CTNF 18/710,244 CTNF 92610 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 07-20-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 1-6,10,11-15,19 are rejected under 35 U.S.C. 103 as being unpatentable over Moradi et al. ( US20230259744A1 ) in view of Desai et al. ( US20190325350A1 ) Regarding Claim 1, Moradi teaches, A machine-learning (ML) orchestrator entity in a wireless communication network, comprising : at least one processor; and at least one memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the ML orchestrator entity to: group a set of network nodes present in the wireless communication network into at least one node cluster based on at least one radio condition of a set of cells served by the set of network nodes, fig 2[38]- In particular, the steps of the method include grouping each worker node into one of a plurality of groups based on characteristics of a data distribution of each of the plurality of worker nodes (S100). [42] The local data of a worker node may be at least one of: Quality of Service (QoS) data such as QoS performance counter dataset collected on the network elements (such as the worker nodes) used in key performance indicators (=based on at least one radio condition of a set of cells).[66]- The local dataset may be dynamically updated when new data arrives, e.g., in the telecommunications domain where the worker nodes may be base stations, the local dataset may be updated after receiving new performance measure (PM) counters at each time interval. (=wireless network ). each network node from the set of network nodes having an ML agent installed thereon, [38]- subgrouping worker nodes within a group of the plurality of groups into subgroups based on characteristics of a worker neural network model of each worker node the ML agent being configured to run based on radio measurements in a training mode and an inference mode; and [40]- Each worker node trains a neural network model using their own local data and a neural network. (=training mode). As a result of the grouping and/or subgrouping, one or more of the worker models may be updated using the average model which is an average of models within the same group or subgroup of the worker node(=inference mode) for each node cluster from the at least one node cluster: transmit, to at least two network nodes of the node cluster, an indication to obtain a set of parameters for the ML agent by running the ML agent in the training mode; [53]- the clustering module may generate cluster representations(=transmit, to at least two network nodes of the node cluster,) which may be sent to each worker node 502 a , 502 b , 502 c , and may receive indications from the worker nodes as to which subgrouping the worker nodes belong. See Fig 4 and 5 of multiple network nodes with operator A/B/C(more than 2) Moradi does not teach, receive the set of parameters from each of the at least two network nodes of the node cluster; and based on the set of parameters received from each of the at least two network nodes of the node cluster, generate a common set of parameters suitable for the inference mode of the ML agents within the node cluster. Desai teaches, receive the set of parameters from each of the at least two network nodes of the node cluster; and based on the set of parameters received from each of the at least two network nodes of the node cluster, generate a common set of parameters suitable for the inference mode of the ML agents within the node cluster. receive the set of parameters from each of the at least two network nodes of the node cluster; and [53]- transmit, to at least two network nodes of the node cluster, based on the set of parameters received from each of the at least two network nodes of the node cluster, generate a common set of parameters suitable for the inference mode of the ML agents within the node cluster. [53]- Upon receiving one or more reports of model parameters from nodes of a cluster, the training system aggregates or normalizes using a suitable function (collectively referred to hereinafter as “normalization” or a variant thereof) (=normalization is generate common set of parameters) a model parameter across all such node-specific reports(=generate a common set of parameters suitable for the inference mode of the ML agents within the node cluster).Figure 3A shows multiple nodes .inference mode is taught in [59]- At any iteration, an embodiment can also optionally improve or adjust the base model in a manner described herein. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi receive the set of parameters from each of the at least two network nodes of the node cluster; and based on the set of parameters received from each of the at least two network nodes of the node cluster, generate a common set of parameters suitable for the inference mode of the ML agents within the node cluster as taught by Desai to come up with a centralized parameter based on inference and training. Regarding Claim 10, Moradi teaches , A method for operating a machine-learning (ML) orchestrator entity in a wireless communication network, said method comprising: grouping a set of network nodes present in the wireless communication network into at least one node cluster based on at least one radio condition of a set of cells served by the set of network nodes, each network node from the set of network nodes having an ML agent installed thereon, the ML agent being configured to run based on radio measurements in a training mode and an inference mode; and fig 2[38]- In particular, the steps of the method include grouping each worker node into one of a plurality of groups based on characteristics of a data distribution of each of the plurality of worker nodes (S100). [42] The local data of a worker node may be at least one of: Quality of Service (QoS) data such as QoS performance counter dataset collected on the network elements (such as the worker nodes) used in key performance indicators (=based on at least one radio condition of a set of cells). [66]- The local dataset may be dynamically updated when new data arrives, e.g., in the telecommunications domain where the worker nodes may be base stations, the local dataset may be updated after receiving new performance measure (PM) counters at each time interval. (=wireless network ). or each node cluster from the at least one node cluster: [38]- subgrouping worker nodes within a group of the plurality of groups into subgroups based on characteristics of a worker neural network model of each worker node transmitting, to at least two network nodes of the node cluster, an indication to obtain a set of parameters for the ML agent by running the ML agent in the training mode; [53]- the clustering module may generate cluster representations(=transmit, to at least two network nodes of the node cluster,) which may be sent to each worker node 502 a , 502 b , 502 c , and may receive indications from the worker nodes as to which subgrouping the worker nodes belong. See Fig 4 and 5 of multiple network nodes with operator A/B/C(more than 2) Moradi does not teach, receiving the set of parameters from each of the at least two network nodes of the node cluster; and based on the set of parameters received from each of the at least two network nodes of the node cluster, generating a common set of parameters suitable for the inference mode of the ML agents within the node cluster. Desai teaches, receiving the set of parameters from each of the at least two network nodes of the node cluster; and [53]- transmit, to at least two network nodes of the node cluster, based on the set of parameters received from each of the at least two network nodes of the node cluster, generating a common set of parameters suitable for the inference mode of the ML agents within the node cluster. [53]- Upon receiving one or more reports of model parameters from nodes of a cluster, the training system aggregates or normalizes using a suitable function (collectively referred to hereinafter as “normalization” or a variant thereof) (=normalization is generate common set of parameters) a model parameter across all such node-specific reports(=generate a common set of parameters suitable for the inference mode of the ML agents within the node cluster).Figure 3A shows multiple nodes .inference mode is taught in [59]- At any iteration, an embodiment can also optionally improve or adjust the base model in a manner described herein. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi receive the set of parameters from each of the at least two network nodes of the node cluster; and based on the set of parameters received from each of the at least two network nodes of the node cluster, generate a common set of parameters suitable for the inference mode of the ML agents within the node cluster as taught by Desai to identify metrics for beam sweep time. Regarding Claim 2, Moradi teaches, The ML orchestrator entity of claim 1, wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the ML orchestrator entity to receive the set of parameters from each of the at least two network nodes of the node cluster via an ML agent-specific signalling interface. [65]- Parameters of models of worker nodes belonging to the same group are sent to the averaging module of the master node for averaging. [19]- According to an aspect of an embodiment of the invention there is provided a master node configured to communicate with a plurality of worker nodes in a machine learning system. Regarding Claim 3, Moradi teaches, The ML orchestrator entity of claim 1 or 2, wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the ML orchestrator entity to transmit a request for the set of parameters to each of the at least two network nodes of the node cluster and, [82]- The master node initiates group-level federated learning by sending the worker nodes' models belonging to the same group to the averaging module in response to the request, receive the set of parameters from each of the at least two network nodes of the node cluster. [58] In one example embodiment, the model inverter 512 takes as input the parameters of the worker neural network of the local trainer module of a worker node. Regarding Claim 4, Moradi does not teach, The ML orchestrator entity of claim1,wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the ML orchestrator entity to transmit the common set of parameters to each network node of the node cluster after the common set of parameters is generated Desai teaches, The ML orchestrator entity of claim1,wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the ML orchestrator entity to transmit the common set of parameters to each network node of the node cluster after the common set of parameters is generated. [124]- The application distributes the normalized model parameters to the nodes in the cluster (block 622). The application optionally adjusts the base model based on the normalized model parameters (block 624).[117]- configured differently depending on whether an implementation uses the cluster notification method or the cluster model method. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi The ML orchestrator entity of claim1,wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the ML orchestrator entity to transmit the common set of parameters to each network node of the node cluster after the common set of parameters is generated as taught by Desai to come up with a centralized parameter based on inference and training. Regarding Claim 5, Moradi does not teach, The ML orchestrator entity of any one of claims 1 to 3claim1,wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the ML orchestrator entity to:receive a request for the common set of parameters from at least one network node of the node cluster; and in response to the request, transmit the common set of parameters to each of the at least one network node of the node cluster. Desai teaches, The ML orchestrator entity of any one of claims 1 to 3claim1,wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the ML orchestrator entity to:receive a request for the common set of parameters from at least one network node of the node cluster; and in response to the request, transmit the common set of parameters to each of the at least one network node of the node cluster. [114]- When the exit condition is not satisfied, component 418 normalizes the model parameters and causes component 416 to distribute the normalized model parameters to the nodes in the clusters. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi The ML orchestrator entity of any one of claims 1 to 3claim1,wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the ML orchestrator entity to: receive a request for the common set of parameters from at least one network node of the node cluster; and in response to the request, transmit the common set of parameters to each of the at least one network node of the node cluster as taught by Desai to come up with a centralized parameter based on inference and training. Regarding Claim 6, Moradi does not teach, The ML orchestrator entity of claim 4 or 5, wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the ML orchestrator entity to transmit, together with the common set of parameters, a time instant from which the common set of parameters is to be used in the inference mode Desai teaches, The ML orchestrator entity of claim 4 or 5, wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the ML orchestrator entity to transmit, together with the common set of parameters, a time instant from which the common set of parameters is to be used in the inference mode. [115]- In order to cause such operations as the node, application 402 of FIG. 4 may deliver to the node client-side application 502 in a suitable form and at a suitable time during the interactions with the node. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi The ML orchestrator entity of claim 4 or 5, wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the ML orchestrator entity to transmit, together with the common set of parameters, a time instant from which the common set of parameters is to be used in the inference mode as taught by Desai to come up with a centralized parameter based on inference and training. Regarding Claim 11, Moradi teaches, The method of claim 10, wherein the set of parameters is received from each of the at least two network nodes of the node cluster via an ML agent-specific signalling interface. [65]- Parameters of models of worker nodes belonging to the same group are sent to the averaging module of the master node for averaging. [19]- According to an aspect of an embodiment of the invention there is provided a master node configured to communicate with a plurality of worker nodes in a machine learning system. Regarding Claim 12, Moradi teaches, The method of claim 10, wherein said receiving comprises: transmitting a request for the set of parameters to each of the at least two network nodes of the node cluster; and in response to the request, receiving the set of parameters from each of the at least two network nodes of the node cluster., [82]- The master node initiates group-level federated learning by sending the worker nodes' models belonging to the same group to the averaging module in response to the request, receive the set of parameters from each of the at least two network nodes of the node cluster . [58] In one example embodiment, the model inverter 512 takes as input the parameters of the worker neural network of the local trainer module of a worker node. Regarding Claim 13, Moradi does not teach, The method of any one of claim 10 , further :comprising transmitting the common set of parameters to each network node of the lode cluster after the common set of parameters is generated. Desai teaches, The method of any one of claim 10 , further :comprising transmitting the common set of parameters to each network node of the lode cluster after the common set of parameters is generated. [124]- The application distributes the normalized model parameters to the nodes in the cluster (block 622). The application optionally adjusts the base model based on the normalized model parameters (block 624).[117]- configured differently depending on whether an implementation uses the cluster notification method or the cluster model method. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi The method of any one of claim 10 , further :comprising transmitting the common set of parameters to each network node of the lode cluster after the common set of parameters is generated as taught by Desai to come up with a centralized parameter based on inference and training. Regarding Claim 14, Moradi does not teach, The method of any one of claim 10 , further comprising: receiving a request for the common set of parameters from at least one network node of the node cluster; and in response to the request, transmitting the common set of parameters to each of the at least one network node of the node cluster. Desai teaches, The method of any one of claim 10 , further comprising: receiving a request for the common set of parameters from at least one network node of the node cluster; and in response to the request, transmitting the common set of parameters to each of the at least one network node of the node cluster. [114]- When the exit condition is not satisfied, component 418 normalizes the model parameters and causes component 416 to distribute the normalized model parameters to the nodes in the clusters. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi The method of any one of claim 10 , further comprising: receiving a request for the common set of parameters from at least one network node of the node cluster; and in response to the request, transmitting the common set of parameters to each of the at least one network node of the node cluster. as taught by Desai to come up with a centralized parameter based on inference and training. Regarding Claim 15, Moradi does not teach, The method of claim 13, further comprising transmitting, together with the common set of parameters, a time instant from which the common set of parameters is to be used in the inference mode. Desai teaches, The method of claim 13, further comprising transmitting, together with the common set of parameters, a time instant from which the common set of parameters is to be used in the inference mode. [115]- In order to cause such operations as the node, application 402 of FIG. 4 may deliver to the node client-side application 502 in a suitable form and at a suitable time during the interactions with the node. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi The method of claim 13, further comprising transmitting, together with the common set of parameters, a time instant from which the common set of parameters is to be used in the inference mode. as taught by Desai to come up with a centralized parameter based on inference and training. Regarding Claim 19, Moradi teaches, A non-transitory computer-readable storage medium having a computer program comprising computer code encoded thereon, wherein the computer code, when executed by at least one processor, causes the at least one processor to perform the method according to claim 10. See Fig 1.[27]- is a block diagram of a system illustrating master based federated learning; 07-21-aia AIA Claim s 7,16 are rejected under 35 U.S.C. 103 as being unpatentable over Moradi et al. ( US20230259744A1 ) in view of Desai et al. ( US20190325350A1 ) In further view of Jeon et al. ( US20220287104A1 ) Regarding Claim 7, Moradi in view of Desai does not teach, The ML orchestrator entity of claim1,wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the ML orchestrator entity to generate the common set of parameters by using at least one of a linear function, a non-linear function, and a Boolean function. Jeon teaches, The ML orchestrator entity of claim1,wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the ML orchestrator entity to generate the common set of parameters by using at least one of a linear function, a non-linear function, and a Boolean function. [95]- he configuration can indicate the indexes of the operations which are enabled, or there can be a Boolean parameter to enable or disable the ML/AI approach for each operation.[96]- the federated learning can be defined as one of the ML algorithm. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi in view of Desai The ML orchestrator entity of claim1,wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the ML orchestrator entity to generate the common set of parameters by using at least one of a linear function, a non-linear function, and a Boolean function as taught by Jeon to come up with a centralized parameter based on inference and training. Regarding Claim 16, Moradi in view of Desai does not teach, The method oof claims 10,wherein said generating comprises generating the common set of parameters by using at least one of a linear function, a non-linear function, and a Boolean function. Jeon teaches, The method oof claims 10,wherein said generating comprises generating the common set of parameters by using at least one of a linear function, a non-linear function, and a Boolean function. [95]- he configuration can indicate the indexes of the operations which are enabled, or there can be a Boolean parameter to enable or disable the ML/AI approach for each operation.[96]- the federated learning can be defined as one of the ML algorithm. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi in view of Desai The method oof claims 10,wherein said generating comprises generating the common set of parameters by using at least one of a linear function, a non-linear function, and a Boolean function as taught by Jeon to come up with a centralized parameter based on inference and training . 07-21-aia AIA Claim s 8,9,17,18 are rejected under 35 U.S.C. 103 as being unpatentable over Moradi et al. ( US20230259744A1 ) in view of Desai et al. ( US20190325350A1 ) In further view of Wu et al. (US20220051135A1) Regarding Claim 8, Moradi in view of Desai does not teach, The ML orchestrator entity of claim1,wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the ML orchestrator entity to generate the common set of parameters by using at least one of a linear function, a non-linear function, and a Boolean function Wu teaches, The ML orchestrator entity of any one of claims 1 wherein the ML agent is a reinforcement learning (RL) agent configured to run in an exploration mode as the training mode and in an exploitation mode as the inference mode. [45]- FIG. 1B illustrates a transfer reinforcement learning framework, according to some embodiments. The policy bank, B (item 1-20), stores the policies learned on the source tasks. The source task and target task have the same MDP formulation. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi in view of Desai The ML orchestrator entity of claim1,wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the ML orchestrator entity to generate the common set of parameters by using at least one of a linear function, a non-linear function, and a Boolean function as taught by Wu to come up with a centralized parameter based on inference and training. Regarding Claim 9, Moradi in view of Desai does not teach, The ML orchestrator entity of claim 8, wherein the RL agent is based on a Q-learning approach, and the set of parameters from each of the at least two network nodes of the node cluster is presented as a Q-table, and wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the ML orchestrator entity to generate the common set of parameters as a common Q-table Wu teaches, The ML orchestrator entity of claim 8, wherein the RL agent is based on a Q-learning approach, and the set of parameters from each of the at least two network nodes of the node cluster is presented as a Q-table, and wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the ML orchestrator entity to generate the common set of parameters as a common Q-table. [60]- Thus, FIG. 4A illustrates obtaining a random sample batch from the replay buffer; updating θ parameters of a Q network for a soft Q value; updating parameters of a model network for the base target policy; updating bar parameters of a target value network for the base target policy; and updating ϕ model parameters for the base target policy. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi in view of Desai The ML orchestrator entity of claim 8, wherein the RL agent is based on a Q-learning approach, and the set of parameters from each of the at least two network nodes of the node cluster is presented as a Q-table, and wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the ML orchestrator entity to generate the common set of parameters as a common Q-table as taught by Jeon to come up with a centralized parameter based on inference and training. Regarding Claim 17, Moradi in view of Desai does not teach, The method of claim 10,wherein the ML agent is a reinforcement learning (RL) agent configured to run in an exploration mode as the training mode and in an exploitation mode as the inference mode. Wu teaches, The method of claim 10,wherein the ML agent is a reinforcement learning (RL) agent configured to run in an exploration mode as the training mode and in an exploitation mode as the inference mode . [45]- FIG. 1B illustrates a transfer reinforcement learning framework, according to some embodiments. The policy bank, B (item 1-20), stores the policies learned on the source tasks. The source task and target task have the same MDP formulation. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi in view of Desai The method of claim 10,wherein the ML agent is a reinforcement learning (RL) agent configured to run in an exploration mode as the training mode and in an exploitation mode as the inference mode. as taught by Wu to come up with a centralized parameter based on inference and training. Regarding Claim 18, Moradi in view of Desai does not teach, The method of claim 17, wherein the RL agent is based on a Q-learning approach, and the set of parameters from each of the at least two network nodes of the node cluster is presented as a Q-table, and wherein said generating comprises generating the common set of parameters as a common Q-table Wu teaches, The method of claim 17, wherein the RL agent is based on a Q-learning approach, and the set of parameters from each of the at least two network nodes of the node cluster is presented as a Q-table, and wherein said generating comprises generating the common set of parameters as a common Q-table. [60]- Thus, FIG. 4A illustrates obtaining a random sample batch from the replay buffer; updating θ parameters of a Q network for a soft Q value; updating parameters of a model network for the base target policy; updating bar parameters of a target value network for the base target policy; and updating ϕ model parameters for the base target policy. It would have been obvious to a person having an ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Moradi in view of Desai The method of claim 17, wherein the RL agent is based on a Q-learning approach, and the set of parameters from each of the at least two network nodes of the node cluster is presented as a Q-table, and wherein said generating comprises generating the common set of parameters as a common Q-table as taught by Jeon to come up with a centralized parameter based on inference and training. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anindita Sen whose telephone number is (571)-272-2390. The examiner can normally be reached 7:30am-5:30pm. 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, Joseph Avellino can be reached on (571)-272-3905. 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. /ANINDITA SEN/ Examiner, Art Unit 2478 /JOSEPH E AVELLINO/Supervisory Patent Examiner, Art Unit 2478 Application/Control Number: 18/710,244 Page 2 Art Unit: 2478 Application/Control Number: 18/710,244 Page 3 Art Unit: 2478 Application/Control Number: 18/710,244 Page 4 Art Unit: 2478 Application/Control Number: 18/710,244 Page 6 Art Unit: 2478 Application/Control Number: 18/710,244 Page 7 Art Unit: 2478 Application/Control Number: 18/710,244 Page 8 Art Unit: 2478 Application/Control Number: 18/710,244 Page 9 Art Unit: 2478 Application/Control Number: 18/710,244 Page 10 Art Unit: 2478 Application/Control Number: 18/710,244 Page 11 Art Unit: 2478 Application/Control Number: 18/710,244 Page 12 Art Unit: 2478 Application/Control Number: 18/710,244 Page 13 Art Unit: 2478 Application/Control Number: 18/710,244 Page 14 Art Unit: 2478 Application/Control Number: 18/710,244 Page 15 Art Unit: 2478 Application/Control Number: 18/710,244 Page 16 Art Unit: 2478 Application/Control Number: 18/710,244 Page 17 Art Unit: 2478 Application/Control Number: 18/710,244 Page 18 Art Unit: 2478 Application/Control Number: 18/710,244 Page 19 Art Unit: 2478
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