CTNF 18/452,740 CTNF 102081 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. Information Disclosure Statement Acknowledgement is made of the information disclosure statement (IDS) submitted on August 2. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “a provision unit configured to provide training data…” in Claims 6 (7, 8, 9 in virtue of inheritance of the parent claim’s limitations) and 10 Regarding claims 6 and 10 and the above-noted three-prong test, the recited provision unit is a generic placeholder, configured to provide training data is functional language, and there is no recitation of sufficient structure to perform the providing. “a training unit configured to train the generative adversarial network” in Claims 6 (7, 8, 9 in virtue of inheritance of the parent claim’s limitations) and 10 Regarding claims 6 and 10 and the above-noted three-prong test, the recited training unit is a generic placeholder, configured to train the generative adversarial network is functional language, and there is no recitation of sufficient structure to perform the providing. “a modeling unit configured to model a profile for controlling the controllable system” in Claim 10 Regarding claim 10 and the above-noted three-prong test, the recited modeling unit is a generic placeholder, configured to model a profile for controlling the controllable system is functional language, and there is no recitation of sufficient structure to perform the providing. “a control unit configured to control the controllable system” in Claim 10 Regarding claims 10 and the above-noted three-prong test, the recited control unit is a generic placeholder, configured to control the controllable system is functional language, and there is no recitation of sufficient structure to perform the providing. Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 1,2,3,5,6,7,8,10,11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention. The term “ corresponding data ” in Claims 1(Line 15, hereinafter L#), 5(L19), 6(L15), 10(L22), 11(L18) is unclear as there is no prior reference to a data made in the claims, which making it indefinite . For examination purposes, this term is interpreted to mean [[to be generated based on the training data and the performance of the realism assessment]] data . Claims 2,3,4,7,8,9 are dependent claims of indefinite Claims 1 ,5, 6, 10, 11. They are therefore rejected by virtue of their dependency. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1, 2, 4-7, 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Munoz et al. ( Munoz ), European Patent Application No. 19176670.8(EP3745309B1) in view of Binkowski et al. ( Binkowski ), Non-Patent Literature, “Demystifying MMD GANs.” As to independent Claim 1 , Munoz teaches a method for training a generative adversarial network, the generative adversarial network having a generator and a discriminator ( Munoz, paragraph[0043] Lines32,36: “The GAN as defined by the generative model data 142 may comprise a discriminative part” and “may further comprise a generative part” ), and the method comprising the following steps: providing training data for training the generative adversarial network ( Munoz, paragraph[0040], Lines15-16, 18-20: “The training system 100 is shown to comprise an input interface 120 configured to access generative model data 142” and “further configured to access training data 144 which may comprise a set of training instances for training the GAN… which the GAN is to be trained ); and iteratively training the generative adversarial network based on the training data, the training of the generative adversarial network including an alternating training of the generator and the discriminator based on the training data ( Munoz, paragraph[0045], Lines41-44, “train the GAN based on the training data 144 in a iterative manner in which the generative part may generate synthesized output instances from noisy samples drawn from a latent space, and in which the discriminative part may be trained to discriminate between input instances originating from the generative part and the training data” ), the training of the generator including a training of the generator based on the training data and results of realism assessments performed by the discriminator ( Munoz, paragraph[0045], Lines44-49, “the generative part may be trained to generate synthesized output instances from noisy samples which maximize a discrimination error when the discriminative part is applied to the synthesized output instances. Such training of the discriminative part and the generative part may be iterative, wherein in each iteration, weights of the discriminative part and the generative part may be updated, for example by computing gradients through backpropagation. Such training may be known per se, and may in the following also be referred to as an ’outer iterative loop’”) , and the training of the generative adversarial network in each iteration step including a generation of corresponding data by the generator ( Munoz, paragraph[0045], Lines44-49, mentioned above ), a performance of a realism assessment of the corresponding data by the discriminator ( Munoz, paragraph[0029], Lines46-47, “the discriminative part is configured to discriminate between input images originating from the generative part and the training data” ), and a performance of an additional realism assessment of at least one specific feature derived from the corresponding data by the discriminator ( Munoz, paragraph[0043], Lines46-47, “discriminative part configured to discriminate between input instances originating from the generative part and the training data and to output a classification and an attention mask identifying parts of a respective input instance which contribute to the classification of the respective input instance by the discriminative part” and [0025], Lines19-20, “The inner iterative loop may thus seek maximize mutual information between the attention mask produced by the discriminative part, and the proposed changes by the generative part ). Munoz , however, does not teach and the performance of the additional realism assessment of at least one specific feature derived from the corresponding data including an application of at least one deterministic function. Munoz teaches that a discriminator may output an attention map or embedding and that such learned discriminator outputs may be used to guide generator training, for example by including an information-theoretic term in the loss (attention/mutual-information based feedback). However, Munoz does not teach performing the additional realism assessment by applying a deterministic distribution-distance function to user-selected numeric features. In the same field of endeavor, Binkowski teaches the realism assessment including an application of at least one deterministic function ( Binkowski, Abstract, Lines 1-2, “We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic”, and pg3, Section 2.1, paragraph2, Lines4-5, “make use of a class of witness functions to distinguish between P and Q, choosing the function with the largest discrepancy in expectation over P,Q”, wherein Binkowski teaches the use of a kernel-based, deterministic distribution distance (Maximum Mean Discrepancy) as a critic to evaluate differences between generated and reference distributions; MMD thus provides a deterministic metric for realism assessment on feature representations). Munoz and Binkowski are analogous to the claimed invention as both are from the same field of endeavor of training GANs. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the Munoz ’s GAN training framework with an MMD-based deterministic realism assessment of Binkowski . The motivation to combine is to as recited by Binkowski ( Abstract, Lines10-12, “In experiments, the MMD GAN is able to employ a smaller critic network than the Wasserstein GAN, resulting in a simpler and faster-training algorithm with matching performance”) such that seek to reduce critic complexity and accelerate training by replacing or augmenting learned attention/embedding-based assessments with a kernel-based distribution distance, since MMD has been shown to enable smaller critics and faster training while retaining performance. As to dependent Claim 2 , Munoz does not teach but Binkowski further teaches wherein the application of at least one deterministic function for each specific feature of the at least one specific feature includes an evaluation of a distance between at least one distribution characterizing the specific feature and at least one distribution characterizing the specific feature derived from the corresponding data ( Binkowski, pg3, Section2.1, paragraph 2 and pg4, Section2.2, paragraph1, Lines4-5, “the samples first pass through a mapping function h, generally a convolutional network”, wherein computing a kernel-based distribution distance as a critic, and explicitly state that the MMD is computed on feature representations obtained via a mapping function h where the distribution of values produced by applying the feature extractor h to samples corresponds to the specific feature and the empirical distribution of h(x) over the training set corresponds to the corresponding data distribution). As to dependent Claim 4 , Munoz teaches wherein the generative adversarial network is trained to generate vehicle variables of a motor vehicle ( Munoz, paragraph[0072], Lines1-3, “Fig. 5 shows the data synthesis system 200 being used in an autonomous vehicle 310 which operates in an environment 300 to synthesize missing sensor data outside of the field of view of an image sensor 320. Based on the sensor data and the synthesized data, an electric motor 330 may be controlled” and paragraph[0029], “,the GAN may be configured and trained to be used with non-image data, such as measurement data obtained from non-image sensors” , wherein the synthesized data generated by the GAN is used to control an autonomous vehicle concluding the data must be vehicle-related parameters as the data also includes the non-sensor data). Claim 5 contains similar limitations of Claim 1 and thus rejected under the same rationale except the following limitations: As to independent claim 5 , Munoz teaches a method for controlling a controllable system, the method comprising the following steps: modeling a profile for controlling the controllable system, the profile being modeled by a generative adversarial network having a generator and a discriminator ( Munoz, paragraph[0006], Lines36-39, “For example, in autonomous driving, various types of sensor data may be fused to establish a 360-degree view around a vehicle. In case the field of view of different types of sensors is different, a generative model may be trained and used to synthesize missing sensor data outside of the field of view of a sensor based on the sensor data of another sensor”, paragraph[0043-0044], wherein the GAN is trained to synthesize missing sensor data or profile of a vehicle to control an autonomous vehicle). and controlling the controllable system based on the modeled profile for controlling the controllable system ( Munoz, paragraph[0072], Lines1-3, “Fig. 5 shows the data synthesis system 200 being used in an autonomous vehicle 310 which operates in an environment 300 to synthesize missing sensor data outside of the field of view of an image sensor 320. Based on the sensor data and the synthesized data, an electric motor 330 may be controlled” ). Claim 6 contains similar limitations of Claim 1 and thus rejected under the same rationale except the following limitations: As to independent Claim 6 , Munoz teaches a system for training ( Munoz, Figure 1, the training system) a generative adversarial network, the generative adversarial network having a generator and a discriminator ( Munoz, paragraph[0044], “the generative part of the GAN is also referred to as generative model or simply as generator or G, and the discriminative part of the GAN is also referred to as discriminative model or simply as discriminator or D”), and the system comprising: a provision unit configured to provide training data for training the generative adversarial network; and a training unit configured to train the generative adversarial network based on the training data ( Munoz, paragraph[0040], “Fig. 1 shows a training system 100 for training a generative adversarial network. The training system 100 is shown to comprise an input interface 120 configured to access generative model data 142 defining a GAN, which may initially be considered an ’untrained’ GAN in that parameters of the GAN may not yet be trained, or at least not yet trained to what may be considered a sufficient degree. The input interface 120 may be further configured to access training data 144 which may comprise a set of training instances for training the GAN, e.g., images, text segments, audio segments or other type of data on which the GAN is to be trained” and paragraph[0072], Lines1-3, “Fig. 5 shows the data synthesis system 200 being used in an autonomous vehicle 310 which operates in an environment 300 to synthesize missing sensor data outside of the field of view of an image sensor 320. Based on the sensor data and the synthesized data, an electric motor 330 may be controlled” wherein the input interface uses the data collected by the sensors). As to dependent Claim 7 and 9 , they contain similar limitations of Claim 2 and 4, respectively, thus rejected under the same rationale. Claim 10 contains similar limitations of Claims 1,5,6 and thus rejected under the same rationale except the following limitations: As to independent Claim 10 , Munoz teaches a system for controlling a controllable system, comprising: a modeling unit configured to model a profile for controlling the controllable system, the profile being modeled by a generative adversarial network having a generator and a discriminator, the generative adversarial having been trained using a system for training including: a provision unit configured to provide training data for training the generative adversarial network, and a training unit configured to train the generative adversarial network iteratively based on the training data, the training of the generative adversarial network in the training unit including an alternating training of the generator and the discriminator based on the training data, the training of the generator including a training of the generator based on the training data and results of realism assessments performed by the discriminator, and the training of the generative adversarial network in each iteration step including a generation of corresponding data by the generator, a performance of a realism assessment of the corresponding data by the discriminator, and a performance of an additional realism assessment of at least one specific feature derived from the corresponding data by the discriminator, and the performance of the additional realism assessment of at least one specific feature derived from the corresponding data including an application of at least one deterministic function; and a control unit configured to control the controllable system based on the modeled profile for controlling the controllable system ( Munoz, paragraph[0073], Lines30-34, “Any method described in this specification may be implemented on a computer as a computer implemented method, as dedicated hardware, or as a combination of both. As also illustrated in Fig.6, instructions for the computer, e.g., executable code, may be stored on a computer readable medium 400, e.g., in the form of a series 410 of machine-readable physical marks and/or as a series of elements having different electrical, e.g., magnetic, or optical properties or values. The executable code may be stored in a transitory or non-transitory manner” , wherein the modeling unit and the control unit are realized on the basis of a code that is stored in a memory, which is executed by a processor (Claim 5 contains similar limitations); Claim 6 contains similar limitations for the provision unit and the training unit; Claim 1 has similar limitation for the training of the GAN.) Claim 11 contains similar limitations of Claim 1 and thus rejected under the same rationale except the following limitations: As to independent Claim 11 , it contains similar limitations of Claim 1 in a non-transitory computer-readable data carrier on which is stored program code of a computer program ( Munoz, paragraph[0073], Lines30-34, “Any method described in this specification may be implemented on a computer as a computer implemented method, as dedicated hardware, or as a combination of both. As also illustrated in Fig.6, instructions for the computer, e.g., executable code, may be stored on a computer readable medium 400, e.g., in the form of a series 410 of machine-readable physical marks and/or as a series of elements having different electrical, e.g., magnetic, or optical properties or values. The executable code may be stored in a transitory or non-transitory manner”) . 07-21-aia AIA Claim s 3 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Munoz , in view of Binkowski , and further in view of Kristan et al. (Kristan), NPL “Multivariate Kernel Density Estimation”, published on 2010, pages: 8 . As to dependent Claim 3 , the combination of Munoz and Binkowski does not teach for each specific feature of the at least one specific feature storing at least one distribution for the specific feature, the at least one distribution for the specific feature being adjusted in each iteration step based on the corresponding data in order to generate at least one adjusted distribution. In the same field of endeavor, Kristan teaches this limitation ( Kristan, pg2, Lines7-15, “During online operation the sample distribution is updated by each new observation in essentially the following three steps (Figure 1a): In the first step, we update the sample model with the observed data point. In the second step, the updated model is used to recalculate the optimal bandwidth for the KDE– here, the main issue is how to calculate the bandwidth without having access to the previously observed individual samples. In the third step, the sample distribution is refined and compressed” and Equation25, wherein Equation 25, PNG media_image1.png 22 437 media_image1.png Greyscale denotes the model of samples observed which can corresponds to each specific feature, up to time-step (t-1) representing data at each iteration which can be stored and accessed; steps 1-3 show how at each iteration step do the followings: updates and adjusts a maintained sample distribution P S (x) at each iteration (Step1: update the sample distribution, Step2: re-estimate the bandwidth from the empirical covariance of P S , and Step3: refine/compress the model, P S , which generates an adjusted distribution based on the sample data). The combination of Munoz, Binkowski and Kristan teaches for each specific feature of the at least one specific feature, the performance of the realism assessment including an evaluation of a distance between the corresponding at least one adjusted distribution and an empirical probability distribution of the at least one specific feature (as mentioned in Claim1 and 2, Munoz and Binkowski teach the evaluation between the two distributions where the two distributions are the sample distribution and the empirical distribution during the additional realism assessment; as mentioned above, Kristan teaches to generating an adjusted distribution using the per-iteration online update thus combining them together can yield the same limitation). Munoz, Binkowski, and Kristan are analogous to the claimed invention as they are from the same field of endeavor of evaluating a distance between two distributions to reduce the complexity of the GAN training. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the training architecture of Munoz with the deterministic MMD kernel of Binkowski and the online kernel density estimation of Kristan. The motivation to combine is to as recited by Kristan (Kristan, Abstract, Lines12-16, “The results show that the online KDE generally achieves comparable performance to the batch approaches, while producing models with lower complexity and allowing online updating using only a single observation at a time” and pg2, paragraph1, Lines1-5, “Our main contribution is the approach for multivariate online kernel density estimation (oKDE), which enables construction of a multivariate density estimate by observing only a single sample at a time and automatically adjusts its complexity”) such that by incorporating online-updating iteration will make the evaluation and its reproduction faster, accurate, efficient while reducing the complexity. As to dependent Claim 8 , Munoz teaches wherein the system further comprises a memory in which, for each specific feature of the at least one specific feature, at least one distribution for the specific feature is stored, and the training of the generative adversarial network in the training unit for each specific feature of the at least one specific feature including an adjustment of the at least one distribution for the specific feature in each iteration step based on the corresponding data in order to generate at least one adjusted distribution, and, for each specific feature of the at least one specific feature, the performance of the additional realism assessment including an evaluation of a distance between the corresponding at least one adjusted distribution and an empirical probability distribution of the at least one specific feature ( Munoz, paragraph[0040][0045], “Fig. 1 shows a training system 100 for training a generative adversarial network. The training system 100 is shown to comprise an input interface 120 configured to access generative model data 142 defining a GAN, which may initially be considered an ’untrained’ GAN in that parameters of the GAN may not yet be trained, or at least not yet trained to what may be considered a sufficient degree. The input interface 120 may be further configured to access training data 144 which may comprise a set of training instances for training the GAN, e.g., images, text segments, audio segments or other type of data on which the GAN is to be trained” and “The training system 100 is further shown to comprise a processor subsystem 160 configured to train the GAN based on the training data 144 in a iterative manner …”, wherein it has a memory to store input data and the GAN training system, which can further store data generated from the batching process ) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mattyus et al (US patent, US 2019/0147320 A1), Salehi et al (NPL, "Generative Adversarial Networks (GANs): An overview of theoretical model, evaluation metrics, and recent developments", 17pages) and Silverman et al (NPL, “Density Estimation for statistics and data analysis”, 22pages) . Any inquiry concerning this communication or earlier communications from the examiner should be directed to DONG YOON JUNG whose telephone number is (571)270-0198. The examiner can normally be reached 8am-5pm. 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, Cesar Paula can be reached at (571) 272-4128. 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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. /DONG YOON JUNG/Examiner, Art Unit 2145 /CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145 Application/Control Number: 18/452,740 Page 2 Art Unit: 2145 Application/Control Number: 18/452,740 Page 3 Art Unit: 2145 Application/Control Number: 18/452,740 Page 4 Art Unit: 2145 Application/Control Number: 18/452,740 Page 5 Art Unit: 2145 Application/Control Number: 18/452,740 Page 6 Art Unit: 2145 Application/Control Number: 18/452,740 Page 7 Art Unit: 2145 Application/Control Number: 18/452,740 Page 8 Art Unit: 2145 Application/Control Number: 18/452,740 Page 9 Art Unit: 2145 Application/Control Number: 18/452,740 Page 10 Art Unit: 2145 Application/Control Number: 18/452,740 Page 11 Art Unit: 2145 Application/Control Number: 18/452,740 Page 12 Art Unit: 2145 Application/Control Number: 18/452,740 Page 13 Art Unit: 2145 Application/Control Number: 18/452,740 Page 14 Art Unit: 2145 Application/Control Number: 18/452,740 Page 15 Art Unit: 2145 Application/Control Number: 18/452,740 Page 16 Art Unit: 2145 Application/Control Number: 18/452,740 Page 17 Art Unit: 2145
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