CTNF 18/534,299 CTNF 90574 DETAILED ACTION This is a non-final, first office action on the merits. Claims 1-20 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. With respect to Step 2A Prong One of the framework, claims 1, 10, and 18 recite an abstract idea. Claims 1, 10, and 18 include “generating a task input and a plurality of data inputs associated with the task input, the plurality of data inputs having a first order; generating one or more batch permutations, each batch permutation of the one or more batch permutations including data inputs from the plurality of data inputs in a different order from the first order; applying a model and the one or more batch permutations to generate: a first set of outputs responsive to the task input based on the plurality of data inputs; and a second set of outputs responsive to the task input based on the data inputs from the plurality of data inputs of the one or more batch permutations; and generating an output responsive, the output including a plurality of outputs based on the first set of outputs and the second set of outputs”. The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and mathematical calculations because the elements describe a process for processing batches. As a result, claims 1, 10, and 18 recite an abstract idea under Step 2A Prong One. Claims 2-9, 11-17, and 19-20 further describe the process for processing batches. As a result, claims 2-9, 11-17, and 19-20 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1, 10, and 18. With respect to Step 2A Prong Two of the framework, claims 1, 10, and 18 do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 10, and 18 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 10, and 18 include a batch prompt, a large language model, a processor, a memory, and a non-transitory computer readable medium. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1, 10, and 18 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two. Claims 2-7, 11-15, and 19-20 do not include any additional elements beyond those recited with respect to claims 1, 10, and 18. As a result, claims 2-7, 11-15, and 19-20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1, 10, and 18. Claims 8-9 and 16-17 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 8-9 and 16-17 include a batch prompt and a large language model. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 8-9 and 16-17 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two. With respect to Step 2B of the framework, claims 1, 10, and 18 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1, 10, and 18 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 10, and 18 include a batch prompt, a large language model, a processor, a memory, and a non-transitory computer readable medium. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1, 10, and 18 do not include additional elements that amount to significantly more than the abstract idea under Step 2B. Claims 2-7, 11-15, and 19-20 do not include any additional elements beyond those recited with respect to claims 1, 10, and 18. As a result, claims 2-7, 11-15, and 19-20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1, 10, and 18. Claims 8-9 and 16-17 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 8-9 and 16-17 include a proximal sensor, a remote sensor, a sensor, a remote sensing satellite, an airplane, an unmanned aerial vehicle (UAV). The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 8-9 and 16-17 do not include additional elements that amount to significantly more than the abstract idea under Step 2B. Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 U.S.C. 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 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 of this title, 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 set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-6, 8-14, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhoujun Cheng, Jungo Kasai, Tao Yu et al. (Batch Prompting: Efficient Inference with Large Language Model APIs) Published in Conference on Empirical… 19 January 2023 Computer Science, DOI:10.48550/arXiv.2301.08721Corpus ID: 256080546 (hereinafter Cheng et al. ) in view of Z Zhao, E Wallace, S Feng, D Klein (Calibrate before use: Improving few-shot performance of language models)- … on machine learning, 2021 - proceedings.mlr.press (hereinafter Zhao et al. ), and further in view of Gardner et al. (US Pub No. 2025/0061290) (hereinafter Gardner et al. ) . Regarding claims 1, 10, and 18, Cheng, Zhao, and Gardner disclose a method for processing batches of inputs using one or more large language models, the method comprising:9 generating a batch prompt including a task input and a plurality of data inputs associated with the task input, the plurality of data inputs having a first order (see Cheng, pages 792-793, wherein Table 17 & Figure 1: Illustration of batch prompting compared with standard prompting. Batch prompting groups multiple samples in one batch (b=2 in the figure) and lets the LLM generate multiple responses (highlighted in yellow) for the batch in inference…..Demonstration contexts are arranged in a specific order at the beginning, with their corresponding outputs placed in the same order afterwards); and generating a batch prompt output responsive to the batch prompt, the batch prompt output including a plurality of outputs based on the first set of outputs and the second set of outputs (see Cheng, page 810, wherein Table 17: An example GPT-3.5 (ChatGPT) and GPT-4 prompt we use for batch prompting. Specifically, the task instruction is given in the system message. In the next a few rounds, one batch of in-context exemplars is input in one round as the role “user", and the answers are output as the role “assistant”). Cheng et al. fails to explicitly disclose generating one or more batch permutations based on the batch prompt, each batch permutation of the one or more batch permutations including data inputs from the plurality of data inputs in a different order from the first order; applying a large language model to the batch prompt and the one or more batch permutations to generate; a first set of outputs responsive to the task input based on the plurality of data inputs; and a second set of outputs responsive to the task input based on the data inputs from the plurality of data inputs of the one or more batch permutations. Analogous art Zhao discloses generating one or more batch permutations based on the batch prompt, each batch permutation of the one or more batch permutations (see Zhao, page 1, wherein a prompt contains three components: a format, a set of training examples, and a permutation (ordering) for those examples. We show that different choices for these factors can lead to highly different accuracies, e.g., changing the permutation of the training examples in a sentiment analysis prompt can change accuracy from near chance (54%) to near state-of-the-art (93%). This instability implies that GPT-3 users, who typically design prompts manually, cannot expect to consistently obtain good accuracy); Analogous art Zhao discloses applying a large language model to the batch prompt and the one or more batch permutations to generate (see Zhao, page 1, wherein a prompt contains three components: a format, a set of training examples, and a permutation (ordering) for those examples. We show that different choices for these factors can lead to highly different accuracies, e.g., changing the permutation of the training examples in a sentiment analysis prompt can change accuracy from near chance (54%) to near state-of-the-art (93%). This instability implies that GPT-3 users, who typically design prompts manually, cannot expect to consistently obtain good accuracy): Analogous art Zhao discloses a first set of outputs responsive to the task input based on the plurality of data inputs (see Zhao, page 3, wherein Figure 2. There is high variance in GPT-3’s accuracy as we change the prompt’s training examples, as well as the permutation of the examples); and Analogous art Zhao discloses a second set of outputs responsive to the task input based on the data inputs from the plurality of data inputs of the one or more batch permutations (see Zhao, page 3, wherein Figure 3. There is high variance in GPT-3’s accuracy as we change the prompt format). Cheng directed to a system for performing inference on large volumes of samples with large language models (LLMs). Zhao directed to improving few-shot performance of language models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Johnson, regarding the Efficient Inference with Large Language Model APIs, to have included generating one or more batch permutations based on the batch prompt, each batch permutation of the one or more batch permutations; applying a large language model to the batch prompt and the one or more batch permutations to generate; a first set of outputs responsive to the task input based on the plurality of data inputs; and a second set of outputs responsive to the task input based on the data inputs from the plurality of data inputs of the one or more batch permutations because both inventions teach improving efficiency to batch data. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Analogous art Gardner discloses data inputs from the plurality of data inputs in a different order from the first order (see Gardner, para [0149], wherein the order of the sentences from the original content can be rearranged in the prompt to improve context and logical flow for the LLM. Templates may determine optimal sentence positioning based on relationships between entities, topics, and other semantic factors). Cheng directed to a system for performing inference on large volumes of samples with large language models (LLMs). Gardner directed to generating summaries of content items using one or more large language models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Johnson, regarding the Efficient Inference with Large Language Model APIs, to have included data inputs from the plurality of data inputs in a different order from the first order because both inventions teach improving results. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Regarding claims 2, 11, and 19, Cheng, Zhao, and Gardner disclose the method of claim 1, wherein the one or more batch permutations, as set forth above with claim 1. Cheng et al. fails to explicitly disclose a first permutation of the plurality of data inputs, the first permutation including a first reordered set data inputs in which the plurality of data inputs are reordered relative to the first order. Analogous art Zhao discloses a first permutation of the plurality of data inputs, the first permutation (see Zhao, page 1, wherein a prompt contains three components: a format, a set of training examples, and a permutation (ordering) for those examples. We show that different choices for these factors can lead to highly different accuracies, e.g., changing the permutation of the training examples in a sentiment analysis prompt can change accuracy from near chance (54%) to near state-of-the-art (93%). This instability implies that GPT-3 users, who typically design prompts manually, cannot expect to consistently obtain good accuracy……; and page 3, wherein GPT-3’s accuracy depends highly on both selection and permutation of training examples…..For each set of training examples, we evaluate the accuracy for all possible permutations…Figure 2 shows the results for SST-2 (4-shot, GPT-3 2.7B). Surprisingly, varying the permutation can be as important, or even more important, than which training examples are chosen. For example, varying the permutation of the training examples can cause accuracy to go from near chance (54.3%) to near state-of-the-art (93.4%). For a qualitative example of the sensitivity to permutations, see Table 2 in Appendix A.). One of ordinary skill in the art would have recognized that applying the known technique of Zhao would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Analogous art Gardner discloses a first reordered set data inputs in which the plurality of data inputs are reordered relative to the first order (see Gardner, para [0081], wherein sentence reordering to improve coherence, replacing named entities with placeholders to control abstraction, inserting keywords and example outputs to guide the LLM, and providing instructions specifying the target abstraction level; and para [0149], wherein the order of the sentences from the original content can be rearranged in the prompt to improve context and logical flow for the LLM. Templates may determine optimal sentence positioning based on relationships between entities, topics, and other semantic factors). One of ordinary skill in the art would have recognized that applying the known technique of Gardner would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Regarding claims 3 and 12, Cheng, Zhao, and Gardner disclose the method of claim 2, wherein the one or more batch permutations, as set forth above with claim 1. Cheng et al. fails to explicitly disclose a second permutation of the plurality of data inputs, the second permutation including a second reordered set of data inputs in which the plurality of data inputs are reordered relative to the first order. Analogous art Zhao discloses a second permutation of the plurality of data inputs, the second permutation (see Zhao, page 1, wherein a prompt contains three components: a format, a set of training examples, and a permutation (ordering) for those examples. We show that different choices for these factors can lead to highly different accuracies, e.g., changing the permutation of the training examples in a sentiment analysis prompt can change accuracy from near chance (54%) to near state-of-the-art (93%). This instability implies that GPT-3 users, who typically design prompts manually, cannot expect to consistently obtain good accuracy……; and page 3, wherein GPT-3’s accuracy depends highly on both selection and permutation of training examples…..For each set of training examples, we evaluate the accuracy for all possible permutations…Figure 2 shows the results for SST-2 (4-shot, GPT-3 2.7B). Surprisingly, varying the permutation can be as important, or even more important, than which training examples are chosen. For example, varying the permutation of the training examples can cause accuracy to go from near chance (54.3%) to near state-of-the-art (93.4%). For a qualitative example of the sensitivity to permutations, see Table 2 in Appendix A.). One of ordinary skill in the art would have recognized that applying the known technique of Zhao would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Analogous art Gardner discloses a second reordered set of data inputs in which the plurality of data inputs are reordered relative to the first order (see Gardner, paras [0185]-[0193], wherein the trained models may employ diverse techniques to optimize prompts, including one or more of the following: Content Reordering-Restructures long input text for better context and coherence. Improves LLM comprehension;…..Entity Replacement-Substitutes non-critical entities with placeholders to control abstraction. Allows focusing LLM on key information;….Keyword Insertion-Inserts keywords like "summarize" and "paraphrase" to guide LLM behaver; and para [0149], wherein the order of the sentences from the original content can be rearranged in the prompt to improve context and logical flow for the LLM. Templates may determine optimal sentence positioning based on relationships between entities, topics, and other semantic factors). One of ordinary skill in the art would have recognized that applying the known technique of Gardner would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Regarding claim 4, Cheng, Zhao, and Gardner disclose the method of claim 3, wherein the one or more batch permutations, as set forth above with claim 1. Cheng et al. fails to explicitly disclose a plurality of additional permutations of the plurality of data inputs, wherein the second set of outputs includes a set of outputs for each permutation from the first permutation, second permutation, and the plurality of additional permutations. Analogous art Zhao discloses a plurality of additional permutations of the plurality of data inputs, wherein the second set of outputs includes a set of outputs for each permutation from the first permutation, second permutation, and the plurality of additional permutations (see Zhao, page 3, wherein GPT-3’s accuracy depends highly on both selection and permutation of training examples…..For each set of training examples, we evaluate the accuracy for all possible permutations…Figure 2 shows the results for SST-2 (4-shot, GPT-3 2.7B). Surprisingly, varying the permutation can be as important, or even more important, than which training examples are chosen. For example, varying the permutation of the training examples can cause accuracy to go from near chance (54.3%) to near state-of-the-art (93.4%). For a qualitative example of the sensitivity to permutations, see Table 2 in Appendix A; and pages 4-5, wherein GPT-3’s accuracy varies across different training examples, permutations, and prompt formats. Concretely, we show that the variance arises because LMs are biased towards outputting answers that are (1) frequent in the prompt (majority label bias), (2) towards the end of the prompt (recency bias), and (3) common in the pre-training data (common token bias). One of ordinary skill in the art would have recognized that applying the known technique of Zhao would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Regarding claims 5 and 13, Cheng, Zhao, and Gardner disclose the method of claim 4. Cheng et al. fails to explicitly disclose further comprising determining a confidence value for each output from the first set of outputs and the second set of outputs. Analogous art Zhao discloses determining a confidence value for each output from the first set of outputs and the second set of outputs (see Zhao, page 5, wherein the output of GPT-3 is biased (its outputs are shifted), similar to how measurement devices such as voltage meters or weighing scales are biased. Just like how these devices require “calibration before use”, where the devices’ outputs are scaled/zeroed- out, we hope to apply a similar calibration procedure to LMs……aligning a model’s confidence estimate with its true accuracy). One of ordinary skill in the art would have recognized that applying the known technique of Zhao would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Regarding claims 6 and 14, Cheng, Zhao, and Gardner disclose the method of claim 1, wherein one or more of the batch permutations includes a reduced sets of data inputs (see Cheng, page 793, wherein one batch in a single inference run, so that it reduces the LLM inference time from N to N/b, where b is the number of samples in one batch). Regarding claims 8 and 16, Cheng, Zhao, and Gardner disclose the method of claim 1, wherein generating the batch prompt output, as set forth above with claim 1. Cheng et al. fails to explicitly disclose determining one or more weights for each output from the first set of outputs and the second set of outputs based on a confidence score determined by the large language model with respect to each output from the first set of outputs and the second set of outputs. Analogous art Gardner discloses determining one or more weights for each output from the first set of outputs and the second set of outputs based on a confidence score determined by the large language model with respect to each output from the first set of outputs and the second set of outputs (see Gardner, paras [0168]-[0173], wherein the model shares encoder weights between tasks while maintaining tasks pacific decoders. The primary task predicts prompt text conditioned on the encoder. Secondary tasks predict properties like target length….. paras [0154]-[0155], wherein prompt may be engineered to request inferences with over 80% confidence and/or appropriate external sources. At -50% zoom, the prompt may be engineered to request inferences over 50% confidence…..abstraction capabilities of large language models (LLMs ). On innovation is a prompt engineering model that is trained to dynamically construct prompts tailored to the nuances of the input content and desired level of abstraction.). One of ordinary skill in the art would have recognized that applying the known technique of Gardner would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Regarding claims 9 and 17, Cheng, Zhao, and Gardner disclose the method of claim 8, wherein the one or more weights, as set forth above with claim 8. Cheng et al. fails to explicitly disclose one or more high weight values based on the large language model determining that corresponding outputs are likely accurate, and the one or more weights include one or more low weight values based on the large language model determining that the that corresponding outputs are likely inaccurate. Analogous art Gardner discloses one or more high weight values based on the large language model determining that corresponding outputs are likely accurate, and the one or more weights include one or more low weight values based on the large language model determining that the that corresponding outputs are likely inaccurate (see Gardner, para [0275], wherein comparing confidence ratings across models and preferentially weighting higher confidence responses.…..; para [0157], wherein during training, the model learns prompt engineering strategies that elicit the most accurate summarization from the LLM for each content type and abstraction level; paras [0154]-[0155], wherein prompt may be engineered to request inferences with over 80% confidence and/or appropriate external sources. At -50% zoom, the prompt may be engineered to request inferences over 50% confidence…..abstraction capabilities of large language models (LLMs ). On innovation is a prompt engineering model that is trained to dynamically construct prompts tailored to the nuances of the input content and desired level of abstraction; para [0127], wherein Error rates, failures, and alerts highlight reliability issues). One of ordinary skill in the art would have recognized that applying the known technique of Gardner would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Regarding claim 10 is rejected based upon the same rationale as the rejection of claim 1, respectively, since it is a system claim corresponding to the method claim. Cheng et al. fails to explicitly disclose at least one processor and memory. Analogous art Gardner discloses additional feature at least one processor and memory (see Gardner, Fig. 8). One of ordinary skill in the art would have recognized that applying the known technique of Gardner would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Regarding claim 18 is rejected based upon the same rationale as the rejection of claim 1, respectively, since it is a CRM claim corresponding to the method claim. Cheng et al. fails to explicitly disclose a non-transitory computer readable medium. Analogous art Gardner discloses additional feature a non-transitory computer readable medium (see Gardner, Fig. 8). One of ordinary skill in the art would have recognized that applying the known technique of Gardner would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Allowable Subject Matter Regarding claims 7, 15, and 20 objected to as being dependent upon a rejected base claim, but it appears they would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and rewritten to overcome the 35 USC 101 rejection. Conclusion The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2012/0330647; US Pub No. 2024/0394641; US Pub No. 2024/0354792; US Pub No. 2024/0411994; US Pub No. 2023/0252224; US Pub No. 2024/0394289; US Pub No. 2019/0188612; H Ma, C Zhang, Y Bian, L Liu, Z Zhang, P Zhao, S Zhang, H Fu, Q Hu, B Wu et al. (Fairness-guided few-shot prompting for large language models) Advances in Neural Information Processing Systems, 2023•proceedings.neurips.cc (hereinafter Ma et al.); and V Liu, LB Chilton et al. (Design guidelines for prompt engineering text-to-image generative models) - Proceedings of the 2022 CHI conference on human …, 2022 - dl.acm.org (hereinafter Liu et al.). Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAFIZ A KASSIM whose telephone number is (571)272-8534. The examiner can normally be reached 9:00 - 5:00 PM. 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, Rutao Wu can be reached at 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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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. /HAFIZ A KASSIM/Primary Examiner, Art Unit 3623 05/22/2026 Application/Control Number: 18/534,299 Page 2 Art Unit: 3623 Application/Control Number: 18/534,299 Page 3 Art Unit: 3623 Application/Control Number: 18/534,299 Page 4 Art Unit: 3623 Application/Control Number: 18/534,299 Page 5 Art Unit: 3623 Application/Control Number: 18/534,299 Page 6 Art Unit: 3623 Application/Control Number: 18/534,299 Page 7 Art Unit: 3623 Application/Control Number: 18/534,299 Page 8 Art Unit: 3623 Application/Control Number: 18/534,299 Page 9 Art Unit: 3623 Application/Control Number: 18/534,299 Page 10 Art Unit: 3623 Application/Control Number: 18/534,299 Page 11 Art Unit: 3623 Application/Control Number: 18/534,299 Page 12 Art Unit: 3623 Application/Control Number: 18/534,299 Page 13 Art Unit: 3623 Application/Control Number: 18/534,299 Page 14 Art Unit: 3623 Application/Control Number: 18/534,299 Page 15 Art Unit: 3623 Application/Control Number: 18/534,299 Page 16 Art Unit: 3623 Application/Control Number: 18/534,299 Page 17 Art Unit: 3623 Application/Control Number: 18/534,299 Page 18 Art Unit: 3623 Application/Control Number: 18/534,299 Page 19 Art Unit: 3623 Application/Control Number: 18/534,299 Page 20 Art Unit: 3623
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