CTNF 18/747,727 CTNF 80528 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. Claims 1-19 have been examined. P = paragraph, e.g. p5 = paragraph 5. Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ferone, USPAP 2025/0262975, and further in view of Cella, USPAP 2020/0294530 . As per claims 1, 18 and 19, Ferone discloses a method/computer readable media/system for serving by a distributed communication system a vehicle traveling from an origin to a destination, the distributed communication system comprising computer systems, referred to as initial set of computer systems, the method comprising: predicting a route of the vehicle from a current location of the vehicle to the destination; using resource information of the initial set of computer systems and the vehicle for selecting from the initial set of computer systems a set of computer systems that can provide machine learning based content to the vehicle along the route; predicting a content that can be requested at the vehicle at a specific set of one or more space-time points along the route (p’s 51-58, 66, 81; ab; fig’s 3c, 5b); Ferone discloses via p54: [0054] In a further embodiment, there are various levels of confirmation. For example, in a first-level confirmation, there is an originating or initial location of the electric vehicle 102 . As the vehicle begins to maneuver, at some point (e.g., first minutes or first movements of the electric vehicle 102 ), the processor 111 may determine an initial prediction of the predicted destination 106 . The processor 111 may repeatedly or continually check this prediction by monitoring the GPS 113 . If the prediction is no longer correct (based on the GPS 113 indicating a turn of the electric vehicle 102 , for example), the prediction can be changed, but this changed prediction is not yet confirmed. By the time the electric vehicle 102 is 50% (by example only, any other percentage may be used), of the way to the predicted destination 106 as determined by the processor 111 monitoring the GPS 113 , the processor 111 may ascertain a first-level confirmation of the predicted destination 106 . In response to ascertaining the first-level confirmation, the processor may take a first action at the predicted destination 106 , such as directing the smart electrical panel 133 to activate the HVAC system at the predicted destination 106 . This level of engagement can be based on an identity of one or more occupants of the electric vehicle 102 , a characteristic of one or more occupants of the electric vehicle 102 , an identity of one or more occupants of the predicted destination 106 , or a characteristic of one or more occupants of the predicted destination 106 . For example, the identities and/or characteristics for one or more occupants of the electric vehicle 102 can be stored in an occupant database 117 in the memory 110 . Likewise, the identities and/or characteristics for one or more occupants of the predicted destination 106 may be stored in the memory 130 of the processor 131 associated with the predicted destination 106 . P66 of Ferone discloses: [0066] In some embodiments, the processor 111 may calculate a respective probability of the electric vehicle 102 heading towards each of the one or more recurrent destinations based on the current route 121 and/or the current trajectory of the electric vehicle 102 . The current route 121 may comprise a path followed by the electric vehicle 102 as a function of time to reach the current location of the electric vehicle 102 . The current trajectory may comprise a bearing or direction in which the electric vehicle 102 is currently moving. The processor 111 may determine the current route 121 and/or the current trajectory by monitoring the GPS 113 or the motion sensor 112 . The processor 111 may compare the current route 121 and/or the current trajectory with the one or more recurrent destinations to determine a respective likelihood that the electric vehicle 102 is proceeding towards each of the recurrent destinations. For example, the processor 111 may access the memory 110 or the GPS 113 to identify a school and a church as two recurrent destinations for the electric vehicle 102 . Based on the current trajectory of the electric vehicle 102 , the processor may determine that the electric vehicle 102 is headed in a direction and/or bearing towards the school and away from the church. For example, the electric vehicle may be headed in a northeasterly direction, with the school being to the northeast of the current location of the electric vehicle 102 , and the church being to the southwest of the current location of the electric vehicle 102 . Thus, the processor 111 may calculate a higher probability for the school being the predicted destination 106 , and a lower probability for the church being the predicted destination 106 . In some embodiments, the processor 111 identifies a highest-probability destination from among the one or more recurrent destinations. selecting a subset of one or more computer systems of the set of computer systems for generating the predicted content; offloading a generation of the predicted content to the subset of computer systems; and controlling content delivery computer systems of the initial set of computer systems to deliver the generated content to the vehicle in accordance with the specific set of space-time points (p’s 60-63, 76, 104; fig’s 3b, 2f). figure 3b of Ferone discloses: PNG media_image1.png 509 732 media_image1.png Greyscale Ferone discloses all the limitations of the invention, however, arguendo, if Ferone is or might be interpreted such that it might not explicitly disclose content, then Cella discloses content (p’s 432, 509, 590, 385, 295, 58, 60, 423, 445; ab; fig’s 32, 39, 55). If this interpretation is taken, then it would have been obvious, before the effective filing date of the claimed invention, to modify Ferone to include content such as that taught by Cella in order to watch a piece of content causing a routing system 1692 to select a route that permits adequate time to view or hear the content (Cella, p432). Cella further discloses via p445: [0445] Referring to FIG. 18, in embodiments provided herein are transportation systems 1811 having a data processing system 1862 for taking data 18114 from a plurality 1869 of social data sources 18107 and using a neural network 18108 to predict an emerging transportation need 18112 for a group of individuals. Among the various social data sources 18107 , such as those described above, a large amount of data is available relating to social groups, such as friend groups, families, workplace colleagues, club members, people having shared interests or affiliations, political groups, and others. The expert system described above can be trained, as described throughout, such as using a training data set of human predictions and/or a model, with feedback of outcomes, to predict the transportation needs of a group. For example, based on a discussion thread of a social group as indicated at least in part on a social network feed, it may become evident that a group meeting or trip will take place, and the system may (such as using location information for respective members, as well as indicators of a set of destinations of the trip), predict where and when each member would need to travel in order to participate. Based on such a prediction, the system could automatically identify and show options for travel, such as available public transportation options, flight options, ride share options, and the like. Such options may include ones by which the group may share transportation, such as indicating a route that results in picking up a set of members of the group for travel together. Social media information may include posts, tweets, comments, chats, photographs, and the like and may be processed as noted above. Cello further discloses via figure 32: PNG media_image2.png 1011 694 media_image2.png Greyscale As per claim 2, Ferone discloses wherein the selecting further comprises: assigning suitability scores to the initial set of computer systems based on respective resource information, the suitability scores indicating a capability of the initial set of computer systems for content generation for the vehicle along the route; using the suitability scores for selecting the set of computer systems; predicting a spatiotemporal map of a travel of the vehicle along the route; and selecting the subset of the computer systems whose locations align with the spatiotemporal map and that is sufficient to generate the content (p’s 51-58, 66, 81; ab; fig’s 3c, 5b; p’s 60-63, 76, 104; fig’s 3b, 2f) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via p104: [0104] A number of the steps/features that may utilize the AI/ML process described herein include one or more of: analyzing a driving pattern of a vehicle; predicting a destination of the vehicle based on the analyzing; and performing an action at the predicted destination based on an amount of time until an arrival of the vehicle at the predicted destination ; determining a route optimization for the vehicle, based on the predicted destination ; determining a probability score for the predicted destination based on one or more of a movement of the vehicle, or a time of day; and controlling one or more energy-consuming devices at the predicted destination , in response to the probability score being above a threshold; determining a route for the vehicle, based on a current location of the vehicle and the predicted destination ; identifying one or more intermediate destinations situated in closest proximity to the determined route, based on one or more characteristics of one or more occupants of the vehicle; and modifying the determined route to include the identified one or more intermediate destinations; wherein the analyzing comprises: identifying one or more recurrent destinations for the vehicle; calculating a respective probability of the vehicle heading towards each of the one or more recurrent destinations based on a current trajectory of the vehicle; and identifying a highest-probability destination from among the one or more recurrent destinations ; determining a first probability score for the predicted destination based on one or more of historical travel data for the vehicle, an initial movement of the vehicle, or a time of day; and activating one or more energy-consuming devices at the predicted destination , in response to the first probability score being above a threshold; determining a second probability score for the predicted destination based on a subsequent movement of the vehicle; and deactivating the one or more energy-consuming devices at the predicted location, in response to the second probability score being below the threshold. As per claim 3, Ferone discloses wherein the offloading further comprises: controlling the subset of computer systems to perform the generation by: pre-generating the content before the vehicle starts traveling along the route; or partially generating the content before the vehicle starts traveling along the route and complete the generation of the content after the vehicle starts traveling along the route and before reaching the destination; or entirely generating the content after the vehicle starts traveling along the route and before reaching the destination (fig’s 3b, 2f; p’s 51-58, 66, 81; ab; fig’s 3c, 5b; p’s 60-63, 76, 104) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via figure 3c: PNG media_image3.png 484 692 media_image3.png Greyscale As per claim 4, Ferone discloses further comprising: controlling the subset of computer systems to load the pre-generated content or the partially generated content to the content delivery computer systems before the vehicle starts traveling along the route (p’s 60-63, 76, 104; fig’s 3b, 2f; p’s 51-58, 66, 81; ab; fig’s 3c, 5b) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via p61: [0061] In some embodiments, the processor 111 determines that a battery 119 of the electric vehicle 102 has an insufficient state-of-charge to reach the predicted destination 106 . For example, the processor 111 may monitor a battery management system 116 to determine the state-of-charge of the battery 119 . The processor 111 may identify one or more charging stations situated along the planned route to the predicted destination 106 , such as the charging station 107 , by communicating with the GPS 113 . The processor 111 may send a notification to a device associated with the electric vehicle 102 to stop the electric vehicle 102 at the one or more charging stations. For example, the notification may be sent to the mobile device 118 . Alternatively or additionally, the notification may be sent to a display associated with the electric vehicle 102 , such as a display on the GPS 113 , or a display on an infotainment system of the electric vehicle 102 . As per claim 5, Ferone discloses in case the content is generated partially or entirely after the vehicle starts traveling along the route, controlling the content delivery computer systems and the subset of computer systems to communicate generated content to meet the set of space-time points (fig’s 3c, 5b; p’s 60-63, 76, 104; fig’s 3b, 2f; p’s 51-58, 66, 81; ab) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein. As per claim 6, Ferone discloses selecting from the initial set of computer systems the content delivery computer systems whose locations align with the specific space-time points or align with a spatiotemporal map of a travel of the vehicle along the route (ab; fig’s 3c, 5b; p’s 60-63, 76, 104; fig’s 3b, 2f; p’s 51-58, 66, 81) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via figure 2f: PNG media_image4.png 985 633 media_image4.png Greyscale As per claim 7, Ferone discloses determining a current location of the vehicle; and in response to determining that the vehicle is in proximity of a given computer system of the content delivery computer systems, controlling the given computer system to deliver to the vehicle the content that has been generated by or loaded at the given computer system (p’s 51-58, 66, 81; ab; fig’s 3c, 5b; p’s 60-63, 76, 104; fig’s 3b, 2f) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via p60: [0060] In another embodiment, the processor 111 determines the planned route for the electric vehicle 102 , based on a current location of the electric vehicle 102 and the predicted destination 106 . In a further embodiment, the planned route may be a route from the current location of the electric vehicle 102 to the predicted destination 106 . The processor 111 may monitor the GPS 113 to determine the current location. The processor 111 may identify one or more charging stations that are situated in closest proximity to the planned route, such as a charging station 107 . The processor 111 may communicate over the network 104 with a processor 123 at the charging station 107 to determine whether or not the charging station 107 has an available charging spot for the electric vehicle 102 . In response to determining that an available charging spot at the charging station 107 exists for the electric vehicle 102 , the processor 111 may modify the planned route to the predicted destination 106 to include the charging station 107 . As per claim 8, Ferone discloses while the vehicle is traveling the route, repeatedly performing the predicting of the route, in each repetition: if the route changed from a last predicted route, repeating selection of the set of computer systems, and the content prediction; and if the predicted content is different from a last predicted content, repeating the selection of the subset of computer systems, the offloading, and the controlling (fig’s 3b, 2f; p’s 51-58, 66, 81; ab; fig’s 3c, 5b; p’s 60-63, 76, 104) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via p58: [0058] In some embodiments, the processor 111 determines a probability score for the predicted destination 106 based on one or more of a movement of the electric vehicle 102 , or a time of day. For example, the processor 111 may detect a movement of the electric vehicle 102 by monitoring the motion sensor 112 or the GPS 113 . Likewise, the processor 111 may determine a time of day by monitoring a clock 115 . For example, the probability score may represent a percentage or likelihood that the predicted destination 106 is correct. In a further embodiment, the processor 111 may determine the probability score based on historical travel data for the electric vehicle 102 retrieved, for example, from the GPS 113 and/or the memory 110 . In some embodiments, the processor 111 controls one or more energy-consuming devices at the predicted destination 106 , such as the energy-consuming device 137 , in response to the probability score being above a threshold. As per claim 9, Ferone discloses while the vehicle is traveling the route, repeatedly performing the predicting of the content, in each repetition: if the predicted content is different from a last predicted content, repeating the selecting, the offloading and the controlling (p’s 60-63, 76, 104; fig’s 3b, 2f; p’s 51-58, 66, 81; ab; fig’s 3c, 5b) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via p54: [0054] In a further embodiment, there are various levels of confirmation. For example, in a first-level confirmation, there is an originating or initial location of the electric vehicle 102 . As the vehicle begins to maneuver, at some point (e.g., first minutes or first movements of the electric vehicle 102 ), the processor 111 may determine an initial prediction of the predicted destination 106 . The processor 111 may repeatedly or continually check this prediction by monitoring the GPS 113 . If the prediction is no longer correct (based on the GPS 113 indicating a turn of the electric vehicle 102 , for example), the prediction can be changed, but this changed prediction is not yet confirmed. By the time the electric vehicle 102 is 50% (by example only, any other percentage may be used), of the way to the predicted destination 106 as determined by the processor 111 monitoring the GPS 113 , the processor 111 may ascertain a first-level confirmation of the predicted destination 106 . In response to ascertaining the first-level confirmation, the processor may take a first action at the predicted destination 106 , such as directing the smart electrical panel 133 to activate the HVAC system at the predicted destination 106 . This level of engagement can be based on an identity of one or more occupants of the electric vehicle 102 , a characteristic of one or more occupants of the electric vehicle 102 , an identity of one or more occupants of the predicted destination 106 , or a characteristic of one or more occupants of the predicted destination 106 . For example, the identities and/or characteristics for one or more occupants of the electric vehicle 102 can be stored in an occupant database 117 in the memory 110 . Likewise, the identities and/or characteristics for one or more occupants of the predicted destination 106 may be stored in the memory 130 of the processor 131 associated with the predicted destination 106 . As per claim 10, Ferone discloses further comprising: performing a federated learning across the initial set of computer systems for generating a federated learning model, the federated learning model being configured to predict a resource availability and resource usage by each computer system of the initial set of computer systems; and using the federated learning model for predicting the resource information of the initial set of computer systems (fig’s 3c, 5b; p’s 60-63, 76, 104; fig’s 3b, 2f; p’s 51-58, 66, 81; ab) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via figure 2f: PNG media_image4.png 985 633 media_image4.png Greyscale As per claim 11, Ferone discloses wherein the prediction of the content is performed using at least one of: user preferences of a user of the vehicle, stored data of vehicles or users of the vehicles, and conditions of the route (ab; fig’s 3c, 5b; p’s 60-63, 76, 104; fig’s 3b, 2f; p’s 51-58, 66, 81) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via p51: [0051] In some embodiments, the processor 111 predicts a destination of the electric vehicle 102 based on the analyzing. For example, the predicted destination 106 can be predicted by the processor 111 comparing a current route 121 and/or a current trajectory of the vehicle 102 with the gathered information. The current route 121 may comprise a path followed by the electric vehicle 102 as a function of time to reach a current location of the electric vehicle 102 . The current trajectory may comprise a bearing or direction in which the electric vehicle 102 is currently moving. The current route 121 and the current trajectory may be determined by the processor 111 monitoring at least one of the GPS 113 or a motion sensor 112 . For example, when the user of the electric vehicle 102 typically drives to a particular location after work on a certain day of the week, the processor 111 may recognize this driving pattern. As per claim 12, Ferone discloses the set of computer systems comprising first computer systems and second computer systems, wherein the first computer systems are multi-access edge computing (MEC) nodes and the second computer systems are cloud systems (p’s 51-58, 66, 81; ab; fig’s 3c, 5b; p’s 60-63, 76, 104; fig’s 3b, 2f) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via p64: [0064] FIG. 1 B illustrates a further example of a system diagram 150 , according to example embodiments. In some embodiments, the instant solution fully or partially executes in the memory 105 of the server 103 , in the memory 110 of the processor 111 associated with the electric vehicle 102 , in the memory 130 of the processor 131 associated with the predicted destination 106 , or in a memory of one or more other processors associated with devices and/or entities mentioned herein. In some embodiments, one or more of the server 103 , the processor 111 , or the processor 131 may include a microcontroller that contains one or more central processing unit (CPU) cores, along with program memory and programmable input/output peripherals. Program memory can be provided, for example, in the form of flash memory. As per claim 13, Ferone discloses each computer system of the content delivery computer systems is associated with a base station of the distributed communication system, wherein delivery of content by each content delivery computer system comprises using the base station associated with the content delivery computer system for sending radio frequency signals comprising the content (fig’s 3b, 2f; p’s 51-58, 66, 81; ab; fig’s 3c, 5b; p’s 60-63, 76, 104) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via p61: [0064] FIG. 1 B illustrates a further example of a system diagram 150 , according to example embodiments. In some embodiments, the instant solution fully or partially executes in the memory 105 of the server 103 , in the memory 110 of the processor 111 associated with the electric vehicle 102 , in the memory 130 of the processor 131 associated with the predicted destination 106 , or in a memory of one or more other processors associated with devices and/or entities mentioned herein. In some embodiments, one or more of the server 103 , the processor 111 , or the processor 131 may include a microcontroller that contains one or more central processing unit (CPU) cores, along with program memory and programmable input/output peripherals. Program memory can be provided, for example, in the form of flash memory. As per claim 14, Ferone discloses the subset of computer systems comprising one computer system (p’s 60-63, 76, 104; fig’s 3b, 2f; p’s 51-58, 66, 81; ab; fig’s 3c, 5b) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via figure 3b: PNG media_image5.png 494 685 media_image5.png Greyscale As per claim 15, Ferone discloses the subset of computer systems comprising one computer system per point of the space-time points, wherein each computer system of the subset of computer systems is located with respect to the respective space point such that the computer system can generate a content that can be delivered at the respective time point (fig’s 4d, 3c, 5b; p’s 60-63, 76, 104; fig’s 3b, 2f; p’s 51-58, 66, 81; ab) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via figure 4d: PNG media_image6.png 454 676 media_image6.png Greyscale As per claim 16, Ferone discloses the content delivery computer systems being the subset of computer systems (ab; fig’s 3c, 5b; p’s 60-63, 76, 104; fig’s 3b, 2f; p’s 51-58, 66, 81) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein. As per claim 17, Ferone discloses the resource information comprising real-time resource information and predicted resource information (p’s 51-58, 66, 81; ab; fig’s 3c, 5b; p’s 60-63, 76, 104; fig’s 3b, 2f) as per the discussion above and the rejection of corresponding parts of the claims above incorporated herein and further, Ferone discloses via p127: [0127] The navigation system 418 E may describe at least one navigation route including a start point and an endpoint. In some embodiments, the navigation system 418 E of the vehicle 410 E receives a request from a user for navigation routes wherein the request includes a starting point and an ending point. The navigation system 418 E may query a real-time data server 404 E (via a network 402 E), such as a server that provides driving directions, for navigation route data corresponding to navigation routes, including the start point and the endpoint. The real-time data server 404 E transmits the navigation route data to the vehicle 410 E via a wireless network 402 E, and the communication system 424 E stores the navigation data 418 E in the memory 422 E of the vehicle 410 E . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kundu (U.S. patent application publication 2024/0414337) discloses determining a plurality of candidate routes to a destination location for a vehicle , and may segment each candidate route into multiple road segments. The system determines one or more compression methods to be utilized for compressing images captured by one or more vehicle cameras on board the vehicle while the vehicle is traversing at least one road segment of at least one route of the plurality of candidate routes. The system selects, for the vehicle , a first route based at least on the one or more compression methods. The system sends, to the vehicle , information related to the first route and the one or more compression methods. The vehicle may utilize the one or more compression methods for compressing the images captured by the one or more vehicle cameras during traversal of the first route. Abbas et al. (U.S. patent application publication 2025/0298568) discloses communicating with a service provider via a head unit of a vehicle , capturing sensor data from an interior of the vehicle during an interaction between the service provider and an occupant within the vehicle , determining a location of the occupant within the vehicle based on the sensor data captured from the interior of the vehicle , and displaying data related to the interaction between the service provider and the occupant on a user interface from among a plurality of available user interfaces within the vehicle based on the location of the occupant within the vehicle . An artificial intelligence (AI) model can be trained and/or executed when performing at least one portion of the example operation. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEHRANG BADII whose telephone number is (571)272-6879. The examiner can normally be reached Monday-Friday. 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, Hunter Lonsberry can be reached at 571-272-7298. 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. /Behrang Badii/ Primary Examiner Art Unit 3665 Application/Control Number: 18/747,727 Page 2 Art Unit: 3665 Application/Control Number: 18/747,727 Page 3 Art Unit: 3665 Application/Control Number: 18/747,727 Page 4 Art Unit: 3665 Application/Control Number: 18/747,727 Page 5 Art Unit: 3665 Application/Control Number: 18/747,727 Page 6 Art Unit: 3665 Application/Control Number: 18/747,727 Page 7 Art Unit: 3665 Application/Control Number: 18/747,727 Page 8 Art Unit: 3665 Application/Control Number: 18/747,727 Page 9 Art Unit: 3665 Application/Control Number: 18/747,727 Page 10 Art Unit: 3665 Application/Control Number: 18/747,727 Page 11 Art Unit: 3665 Application/Control Number: 18/747,727 Page 12 Art Unit: 3665 Application/Control Number: 18/747,727 Page 13 Art Unit: 3665 Application/Control Number: 18/747,727 Page 14 Art Unit: 3665 Application/Control Number: 18/747,727 Page 15 Art Unit: 3665 Application/Control Number: 18/747,727 Page 16 Art Unit: 3665 Application/Control Number: 18/747,727 Page 17 Art Unit: 3665 Application/Control Number: 18/747,727 Page 18 Art Unit: 3665 Application/Control Number: 18/747,727 Page 19 Art Unit: 3665 Application/Control Number: 18/747,727 Page 20 Art Unit: 3665
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