Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Detailed Action 2. Claims 1- 20 are pending in Instant Application. Information Disclosure Statement The information disclosure statement (IDS) submitted on 0 7 / 20 /202 3 and 11/14/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Priority Examiner acknowledges that this application is the U.S. National Stage Entry of International Application No. PCT/US2022/013190, filed January 20, 2022, which claims the benefit of and priority to U.S. Provisional Patent Application No. 63/140,136 filed January 21, 2021, and U.S. Provisional Patent Application No. 63/194,722, filed May 28, 2021, the entirety of each of which is incorporated herein by reference. 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. 5 . Claims 1, 5, 6, 10-14, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0154670 A1 issued to Memorial Sloan Kettering Cancer Center (hereinafter "Memorial") (Applicant IDS) in view of US 2020/0008299 A1 to Tran (hereinafter “ Tran") (Applicant IDS) . Regarding claim 1, Memorial discloses a method comprising: receiving, by a computing system, emission data corresponding to fluorescence spectral responses of nanosensor in contact with a plurality of biological samples (para [0037]- detecting a wavelength shift (e.g., a blueshift or a redshift) in emission EMR and/or an intensity shift (e.g., amplitude shift) and/or another change in the spectral characteristics of emission EMR, whereupon binding of the analyte to a particular analyte-binding species (e.g., antibody) results in a detectable change in the emission EMR (e.g., intrinsic fluorescence of the SWCNT); para [0042]- a single-walled carbon nanotube (SWCNT) sensor) collected from a cohort of subjects, the cohort of subjects including subjects with a medical condition and subjects without the medical condition (para (0010)- This device can be used for the specific and rapid detection of biomarkers in patient samples such as whole blood, serum, urine, and the like; para (0138)- The complexes measured HE4 with nanomolar sensitivity to differentiate disease from healthy patient biofluids; para (0145)- with biofluid samples collected from ovarian cancer patients) , each nanosensor comprising a semiconducting single - walled carbon nanotubes (SWCNT) that is (i) covalently functionalized and (ii) encapsulated by a nucleic acid (para (0051]- a "nucleic acid" is or comprises DNA; para (0107 ) - DNA-encapsulated single-walled carbon nanotubes (SWCNTs) functionalized with an antibody or other analyte-binding species; para [0109]- dispersed semiconducting SWCNTs exhibit ideal qualities as optical biomedical sensors; para (0111 ]- a polymer capable of being non-covalently or covalently conjugated to the SWCNT) ; generating, by the computing system, based on the emission data, a dataset comprising a plurality of spectral feature changes caused by the biological samples, the spectral feature changes corresponding to intensity and wavelength of emissions from the nanosensor in response to excitation by coherent light from a light source (para [0037] - detecting a wavelength shift (e.g., a blueshift or a redshift) in emission EMR and/or an intensity shift (e.g., amplitude shift) and/or another change in the spectral characteristics of emission EMR, whereupon binding of the analyte to a particular; an alyte-binding species (e.g., antibody) results in a detectable change in the emission EMR (e.g., intrinsic fluorescence of the SWCNT); para (0167 ) - Sensor devices ... The emission center wavelengths were compared to control un - injected mice to determine the magnitude of the shifts .... spectra of the devices were acquired; para ( 0013 ) - The sensor can be interrogated from outside the body using a light source that can be directed at the device using near-infrared excitation light) ; Memorial fails to disclose nanosensor arrays; training, by the computing system, a machine learning model based on the dataset and on clinical data corresponding to the medical condition for each subject in the cohort of subjects, wherein the machine learning model comprises at least one of logistic regression, decision tree, artificial neural networks (ANN), random forest, or support vector machine (SVM), wherein the machine learning model is configured to receive emission data and provide a classification corresponding to the medical condition; and providing, by the computing system, the machine learning model for classification of the medical condition in one or more patients based on spectral responses of nanosensor arrays in contact with one or more patient samples, wherein providing the machine learning model comprises at least one of storing the machine learning model in a non-volatile computer-readable sto rage medium of the computing system or transmitting the machine learning model to a second computing system. Tran, drawn to a computing method, however discloses nanosensor arrays (para (0096)- Direct electron transfer can be done with various types of CNT electrodes ... aligned CNT arrays) ; training, by the computing system, a machine learning model based on the dataset and on c linical data corresponding to the medical condition for each subject in the cohort of subjects (para (0200 ) - The EMG sensors can detect muscle fatigue and can warn the pa t ient...The data can include in-door positioning information, 3D acceleration information, or EMG/EKG/EEG data ... Training data is acquired and a training method is built for the Bayesian network engine; para (0206 ) - Al approaches towards signal recognition include Artificial Neural Networks (ANN)); para [0283 ) - the processor may be located centrally and receive sensor signals relating indicative of wetness of a number of absorbent articles worn by different patients) , wherein the machine learning model comprises at least one of logistic regression, decision tree, artificial neural networks (ANN), random forest, or support vector machine (SVM) (para ( 0206 ) - Al approaches towards signal recognition include Artificial Neural Networks (ANN))), wherein the machine learning model is configured to receive data and provide a classification corresponding to the medical condition; and providing, by the computing system, the machine learning model for classification of the medical condition in one or more patients based on spectral responses of nanosensor arrays in contact with one or more patient samples (para ( 0209 ) - The WT (Wavelet Transform) represents a very suitable method for the classification of EMG signals; para (0210) - Fuzzy logic systems are advantageous in biomedical signal processing and classification. Biomedical signals such as EMG signals) . wherein providing the machine learning model comprises at least one of storing the machine learning model in a nonvolatile computer-readable storage medium of the computing system or transmitting the machine learning model to a second computing system (para (0005 ) - forming a biodegradable processor, memory, and a wireless or optical communication circuit on the substrate; para (0025)- a stored dataset; para (0028 ) - The processor and wireless communication system can communicate with a remote computer; para (0178 ) - a volatile memory integrated with the non-volatile memory array and the processor) . It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of the invention to combine nanosensor arrays and machine learning method disclosed by Tran to the method disclosed by Memorial to reduce the cost (See Tran, para (0006) . Regarding claim 5, Memorial in view of Tran disclose the method of claim 1. Memorial further discloses wherein the SWCNTs are en capsulated by a single-strand deoxyribonucleic acid (ssDNA) (para (0113)- the nucleotide sequence is a single-stranded DNA molecule (ssDNA): para ( 0132)- ssDNA-encapsulated SWCNT) . Regarding claim 6, Memorial in view of Tran disclose the method of claim 5. Memorial further discloses wherein the ssDNA comprises a sequence selected from a group consisting of CTTC 3 TTC, (TAT) 4 , or (GT) 15 (para ( 0113 ) - fewer than 20, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3 or 2 nucleotides ... poly (GT)) . Regarding claim 10, Memorial in view of Tran disclose the method of claim 1. Memorial further discloses comprising: receiving, by the computing system, emission data corresponding to fluorescence spectral responses of a nanosensor in contact with a biological sample of a patient (para (0037 ) - detecting a wavelength shift (e.g., a blueshift or a redshift) in emission EMR and/or an intensity shift (e.g., amplitude shift) and/or another change in the spectral characteristics of emission EMR, whereupon binding of the analyte to a particular analyte - binding species (e.g., antibody) results in a detectable change in the emission EMR (e.g., intrinsic fluorescence of the SWCNT): para (0042 ) - a single-walled carbon nanotube (SWCNT) sensor; para (0145]- with biofluid samples collected from ovarian cancer patients) ; and processing, by the computing system, the emission data corresponding to the medical condition in the patient (para (0138 ) - The complexes measured HE4 with nanomolar sensitivity to differentiate disease from healthy patient biofluids). Tran further discloses nanosensor array (para (0096]- Direct electron transfer can be done with various types of CNT electrodes ... aligned CNT arrays) using the machine learning model to obtain a classification (para (0206 ) - Al approaches towards signal recognition include Artificial Neural Networks (ANN); para (0209 ) - The WT represents a very suitable method for the classification of EMG signals) . It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of the invention to combine nanosensor arrays and machine learning method disclosed by Tran to the method disclosed by Memorial to reduce the cost (See Tran, para (0006) . Regarding claim 11, Memorial in view of Tran disclose the method of claim 10. Memorial further-discloses the method further comprising administering a treatment to the patient based on the classification (para (0013 ) - Thus, such embodiments address the need for rapid, transient, specific, and sensitive detection of disease biomarkers in a patient. This allows for more informed decisions by the patient and physician about ther apies /courses of treatm e nt) . Regarding claim 12, Memorial in view of Tran disclose the method of claim 10. Memorial further discloses wherein the biological sample of the patient is a serum sample from the patient (para (0022 ) - the biological sample comprises a member selected from the group consisting of blood, serum, plasma, urine, c1scites, and uterine washing) . Regarding claim 13, Memorial in view of Tran disclose the method of claim 1. Although Memorial doesn't specifically disclose wherein the coherent light used for excitation has a wavelength bandwidth centered at 575 nanometers (nm), it would have been obvious to a person having ordinary skill in art at the time of the invention to have the coherent light used for excitation has a wavelength bandwidth centered at G75 nanometers (nm), for when the general conditions of a claim are disclosed by the prior art it is not inventive to discover an optimum or workable range by routine experimentation (See Memorial, para (0120 ) - the excitation EMR is visible light. In certain embodiments, the excitation EMR has a wavelength between 100 nm and 3000 nm, 200 nm and 2000 nm, between 300 and 1500 nm, or between 500 and 1000 nm) . Regarding claim 14, Memorial in view of Tran disclose the method of claim 1. Memorial further discloses comprising synthesizing the nanosensor (para (0126 ) - These activated groups were conjugated to amine-functionalized DNA encapsulating SWCNT via a simple amidation reaction. Unconjugated antibody was dialyzed away to obtain purified antibody-DNA-SWCNT complexes) . Tran further discloses nanosensor arrays (para (0096]- Direct electron transfer can be done with various types of CNT electrodes ... aligned CNT arrays) . It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of the invention to combine nanosensor arrays and machine learning method disclosed by Tran to the method disclosed by Memorial to reduce the cost (See Tran, para (0006) . Regarding claim 16, Memorial in view of Tran disclose the method of claim 1. Memorial further discloses wherein the biological samples comprise sera of subjects in the cohort of subjects (para (0010 ) -biomarkers in patient samples such as whole blood, serum, urine; para (0086 ) - serum from HGSC three patients or healthy donors; para (0145 ) - biofluid samples collected from ovarian cancer patients) . Regarding claim 17, Memorial discloses a method comprising: receiving, by a computing system, emission data corresponding to fluorescence spectral responses of nanosensor in contact with a biological sample of the patient (para [0037 ) - detecting a wavelength shift (e.g., a blueshift or a redshift) in emission EMR and/or an intensity shift (e.g., amplitude shift) and/or another change in the spectral characteristics of emission EMR, whereupon binding of the analyte to a particular analyte-binding species (e.g., antibody) results in a detectable change in the emission EMR (e.g., intrinsic fluorescence of the SWCNT); para (0042]- a single-walled carbon nanotube (SWCNT) sensor; para (0145]- with biofluid samples collected from ovarian cancer patients) , the nanosensor comprising a semiconducting single-walled carbon nanotubes (SWCNT) that is (i) covalently functionalized and (ii) encapsulated by a nucleic acid (para (0051 ) - a "nucleic acid" is or comprises DNA; para (0107]- DNA-encapsulate d single-walled carbon nanotubes (SWCNTs) functionalized with an antibody or other analyte-binding species; para (0109 ) - dispersed semiconducting SWCNTs exhibit ideal qualities as optical biomedical sensors; para ( 0111) - a polymer capabl e of being non-covalently or covalently conjugated to the SWCNT) ; and processing, by the computing system, the emission data corresponding to a medical condition in the patient (para [0138 ) - The complexes measured HE4 with nanomolar sensitivity to differentiate disease from healthy patient biofluids) ; reference emission data and clinical data corresponding to the medical condition for each subject in a cohort of subjects, the reference emission data corresponding to fluorescence spectral responses of nanosensor in contact with a plurality of biological samples collected from the cohort of subjects, the cohort of subjects including subjects with a medical condition and subjects without the medical condition (para (001 0) - This device can be used for the specific and rapid detection of biomarkers in patient samples such as whole blood, serum, urine, and the like; para (0129)- Thus, each sensor is operational in the ideal conditions of PBS as well as in FBS in a range relevant to clinical biomarker detection; para (0138)- The complexes measured HE4 with nanomolar sensitivity to differentiate disease from healthy patient biofluids; para (0145)- with biofluid samples collected from ovarian cancer patients) , dataset comprising a plurality of spectral feature changes caused by the biological samples, the spectral feature changes corresponding to intensity and wavelength of emissions from the nanosensor in response to excitation by coherent light from a light source (para (0037) detecting a wavelength shift (e.g., a blueshift or a redshift) in emission EMR and/or an intensity shift (e.g., amplitude shift) and/or another change in the spectral characteristics of emission EMR, whereupon binding of the analyte to a particular analyte-binding species (e.g., antibody) results in a detectable change in the emission EMR (e.g., intrinsic fluorescence of the SWCNT; para (0167)- Sensor devices ... The emission center wavelengths were compared to control uninjected mice to determine the magnitude of the shifts .... spectra of the devices were acquired; para (0013)- The sensor can be interrogated from outside the body using a light source that can be directed at the device using near-infrared excitation light) . Memorial fails to disclose nanosensor array, using a machine learning model to obtain a classification, the machine learning model configured to provide the classification, the machine learning model having been trained based on reference; generate a training dataset, wherein the machine learning model comprises at least one of logistic regression, decision tree, artificial neural networks (ANN ) , random forest, or support vector machine (SVM). Tran, drawn to a computing method, discloses nanosensor array (para (0096) - Direct electron transfer can be done with various types of CNT electrodes ... aligned CNT arrays) ; using a machine learning model to obtain a classification, the machine learning model configured to provide the classification (para ( 0209) - The WT represents a very suitable method for the classification of EMG signals; para ( 0210) - Fuzzy logic systems are advantageous in biomedical signal processing and classification. Biomedical signals such as EMG signals )) ; the machine learning model having been trained based on reference; generate a training dataset; wherein the machine learning model comprises at least one of logistic regression, decision tree, artificial neural networks (ANN), random forest, or support vector machine (SVM) (para (0200)- The EMG sensors can detect muscle fatigue and can warn the patient...The data can include in-door positioning information, 3D acceleration information, or EMG/EKG/EEG data ... Training data is acquired and a training method is built for the Bayesian network engine; para (0206)- Al approaches towards signal recognition include Artificial Neural Networks (ANN)). It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine nanosensor array and machine learning method disclosed by Tran to the method disclosed by Memorial to reduce the cost (See Tran. para [0006)) . Regarding claim 18, Memorial in view of Tran disclose the method of claim 17. Memorial further discloses the method further comprising administering a treatment to the patient based on the classification (para (0013)- Thus, such embodiments address the need for rapid, transient, specific. and sensitive detection of disease biomarkers in a patient. This allows for more informed decisions by the patient and physician about therapies/cou rses o f treatment) . 6 . Claims 2 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0154670 issued to Memorial Sloan Kettering Cancer Center (hereinafter "Memorial") (Applicant IDS) in view of US 2020/0008299 A1 to Tran (hereinafter “ Tran") (Applicant IDS) and further in view of US 2018/0265779 A1 to Wang et al. (hereinafter "Wang") (Applicant IDS) . Regarding claim 2, Memorial in view of Tran disclose the method of claim 1. Memorial further discloses wherein the spectral feature changes correspond to a plurality of an intensity and a wavelength (para (0037)- detecting a wavelength shift (e.g., a blueshift or a redshift) in emission EMR and/or an intensity shift (e.g., amplitude shift} and/or another change in the spectral characteristics of emission EMR) . Memorial in view of Tran fail to disclose E 11 peak (int) and E 11 -peak (int*) . Wang, drawn to fluorescence emitters, discloses E 11 peak and E 11 -peak (Fig. 1; para (0125) - E 11 and E 11 - emission peaks) . It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the E 11 peak and E 11 -peak disclosed by Wang to the intensity and wavelength disclosed by Memorial to overcome the difficulties and limitations of the conventional approaches for creating emitters (See Wang, para (0081)) . Regarding claim 15, Memorial in view of Tran disclose the method of claim 14. Memorial further discloses wherein synthesizing the nanosensor arrays comprises encapsulating the SWCNTs with a library ssDNA to solubilize the nanosensors in biofluids (para ( 0126) SWCNTs were suspended in solution with ssDNA oligonudeotides ... These activated groups were conjugated to amine-functionalized DNA encapsulating SWCNT via a simple amidation reaction; para (0117) - the device is in contact with a biofluid or bodily fluid sample) . Memorial in view of Tran fail to disclose introducing sp 3 defects to (6,5) SWCNTs via diazonium chemistry. Wang, drawn to fluorescence emitters, discloses disclose introducing sp 3 defects to (6,5) SWCNTs via diazonium chemistry (para ( 0007)- incorporation of defects through diazonium chemistry; para (0020)- the carbon nanotube may be a SWCNT selected from the group consisting of a (6,5)-SWCNT; para (0066)- the synthesis of more than thirty new exemplary fluorescent nanostructures was achieved from SWCNTs of the same crystal structure by creating molecularly tunable fluorescent quantum defects in the sp 3 carbon lattice) . It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine introducing sp 3 defects to (6,5) SWCNTs via diazonium chemistry, dis closed Wang to the method disclosed by Memorial to overcome the difficulties and limitations of the conventional approaches for creating emitters (See Wang, para (0081)) . 7 . Claims 3, 4, 7, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0154670 issued to Memorial Sloan Kettering Cancer Center (hereinafter "Memorial") (Applicant IDS) in view of US 2020/0008299 A1 to Tran (hereinafter “ Tran") (Applicant IDS) and further in view of US 2020/0048489 A1 to University of Maryland, College Park (hereinafter "Maryland") (Applicant IDS) . Regarding claim 3, Memorial in view of Tran disclose the method of claim 1, but fail to disclose wherein the SWCNTs are functionalized by organic color centers (OCCs). Maryland, drawn to SWCNTs, discloses the SWCNTs are functionalized by organic color centers (OCCs) (para (0013)- the carbon nanomaterials are SWCNTs ... the carbon nanomaterials are organic- color-center-tailored carbon nanotubes; para ( 0089)- organic color centers) . It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the organic color centers disclosed by Maryland to the SWCNTs disclosed by Memorial to improve the dispersion of CNTs (See Maryland, para (0007)) . Regarding claim 4, Memorial in view of Tran and further in view of Maryland disclose t he method of claim 3. Maryland further discloses wherein the OCCs comprise an aryl functional group selected from the group consisting of 4-N,N-diethylamino (-4-N(C 2 H 5 ) 2 ), 3,4,5- trifluoro (-3,4,5-F3}, or 3 -fluorn-4-carboxy (-3 -F -4-CO 2 H) (para (0090 ) - organic color centers ... aryl groups ... one or more fluorine s , one or more amino groups ... alkylated amino groups (-N(RN) 2 ) where RN is H or an alkyl group, one or more carboxylic acid or carboxylate groups ) . It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the organic color centers disclosed by Maryland to the SWCNTs disclosed by Memorial to improve the dispersion of CNTs (See Maryland, para (0007)) . Regarding claim 7, Memorial in view of Tran disclose the method of claim 1. Memorial further discloses wherein the nanosensor arrays comprise ssDNA-encapsulated SWCNTs selected from a group consisting of CTTC 3 TTC, (TAT) 4 , or (GT) 15 (para (0132)- ssDNA encapsulated SWCNT; para (0113)- fewer than 20, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3 or 2 nucleotides ... poly (GT)) . Memorial in view of Tran fail to disclose OCC-functionalized, selected from group consisting of NE t2 *CTTC 3 TTC, NE t2 *(TAT) 4 , NE 12 *(GT) 15 , 3F*CTTC 3 TTC, 3F*(TAT) 4 , 3F*(AT) 15 , 3F*(GT) 15 , FCO 2 H* CTTC 3 TTC, F-CO 2 H *(AT) 15 , or F-CO 2 H *(GT) 15 , where NE t2 represents 4-N,Ndiethylamino, 3F represents F-CO 2 H 3,4,5-trifluoro, and F-CO 2 H represents 3-fluoro-4- carboxy aryl OCCs. Maryland, drawn to SWCNTs, discloses the SWCNTs are functionalized by organic color centers (OCCs) (para (0013)- the carbon nanomaterials are SWCNTs ... the carbon nanomaterials are organic- color-center-tailored carbon nanotubes; para (0089)- organic color centers) , wherein the OCCs selected from the group consisting of 4-N,N-diethylamino (-4-N(C 2 H 5 ) 2 ) , 3,4,5-trifluoro (-3,4,5-F3), or 3 -f l uoro - 4-carboxy (-3 -F -4-CO 2 H) (para (0090]- organic color centers ... aryl groups ... one or more fluorines, one or more amino groups ... alkylated amino groups (-N(RN) 2 ) where RN is H or an alkyl group, one or more carboxylic acid or carboxylate groups (- COOH)) . It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the organic color centers disclosed by Maryland to the ssDNA-encapsulated SWCNTs disclosed by Memorial to improve the dispersion of CNTs (See Maryland, para ( 0007)) . Regarding claim 19, Memorial in view of Tran disclose the method of claim 17. Memorial further discloses the SWCNTs are encapsulated by a single-strand deoxyribonucleic acid (ssDNA) (para (0113 ) - the nucleotide sequence is a single-stranded DNA molecule (ssDNA); para (0132 ) - ssDNA-encapsulated SWCNT) . Memorial in view of Tran fail to disclose wherein the SWCNTs are functionalized by organic color centers (OCCs). Maryland, drawn to SWCNTs, discloses the SWCNTs are functionalized by organic color centers (OCCs) (para (0013)- the carbon nanomaterials are SWCNTs ... the carbon nanomaterials are organic- color-center-tailored carbon nanotubes; para ( 0089 ) - organic color centers) . It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the organic color centers disclosed by Maryland to the SWCNTs disclosed by Memorial to improve the dispersion of CNTs (See Maryland, p ara - (0007 ) ) . Regarding claim 20, Memorial in view of Tran and further in view of Maryland disclose the method of claim 19. Memorial further discloses the ssDNA comprises a sequence selected from a group consisting of CTTC 3 TTC, (TAT) 4 , or (GT) 15 (para (0113]- fewer than 20, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3 or 2 nucleotides ... poly (GT)) . Maryland further discloses wherein the OCCs comprise an aryl functional group selected from the group consisting of 4-N,N-diethylamino (-4-N(C 2 H 5 ) 2 ), 3,4,5-trifluoro (-3,4,5-F3), or 3-fluoro-4-carboxy (-3-F-4- CO 2 H) (para (0090 ) - organic color centers ... aryl groups ... one or more fluorines, one or more amino groups ... alkylated amino groups (N(RN) 2 ) where RN is H or an alkyl group, one or more carboxylic acid or carboxylate groups (- COOH)) . It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the organic color centers disclosed by Maryland to the SWCNTs disclosed by Memorial to improve the dispersion of CNTs (See Maryland, p ara - (0007 ) ) . 8 . Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0154670 issued to Memorial Sloan Kettering Cancer Center (hereinafter "Memorial") (Applicant IDS) in view of US 2020/0008299 A1 to Tran (hereinafter “ Tran") (Applicant IDS) further in view of US 2010/0093092 A1 to Howard (hereinafter "Howard") (Applicant IDS) and further in view of US 2020/0048489 A1 to University of Maryland, College Park (hereinafter "Maryland") (Applicant IDS) . Regarding claim 8, Memorial in view of Tran disclose the method of claim 1. Memorial further discloses spectral responses of a plurality of DNA SWCNTs (para (0037 ) - detecting a wavelength shift (e.g., a blueshift or a redshift) in emission EMR and/or an intensity shift (e.g., amplitude shift) and/or another change in the spectral characteristics of emission EMR, whereupon binding of the analyte to a particular analyte-binding species (e.g., antibody) results in a detectable change in the emission EMR (e.g., intrinsic fluorescence of the SWCNT); para (0132 ) - ssDNA-encapsulated SWCNT) . Memorial in view of Tran fail to disclose wherein the machine learning model is an SVM model trained; OCC-DNA SWCNTs. Howard, drawn to data training, discloses the machine learning model is an SVM model trained (para (0114) - (0115)- the reconstruction model is trained ... Support vector machines (SVMs)) . It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the SVM model disclosed by Howard to the spectral responses disclosed by Memorial to facilitate the data analysis (See Howard, para (0114 ) - ( 0115 ) ) . Further, Maryland, drawn to SWCNTs, discloses OCCSWCNTs. (para (0013)- the carbon nanomaterials are SWCNTs ... the carbon nanomaterials are organic- color-center-tailored carbon nanotubes; para [0089)- organic color centers) . It would have been obvious to a person having ordinary skill in the art at the lime of the invention to combine the organic color centers disclosed by Maryland to the DNA-SWCNTs disclosed by Memorial to improve the dispersion of CNTs (See Maryland, para (0007)) . Regarding claim 9, Memorial in view of Tran, Howard and Maryland disclose the method of claim 8. Memorial further discloses the plurality of DNA SWCNTs comprise at least one DNA SWCNT selected from a group consisting of CTTC 3 TTC, (TAT ) 4 , or (GT) 15 (para ( 0132 ) ssDNA - encapsulated SWCNT; (para (0113 ) - fewer than 20, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3 or 2 nucleotides ... poly (GT)) . Maryland further discloses OCCs selected from the group consisting of 4-N,N-diethylamino (-4-N(C 2 H 5 ) 2 ) , 3,4,5-trifluoro (-3,4,5-F3 ) or 3 -fluoro-4-carboxy (-3 -F -4-CO2H) (para (0090 ) - organic color centers ... aryl groups ... one or more fluorines, one or more amino groups ... alkylated amino groups (-N(RN) 2 ) wh ere RN is H or an alkyl g roup, one or more carboxylic acid or carboxylate groups (-COOH)) . It would have been obvious to a person having ordinary skill in the art at the lime of the invention to combine the organic color centers disclosed by Maryland to the DNA-SWCNTs disclosed by Memorial to improve the dispersion of CNTs (See Maryland, para (00 90 )) . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT SM AZIZUR RAHMAN whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571) 270-7360 . The examiner can normally be reached on FILLIN "Work schedule?" \* MERGEFORMAT M-F Telework; If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ali Shayanfar can be reached on FILLIN "SPE Phone?" \* MERGEFORMAT 571-270-1050 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov . Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free) . If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SM A RAHMAN/ Primary Examiner, Art Unit 2434