CTNF 18/476,455 CTNF 96330 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. Detailed Action This action is in response to the claims filed 9/28/2023: Claims 1 – 20 are pending. Claims 1, 8, and 15 are independent. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 2, 9, 16, 18, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Regarding claims 2, 9, and 16, claims 2, 9, and 16 recite “based on the first neuron having a run time that is statistically different ” where statistically different is indefinite. Neither the claims or instant specification place bounds on what it means to be statistically different such that the scope of the claim cannot reasonably be determined. In the interest of further examination the claim is interpreted as “based on a first neuron having a first runtime and a second neuron having a second runtime”. Regarding claim 20, “the system” lacks antecedent basis. “A system” or “the computer program product” are recommended. Claims 18 is rejected with respect to its dependence on rejected claim 16. Claim Rejections - 35 USC § 101 101 Rejection 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 USC § 101 because the claimed invention is directed to non-statutory subject matter. Regarding Claim 1: Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 1 is directed to an non-transitory computer-readable recording medium having stored therein a program that causes a computer to execute a process, which is directed to a product, one of the statutory categories. Step 2A Prong One Analysis: Claim 1 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass machine learning processing, including the following: an identification component that , employing an operating, multi-layered virtual computation module of looped neurons, identifies a first neuron of a first cluster of a first layer of the looped neurons as being an outlier neuron (observation, evaluation, and judgement), adjustment component that reassigns the outlier neuron from the first cluster to a second cluster of the first layer (observation, evaluation, and judgement) a scheduling component that, based on a dependency among layers of the multi-layered virtual computation module, including the first layer, adjusts a cross-layer functionality of the looped neurons for a workload currently being performed by the multi-layered virtual computation module (observation, evaluation, and judgement) Therefore, claim 1 recites an abstract idea which is a judicial exception. Step 2A Prong Two Analysis: Claim 1 recites additional elements “ a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise ”. However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Therefore, claim 1 is directed to a judicial exception. Step 2B Analysis: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 1 amount to no more than mere instructions to apply the judicial exception using a generic computer component. For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 8 and 15, which recite a system and a computer program product, respectively, as well as to dependent claims 2-7, 9-14, and 16-20. The additional limitations of the dependent claims are addressed briefly below: Dependent claims 2, 9, and 16 recite additional insignificant extra-solution activity of gathering and outputting data “ wherein the identifying, by the identification component, that the first neuron is an outlier neuron, is based on the first neuron having a run time that is statistically different than other neurons, of the looped neurons, of the first cluster” Dependent claims 3, 10, and 17 recites additional observation, evaluation, and judgement “ a prediction component that, employing an adjusted multi-layered virtual computation module resulting from the adjustment performed by the scheduling component, generates a prediction based on data input into the adjusted multi-layered virtual computation module.” Dependent claim 4, 11, and 18 recites additional observation, evaluation, and judgement “ for a plurality of clusters of the first layer and of additional layers of the layers of the multi-layered virtual computation module, other than the first cluster, the identification component identifies an additional outlier neuron for each cluster of the plurality of clusters of the first layer and of the additional layers; and for the plurality of clusters of the first layer and of the additional layers, the adjustment component reassigns each outlier neuron of the additional outlier neurons to a different cluster of a same layer, of the layers, comprising the cluster initially having the outlier neuron” . Dependent claims 5, 12, and 19 recite additional observation, evaluation, and judgement “ wherein the cross layer functionality is further adjustably varied, by the scheduling component, based on a dependency between neurons of different layers of the multi-layered virtual computation module” Dependent claims 6 and 13 recite additional observation, evaluation, and judgement “ a derived field extractor component that classifies derived fields corresponding to the looped neurons; and a neuron extractor component that, employing classifications of the classified derived fields, assigns the classifications to the looped neurons” Dependent claims 7, 14, and 20 recite additional observation, evaluation, and judgement “ a neuron clustering component that groups the looped neurons into clusters based on results of the assigning of the classifications to the looped neurons; and a layering component that defines the layers of the multi-layered virtual computation module by grouping the clusters into the layers ” Therefore, when considering the elements separately and in combination, they do not add significantly more to the inventive concept. Accordingly, claims 1-20 are rejected under 35 U.S.C. § 101. 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, 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. Claims 1, 3-8, 10-15, 17, and 19-20 are rejected under U.S.C. §103 as being unpatentable over the combination of Kauffmann (“From Clustering to Cluster Explanations via Neural Networks”, 2021) and Rakib (“Short Text Stream Clustering via Frequent Word Pairs and Reassignment of Outliers to Clusters”, 2020). PNG media_image1.png 594 1584 media_image1.png Greyscale FIG. 1 of Kauffmann PNG media_image2.png 346 1590 media_image2.png Greyscale FIG. 5 of Kauffmann Regarding claim 1, Kauffmann teaches A system, comprising: a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: ([p. 24] "We used the Python function time.process_time_ns to measure runtime. […] The number of clusters has limited impact on runtime. Deviations from our theoretical runtime analysis in Table 2 in the main text can be mainly attributed to side computations and to hardware optimization, such as cache optimization") an identification component that, employing an operating, multi-layered virtual computation module of looped neurons, ([Abstract] "We propose a new framework that can, for the first time, explain cluster assignments in terms of input features in an efficient and reliable manner. It is based on the novel insight that clustering models can be rewritten as neural networks—or ‘neuralized’" [p. 1 §1] "The method we propose, puts forward the novel insight that a broad range of clustering models can be rewritten, without retraining, as functionally equivalent neural networks, which then serve as a backbone to guide the explanation process. Technically, we suggest to apply the following two steps: (1) The cluster model is ‘neuralized’ by rewriting it as a functionally equivalent neural network with standard detection/pooling layers. (2) Cluster assignments formed at the output of the neural network are then propagated backwards using an LRP-type procedure (cf. [17], [21], [22]) until the input variables (e.g. pixels or words) are reached" Multi-layer neural network performed forwards and backwards interpreted as an operating multi-layered virtual computation module of looped (forward and backwards) neurons ) identifies a first neuron of a first cluster of a first layer of the looped neurons as being an outlier neuron; ([p. 4] "Rk is the ‘relevance’ of neuron hk to the cluster assignment fc" [p. 5] "(ui)i and (uj)j are sets of data points (or support vectors) representing the two clusters, Cc,Ck ⊂ N are the non overlapping sets of indices of support vectors that represent these clusters [...] hijk = w^T ijx +bij (layer 1) [...] jECk [...] iECc" [p. 6] "we must handle the case where some relevance lands on a deactivated (or weakly activated) neuron hijk, as the latter does not provide directionality in input space. Such special case can be handled by only propagating part of the relevance (and dissipating the rest), specifically, by performing the reassignment:" Deactivated or weakly activated neuron interpreted as outlier neuron of a first cluster of a first layer. Kaufmann explicitly indexes neurons by cluster member sets Cc and Ck. See also FIG. 5 ) an adjustment component that reassigns the outlier neuron from the first cluster [to a second cluster] of the first layer; and ([p. 4] "Rk is the ‘relevance’ of neuron hk to the cluster assignment fc" [p. 6] "we must handle the case where some relevance lands on a deactivated (or weakly activated) neuron hijk, as the latter does not provide directionality in input space. Such special case can be handled by only propagating part of the relevance (and dissipating the rest), specifically, by performing the reassignment [See Eqn. 10] The latter ensures that the relevance continuously converges to zero as the neuron hijk becomes deactivated" Kauffmann identifies a neuron level outlier condition: nonzero relevance landing on a deactivated or weakly activated neuron and then explicitly performs a reassignment of the neuron-wise relevance, the relevance being explicitly tied to a cluster contribution. specifically: in Kauffmann, cluster membership is computed from neuron-wise contributions such that reassigning said contributions necessarily reassigns the cluster. fc is maps to a specific cluster c, such that each neuron has a relevance score for each cluster. ) a scheduling component that, based on a dependency among layers of the multi-layered virtual computation module, including the first layer, ([p. 1] "Cluster assignments formed at the output of the neural network are then propagated backwards using an LRP-type procedure" [p. 7 FIG. 5] "propagation rules applied at each layer" [p. 6] "This lets us rewrite the full model as a the stacking of the L layers of the neural network Ψ with the neuralized k-means model defined in Proposition 1" The scheduling component is interpreted as synonymous with Kauffmann's NEON propagation procedure that applies layer-specific rules in sequential order ) adjusts a cross-layer functionality of the looped neurons for a workload currently being performed by the multi-layered virtual computation module. ([p. 6] "The directional redistribution in the first layer can be achieved using Eq. (4)." [p. 7] "we perform the reassignment Rk ← Rk · (hk/fc). For further propagation of relevance scores into the neural network, we notice that all layers up to layer L+1 form a standard neural network. Hence, propagation rules designed in the context of neural network are applicable" Kauffmann adjusts cross-layer functionality through layer-wise relevance redistribution. When relevance lands on weak/deactivated neurons, Kauffmann reassigns neuron-wise relevance and then continues propagation into lower layers. That is a cross-layer adjustment because the reassignment changes how relevance is passed from the cluster-assignment output through pooling/detection layers and into the neural network ). However, Kauffmann does not explicitly teach an adjustment component that reassigns the outlier neuron from the first cluster to a second cluster . Rakib, in the same field of endeavor, teaches an adjustment component that reassigns the outlier neuron from the first cluster to a second cluster ([p. 3] "For each outlier removed from a cluster during outlier removal […] We assign the outlier to the cluster with the highest cosine similarity [...] Otherwise, we create a new cluster containing this outlier. Thus the outliers are assigned to clusters based on the dynamic similarity thresholds. For every outlier added to a new or an existing cluster, we create (or update) the 𝐶𝐹 vector, cluster vector and cluster center to reflect the addition of this outlier to a new or an existing cluster"). Kauffmann as well as Rakib are directed towards machine learning clustering. Therefore, Kauffmann as well as Rakib are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Kauffmann with the teachings of Rakib by applying Rakib's known outlier reassignment within Kauffmann's neuralized clustering model necessarily results in reassignment of the neuron's cluster-specific contribution from a first cluster to a second cluster. Rakib provides as additional motivation for combination ([Abstract] “the proposed method efficiently deals with the concept drift problem. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art short text stream clustering algorithms by a statistically significant margin on several short text datasets”). This motivation for combination also applies to the remaining claims which depend on this combination. Regarding claim 3, the combination of Kauffmann and Rakib teaches The system of claim 1, further comprising: a prediction component that, employing an adjusted multi-layered virtual computation module resulting from the adjustment performed by the scheduling component, (Kauffmann [p. 6] "The directional redistribution in the first layer can be achieved using Eq. (4)." [p. 7] "we perform the reassignment Rk ← Rk · (hk/fc). For further propagation of relevance scores into the neural network, we notice that all layers up to layer L+1 form a standard neural network. Hence, propagation rules designed in the context of neural network are applicable" Kauffmann adjusts cross-layer functionality through layer-wise relevance redistribution. When relevance lands on weak/deactivated neurons, Kauffmann reassigns neuron-wise relevance and then continues propagation into lower layers. That is a cross-layer adjustment because the reassignment changes how relevance is passed from the cluster-assignment output through pooling/detection layers and into the neural network ) generates a prediction based on data input into the adjusted multi-layered virtual computation module. (Kauffmann [Abstract] "Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features"). Regarding claim 4, the combination of Kauffmann and Rakib teaches The system of claim 1, wherein for a plurality of clusters of the first layer and of additional layers of the layers of the multi-layered virtual computation module, other than the first cluster, (Kauffmann [p. 9] "For each cluster, we consider the model outputs fc, and propagate these outputs backward through the network using LRP" [p. 3] "Layer-wise Relevance Propagation (LRP)" [p. 6] "This lets us rewrite the full model as a the stacking of the L layers of the neural network Ψ with the neuralized k-means model defined in Proposition 1 […] (layers 1...L)" See FIG. 5 ) the identification component identifies an additional outlier neuron for each cluster of the plurality of clusters of the first layer and of the additional layers; and (Kauffmann [p. 4] "Rk is the ‘relevance’ of neuron hk to the cluster assignment fc" [p. 6] "where some relevance lands on a deactivated (or weakly activated) neuron hijk" [p. 7] " deactivated neurons " Kauffmann explicitly computes the cluster wise relevance for each neuron and explicitly anticipates having a plurality (additional) of deactivated neurons (outliers) ) for the plurality of clusters of the first layer and of the additional layers, the adjustment component reassigns each outlier neuron of the additional outlier neurons to a different cluster of a same layer, of the layers, comprising the cluster initially having the outlier neuron. (Kauffmann [p. 4] "Rk is the ‘relevance’ of neuron hk to the cluster assignment fc" [p. 6] "we must handle the case where some relevance lands on a deactivated (or weakly activated) neuron hijk, as the latter does not provide directionality in input space. Such special case can be handled by only propagating part of the relevance (and dissipating the rest), specifically, by performing the reassignment [See Eqn. 10] The latter ensures that the relevance continuously converges to zero as the neuron hijk becomes deactivated" Kauffmann identifies a neuron level outlier condition: nonzero relevance landing on a deactivated or weakly activated neuron and then explicitly performs a reassignment of the neuron-wise relevance, the relevance being explicitly tied to a cluster contribution. specifically: in Kauffmann, cluster membership is computed from neuron-wise contributions such that reassigning said contributions necessarily reassigns the cluster. fc is maps to a specific cluster c, such that each neuron has a relevance score for each cluster. ). Regarding claim 5, the combination of Kauffmann and Rakib teaches The system of claim 1, wherein the cross layer functionality is further adjustably varied, by the scheduling component, based on a dependency between neurons of different layers of the multi-layered virtual computation module. (Kauffmann [Abstract] "clustering models can be rewritten as neural networks—or ‘neuralized’. Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features. Several showcases demonstrate the ability of our method to assess the quality of learned clusters and to extract novel insights from the analyzed data and representations" [p. 4] "Propagation of the Cluster Assignment [...] LRP applies propagation rules that redistribute the quantity to explain from layer to layer"). Regarding claim 6, the combination of Kauffmann and Rakib teaches The system of claim 1, further comprising: a derived field extractor component that classifies derived fields corresponding to the looped neurons; and (Kauffmann [p. 8] "Our second showcase example demonstrates how cluster explanations can be applied beyond clusters assessment, in particular, how it can be used as a way of getting insights into some given data representation Ψ" Extracted data features/representations in Kauffmann interpreted as synonymous with derived fields corresponding to the looped neurons ) a neuron extractor component that, employing classifications of the classified derived fields, assigns the classifications to the looped neurons. (Kauffmann [Abstract] "clustering models can be rewritten as neural networks—or ‘neuralized’. Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features. Several showcases demonstrate the ability of our method to assess the quality of learned clusters and to extract novel insights from the analyzed data and representations"). Regarding claim 7, the combination of Kauffmann and Rakib teaches The system of claim 6, further comprising: a neuron clustering component that groups the looped neurons into clusters based on results of the assigning of the classifications to the looped neurons; and (Kauffmann [p. 3] "The k-means model assigns points to clusters based on their distance to each centroid" [Abstract] "clustering models can be rewritten as neural networks—or ‘neuralized’" [p. 10] "the baselines we use were originally proposed for explaining classification") a layering component that defines the layers of the multi-layered virtual computation module by grouping the clusters into the layers. (Kauffmann [p. 3] "Layer-wise Relevance Propagation (LRP)" [p. 6] "(layers 1...L)" See also FIG. 5 ). Regarding claims 8 and 10-14, claims 8 and 10-14 are directed towards the method performed by the system of claims 1 and 3-7. Therefore, the rejections applied to claims 1 and 3-7 also apply to claims 8 and 10-14. Regarding claim 15, claim 15 is directed towards a computer program product for performing the method of claim 1. Therefore, the rejection applied to claim 1 also applies to claim 15. Claim 15 also recites additional elements A computer program product facilitating a process to optimize performance of an operating neural network (Kauffmann [Abstract] "clustering models can be rewritten as neural networks—or ‘neuralized’" [p. 6] "Clustering solutions produced by [14], [15] optimize a hard k-means objective based on distances in feature space" [p. 7] "the cluster assignment converges to a local optimum with the final assignment visualized in Fig. 6 (middle)" [p. 12] 'optimization of the NEON hyperparameters"). Similarly, regarding claims 17 and 19-20, claims 17 and 19-20 are directed towards a computer program product for performing the methods of claims 3, 5 and 7, respectively. Therefore, the rejections applied to claims 3, 5 and 7 also apply to claims 17 and 19-20. Claims 2, 9, 16, and 18 are rejected under U.S.C. §103 as being unpatentable over the combination of Kauffmann and Rakib and in further view of Shen (“HALP: HARDWARE-AWARE LATENCY PRUNING”, 2021). Regarding claim 2, the combination of Kauffmann and Rakib teaches The system of claim 1. However, the combination of Kauffmann and Rakib doesn't explicitly teach wherein the identifying, by the identification component, that the first neuron is an outlier neuron, is based on the first neuron having a run time that is statistically different than other neurons, of the looped neurons, of the first cluster. Shen, in the same field of endeavor, teaches identifying, by the identification component, that the first neuron is an outlier neuron, is based on the first neuron having a run time that is statistically different than other neurons, of the looped neurons, of the first cluster. ([p. 2] "Pruning different layers in the deep neural network will result in different accuracy-latency trade-off." [p. 4] "we rank the neurons in the lth layer according to their importance score in a descending order and denote the importance score of the jth-ranked neuron as Ij [...] The latency contribution of the j-th neuron in the l-th layer can also be computed using the entries in the look up table as"). The combination of Kauffmann and Rakib as well as Shen are directed towards neural network neuron importance determination. Therefore, the combination of Kauffmann and Rakib as well as Shen are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Kauffmann and Rakib with the teachings of Shen by incorporating the latency aware importance score for determining an outlier for either pruning or reassignment. Shen provides as additional motivation for combination ([p. 9 §V] “We proposed hardware-aware latency pruning (HALP) that focuses on structured pruning for under lying hardware towards latency budgets. [...] we have shown the efficiency and efficacy of HALP by showing consistent improvements over state-of-the-art methods”). Regarding claim 9, claim 9 is directed towards a system for performing the method of claim 2. Therefore, the rejection applied to claim 2 also applies to claim 9. Regarding claims 16 and 18, claims 16 and 18 are directed towards a computer program product for performing the methods of claims 2 and 4, respectively. Therefore, the rejection applied to claims 2 and 4 also applies to claims 16 and 18. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Montavon (“Explaining the Predictions of Unsupervised Learning Models”, 2022) is directed towards clustering neural network nodes . Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIDNEY VINCENT BOSTWICK whose telephone number is (571)272-4720. The examiner can normally be reached M-F 7:30am-5:00pm EST. 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, Miranda Huang can be reached on (571)270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124 Application/Control Number: 18/476,455 Page 2 Art Unit: 2124 Application/Control Number: 18/476,455 Page 3 Art Unit: 2124 Application/Control Number: 18/476,455 Page 4 Art Unit: 2124 Application/Control Number: 18/476,455 Page 5 Art Unit: 2124 Application/Control Number: 18/476,455 Page 6 Art Unit: 2124 Application/Control Number: 18/476,455 Page 7 Art Unit: 2124 Application/Control Number: 18/476,455 Page 8 Art Unit: 2124 Application/Control Number: 18/476,455 Page 9 Art Unit: 2124 Application/Control Number: 18/476,455 Page 10 Art Unit: 2124 Application/Control Number: 18/476,455 Page 11 Art Unit: 2124 Application/Control Number: 18/476,455 Page 12 Art Unit: 2124 Application/Control Number: 18/476,455 Page 13 Art Unit: 2124 Application/Control Number: 18/476,455 Page 14 Art Unit: 2124 Application/Control Number: 18/476,455 Page 15 Art Unit: 2124 Application/Control Number: 18/476,455 Page 16 Art Unit: 2124 Application/Control Number: 18/476,455 Page 17 Art Unit: 2124 Application/Control Number: 18/476,455 Page 18 Art Unit: 2124 Application/Control Number: 18/476,455 Page 19 Art Unit: 2124 Application/Control Number: 18/476,455 Page 20 Art Unit: 2124 Application/Control Number: 18/476,455 Page 21 Art Unit: 2124
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