of nature, or natural phenomena [Prong One] without the claim as a whole integrating the exception into a practical application [Prong Two]. Abstract ideas include mathematical concepts, mental processes, and certain methods of organizing human activity. Mathematical concepts encompass mathematical relationships, formulas, equations, and mathematical calculations. See MPEP § 2106.04(a)(2)(I). Mental processes involve concepts that can be performed in the human mind or by a human with the aid of pen and paper, such as observations, evaluations, judgments, or opinions. See MPEP § 2106.04(a)(2)(III). Certain methods of organizing human activity include fundamental economic principles, commercial or legal interactions, and managing personal behavior or relationships. See MPEP § 2106.04(a)(2)(II). Laws of nature and natural phenomena, include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature. See MPEP § 2106.04(b)-(c).
Prong One
A claim recites a judicial exception when it sets forth or describes a law of nature, natural phenomenon, or abstract idea. Claims 1-11 recite abstract ideas that fall into the groupings of mathematical concepts and mental processes.
Claim 1 recites the following limitations, which describe mathematical concepts and/or mental processes:
outputting a first prediction value that predicts immunogenicity of the peptide sequence, by acquiring T cell activity data from the peptide sequence and inputting the T cell activity data into an immunogenicity prediction model;
outputting a second prediction value that predicts a binding affinity of the peptide sequence and the HLA allele sequence, by acquiring binding data from the HLA allele sequence and inputting the binding data into a binding prediction model;
outputting a third prediction value that predicts immune tolerance of the target cancer tissue, by inputting the T cell activity data and the binding data to an immune tolerance prediction model; and
generating neoantigen information of a target cell by using the T cell activity data and the first to third prediction value.
The limitations of outputting a prediction value involves determining a value based on specific input data, which are abstract ideas within the mathematical concepts and mental processes groupings. The limitation of generating neoantigen information involves observation, evaluation, and judgment of input data to produce information, which is an abstract idea within the mental processes grouping.
Claims 2 and 8-11 recite the following limitations, which describe mathematical concepts:
wherein at least one of the immunogenicity prediction model, the binding predictive model, and the immune tolerance prediction model is trained by a machine learning algorithm, based on a training data set including a peptide sequence and an HLA allele sequence present in a plurality of target cancer tissues.
wherein the training data set includes at least one of data related to a proteomic sequence related to the target cancer tissue, data related to an HLA peptide sequence related to the target cancer tissue, binding data between a peptide and an HLA allele that are related to the target cancer tissue; data related to a transcriptome related to the target cancer tissue; and data related to a genome related to the target cancer tissue.
wherein the immunogenicity prediction model is a model trained with the T cell activity data from the peptide sequence as an input and the immunogenicity of the peptide sequence as an output.
wherein the binding prediction model is a model trained with the binding data from the HLA allele sequence and the peptide sequence as an input and the binding affinity of the peptide sequence and the HLA allele sequence as an output.
wherein the immune tolerance prediction model is a model trained with the T cell activity data from the peptide sequence and the HLA allele sequence, and the binding data from the HLA allele sequence and the peptide sequence as an input and the immune tolerance between the peptide sequence and the HLA allele sequence.
The limitations of claims 2 and 8-11 recite training a prediction model with the specified data, which involves optimizing a complex mathematical function using linear algebra, calculus, and probability, constituting abstract ideas within the mathematical concepts grouping. Claims 3-7 do not describe any additional judicial exceptions, but inherit the abstract ideas of claim 1 from which they depend.
Therefore, claims 1-11 recite abstract ideas – namely mathematical concepts and mental processes [Step 2A, Prong One: Yes].
Prong Two
Claims 1-11 as a whole do not integrate the recited judicial exception into a practical application. A claim that recites a judicial exception [Prong One] is deemed to be directed to a judicial exception [Step 2A] unless the claim as a whole contains additional elements that integrate the exception into a practical application [Prong Two]. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, 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. See MPEP §§ 2106.04(d) and 2106.05(e). A claim does not integrate a judicial exception into a practical application by reciting insignificant extra-solution activity, generally linking the exception to a particular technological environment or field of use, merely reciting to apply the exception, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP § 2106.04(d)(I). Insignificant extra-solution activities are nominal or tangential additions to a claim that are incidental to the primary process or product, including both pre-solution and post-solution activity (e.g. pre-solution data gathering for use in a process). If integrated into a practical application, the claim is eligible; otherwise, it is directed to the judicial exception, necessitating further analysis at Step 2B.
Claim 1 recites the additional element of receiving a peptide sequence and an HLA allele sequence both extracted from a target cancer tissue as an input. This limitation constitutes insignificant extra-solution activity that does not integrate the judicial exceptions into a practical application because it is a mere data gathering step that does not transform the nature of the claim into a patent-eligible application of the judicial exception. See MPEP § 2106.04(g). Additionally, this limitation is a data gathering step that is limited to a particular type of data, which merely indicates a field of use or technological environment in which to apply a judicial exception and cannot integrate a judicial exception into a practical application. See MPEP § 2106.04(h).
Claims 3-7 recite the following limitations, which are additional elements:
wherein the target cancer tissue includes a cell engineered to express a single HLA class I allele or single HLA class II allele.
wherein the target cancer tissue includes a human cell obtained from or derived from a plurality of patients.
wherein the target cancer tissue includes a fresh or frozen tumor cell obtained from a plurality of patients.
wherein the target cancer tissue includes a fresh or frozen tissue cell obtained from a plurality of patients.
wherein the target cancer tissue includes a peptide identified by using T-cell analysis.
These limitations narrow the additional element of claim 1 by reciting a particular data source or particular type of data, which merely indicates a field of use or technological environment in which to apply a judicial exception and cannot integrate a judicial exception into a practical application. See MPEP § 2106.04(h). Finally, claims 2 and 8-11 do not include any additional elements.
The claims as a whole merely recite insignificant extra-solution activities that merely indicate a field of use or technological environment in which to apply a judicial exception and cannot integrate a judicial exception into a practical application. Therefore, claims 1-11 do not contain additional elements that integrate the recited abstract ideas into a practical application [Step 2A, Prong Two: No].
Step 2B
Claims 1-11 do not include additional elements, whether considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception itself. Under Step 2B, the claim is analyzed to determine whether there are any additional elements that, individually or in combination, constitute an “inventive concept" sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. See MPEP § 2106.05; and Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217-18, 110 USPQ2d 1976, 1981 (2014).
Claim 1 recites the additional element of receiving a peptide sequence and an HLA allele sequence both extracted from a target cancer tissue as an input. This limitation constitutes conventional insignificant extra-solution activity that merely indicates a field of use or technological environment in which to apply a judicial exception and does not amount to significantly more than the exception itself. See OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016); and MPEP §§ 2106.04(g)-(f).
Claims 3-7 recite the following limitations, which are additional elements:
wherein the target cancer tissue includes a cell engineered to express a single HLA class I allele or single HLA class II allele.
wherein the target cancer tissue includes a human cell obtained from or derived from a plurality of patients.
wherein the target cancer tissue includes a fresh or frozen tumor cell obtained from a plurality of patients.
wherein the target cancer tissue includes a fresh or frozen tissue cell obtained from a plurality of patients.
wherein the target cancer tissue includes a peptide identified by using T-cell analysis.
These limitations narrow the additional element of claim 1 by reciting a particular data source or particular type of data, which merely indicates a field of use or technological environment in which to apply a judicial exception and do not amount to significantly more than the exception itself. See MPEP § 2106.04(f). Additionally, the particular data source or particular type of data recited in these limitations are conventional in the field. See Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546; Laura Riolobos et al., HLA Engineering of Human Pluripotent Stem Cells, 21(6) Mol. Ther. 1232, abstract (30 April 2013) (engineering human cells to express a single HLA class allele is well-understood, routine, and conventional); and Hiroya Kobayashi and Esteban Celis, Peptide epitope identification for tumor-reactive CD4 T cells, 20(2) Curr Opin Immunol. 221, abstract (1 April 2009) (peptides identified by using T-cell analysis are well-understood, routine, and conventional).
Overall, claims 1-11 amount to no more than conventional insignificant extra-solution activities that indicate a field of use or technological environment in which to apply a judicial exception. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Therefore, claims 1-11 are rejected for failing to set forth patent eligible subject matter under 35 U.S.C. 101 because the claimed invention recites abstract ideas [Step 2A, Prong One: Yes] and the additional elements do not integrate the judicial exception into a practical application [Step 2A, Prong Two: No] and do not amount to claiming significantly more than the recited exception [Step 2B: No].
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.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (Jingcheng Wu et al., 10:2559 Front Immunol. (1 November 2019)) in view of Richman (Lee P. Richman et al., 9(4) Cell Systems 375 (23 October 2019)) as evidenced by Cruz-Tapias (Paola Cruz-Tapias et al., Autoimmunity: From Bench to Bedside, (18 July 2013)) and C3.ai. (Infrastructure: Machine Learning Hardware Requirements (15 May 2021)).
Regarding claim 1, Wu discloses a framework to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). At abstract (a method of predicting a neoantigen by using a peptide sequence and an HLA allele sequence). Wu receives mutant peptide sequences and patient HLA allele sequences from tumor mutations/cancer tissue as inputs for neoantigen prediction. At 3 col.1 paras.4-5; 1 para.1; 8 col.2 para.1 (receiving a peptide sequence and an HLA allele sequence both extracted from a target cancer tissue as an input). Wu discloses a dedicated immunogenicity model trained on peptides with T cell response data that outputs an immunogenic score. At 3 col.1 paras.3-5; 6 col.1 para.3 – col.2 para.1 (outputting a first prediction value that predicts immunogenicity of the peptide sequence, by acquiring T cell activity data from the peptide sequence and inputting the T cell activity data into an immunogenicity prediction model). Wu discloses a dedicated binding model trained on HLA-peptide pairs that outputs binding scores for each input HLA-peptide pair. At 2 col.2 paras.4-5; 3 col.1 para.5 – col.2 para.2 (outputting a second prediction value that predicts a binding affinity of the peptide sequence and the HLA allele sequence, by acquiring binding data from the HLA allele sequence and inputting the binding data into a binding prediction model). Based on the T cell response data and the outputs from the models, Wu ranks the mutations by binding affinity with an immunogenicity score filter to indicate the probability that the neoantigen is present within the patient. At 7 col.1 para.1 – col.2 para.1; Figure 5 caption; 2 col.2 para.1 (generating neoantigen information of a target cell by using the T cell activity data and the first to third prediction value).
Wu notes that a limitation of the method is that all the HLA-peptide pairs are considered to be immunogenic if they have been validated to elicit T-cell activation at least once when training an immunogenic model, which simplifies the evaluation of potential immunogenicity because whether the pHLA-matched TCR exists in the body is unknown and alterations in the tumor microenvironment or immunoediting could render previous immunogenic neoantigens non-immunogenic. At 9 col.1 para.1. As such, Wu fails to teach outputting a third prediction value that predicts immune tolerance of the target cancer tissue, by inputting the T cell activity data and the binding data to an immune tolerance prediction model. However, Richman discloses a neoantigen quality analysis methodology to predict immunogenic peptides. At abstract. Richman discloses a dissimilarity-to-self-proteome model that distinguishes immunogenic from non-immunogenic binders based on T cell response and binding energies. At 378 col.1 para.2; col.2 paras.1-3. Richman teaches that a high dissimilarity is more likely to break tolerance and elicit T cell responses, while a low dissimilarity correlates with tolerance/non-immunogenicity despite potential binding. At 378 col.1 para.2; 380 col.1 para.1. Richman notes that the quality criteria in addition to binding affinity may dictate the likelihood of a predicted neoantigen to drive an antitumor T cell response. At 375 col.2 para.2. Richman suggests that the success of vaccines may be improved by integrating the disclosed neoantigen quality analysis into an immunogenicity classifier to sort the immunogenic “needles” from the bulk peptide “haystack.” At 381 col.1 para.1.
A person having ordinary skill in the art would be motivated to combine the dissimilarity model of Richman with the framework of Wu because Wu notes some limitations in fully capturing why some strong binders are non-immunogenic, which are directly addressed by Richman’s dissimilarity metric that improves immunogenicity prediction and patient outcome. One of ordinary skill in the art would reasonably expect success in this combination because integrating Richman’s neoantigen quality analysis into an immunogenicity classifier can improve vaccine success by distinguishing immunogenic neoantigens from the bulk potential peptides. Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, G.
Additionally, Wu discloses a base method of predicting a neoantigen and Richman implements the quality analysis methodology into a tool for neoantigen prediction. Richman, at 375 col.2 para.3. A person having ordinary skill in the art would have recognized that applying the quality analysis of Richman to the method of Wu would predictably result in an improved system for neoantigen prediction because Richman’s analysis directly addresses the limitations of Wu’s method and improves the success of vaccines and patient outcomes. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to take the framework for neoantigen prediction of Wu and apply the dissimilarity model quality analysis as taught by Richman. Applying a known technique to a known device (method or product) ready for improvement to yield predictable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, D.
Regarding claim 2, Wu discloses that the immunogenicity model and the binding model are machine learning models trained on large datasets of peptides and HLA alleles derived from cancer/tumor samples. At 2 col.2 para.2; 4 col.1 para.2 (the method of claim 1, wherein at least one of the immunogenicity prediction model, the binding predictive model, and the immune tolerance prediction model is trained by a machine learning algorithm, based on a training data set including a peptide sequence and an HLA allele sequence present in a plurality of target cancer tissues).
Regarding claim 3, Wu discloses evaluating the performance of the binding model on single-labeled HLA alleles. At 4 col.1 para.4; Figure 2 caption (the method of claim 2, wherein the target cancer tissue includes a cell engineered to express a single HLA class I allele or single HLA class II allele).
Regarding claim 4, Wu discloses analyzing single-nucleotide variants from 17 patients using HLA alleles, at 3 col.1 para.4, which is the human-specific subset of the Major Histocompatibility Complex (MHC), as evidenced by Cruz-Tapias, at 169 col.1 para.2 (the method of claim 2, wherein the target cancer tissue includes a human cell obtained from or derived from a plurality of patients).
Regarding claims 5 and 6, Wu discloses analyzing single-nucleotide variants from tumor cells from 17 patients. At 3 col.1 para.4 (the method of claim 2, wherein the target cancer tissue includes a fresh or frozen tumor cell obtained from a plurality of patients; the method of claim 2, wherein the target cancer tissue includes a fresh or frozen tissue cell obtained from a plurality of patients).
Regarding claim 7, Wu discloses that the immunogenicity model is trained/validated on peptides with known T cell assay results. At 3 col.1 paras.4-5 (the method of claim 2, wherein the target cancer tissue includes a peptide identified by using T-cell analysis).
Regarding claim 8, Wu discloses that the training datasets include binding data for HLA-peptide pairs related to the tumor tissue. At 2 col.2 para.2; 3 col.1 para.5 (the method of claim 2, wherein the training data set includes at least one of data related to a proteomic sequence related to the target cancer tissue, data related to an HLA peptide sequence related to the target cancer tissue, binding data between a peptide and an HLA allele that are related to the target cancer tissue; data related to a transcriptome related to the target cancer tissue; and data related to a genome related to the target cancer tissue).
Regarding claim 9, Wu discloses that the immunogenicity model, which outputs the immunogenicity of the input peptide, is trained/validated on peptides with known T cell assay results. At 3 col.1 paras.4-5 (the method of claim 1, wherein the immunogenicity prediction model is a model trained with the T cell activity data from the peptide sequence as an input and the immunogenicity of the peptide sequence as an output).
Regarding claim 10, Wu discloses that the binding model, which outputs the binding affinity of the input peptide and HLA allele, is trained with the binding data from HLA-peptide pairs. At 2 col.2 paras.1-3 (the method of claim 1, wherein the binding prediction model is a model trained with the binding data from the HLA allele sequence and the peptide sequence as an input and the binding affinity of the peptide sequence and the HLA allele sequence as an output).
Regarding claim 11, Wu discloses training the models with the T cell response data and binding data from HLA-peptide pairs. At 2 col.2 paras.1-3; 3 col.1 paras.4-5 (a model trained with the T cell activity data from the peptide sequence and the HLA allele sequence, and the binding data from the HLA allele sequence and the peptide sequence as an input). Richman discloses a dissimilarity-to-self-proteome model, which outputs a dissimilarity metric indicative of immune tolerance between the HLA-peptide pair. At 378 col.1 paras.2 & 4. In combining the Richman’s dissimilarity model with the framework of Wu, one of ordinary skill in the art would know to train the dissimilarity model with the T cell response data and binding data from HLA-peptide pairs used to train the other models.
Regarding claim 12, neither Wu nor Richman disclose a computer program stored in a computer-readable storage medium for executing the method of claim 1 using a computer. However, both Wu and Richman disclose machine learning models, which necessarily involves a computer-readable medium with instructions stored thereon. See C3.ai., § Memory and Storage.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Marta Łuksza et al., A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy, 551 Nature 517-520 (1 November 2017). Discloses a framework to determine neoantigen fitness based on immune interactions of neoantigens that predicts response to immunotherapy.
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/E.A.D./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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