CTFR 18/307,227 CTFR 82132 DETAILED ACTION 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. Response to Arguments Applicant's arguments and amendments received March 23, 2026 have been fully considered. with regard to 35 U.S.C. § 102, Applicant argues that the cited prior art does not disclose “see applicant argument pages 6-13”. This language corresponds to claims 1-19 and 20. As such, these have been considered but they are not persuasive as addressed below. See the rejection how the art on record reads on the claimed invention as well as the examiner's interpretation of the cited art in view of the presented claim set as outlined below. Furthermore, in response to applicant argument, Ali teaches: [0033] FIG. 2 illustrates a system 200 for training a synthetic data generator 205 for generating synthetic data, in accordance with at least one embodiment of the present disclosure. In system 200 , the synthetic data generator 205 is fed noise data 210 and latent space data 215 . The noise data 210 is random data while the latent space data 215 is a representation of compressed data. In some embodiments, the noise data 210 is white noise , where the sequence of random data cannot be predicted, where the variables are independent and identically distributed with a mean of zero. Accordingly, each variable has the same variance, and each value has zero correlation with other values. In some further embodiments, the white noise is generated from a Gaussian distribution. The compressed data represents the structural similarities in real data that may be compressed. The generator 205 uses the noise data 210 and the latent space data 215 to generate fake samples 220 of the real data 115 . A randomizer 225 decides whether to send a generated fake sample 220 or real data 115 to a discriminator 230 . The discriminator 230 is programmed, for example via a machine learning algorithm, to identify whether the data it received is real data 115 or a generated fake sample 220 . [0034] The discriminator's results are then analyzed by a results analyzer 235 to determine if the discriminator 230 was correct that the data that is received was a generated fake sample 220 or real data 115 . If the data was a generated fake sample 220 or a piece of real data 115 , and the discriminator 230 correctly labeled the data, then the results analyzer 235 informs the generator 205 . The generator 205 then configures itself and adjusts its output to improve the generated fake data 220 output to appear more like the real data 115 . If the data was a generated fake sample 220 and the discriminator 230 thought that the data was real, then the results analyzer 235 informs the generator 205 and provides positive reinforcement. If the data was real data 115 and the discriminator 230 was incorrect, then the results analyzer 235 informs the discriminator 230 of the error, and the discriminator 230 then configures itself and adjusts its output to improve its ability to discriminate between fake samples 220 and real data 115 . Applicant argument in regarding “first portion and the second portion are defined by a sampling ration”, in response, as outlined above, Ali teaches generating fake samples using noise data and the latent space data of the real data, further in order to produce a correct fake sample using discriminator, processing or adjusting output data to improve its ability between fake sample and real data. Therefore, the input data and adjusting output data reads the claimed invention sample ration. Furthermore, applicant argument in regarding the claimed invention “applying parameters”, Ali clearly teaches applying parameters based on request in order to produce a desired synthetic data [ desired paramete rs 330 are provided to the synthetic data generator 205 and the synthetic data generator 205 generates data records according to those paramete rs that mimic the real data 115 ]. Applicant argument in regarding the claimed invention “comparing, evaluating and adjusting”, the examine stands with the rejection as outlined above and outlined under 102 rejection below. Ali clearly teaches applying parameters, comparing results or output, evaluating and adjusting to produce a desired synthetic data. The examiners position that Applicant has not yet submitted claims drawn to limitations, which define the operation and apparatus of Applicant's disclosed invention in manner, which distinguishes over the prior art. As it is Applicant's right to continue to claim as broadly as possible their invention. It is also the Examiners right to continue to interpret the claim language as broadly as possible, as such, the examiner stands with the rejection since the claimed invention steps “selecting, applying, comparing, evaluating and adjusting” anticipated as outlined above and under 102 rejection below. Applicant argument in regarding claims 7-9 and 20, the examiner stands with the rejection since the use of Jacobian, matrix approximation and intersectional fairness are well known to implemented within AI system as outlined within the cited art. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-6 and 10-19 are rejected under 35 U.S.C. 102( 1)(2 ) as being anticipated by Ali et al. US 2024/0046012 . In regarding to claim 1 Ali teaches: 1. A computer-implemented method, comprising: selecting a first portion of training data from a set of real data; [0033] FIG. 2 illustrates a system 200 for training a synthetic data generator 205 for generating synthetic data, in accordance with at least one embodiment of the present disclosure. In system 200 , the synthetic data generator 205 is fed noise data 210 and latent space data 215 . The noise data 210 is random data while the latent space data 215 is a representation of compressed data. In some embodiments, the noise data 210 is white noise, where the sequence of random data cannot be predicted, where the variables are independent and identically distributed with a mean of zero. Accordingly, each variable has the same variance, and each value has zero correlation with other values. In some further embodiments, the white noise is generated from a Gaussian distribution. The compressed data represents the structural similarities in real data that may be compressed. The generator 205 uses the noise data 210 and the latent space data 215 to generate fake samples 220 of the real data 115 . A randomizer 225 decides whether to send a generated fake sample 220 or real data 115 to a discriminator 230 . The discriminator 230 is programmed, for example via a machine learning algorithm, to identify whether the data it received is real data 115 or a generated fake sample 220 . [0035] The system 100 is executed until the generator 205 consistently provides fake data samples 220 that the discriminator 230 cannot differentiate from the real data 115 . Depending on the purpose of train ing, the system 100 may only be executed until the fake data samples 220 are mis-classified by the discriminator 230 a percentage of the time, such as, 50% of the time, for example consistent with random guessing. At this point, the generator 205 is generating fake data samples 220 that are practically indistinguishable from the real data 115 , and thus the generator 205 can be used to generate synthetic data that can mimic the real data 115 . For example, trends present in the fields of the real data 115 may be defined by marginal and conditional distributions of the real data 115 , and the trained generator 205 outputs synthetic data that includes the same marginal and conditional distributions as the real data 115 . The synthetic data can then be used for data analysis to detect trends in similar ways that the real data 115 could have been used, while avoiding the potential privacy issues associated with the use of real data 115 , because the synthetic data cannot be traced to any actual individuals. Ali, 0033-0035, 0038-0041 and Fig. 3, emphasis added. selecting a second portion of the training data from a set of synthetic data, wherein the first portion and the second portion are defined by a sampling ratio; [0033] FIG. 2 illustrates a system 200 for training a synthetic data generator 205 for generating synthetic data, in accordance with at least one embodiment of the present disclosure. In system 200 , the synthetic data generator 205 is fed noise data 210 and latent space data 215 . The noise data 210 is random data while the latent space data 215 is a representation of compressed data. In some embodiments, the noise data 210 is white noise, where the sequence of random data cannot be predicted, where the variables are independent and identically distributed with a mean of zero. Accordingly, each variable has the same variance, and each value has zero correlation with other values. In some further embodiments, the white noise is generated from a Gaussian distribution. The compressed data represents the structural similarities in real data that may be compressed. The generator 205 uses the noise data 210 and the latent space data 215 to generate fake samples 220 of the real data 115 . A randomizer 225 decides whether to send a generated fake sample 220 or real data 115 to a discriminator 230 . The discriminator 230 is programmed, for example via a machine learning algorithm, to identify whether the data it received is real data 115 or a generated fake sample 220 . [0035] The system 100 is executed until the generator 205 consistently provides fake data samples 220 that the discriminator 230 cannot differentiate from the real data 115 . Depending on the purpose of train ing, the system 100 may only be executed until the fake data samples 220 are mis-classified by the discriminator 230 a percentage of the time, such as, 50% of the time, for example consistent with random guessing. At this point, the generator 205 is generating fake data samples 220 that are practically indistinguishable from the real data 115 , and thus the generator 205 can be used to generate synthetic data that can mimic the real data 115 . For example, trends present in the fields of the real data 115 may be defined by marginal and conditional distributions of the real data 115 , and the trained generator 205 outputs synthetic data that includes the same marginal and conditional distributions as the real data 115 . The synthetic data can then be used for data analysis to detect trends in similar ways that the real data 115 could have been used, while avoiding the potential privacy issues associated with the use of real data 115 , because the synthetic data cannot be traced to any actual individuals. Ali, 0033-0035, 0038-0041 and Fig. 3, emphasis added. applying parameters (weights), by a neural network model, to the first portion and the second portion to predict results; [0041] In the exemplary embodiment, the secured environment 305 provides one or more paramete rs 330 about the desired synthetic data 310 to the API call. For example, a first paramete r 330 can be the number of data records desired in the synthetic data 310 . In one example, the records are financial transactions and/or payment transactions between a merchant and cardholder (or accountholder) that are processed over a payment network. For these records, the requested paramete rs 330 can include, but are not limited to, duration, date/time, industry, category, merchant country, number of issuing countries, number of merchants, transaction level, summary, number of cardholders, cardholder age, cardholder home location, and/or any other paramete rs desired based on the fields available in the real data 115 . In at least one embodiment, the paramete rs 330 could be provided in a JSON format. The desired paramete rs 330 are provided to the synthetic data generator 205 and the synthetic data generator 205 generates data records according to those paramete rs that mimic the real data 115 . The output synthetic data 310 includes the marginal and conditional distributions of the real data 115 . In at least some embodiments, the output synthetic data 310 can be provided in a JSON format and can feature one or more parameters, such as, but not limited to, date/time, industry, category, transaction amount, transaction amount in merchant/issuer currency, merchant/issuer country, merchant issuer currency, and cardholder present code. In these embodiments, synthetic merchant names may be provided by the synthetic data generator 205 to distinguish the different merchants in the synthetic data 310 . Ali, 0041, 0046, 0051-0055 and Fig. 3, emphasis added. comparing the results with desired results to compute an accuracy of the neural network model; [0004] In many cases, PII may be stored in hashed datasets. However, hashed datasets can be traced back to individuals using machine learning techniques, despite many of the anonymization techniques that are used. Research has shown with hashed datasets that with just 15 characteristics or parameters, individuals can be traced back with over 95% accura cy. Therefore, in many cases, merely hashing data is not enough in today's world to protect the data . Ali, 0004, 0076, emphasis added. evaluating a fairness metric for the results; [0066] One side effect of artificial intelligence generated data and analysis is the possibility of introducing or keeping bias in the data. Different types of bias can be introduced in the data including, but not limited to, historical bias, aggregation bias, temporal bias, and social bias. Other types of bias can be introduced by the algorithms used, such as, but not limited to, popularity bias, ranking bias, evaluation bias, and emergent bias. The subsequent user interactions can also introduce behavioral bias, presentation bias, linking bias, and/or content production bias. In some embodiments, the goal is to generate synthetic data 310 (shown in FIG. 3 ) that is fair and bias-free . Having the training algorithm simply ignore or remove protected variables such as race, color, religion, gender, disability, or family status may, without more, be insufficient due to the existence of redundant encodings, which are methods of predicting protected attributes from other features. The data generator 205 uses noise data 210 (both shown in FIG. 2 ) to generate data, but in using real data 115 (shown in FIG. 1 ), the biases can inadvertently be trained into the generator 205 (shown in FIG. 2 ). Training a generator 205 to produce unbiased data can be very difficult and time consuming. Accordingly, applying bias mitigation techniques to the data pre-processing 420 can assist in generating fair and unbiased synthetic data 310 independent of the GAN architecture . Ali, 0066 and 0070, emphasis added. and adjusting the sampling ratio based on the accuracy and the fairness metric. [0034] The discriminator's results are then analyzed by a results analyzer 235 to determine if the discriminator 230 was correct that the data that is received was a generated fake sample 220 or real data 115 . If the data was a generated fake sample 220 or a piece of real data 115 , and the discriminator 230 correctly labeled the data, then the results analyzer 235 informs the generator 205 . The generator 205 then configures itself and adjust s its output to improve the generated fake data 220 output to appear more like the real data 115 . If the data was a generated fake sample 220 and the discriminator 230 thought that the data was real, then the results analyzer 235 informs the generator 205 and provides positive reinforcement . If the data was real data 115 and the discriminator 230 was incorrect, then the results analyzer 235 informs the discriminator 230 of the error, and the discriminator 230 then configures itself and adjust s its output to improve its ability to discriminate between fake samples 220 and real data 115 . Ali, 0033-0035, emphasis added. In regarding to claim 2 Ali teaches: 2. The computer-implemented method of claim 1, wherein the fairness metric is a bias measurement of the neural network model for different groups. [0066] One side effect of artificial intelligence generated data and analysis is the possibility of introducing or keeping bias in the data. Different types of bias can be introduced in the data including, but not limited to, historical bias, aggregation bias, temporal bias, and social bias. Other types of bias can be introduced by the algorithms used, such as, but not limited to, popularity bias, ranking bias, evaluation bias, and emergent bias. The subsequent user interactions can also introduce behavioral bias, presentation bias, linking bias, and/or content production bias. In some embodiments, the goal is to generate synthetic data 310 (shown in FIG. 3 ) that is fair and bias-free . Having the training algorithm simply ignore or remove protected variables such as race, color, religion, gender, disability, or family status may, without more, be insufficient due to the existence of redundant encodings, which are methods of predicting protected attributes from other features . The data generator 205 uses noise data 210 (both shown in FIG. 2 ) to generate data, but in using real data 115 (shown in FIG. 1 ), the biases can inadvertently be trained into the generator 205 (shown in FIG. 2 ). Training a generator 205 to produce unbiased data can be very difficult and time consuming. Accordingly, applying bias mitigation techniques to the data pre-processing 420 can assist in generating fair and unbiased synthetic data 310 independent of the GAN architecture . Ali, 0066 and 0070, emphasis added. In regarding to claim 3 Ali teaches: 3. The computer-implemented method of claim 2, wherein each one of the groups is associated with a different attribute comprising at least one of gender, age, skin color, hair color, smile, or glasses. [0066] One side effect of artificial intelligence generated data and analysis is the possibility of introducing or keeping bias in the data. Different types of bias can be introduced in the data including, but not limited to, historical bias, aggregation bias, temporal bias, and social bias. Other types of bias can be introduced by the algorithms used, such as, but not limited to, popularity bias, ranking bias, evaluation bias, and emergent bias. The subsequent user interactions can also introduce behavioral bias, presentation bias, linking bias, and/or content production bias. In some embodiments, the goal is to generate synthetic data 310 (shown in FIG. 3 ) that is fair and bias-free . Having the training algorithm simply ignore or remove protected variables such as race, color, religion, gender, disability, or family status may, without more, be insufficient due to the existence of redundant encodings, which are methods of predicting protected attributes from other features . The data generator 205 uses noise data 210 (both shown in FIG. 2 ) to generate data, but in using real data 115 (shown in FIG. 1 ), the biases can inadvertently be trained into the generator 205 (shown in FIG. 2 ). Training a generator 205 to produce unbiased data can be very difficult and time consuming. Accordingly, applying bias mitigation techniques to the data pre-processing 420 can assist in generating fair and unbiased synthetic data 310 independent of the GAN architecture . Ali, 0066 and 0070, emphasis added. In regarding to claim 4 Ali teaches: 4. The computer-implemented method of claim 2, wherein the sampling ratio comprises a value for each one of the different groups. [0070] The data generation computer device 105 ranks 830 the similar instances based on count of instance that are similar to the opposite group. In this case, the higher the count, the higher the rank. Then based on the ranks, the data generation computer device 105 removes 835 the top X perce ntage of instances from each of the unprivileged and privileged groups. These instances are biased as the output labels differ because of the protected attribute(s). This data pre-processing 420 improves both performance of the model as well as fairness of the model. Ali, Ali, 0035, 0070, emphasis added. In regarding to claim 5 Ali teaches: 5. The computer-implemented method of claim 2, wherein a second sampling ratio comprises a value for each one of the different groups and further comprising adjusting the second sampling ratio based on the accuracy and the fairness metric. Ali, 0034-0035, 0070 In regarding to claim 6 Ali teaches: 6. The computer-implemented method of claim 1, wherein the training data comprises one of images, text, tabular data, or audio sounds. Ali, 0057, 0072 In regarding to claim 10 Ali teaches: 10. The computer-implemented method of claim 1, wherein the fairness metric is equalized odds. Ali, 0033-0035, 0038-0041 In regarding to claim 11 Ali teaches: 11. The computer-implemented method of claim 1, wherein the synthetic data is generated using group-specific constraints. Ali, 0033-0035, 0038-0041, 0076 In regarding to claim 12 Ali teaches: 12. The computer-implemented method of claim 1, wherein at least one of the steps of selecting the first portion, selecting the second portion, and applying the parameters is performed on a server or in a data center to adjust the sampling ratio, and the sampling ratio is streamed to a user device. Ali, 0033-0035, 0038-0041 and Fig. 3 In regarding to claim 13 Ali teaches: 13. The computer-implemented method of claim 1, wherein at least one of the steps of selecting the first portion, selecting the second portion, and applying the parameters is performed within a cloud computing environment. Ali, 0033-0035, 0038-0041 and Figs. 3-4 In regarding to claim 14 Ali teaches: 14. The computer-implemented method of claim 1, wherein at least one of the steps of selecting the first portion, selecting the second portion, and applying the parameters is performed for training, testing, or certifying the neural network for use in a machine, robot, or autonomous vehicle. Ali, 0033-0035, 0038-0041 and Figs. 3-4 In regarding to claim 15 Ali teaches: 15. The computer-implemented method of claim 1, wherein at least one of the steps of selecting the first portion, selecting the second portion, and applying the parameters is performed on a virtual machine comprising a portion of a graphics processing unit. Ali, 0033-0035, 0038-0041, 0057, 0083 and Figs. 3-4 Claims 16-18 list all similar elements of claims 1-3, but in system form rather than method form. Therefore, the supporting rationale of the rejection to claims 1-3 applies equally as well to claims 16-18. Furthermore, Ali teaches a system [ see at least Fig. 3]. Claim 19 list all similar elements of claim 1, but in a non-transitory computer-readable media form rather than method form. Therefore, the supporting rationale of the rejection to claims 1-3 applies equally as well to claims 16-18. Furthermore, Ali teaches a non-transitory computer-readable media [see at least Fig. 6] . Claim Rejections - 35 USC § 103 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. 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. 241-nonobviousness. 07-22-aia AIA Claim s 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Ali et al. US 2024/0046012 as applied to claim s 1-6 above, and further in view of Han US 2021/0174142 . In regarding to claim 7 Ali teaches: 7. The computer-implemented method of claim 1, however, Ali fails to explicitly teach but Han teaches wherein adjusting comprises computing a gradient by measuring a best-response Jacobian using an implicit function theorem. Han, 0241-0242, 0256, 0259, 0263 Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Han with the system of Ali in order wherein adjusting comprises computing a gradient by measuring a best-response Jacobian using an implicit function theorem, as such, selectively generating training data by determining whether the collected usage logs are suitable for updating the artificial intelligence model…--0005. Note: The motivation that was applied to claim 7 above, applies equally as well to claim 8 as presented blow. In regarding to claim 8 Ali teaches: 8. The computer-implemented method of claim 1, furthermore, Han teaches: wherein adjusting comprises computing a gradient using an identity matrix approximation. Han, 0241-0242, 0256, 0259, 0263 . Claim Rejections - 35 USC § 103 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. 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. 241-nonobviousness. 07-22-aia AIA Claim 9 are r ejected under 35 U.S.C. 103 as being unpatentable over A li et al. US 2024/0046012 a s applied to claims 1-6 a bove, and further in view of Y urochkin et al. US 2022/0405529. I n regarding to claim 9 Ali teaches: 9. The computer-implemented method of claim 1, however, Ali fails to explicitly teach but Yurochkin teaches wherein the fairness metric comprises intersectional fairness. Yurochkin, 0051. Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Han with the system of Ali in order wherein the fairness metric comprises intersectional fairness, as such, while machine learning models strive to eliminate the biases of a human decision maker, in practice they may reproduce or even exacerbate these factors in the training data, therefore, techniques for learning effective similarity metrics for individually fair machine learning models..—0004, 0008. Note: The motivation that was applied to claim 9 above, applies equally as well to claim 20 as presented blow. Claim 20 list all similar elements of claim 9, but in a non-transitory computer-readable media form rather than method form. Therefore, the supporting rationale of the rejection to claim 9 applies equally as well to claim 20. Furthermore, Ali teaches a non-transitory computer-readable media [see at least Fig. 6]. Conclusion 07-39 AIA THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL T TEKLE whose telephone number is (571)270-1117. The examiner can normally be reached Monday-Friday 8:00-4:30 ET. 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, William Vaughn can be reached at 571-272-3922. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DANIEL T TEKLE/Primary Examiner, Art Unit 2481 Application/Control Number: 18/307,227 Page 2 Art Unit: 2481 Application/Control Number: 18/307,227 Page 3 Art Unit: 2481 Application/Control Number: 18/307,227 Page 4 Art Unit: 2481 Application/Control Number: 18/307,227 Page 5 Art Unit: 2481 Application/Control Number: 18/307,227 Page 6 Art Unit: 2481 Application/Control Number: 18/307,227 Page 7 Art Unit: 2481 Application/Control Number: 18/307,227 Page 8 Art Unit: 2481 Application/Control Number: 18/307,227 Page 9 Art Unit: 2481 Application/Control Number: 18/307,227 Page 10 Art Unit: 2481 Application/Control Number: 18/307,227 Page 11 Art Unit: 2481 Application/Control Number: 18/307,227 Page 12 Art Unit: 2481 Application/Control Number: 18/307,227 Page 13 Art Unit: 2481 Application/Control Number: 18/307,227 Page 14 Art Unit: 2481 Application/Control Number: 18/307,227 Page 15 Art Unit: 2481 Application/Control Number: 18/307,227 Page 16 Art Unit: 2481
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