CTNF 18/429,336 CTNF 94458 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 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. 07-21-aia AIA Claim s 1, 3-4, 8-9 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Barrow et al. (US 20240419921 A1) in view of Chen et al. (US 20230359678 A1) . Regarding Claim 1 , Barrow (US 20240419921 A1) teaches a method of generating data visualizations for interactive recommender systems for analytic assets, the method comprising: obtaining, from a recommender system that is trained to generate analytic asset recommendations, a plurality of recommendations to destination nodes for a source node of an input graph ( para [0087] “As an example, as shown in FIG. 6, the digital syntopical reading system 106 extracts a viewpoint 612 which indicates a topic (e.g., “Nuclear Energy”), an aspect (e.g., A.sub.1), a stance for the viewpoint (e.g., a positive stance) based on a subset of claims from the claim relation graph (e.g., claims C.sub.3 and C.sub.4).” viewpoints (i.e., recommendations) are extracted for nodes (i.e., destination nodes). ), wherein the input graph includes a plurality of nodes including the source node and the destination node ( para [0075] “As further shown in the claim relation graph 510, the digital syntopical reading system 106 determines relationships (as edges) between non-shaded claim nodes (from a second digital content item) and shaded claim nodes (from a first digital content item).” Non-shaded (i.e., source node). Shaded (i.e. destination node). ), wherein each node of the plurality of nodes stores metadata for a respective analytic asset of a plurality of analytic assets ( para [0064] “As mentioned above, in one or more embodiments, the digital syntopical reading system 106 utilizes sentence transformers with claims identified in a collection of content to embed the claims within a metric space as embedded claim nodes.” ), and wherein the input graph encodes asset lineage that captures relationships between the plurality of analytic assets ( para [0069] “To illustrate, in one or more embodiments, the digital syntopical reading system 106 utilizes sentence transformers to encode claims in a metric space. In some embodiments, the digital syntopical reading system 106 trains sentence transformers to (or utilizes sentence transformers trained to) encode individual sentences (e.g., claims) in a metric space such that vector similarity measures between the embedded sentences capture meaningful relationships.” ); generating a data visualization for the plurality of recommendations, wherein the data visualization includes (i) a summary of the plurality of recommendations to the destination nodes ( para [0122] “In response to the query 1006, the digital syntopical reading system 106 retrieves digital content items and displays a content item representation 1008 (e.g., a link to a content item, a download option for a content item, a description of a content item) based on viewpoints (as described above). In addition, the digital syntopical reading system 106 utilizes viewpoints associated with the content item representation 1008 to display viewpoint information (e.g., a graphical user element 1010 indicating a stance distribution and a graphical user element 1012 indicating a viewpoint coverage).” Figure 13 shows a summary option ), (ii) a comparison of the destination nodes ( para [0077] “As mentioned above, the digital syntopical reading system 106 utilizes a similarity measure (e.g., a vector similarity operation) to determine relationships between claim nodes in a metric space (e.g., to generate a claim relation graph).” ), and (iii) a set of factors that contributed to one or more recommendations of the plurality of recommendations ( para [0057] “Furthermore, as shown in FIG. 2, the digital syntopical reading system 106 displays the search result on the graphical user interface 212 with the viewpoint information 216 to indicate stance distributions associated with the content item (from the extracted viewpoints 210) and viewpoint coverage within the content item (e.g., a quantity of viewpoints associated with the content item).” ); and displaying the data visualization using a graphical user interface, wherein the graphical user interface includes a data region, a recommendation overview region and a recommendation detail region, wherein (i) the data region includes the summary of the plurality of recommendations to the destination nodes ( para [0122] “In response to the query 1006, the digital syntopical reading system 106 retrieves digital content items and displays a content item representation 1008 (e.g., a link to a content item, a download option for a content item, a description of a content item) based on viewpoints (as described above). In addition, the digital syntopical reading system 106 utilizes viewpoints associated with the content item representation 1008 to display viewpoint information (e.g., a graphical user element 1010 indicating a stance distribution and a graphical user element 1012 indicating a viewpoint coverage).” Figure 13 shows a summary option ), and (iii) the recommendation detail region includes the set of factors that contributed to the one or more recommendations of the plurality of recommendations ( para [0057] “Furthermore, as shown in FIG. 2, the digital syntopical reading system 106 displays the search result on the graphical user interface 212 with the viewpoint information 216 to indicate stance distributions associated with the content item (from the extracted viewpoints 210) and viewpoint coverage within the content item (e.g., a quantity of viewpoints associated with the content item).” ). Barrow does not explicitly disclose (ii) the recommendation overview region includes the comparison of the destination nodes, However, Chen (US 20230359678 A1) teaches (ii) the recommendation overview region includes the comparison of the destination nodes ( para “[0099] The second training structure graph may refer to a unary graph of content node elements, which may display potential associations between the content nodes.” ), It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the graph neural network of Barrow with the method of displaying node relationships of Chen. Doing so would allow for representing the correlation and similarity between the content nodes (Chen para [0099] ). Regarding Claim 3 , Barrow and Chen teach the method of claim 1. Barrow further teaches further comprising: in response to detecting a user input in the data region, recording a prediction quality for one or more recommendations ( para [0148] “In addition, in one or more embodiments, the digital syntopical reading system 106 utilizes syntopical reading (via viewpoint reconstruction as described above) to analyze user feedback. For example, the digital syntopical reading system 106 extracts claims (or statements) from user feedback (e.g., customer reviews, survey feedback) and extracts viewpoint information for the claims from the user feedback in accordance with one or more embodiments herein.” ). Regarding Claim 4 , Barrow and Chen teach the method of claim 1. Barrow further teaches further comprising: displaying, in the data region, features and/or properties of the destination nodes ( para [0058] “Although FIG. 2 illustrates a search result with a representation of a content item and viewpoint information, the digital syntopical reading system 106, in one or more embodiments, displays various numbers of content item representations and various viewpoint information for the content items. In addition, in certain instances, the digital syntopical reading system 106 displays viewpoints corresponding to the content items (e.g., via stances, topics, aspects) for the content items.” ). Regarding Claim 8 , Barrow and Chen teach the method of claim 1. Barrow further teaches wherein the comparison includes predictive probabilities and node properties for the destination nodes ( para [0104] “In order to identify a representative claim from the cluster of claims, the digital syntopical reading system 106, in some instances, scores and ranks the claims in the cluster (to rank claims based on similarity to other claims in the cluster, such as cosine similarities).” Score (i.e., probabilities). ). Regarding Claim 9 , Barrow and Chen teach the method of claim 1. Barrow further teaches wherein the comparison includes relationships between distributions of prediction probabilities ( para [0142] “Furthermore, as shown in FIG. 16, the digital syntopical reading system 106 also displays a graphical user element 1614 indicating a stance distribution (as described above) from social media posts in relation to the claims (or claim) in the digital content item 1606.” ) and path length between the source node and the destination nodes ( para [0170] “In some cases, the series of acts 1900 include generating a pairwise cosine similarity distance between the claim node and the approximate nearest neighbor claim node for the edge.” ). Regarding Claim 19 , Claim 19 is the system corresponding to the method of claim 1. Claim 19 is substantially similar to claim 1 and is rejected on the same grounds. Regarding Claim 20 , Claim 20 is the computer readable storage medium corresponding to the method of claim 1. Claim 20 is substantially similar to claim 1 and is rejected on the same grounds . 07-21-aia AIA Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barrow/Chen , as applied above, and further in view of Woelki et al. (US 11966230 B1) . Regarding Claim 2 , Barrow and Chen teach the method of claim 1. Barrow and Chen do not explicitly disclose further comprising: displaying, in the data region, prediction probabilities for the plurality of recommendations. However, Woelki (US 11966230 B1) teaches displaying, in the data region, prediction probabilities for the plurality of recommendations ( col. 7 lines 11-16; “The prediction probability and/or the interacting object probability can be utilized to determine a progress bar 134 associated with the bounding box 124. The progress bar 134 can be displayed as part of the bounding box 124, with a level of progress associated with the progress bar being associated with a level of the prediction probability.” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the graph neural network of Barrow with the prediction probability of Woelki. Doing so would allow for associating a level of progress with the prediction probability (Woelki col. 7 lines 11-16; ) . 07-21-aia AIA Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barrow/Chen , as applied above, and further in view of Tokarev et al. (US-20210042589-A1) . Regarding Claim 5 , Barrow and Chen teach the method of claim 1. Barrow further teaches further comprising: displaying, in the table view, the destination nodes of predicted links ( para [0057] “For instance, as shown in FIG. 2, the digital syntopical reading system 106 displays the search result on the graphical user interface 212 with the representation of the content item 214 as a link to a web article related to the query 202 (e.g., manned mission to mars).” ). Barrow and Chen do not explicitly disclose displaying, in the data region, a node selection widget and a table view; in response to a user input via the node selection widget, selecting the source node from the input graph and displaying basic properties of the source node; and However, Tokarev (US 20210042589 A1) teaches displaying, in the data region, a node selection widget and a table view ( para [0054] “The one or more topic nodes may be displayed to the user over the GUI, allowing the user to select any one or more of the topic nodes.” ); in response to a user input via the node selection widget, selecting the source node from the input graph and displaying basic properties of the source node ( para [0054] “Based on the selection of the one or more topic nodes, a dashboard is generated which includes a plurality of widgets, each widget corresponding to one or more queries.” ); and It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the graph neural network of Barrow with the widget of Tokarev. Doing so would allow for displaying one or more data sources associated with the node (Tokarev para [0055] ) . 07-21-aia AIA Claim s 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barrow/Chen/Tokarev , as applied above, and further in view of Palumbo et al. (US 20240346080 A1) . Regarding Claim 6 , Barrow, Chen, and Tokarev teach the method of claim 5. Barrow and Chen do not explicitly disclose further comprising: displaying, in the table view, derived properties of the input graph including information shortest path between two nodes and communities within the input graph. However, Palumbo (US 20240346080 A1) teaches further comprising: displaying, in the table view, derived properties of the input graph including information shortest path between two nodes and communities within the input graph ( para [0091] “providing (e.g., in a display) a suggested query corresponding to the determined nearest neighbor node (e.g., a user query corresponds to a first node, and the system recommends a second node as a search term in accordance with the second node being closest to the first node in the vector space).” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the graph neural network of Barrow with the method of determining the nearest neighbor node of Palumbo. Doing so would allow for retrieving results with the highest level of similarity to the original node selected by the user (Palumbo para [0091] ). Regarding Claim 7 , Barrow, Chen, Tokarev, and Palumbo teach the method of claim 6. Barrow further teaches wherein the derived properties include centrality attributes for nodes of the input graph ( para [0028] “In addition, in one or more embodiments, the digital syntopical reading system also utilizes clustering and ranking to identify a central claim node for the claim node and utilizes the central claim node as a topic for the claim node.” para [0172] “Furthermore, in one or more implementations, the series of acts 1900 include determining a topic of the viewpoint by identifying a central claim from a cluster of claim nodes in the claim relation graph.” ) and Palumbo further teaches graph attributes derived from relationship between multiple nodes including shortest path and community information ( para [0091] “providing (e.g., in a display) a suggested query corresponding to the determined nearest neighbor node (e.g., a user query corresponds to a first node, and the system recommends a second node as a search term in accordance with the second node being closest to the first node in the vector space).” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the graph neural network of Barrow with the method of determining the nearest neighbor node of Palumbo. Doing so would allow for retrieving results with the highest level of similarity to the original node selected by the user (Palumbo para [0091] ) . 07-21-aia AIA Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barrow/Chen , as applied above, and further in view of Mulchandani et al. (US-20230153659-A1) . Regarding Claim 10 , Barrow and Chen teach the method of claim 1. Barrow and Chen does not explicitly disclose wherein the recommendation overview region comprises (i) a probability distribution region based on node type, and (ii) a recommendation attribute region that provides further details of relationship between probabilities and attributes from the input graph, wherein recommendation probability coordinates views in the probability distribution region and the recommendation attribute region, thereby helping a user to compare recommendations. However, Mulchandani (US 20230153659 A1) teaches wherein the recommendation overview region comprises (i) a probability distribution region based on node type ( para [0069] “determining, by the one or more processors and based on the knowledge graph, the first probability distribution, and the second probability distribution, a third probability distribution for the third node; and causing, by the one or more processors, presentation of a user interface comprising at least a portion of the third probability distribution.” ), and (ii) a recommendation attribute region that provides further details of relationship between probabilities and attributes from the input graph, wherein recommendation probability coordinates views in the probability distribution region and the recommendation attribute region, thereby helping a user to compare recommendations ( para [0046] “Thus, in the example of FIG. 6, the selected range for labor costs is $81 to $98 per 100 hours. The probabilistic distribution module 230 of FIG. 2 may generate the probability distribution for the values of a node of the knowledge graph based on user input received by interactive components 640, 650, and 670, indicating a range of values and a distribution curve shape.” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the graph neural network of Barrow with the graphical user interface of Mulchandani. Doing so would allow for deriving Key performance indicators (KPIs) which may be represented as nodes. Semantic nodes may allow for reasoning to uncover conclusions and derive recommended actions (Mulchandani para [0018] ) . 07-21-aia AIA Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barrow/Chen , as applied above, and further in view of Paredes et al. (US-20240386263-A1) . Regarding Claim 11 , Barrow and Chen teach the method of claim 1. Barrow and Chen do not explicitly disclose further comprising: selecting the plurality of recommendations from a representative sample obtained from a range of recommendation probabilities for the source node, based on a size of the input graph. However, Paredes (US 20240386263 A1) teaches selecting the plurality of recommendations from a representative sample obtained from a range of recommendation probabilities for the source node ( para [0060] “For instance, instead of predicting a resulting solution for the minor embedding problem, the prediction engine component 120, via the ML model 10, can predict the likelihood of each physical node occurring in a high-quality embedding for each logical node.” ), based on a size of the input graph ( para [0035] “The complexity of the minor embedding problem is in part due to the combinatorial explosion of potential embedding candidates, which can expand exponentially with the size of the logical graph.” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the graph neural network of Barrow with the prediction graph of Paredes. Doing so would allow for searching for the optimal solution (Paredes para [0060] ) . 07-21-aia AIA Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barrow/Chen , as applied above, and further in view of Taguchi et al. (US-20230140271-A1) . Regarding Claim 12 , Barrow and Chen teach the method of claim 1. Barrow and Chen do not explicitly disclose wherein the recommendation overview region comprises a probability histogram view and a multi-axis scatter plot view. However, Taguchi (US 20230140271 A1) teaches wherein the recommendation overview region comprises a probability histogram view ( para [0050] “Here, each of the first differential value distribution and the second differential value distribution is assumed to be, for example, a histogram, but is not limited thereto, and may be a discrete probability distribution, a probability density function, a cumulative histogram, a discrete cumulative probability distribution, a cumulative probability density function, or the like.” ) and a multi-axis scatter plot view ( para [0152] “A correspondence relationship is illustrated so as to connect corresponding data groups between the plot of the scatter diagram and the histogram of the differential value distribution.” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Barrow and Chen with the graphs of Taguchi. Doing so would allow for allow a user to visualize the data values to easily confirm maximum and minimum values (Taguchi para [0138] ) . 07-21-aia AIA Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barrow/Chen/Taguchi , as applied above, and further in view of Didari et al. (US-20230368507-A1) . Regarding Claim 13 , Barrow, Chen, and Taguchi teach the method of claim 12. Barrow, Chen, and Taguchi do not explicitly disclose wherein the probability histogram view displays recommendation probability distributions according to asset types, wherein the recommendation probability distributions allows users to evaluate a model’s confidence with respect to recommending an asset type. However, Didari (US 20230368507 A1) teaches wherein the probability histogram view displays recommendation probability distributions according to asset types, wherein the recommendation probability distributions allows users to evaluate a model’s confidence with respect to recommending an asset type ( para [0081] “Predicting the plurality of probability distributions by embodiments includes performing inference, using the segmentation model and for a first pixel of the plurality of unlabeled pixels, M times to form a histogram over the set of class labels (see FIG. 3). Embodiments also include normalizing the histogram by M for the first pixel to obtain a first probability distribution for the first pixel (FIG. 4).” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the histogram of Taguchi with the probability distribution of Didari. Doing so would allow for visualizing the probability distribution for each recommendation (Didari para [0081] ) . 07-21-aia AIA Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barrow/Chen/Taguchi , as applied above, and further in view of Wang et al. (US-20210279618-A1) . Regarding Claim 14 , Barrow, Chen, and Taguchi teach the method of claim 12. Barrow, Chen, and Taguchi do not explicitly disclose wherein the multi-axis scatter plot view displays bivariate relationships between recommendation probabilities and different node features and graph attributes. However, Wang (US 20210279618 A1) teaches wherein the multi-axis scatter plot view displays bivariate relationships between recommendation probabilities and different node features and graph attributes ( para [0099] “FIG. 6( c ) shows a scatter plot of the dominant class state distribution of nodes at one of the layers of a classification neural network as determined by the explainer learning machine,” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the scatter plot of Taguchi with the probability distribution of nodes of Wang. Doing so would allow for visualizing the importance of each node in the machine learning model (Wang para [0099] ) . 07-21-aia AIA Claim s 15-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barrow/Chen , as applied above, and further in view of Trim et al. (US-20190007414-A1) . Regarding Claim 15 , Barrow and Chen teach the method of claim 1. Barrow and Chen do not explicitly disclose wherein the recommendation detail region comprises two adjacent views including a wrapped two-dimensional array and an embedding projection that allow users to inspect recommendations they selected in other components of the graphical user interface and analyze them in more detail. However, Trim (US 20190007414 A1) teaches wherein the recommendation detail region comprises two adjacent views including a wrapped two-dimensional array and an embedding projection that allow users to inspect recommendations they selected in other components of the graphical user interface and analyze them in more detail ( para [0052] “For example, as will be illustrated in more detail below, some embodiments may be configured to display a data table (which may or may not be viewable) wherein each actor is included in a row of the table along with an ownership probability score for the actor.” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Barrow and Chen with the interface of Trim. Doing so would allow for ranking the list of assets based on a score (Trim para [0052] ). Regarding Claim 16 , Barrow, Chen, and Trim teach the method of claim 15. Trim further teaches wherein the wrapped two-dimensional array displays probability and feature as a row set, each row set corresponding to one recommendation and comprising a recommendation probability in a top row and feature in a bottom row ( para [0052] “For example, as will be illustrated in more detail below, some embodiments may be configured to display a data table (which may or may not be viewable) wherein each actor is included in a row of the table along with an ownership probability score for the actor.” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Barrow and Chen with the interface of Trim. Doing so would allow for ranking the list of assets based on a score (Trim para [0052] ). Regarding Claim 18 , Barrow, Chen, and Trim teach the method of claim 15. Barrow furher teaches wherein the embedding projection represents a multivariate summary of data that allows users to contextualize recommendations by using distance between points as a proxy for similarity for nodes ( para [0069] “For example, a metric space includes an abstract set (as a vector space) having points (as nodes) and a notion of distance between the points. Indeed, in one or more embodiments, the digital syntopical reading system 106 utilizes a distance between points (e.g., claim nodes) in a metric space to define (or determine) relationships between the points (e.g., relations or similarities between claim nodes).” ) . 07-21-aia AIA Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barrow/Chen , as applied above, and further in view of Collomosse et al. (US-20210397942-A1) . Regarding Claim 17 , Barrow, Chen, and Trim teach the method of claim 15. Barrow, Chen, and Trim do not explicitly disclose wherein the embedding projection uses shape to differentiate between the source node and the destination nodes. However, Collomosse (US 20210397942 A1) teaches wherein the embedding projection uses shape to differentiate between the source node and the destination nodes ( para [0005] “More specifically, in one or more embodiments, the systems and methods generate a graph representation of a UX layout. The graph representation may include nodes and edges, where each node corresponds to a different component of the user interface (e.g., buttons, icons, sliders, text, images, etc.) and each edge corresponds to how a given pair of nodes are related to one another (e.g., relative distance, aspect ratio, orientation, nesting, etc.).” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Barrow and Chen with the interface of Collomosse. Doing so would allow for encoding the structure of the graph representation into a searchable layout (Collomosse para [0005] ). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY K NGUYEN whose telephone number is (571)272-0217. The examiner can normally be reached Mon - Fri 7:00am-4:30pm. 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, Li B Zhen can be reached at 5712723768. 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. /HENRY NGUYEN/Examiner, Art Unit 2121 Application/Control Number: 18/429,336 Page 2 Art Unit: 2121 Application/Control Number: 18/429,336 Page 3 Art Unit: 2121 Application/Control Number: 18/429,336 Page 4 Art Unit: 2121 Application/Control Number: 18/429,336 Page 5 Art Unit: 2121 Application/Control Number: 18/429,336 Page 6 Art Unit: 2121 Application/Control Number: 18/429,336 Page 7 Art Unit: 2121 Application/Control Number: 18/429,336 Page 8 Art Unit: 2121 Application/Control Number: 18/429,336 Page 9 Art Unit: 2121 Application/Control Number: 18/429,336 Page 10 Art Unit: 2121 Application/Control Number: 18/429,336 Page 11 Art Unit: 2121 Application/Control Number: 18/429,336 Page 12 Art Unit: 2121 Application/Control Number: 18/429,336 Page 13 Art Unit: 2121 Application/Control Number: 18/429,336 Page 14 Art Unit: 2121 Application/Control Number: 18/429,336 Page 15 Art Unit: 2121 Application/Control Number: 18/429,336 Page 16 Art Unit: 2121 Application/Control Number: 18/429,336 Page 17 Art Unit: 2121 Application/Control Number: 18/429,336 Page 18 Art Unit: 2121 Application/Control Number: 18/429,336 Page 19 Art Unit: 2121 Application/Control Number: 18/429,336 Page 20 Art Unit: 2121 Application/Control Number: 18/429,336 Page 21 Art Unit: 2121 Application/Control Number: 18/429,336 Page 22 Art Unit: 2121
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