CTNF 19/365,119 CTNF 100426 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority Acknowledgment is made of applicant’s claim for priority under Provisional Application No. 63/727,609, filed on 12/3/2024. Information Disclosure Statement The information disclosure statements (IDS) submitted on 3/20/2026, and 5/4/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Election/Restrictions Applicant’s election with traverse of Group 1 (Claims 1-16) in the reply filed on 3/20/2026 is acknowledged. 08-06 AIA Claim s 17-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention , there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 3/20/2026 . Claim Status Claims 1-20 are pending. Claims 17-20 are withdrawn. Claims 1-16 are rejected . Drawings 06-24-01 AIA Color photographs and color drawings, as seen in Figures 2-12, are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification: The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2). Specification The use of the terms Clonetech Laboratories, Invitrogen, Evrogen, Thermo Fisher, Whatman, Quantifoil, Pelco, Zymo, Illumina, NEB, Sigma, InGex, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term. Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks. Claim Objections 07-29-01 AIA Claim 3 is objected to because of the following informalities: missing a period at the end of the claim . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 2, 5-6, and 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. Specifically claim 5 is directed to the method of claim 1 and the trained model, however, it is unclear if those limitations are intended to require that training the model is performed within the metes and bounds of the claimed invention or if they are merely limiting the process by which the model was previously trained. Claim 6 depends from claim and does not resolve the dependency issue and as such is also rejected. 07-34-03 AIA The term “ suitable conditions ” in claim 2 is a relative term which renders the claim indefinite. The term “ suitable conditions ” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Specifically, the term defines the conditions under which the cel-type-specific activity quantifies an effect on translation or stability, and thereby renders those conditions indefinite and by extension what the cel-type-specific activity is describing . 07-34-03 AIA The term “ suitable conditions ” in claim 12 is a relative term which renders the claim indefinite. The term “ suitable conditions ” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Specifically, the term defines the conditions under which the cell-type-specific activity is exhibited upon expression transference, thereby rendering the cell-type-specific activity that is exhibited indefinite . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method for predicting cell-type-specific activity of UTRs. The judicial exception is not integrated into a practical application because while claims 1-16 attempt to integrated the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and merely implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d). Framework with which to Analyze Subject Matter Eligibility: Step 1: Are the claims directed to a category of stator subject matter (a process, machine, manufacture, or composition of matter)? [ see MPEP § 2106.03 ] Claims are directed to statutory subject matter, specifically methods (Claims 1-11 and 13-16). Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [ see MPEP § 2106.04(a) ] The claims herein recite abstract ideas, specifically mental processes and mathematical concepts. With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts. Claim 1: Processing data using a model, predicting cell-type-specific activity, and predicting delta activity are processes of comparing/contrasting and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes . Delta activity being the difference between predicted activity and average activity is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept . Claim 2: The cell-type-specific activity quantifying the effect of the UTR on translation or stability is merely further limiting the data itself which is an abstract idea, specifically a mental process . Claim 5: The data comprising a plurality of sequences and measurements for each sequence corresponding to cell types, and a trained model are merely further limiting the data itself which are abstract ideas, specifically mental processes . Claim 6: Target cell types comprising the specified group is merely further limiting the data itself which is an abstract idea, specifically a mental process . Claim 10: The outputted metrics comprising the specified list is merely further limiting the data itself which is an abstract idea, specifically a mental process . Claim 13: Processing the sequence data using the model, and determining a correlation are processes of comparing/contrasting and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes . Claim 14: Processing the input data, predicting cell-type-specific and delta activities for each sequence, generating a predicted set of metrics, and selecting a sub-plurality of sequences are processes of comparing/contrasting, calculating, and selecting information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes . Claim 15: Evaluating each sequence, and selecting sequences that maximize the specified score are processes of comparing/contrasting, calculating, and selecting information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes . Claim 16: The model comprising one of those specified is merely further limiting the data itself which is an abstract idea, specifically a mental process . Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [ see MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h) ] Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application. The following claims recite the following additional elements in the form of non-abstract elements: Claim 1: Obtaining sequence data and outputting a predicted set of metrics are insignificant extra solution activities, specifically mere data gathering and necessary data outputting ( See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) , PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis) , Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential) , Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55; See also Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) ) [ See MPEP § 2106.05(g) ]. Claim 3: Validating the outputted sets, and feeding validation data into the model is an insignificant extra solution activity, specifically mere data gathering ( See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) , PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis) , Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential) , and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55 ) [ See MPEP § 2106.05(g) ]. Claim 4: Validating the outputted sets is an insignificant extra solution activity, specifically mere data gathering ( See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) , PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis) , Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential) , and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55 ) [ See MPEP § 2106.05(g) ]. Claim 7: The dataset comprising the specified number of sequences is an insignificant extra solution activity, specifically merely selecting a particular data source or type to be manipulated ( See Limiting a database index to XML tags, Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937, Taking food orders from only table-based customers or drive-through customers, Ameranth, 842 F.3d at 1241-43, 120 USPQ2d at 1854-55 , and Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) ) [ See MPEP § 2106.05(g) ]. Claim 8: The sequences comprising the specified number of nucleotides is an insignificant extra solution activity, specifically merely selecting a particular data source or type to be manipulated ( See Limiting a database index to XML tags, Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937, Taking food orders from only table-based customers or drive-through customers, Ameranth, 842 F.3d at 1241-43, 120 USPQ2d at 1854-55 , and Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) ) [ See MPEP § 2106.05(g) ]. Claim 9: Obtaining additional sequence information is an insignificant extra solution activity, specifically mere data gathering ( See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) , PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis) , Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential) , and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55 ) [ See MPEP § 2106.05(g) ]. Sequence information comprising triplet phase information is an insignificant extra solution activity, specifically merely selecting a particular data source or type to be manipulated ( See Limiting a database index to XML tags, Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937, Taking food orders from only table-based customers or drive-through customers, Ameranth, 842 F.3d at 1241-43, 120 USPQ2d at 1854-55 , and Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) ) [ See MPEP § 2106.05(g) ]. Claim 11: The model comprising a LegNet model is an insignificant extra solution activity, specifically mere data gathering ( See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) , PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis) , Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential) , and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55 ) [ See MPEP § 2106.05(g) ]. Claim 12: Operably linking a UTR RNA sequence to the target gene in an expression construct, and transferring the expression construct to the target cell are insignificant extra solution activities, specifically insignificant applications as they do not integrate the judicial exception into the limitation of the claims ( See In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential) , and Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55 ) [ See MPEP § 2106.05(g) ]. Claim 13: Obtaining sequence data and outputting a predicted list of candidate motifs are insignificant extra solution activities, specifically mere data gathering ( See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) , PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis) , Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential) , and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55 ) [ See MPEP § 2106.05(g) ]. Claim 14: Obtaining input data and outputting the selected sub-plurality of sequences are insignificant extra solution activities, specifically mere data gathering ( See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) , PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis) , Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential) , and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55 ) [ See MPEP § 2106.05(g) ]. Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05] Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept. The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional or nonspecific. These additional elements include: The additional elements of obtaining input data (Conventional: Specification paragraph [00126], [00251], and [00254]), training a model (Conventional: Basheer et al. Page 7, column 2, paragraph 2 - “ANN learning is performed iteratively as the network is presented with training examples, similar to the way we learn from experience”), and outputting data, are insignificant extra solution activities, specifically mere data gathering and necessary data outputting ( See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989), PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential) , and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55 ) [ See MPEP § 2106.05(g) ]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept. Therefore, claims 1-11, and 13-16, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-2 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Davuluri et al. (Genome Research (2000) 1807-16) and Zhang et al. (Rna (2023) 517-530) . Claim 1 is directed to a method for the prediction of cell-type-specific activity of UTR RNA sequences. Davuluri et al. teaches in the abstract “A nonredundant database of 2312 full-length human 5-untranslated regions (UTRs) was carefully prepared using state-of-the-art experimental and computational technologies. A comprehensive computational analysis of this data was conducted for characterizing the 5’ UTR features. Classification and regression tree (CART) analysis was used to classify the data into three distinct classes. Class I consists of mRNAs that are believed to be poorly translated with long 5’ UTRs filled with potential inhibitory features. Class II consists of terminal oligopyrimidine tract (TOP) mRNAs that are regulated in a growth-dependent manner, and class III consists of mRNAs with favorable 5 UTR features that may help efficient translation”, it should be noted that it is inherent to regressions that residuals, or differences between the predicted and the actual values are calculated, which for CART tends to be the residual sum of squares, RSS, thereby reading on a method for predicting cell-type-specific activity of an untranslated region (UTR) RNA sequence, comprising: A) obtaining UTR RNA sequence data corresponding to a plurality of UTR RNA sequences; B) processing the UTR RNA sequence data using a model, wherein the model is configured to, for each respective UTR RNA sequence in the plurality of UTR RNA sequences: predict a cell-type-specific activity of the respective UTR RNA sequence for each target cell type in a plurality of target cell types, and/or predict a delta activity of the UTR RNA sequence for each target cell type in the plurality of target cell types, wherein the delta activity is defined as difference between the predicted cell-type-specific activity specific to the respective target cell type and an average activity across the plurality of target cell types; and C) outputting, for each respective UTR RNA sequence in the plurality of UTR RNA sequences, a predicted set of metrics for the cell-type-specific activity and/or the delta activity for each target cell type in the plurality of target cell types . Davuluri et al. does not teach the cell-type specific classification. Zhang et al. teaches in the abstract “In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets”. It would have been obvious at the time of first filing to have modified the teachings of Davuluri et al. for the method of claim 1 to base the classes on cell-type specific expression as the latter is a common practice within the art as seen in the review paper Zhang et al. where they review methods of single-cell RNA-sequencing in cell-type classification. One would have had a reasonable expectation of success given that the latter is presenting a review of methods currently used for the process and both are working with RNA sequence data. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful. Claim 2 is directed to the method of claim 1 but further specifies that the cell-type-specific activity quantifies an effect on translation or stability of the target gene. Davuluri et al. teaches in the abstract “Class I consists of mRNAs that are believed to be poorly translated with long 5 UTRs filled with potential inhibitory features. Class II consists of terminal oligopyrimidine tract (TOP) mRNAs that are regulated in a growth-dependent manner, and class III consists of mRNAs with favorable 5 UTR features that may help efficient translation”, reading on wherein the cell-type-specific activity of an UTR RNA sequence quantifies effect of the UTR RNA sequence on translation and/or mRNA stability of a target gene under suitable conditions, when the UTR RNA sequence and the target gene are operably linked in an expression construct . Claim 9 is directed to the method of claim 1 but further specifies the inclusion of triplet information. Davuluri et al. teaches on page 1815, in column 2, paragraph 3 “Codon bias: Codon bias was calculated according to the Karlin formula”, the specification teaches in paragraph [00125] “The term “codon” or “triplet” refers to a nucleotide sequence of three nucleotides as three adjacent”, and therefore reads on wherein step A) further comprises obtaining for each respective UTR RNA sequence in the plurality of UTR RNA sequences, additional sequence information comprising triplet phase information for each respective UTR RNA sequence . 07-22-aia AIA Claim s 3-8, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Davuluri et al. (Genome Research (2000) 1807-16) and Zhang et al. (Rna (2023) 517-530) as applied to claim s 1 and 2 above, and further in view of Sample et al. (Nature Biotechnology (2019) 803-809) . Claim 3 is directed to the method of claim 1 but further specifies the use of in vitro or in vivo assays to validate and further train the model. Davuluri et al. and Zhang et al. teach the method of claim 1 as previously described. Sample et al. teaches on page 804, in Figure 1 “A library of 280,000 members was built by inserting a T7 promoter followed by 25 nucleotides of defined 5′ UTR sequence, a random 50-nucleotide sequence and the eGFP CDS into a plasmid backbone. IVT library mRNA was produced by in vitro transcription from a linearized DNA template obtained by PCR from the plasmid library. Cells transfected with IVT library mRNA were grown for 12 h before polysome profiling. Read counts per fraction were used to calculate MRL for each UTR, and the resulting data were used to train a CNN”, reading on further comprising validating the outputted sets of metrics with one or more in vitro or in vivo assays, and feeding validation data obtained from the one or more in vitro or in vivo assays back to the model, thereby improving accuracy of prediction by the model . It would have been obvious at the time of first filing to have modified the teachings of Davuluri et al. for the method of claim 1 with the teachings of Sample et al. for the incorporation of in vitro or in vivo assays to validate and further train the model as the latter teaches in the abstract “We test 35,212 truncated human 5′ UTRs and 3,577 naturally occurring variants and show that the model predicts ribosome loading of these sequences”, and on page 808, column 2, paragraph 2 “Optimus 5-Prime, the CNN trained on the data, has excellent performance, explaining up to 93% of MRL variation in the test set and up to 82% of variation for truncated human UTRs”. One would have a reasonable expectation of success given that both papers are focused on predicting/classifying UTR sequences to translation, the use of training is necessary in both models, and the use of assays for validation and training does not preclude but would rather be necessary for training. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful. Claim 4 is directed to the method of claim 3 and thus claim 1, but further specifies the use of a massively parallel reporter assay. Davuluri et al. and Zhang et al. teach the method of claim 1 as previously described. Sample et al. teaches on page 803, column 1, paragraph 3 “Here we report the development of an MPRA that measures the translation of hundreds of thousands of randomized 5′ UTRs via polysome profiling and RNA sequencing. We then use the data to train a convolutional neural network (CNN) that can predict ribosome loading from sequence alone”, reading on comprising validating the outputted sets of metrics with a Massively Parallel Reporter Assay (MPRA) . Claim 5 is directed to the method of claim 1 but further specifies the incorporation of cell-type-specific activity to target cell types. Davuluri et al. and Zhang et al. teach the method of claim 1 as previously described. Sample et al. teaches on page 806, column 1, paragraph 1 “We also tested Optimus 5-Prime on 77 5′ UTRs that were previously designed by Ferreira et al.27 and characterized using a fluorescent reporter system in six different cell lines. UTRs were designed to result in a range of expression levels by inserting one or multiple uORFs. The MRL predictions from our model correlated well with the independently reported fluorescence levels”, reading on wherein the model is trained with a Massively Parallel Reporter Assay (MPRA) dataset to predict cell-type-specific activity and delta activity of RNA sequences corresponding to one or more target cell types, wherein the MPRA dataset comprises: the plurality of UTR RNA sequences, and for each respective UTR RNA in the plurality of UTR RNA sequences, measurements of corresponding cell-type-specific activity specific to the one or more target cell types, measured from the MPRA . Claim 6 is directed to the method of claim 5 and thus claim 1, but further specifies the cell types coming from the specified tissues. Davuluri et al. and Zhang et al. teach the method of claim 1 as previously described. Sample et al. teaches on page 806, column 1, paragraph 1 “We also tested Optimus 5-Prime on 77 5′ UTRs that were previously designed by Ferreira et al.27 and characterized using a fluorescent reporter system in six different cell lines. UTRs were designed to result in a range of expression levels by inserting one or multiple uORFs. The MRL predictions from our model correlated well with the independently reported fluorescence levels”, and on page 814, under Eukaryotic Cell Lines, cell lines K562 and MPC11 which are derived from blood tissue, thereby reading on wherein the plurality of target cell types comprise cells from one or more tissues selected from the group consisting of blood tissue, colon tissue, ovarian tissue, breast tissue, and liver tissue . Claim 7 is directed to the method of claim 1 but further specifies the amount of sequences necessary for each of the 5’ and 3’ UTR RNA sequences. Davuluri et al. and Zhang et al. teach the method of claim 1 as previously described. Sample et al teaches on page 804, column 2, paragraph 1 “…we trained a CNN with 260,000 sequences from the 280,000-member eGFP library. The remaining 20,000 sequences were withheld for testing”, reading on wherein the plurality of UTR RNA sequences in the MPRA dataset comprises at least 1,000, at least 10,000, at least 100,000, or at least 1 x 10 6 5' UTR RNA sequences; or at least 1,000, at least 10,000, at least 100,000, or at least 1 x 10 6 3' UTR RNA sequences . Claim 8 is directed to the method of claim 1 but further specifies the total number of nucleotides allowable in the sequences. Davuluri et al. and Zhang et al. teach the method of claim 1 as previously described. Sample et al. teaches on page 804, in Figure 1 “A library of 280,000 members was built by inserting a T7 promoter followed by 25 nucleotides of defined 5′ UTR sequence”, reading on wherein each UTR RNA sequence in the plurality of the UTR RNA sequences comprises at most 50, at most 100, at most 150, at most 200, at most 250, at most 300, at most 350, at most 400, at most 450, or at most 500 nucleotides . Claim 12 is directed to the method of claim 1 but further specifies operably linking the UTR RNA to the expression and transferring the expression to a cell. Davuluri et al. and Zhang et al. teach the method of claim 1 as previously described. Sample et al. teaches on page 810, column 1, paragraph 7 “template for in vitro transcription was produced via PCR of the library plasmid with primers 254 and 255 and KAPA Hi-Fi polymerase (Kapa Biosystems). The double-stranded DNA product has a T7 promoter at the 5′ end and a truncated BGH poly(A) signal sequence followed by a 70-nucleotide poly(A) sequence (introduced with primer 254) at the 3′ end. The IVT reaction used the HiScribe T7 high-yield RNA synthesis kit (NEB) and 3′-O-Me-m7G(5′) ppp(5′)G RNA cap (NEB) was used as the cap structure analog. The DNA template was digested with DNase I (NEB) and the IVT mRNA was purified using RNA Clean & Concentrator (Zymo Research). This protocol was used to produce the unmodified eGFP IVT mRNA and mCherry IVT mRNA for transfection”, reading on further comprising regulating expression of a target gene in a target cell, comprising: operably linking an UTR RNA sequence to the target gene in an expression construct, wherein the UTR RNA sequence exhibits cell-type-specific activity when the expression construct is transferred to a corresponding target cell type and under suitable conditions; and transferring the expression construct to the target cell . 07-22-aia AIA Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Davuluri et al. (Genome Research (2000) 1807-16) and Zhang et al. (Rna (2023) 517-530) as applied to claim s 1 and 2 above, and further in view of Cao et al. (Nature biotechnology (2022) 1624-1633) . Claim 10 is directed to the method of claim 1 but further specifies manner in which the metrics are reported for claim 1. Davuluri et al. and Zhang et al. teach the method of claim 1 as previously described. Cao et al. teaches in the abstract “we present a method to quantify tumor-specific total mRNA expression (TmS) from bulk sequencing data”, and on page 1626, in Figure 1, panel e “total UMI counts by cell type in scRNA-seq data”, where the total counts are scaled to between 0 and 1 representing expression levels, thereby reading on wherein, specific to each target cell type in the plurality of target cell types, the outputted set of metrics comprises: a cell type specificity index (T), for each respective UTR RNA sequence in the plurality of UTR RNA sequences, wherein T:ranges from 0 to 1,indicates ubiquitous activity at 0, and indicates exclusive activity in a single cell type at 1; and a delta activity value (A) quantifying difference between the predicted cell-type-specific activity and an average activity across the plurality of target cell types . It would have been obvious at the time of first filing to have modified the teachings of Davuluri et al. for the method of claim 1 with the teachings of Cao et al. for the scaled representation of expression across cell types as the latter teaches in the abstract “our results indicate that measuring cell-type-specific total mRNA expression in tumor cells predicts tumor phenotypes and clinical outcomes”. One would have had a reasonable expectation of success given that it is merely scaling information that is already calculated, and the latter, while calculating UTR regions specifically, is calculating normalized expression of RNA. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful . 07-22-aia AIA Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Davuluri et al. (Genome Research (2000) 1807-16) and Zhang et al. (Rna (2023) 517-530) as applied to claim s 1 and 2 above, and further in view of Penzar et al. (Bioinformatics (2023) 1-9) . Claim 11 is directed to the method of claim 1 but further specifies that the model used is a LegNet model. Davuluri et al. and Zhang et al. teach the method of claim 1 as previously described. Penzar et al. teaches in the abstract “we introduce LegNet, an EfficientNetV2-inspired convolutional network for modeling short gene regulatory regions”, reading on wherein the model comprises a LegNet model . It would have been obvious at the time of first filing to have modified the teachings of Davuluri et al. for the method of claim 1 with the teachings of Penzar et al. for the use of a LegNet model as the latter teaches in the abstract “LegNet secured first place for the autosome.org team in the DREAM 2022 challenge of predicting gene expression from gigantic parallel reporter assays. Using published data, here, we demonstrate that LegNet outperforms existing models and accurately predicts gene expression per se as well as the effects of single-nucleotide variants. Furthermore, we show how LegNet can be used in a diffusion network manner for the rational design of promoter sequences yielding the desired expression level”. One would have had a reasonable expectation of success given that the latter is model specifically designed for modeling regulatory elements, which UTR regions act as, and is merely a change in the model used. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful . 07-21-aia AIA Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Davuluri et al. (Genome Research (2000) 1807-16), Zhang et al. (Rna (2023) 517-530), and Rabani et al. (Proceedings of the National Academy of Sciences (2008) 14885-14890) . Claim 13 is directed a method for predicting regulatory motifs within a UTR RNA sequence. Davuluri et al. teaches in the abstract “A nonredundant database of 2312 full-length human 5-untranslated regions (UTRs) was carefully prepared using state-of-the-art experimental and computational technologies. A comprehensive computational analysis of this data was conducted for characterizing the 5’ UTR features. Classification and regression tree (CART) analysis was used to classify the data into three distinct classes. Class I consists of mRNAs that are believed to be poorly translated with long 5’ UTRs filled with potential inhibitory features. Class II consists of terminal oligopyrimidine tract (TOP) mRNAs that are regulated in a growth-dependent manner, and class III consists of mRNAs with favorable 5’ UTR features that may help efficient translation”. Zhang et al. teaches in the abstract “In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets”. Rabani et al. teaches in the abstract “we develop RNApromo, an efficient computational tool for identifying structural elements within mRNAs that are involved in specifying posttranscriptional regulations. By analyzing experimental data on mRNA decay rates, we identify common structural elements in fast decaying and slow-decaying mRNAs and link them with binding preferences of several RNA binding proteins. We also predict structural elements in sets of mRNAs with common subcellular localization in mouse neurons and fly embryos”, and on page 14889, column 1, paragraph 4 “Having validated that our computational scheme can detect known biological motifs, we used it to predict novel motifs. We applied RNApromo to 3’ and 5’ UTR sequences of several sets of genes for which substantial evidence suggests that they share common posttranscriptional regulation”, and in column 2, paragraphs 3-4 of the same page “we identify a motif in 3 UTRs of 75 mRNAs with a measured short half-life of <6 min (AUC = 0.61, P < 10^-3). The predicted motif sequence is AU rich and folds into a stem–loop structure with a relatively short loop (Fig. 2A). Furthermore, this motif shows a high conservation profile (P < 5 x 10^3), as an independent support for its biological relevance. We also identify a motif in the 3 UTRs of 240 mRNAs with a measured long half-life of >60 min (AUC = 0.59, P < 8 x 10^4). Once again, the motif sequence is AU rich, yet the structural context is different and includes a large unstructured U-rich loop followed by a short stem (Fig. 2B). The role of AU-rich elements located on the 3’ UTR of mRNAs in modulating mRNA stability, both as stabilizing and destabilizing elements, has long been known”, while ranking the motifs in terms of their effects is not specifically articulated it would have been obvious to do so given that the correlation between cell-type-specific activities and motifs are already described, and in view of Davuluri et al., this reads on a method for predicting regulatory motifs in an UTR RNA sequence, comprising: A) obtaining UTR RNA sequence data for each respective UTR RNA sequence in a plurality of UTR RNA sequences, and associated activity for each respective UTR RNA sequence; B) processing the UTR RNA sequence data using a model, wherein the model is configured to, for each respective UTR RNA sequence in the plurality of UTR RNA sequences, identify one or more candidate motifs in each respective UTR RNA sequence; C) determining, for each respective UTR RNA sequence in the plurality of UTR RNA sequences, correlation of each candidate motif identified sequences with the associated UTR RNA activity; and D) outputting a ranked list of candidate motifs identified in the plurality of UTR RNA sequences, wherein the ranking is based on correlation between each respective candidate motif and UTR activity . It would have been obvious at the time of first filing to modify the teachings of Davuluri et al. for the linking of cell-type-specific activity with UTR RNA sequences, with the teachings of Rabani et al. for predicting specific motifs from UTR RNA sequences and linking them cell-type-specific activities such as stability and translation as the latter teaches in the abstract “our results reveal unexplored layers of posttranscriptional regulations in groups of RNAs and are therefore an important step toward a better understanding of the regulatory information conveyed within RNA molecules” and the former teaches “Our classification model and the data we have generated may provide valuable information for experimentalists engaged in translational control and regulation studies”. One would have had a reasonable expectation of success given that both are directed to correlating the cell-type-specific activities of stability and translation with RNA and both focus on the use of UTR RNA sequences to do so. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful . 07-21-aia AIA Claim s 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Davuluri et al. (Genome Research (2000) 1807-16), Zhang et al. (Rna (2023) 517-530), and Runge et al. (arXiv preprint (2018) 1-29) . Claim 14 is directed to a method for generating UTR RNA sequences with a predefined cell-type-specific activity. Davuluri et al. teaches in the abstract “A nonredundant database of 2312 full-length human 5-untranslated regions (UTRs) was carefully prepared using state-of-the-art experimental and computational technologies. A comprehensive computational analysis of this data was conducted for characterizing the 5’ UTR features. Classification and regression tree (CART) analysis was used to classify the data into three distinct classes. Class I consists of mRNAs that are believed to be poorly translated with long 5’ UTRs filled with potential inhibitory features. Class II consists of terminal oligopyrimidine tract (TOP) mRNAs that are regulated in a growth-dependent manner, and class III consists of mRNAs with favorable 5’ UTR features that may help efficient translation”. Zhang et al. teaches in the abstract “In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets”. Runge et al. teaches in the abstract “Since an RNA’s function depends on its structural properties, the RNA Design problem is to find an RNA sequence which satisfies given structural constraints. Here, we propose a new algorithm for the RNA Design problem, dubbed LEARNA. LEARNA uses deep reinforcement learning to train a policy network to sequentially design an entire RNA sequence given a specified target structure. By meta-learning across 65 000 different RNA Design tasks for one hour on 20CPU cores, our extension Meta-LEARNA constructs an RNA Design policy that can be applied out of the box to solve novel RNA Design tasks. Methodologically, for what we believe to be the first time, we jointly optimize over a rich space of architectures for the policy network, the hyperparameters of the training procedure and the formulation of the decision process”, on page 2, paragraph 3 “We describe Meta-LEARNA, a version of LEARNA that learns a single policy across many RNA Design tasks directly applicable to new RNA Design tasks. Specifically, it learns a conditional generative model from which we can sample candidate RNA sequences for a given RNA target structure”, which in view of Davuluri et al. reads on a method for generating a plurality of UTR RNA sequences with predefined cell-type- specific activity, comprising: A) obtaining input data comprising one or more predefined activity and one or more cell type-specificity constraints; B) processing the input data using a first portion of a model to generate a plurality of UTR RNA sequences, wherein the first portion of the model is configured to refine the UTRRNA sequences based on the one or more predefined activity and the one or more cell type- specificity constraints; C) responsive to step B), using a second portion of the model to predict, for each respective UTR RNA sequence in the plurality of UTR RNA sequences generated in step B):a cell-type-specific activity of the respective UTR RNA sequence for each target cell type in a plurality of target cell types, and/or a delta activity of the respective UTR RNA sequence for each target cell type in the plurality of target cell types, wherein the delta activity is defined as difference between the predicted cell-type-specific activity specific to the respective target cell type and an average activity across the plurality of target cell types; D) generating, for each respective UTR RNA sequence in the plurality of UTR RNA sequences generated in step B), a predicted set of metrics for the cell-type-specific activity and/or the delta activity for each target cell type in the plurality of target cell types; E) selecting a sub-plurality of UTR RNA sequences from the plurality of UTR RNA sequences generated in step B), wherein the corresponding predicted sets of metrics for the sub- plurality of UTR RNA sequences satisfy the one or more predefined activity and the one or more cell type-specificity constraints; and F) outputting the selected sub-plurality of UTR RNA sequences . It would have been obvious at the time of first filing to have modified the teachings of Davuluri et al. for the linking of cell-type-specific activity with UTR RNA sequences, with the teachings of Runge et al. for the prediction of RNA sequences based upon various parameters related to the structure of the sequence as the latter teaches in the abstract “our approach achieves new state-of-the-art performance on the former while also being orders of magnitudes faster in reaching the previous state-of-theart performance”. One would have had a reasonable expectation of success given that the former is directed to the classification of UTR RNA sequences based upon sequence metrics such as expression and stability, and the latter is designing RNA sequences based upon the same or similar sequence metrics. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful. Claim 15 is directed to the method of claim 14 but further specifies the calculation of a cell-type activity difference score. Davuluri et al. teaches in the abstract “A nonredundant database of 2312 full-length human 5-untranslated regions (UTRs) was carefully prepared using state-of-the-art experimental and computational technologies. A comprehensive computational analysis of this data was conducted for characterizing the 5’ UTR features. Classification and regression tree (CART) analysis was used to classify the data into three distinct classes. Class I consists of mRNAs that are believed to be poorly translated with long 5’ UTRs filled with potential inhibitory features. Class II consists of terminal oligopyrimidine tract (TOP) mRNAs that are regulated in a growth-dependent manner, and class III consists of mRNAs with favorable 5’ UTR features that may help efficient translation”, it should be noted that it is inherent to regressions that residuals, or differences between the predicted and the actual values are calculated, which for CART tends to be the residual sum of squares, RSS, thereby reading on wherein the method further comprises: evaluating each respective UTR RNA sequence in the plurality of UTR RNA sequences generated to calculate a cell type activity difference (CTAD) score, wherein the CTAD score quantifies the difference in predicted activity of a UTR RNA sequence between two target cell types, and selecting UTR RNA sequences that maximize the CTAD score while satisfying the one or more predefined activity and the one or more cell type-specificity constraints . 07-22-aia AIA Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Davuluri et al. (Genome Research (2000) 1807-16), Zhang et al. (Rna (2023) 517-530), and Runge et al. (arXiv preprint (2018) 1-29) as applied to claim 14 above, and further in view of Liu et al. (Frontiers in Genetics (2022) 1-10) . Claim 16 is directed to the method of claim 14 but further specifies the model as one of those specified. Davuluri et al., Zhang et al. (Rna (2023) 517-530), and Runge et al. teach the method of claim 14 as previously described. Liu et al. teaches in the abstract “we introduce a stepwise Monte Carlo parallelization (SMCP) algorithm for RNA tertiary structure prediction. Millions of conformations were randomly searched using the Monte Carlo algorithm and stepwise ansatz hypothesis, and SMCP uses a parallel mechanism for efficient sampling”, reading on wherein the model comprises a cold diffusion model, a genetic algorithm, a random sampling model, or a combination thereof . It would have been obvious at the time of first filing to have modified the teachings of Davuluri et al. and Runge et al. for the method of claim 14 with the teachings of Liu et al. for the use a random sampling model as the latter teaches in the abstract “benchmark of nine single-stranded RNA loops drawn from riboswitches establishes the general ability of the algorithm to model RNA with high accuracy and integrity, including six motifs that cannot be solved by knowledge mining–based modeling algorithms. Experimental results show that the modeling accuracy of the SMCP algorithm is up to 0.14 Å, and the modeling integrity on this benchmark is extremely high”. One would have had a reasonable expectation of success given that the former two references are describing the process of designing the RNA sequences while the latter is merely specifying a particular algorithm used in a similar process, RNA structure prediction. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEENAN NEIL ANDERSON-FEARS whose telephone number is (571)272-0108. The examiner can normally be reached M-Th, alternate F, 8-5. 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, Karlheinz Skowronek can be reached at 571-272-9047. 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. /K.N.A./ Examiner, Art Unit 1687 /OLIVIA M. WISE/ Supervisory Patent Examiner, Art Unit 1685 Application/Control Number: 19/365,119 Page 2 Art Unit: 1687 Application/Control Number: 19/365,119 Page 3 Art Unit: 1687 Application/Control Number: 19/365,119 Page 4 Art Unit: 1687 Application/Control Number: 19/365,119 Page 5 Art Unit: 1687 Application/Control Number: 19/365,119 Page 6 Art Unit: 1687 Application/Control Number: 19/365,119 Page 7 Art Unit: 1687 Application/Control Number: 19/365,119 Page 8 Art Unit: 1687 Application/Control Number: 19/365,119 Page 9 Art Unit: 1687 Application/Control Number: 19/365,119 Page 10 Art Unit: 1687 Application/Control Number: 19/365,119 Page 11 Art Unit: 1687 Application/Control Number: 19/365,119 Page 12 Art Unit: 1687 Application/Control Number: 19/365,119 Page 13 Art Unit: 1687 Application/Control Number: 19/365,119 Page 14 Art Unit: 1687 Application/Control Number: 19/365,119 Page 15 Art Unit: 1687 Application/Control Number: 19/365,119 Page 16 Art Unit: 1687 Application/Control Number: 19/365,119 Page 17 Art Unit: 1687 Application/Control Number: 19/365,119 Page 18 Art Unit: 1687 Application/Control Number: 19/365,119 Page 19 Art Unit: 1687 Application/Control Number: 19/365,119 Page 20 Art Unit: 1687 Application/Control Number: 19/365,119 Page 21 Art Unit: 1687 Application/Control Number: 19/365,119 Page 22 Art Unit: 1687 Application/Control Number: 19/365,119 Page 23 Art Unit: 1687 Application/Control Number: 19/365,119 Page 24 Art Unit: 1687 Application/Control Number: 19/365,119 Page 25 Art Unit: 1687 Application/Control Number: 19/365,119 Page 26 Art Unit: 1687 Application/Control Number: 19/365,119 Page 27 Art Unit: 1687 Application/Control Number: 19/365,119 Page 28 Art Unit: 1687 Application/Control Number: 19/365,119 Page 29 Art Unit: 1687
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