CTNF 18/717,824 CTNF 89857 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Status of Application 2 This instant Office Action is in response to Original filed on 6/7/2024. 3. This Office Action is made Non-Final. 4. Claims 30-49 are pending. 5. Claims 1-29 are currently cancelled prior to examination. 6. Claim 37 are objected to for allowable subject matter. Information Disclosure Statement 7. The information disclosure statement (IDS) submitted on 6/7/2024 and 12/5/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Specification 06-16-01 AIA 8. The abstract of the disclosure is objected to because the abstract of the disclosure does not commence on a separate sheet in accordance with 37 CFR 1.52(b)(4). A new abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b). Claim Objections 07-29-01 AIA 9. Claim s 36 is objected to because of the following informalities: it recites the abbreviation “RL” that needs to be spelled out at the first occurrence of the abbreviation. See MPEP § 608.01(m) . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 10. Claim 30 and 31-47 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. In addition the claims 31-47 are included in the rejection because of their dependency on claim 30. 07-34-05 AIA Claim 30 recites the limitation “ the greatest increase ” in Line 10 . There is insufficient antecedent basis for this limitation in the claim. Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 1. Claim 37 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claim Rejections - 35 USC § 102 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA 2. Claim s 30-36 and 38-49 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yeh et al. US 20220014963 hereafter Yeh . As to Claim 30. (New) Yeh discloses a performed by an orchestration node [i.e. Coordinator-210 or Edge Compute Node/Agent-140] of a communication network for orchestrating management of a plurality of operational parameters in an environment [Environment-100x] of the communication network, wherein the respective operational parameters are managed by respective Agents, wherein at least one performance parameter of the communication network is operable to be impacted by each of the operational parameters, wherein the method comprises [Figs. 1A-B, 2, Sections 0049, 0064, 0155: FIG. 2 shows Collaborative Multi-Agent DRL architecture, each multi-access UE is configured as agent/multi-agents-101 interact with the environment to collect data, statistics and determine a state of the environment which are sent to a coordinator entity-210 that determines the state of the environment 100x; coordinator entity 210 is employed as a centralized training engine or edge compute node; network parameters also used by the coordinator for the state of the environment; the network parameters may be various performance indicators. Determines a state based on observations from the UEs 101/multi-agents; examples of the observations may include operational parameters. Edge compute node provide orchestration of multiple applications and are coordinated with orchestration functions]: obtaining a representation of a state of the environment [Figs. 1B, 2-3, 6, 8, Sections 0036, 0051, 0061: The Agent-140 obtains a state based on observations from the UEs about environment-100x, and the Agent-140 obtain observation data. Each UE collects observations from the environment, which are used for the state input and UEs report their observations to the central coordinator. Observation information of UEs are used to learn a representation of the current environment dynamics]; using a Machine Learning (ML) process and the obtained state representation [Fig. 2, Sections 0036, 0051, 0125: The Agent-140 obtains a state based on observations from the UEs about environment-100x. Collaborative Multi-Agent DRL procedure where each UE collects observations from the environment, which are used for the state input for RL agent; report their observations to the central coordinator; the coordinator deploy the updated AI/ML (artificial intelligence-AI)/machine learning-ML) model. A deep contextual RL architecture comprises three components, including: (1) representation learning, where features are extracted from observation from a wireless environment], generating a prediction of which of the Agents [i.e. UE-101, Section 0049: A UE is configured as an Agent] , if allowed to execute within the environment respective actions selected by the respective Agent for management of the respective operational parameters, will result in the greatest increase of a performance measure for the communication network [Figs. 1A-B, 2, 5, Sections 0064, 0065, 0100, 0107, 0150: Determines a state based on observations from the UEs 101/multi-agents; examples of the observations include operational parameters. Various observations and measurements used to detect the UE-101 (Agent) which is done at the edge compute node-140 to trigger management policy update for the selected UE-101 (i.e. Agent). Additionally, if performance measurements indicate that the action are drifting towards sub-optimal (i.e. moving towards above optimal) performance outcomes, in response a back-up model Agent (i.e. selected Agent) is selected. The ML algorithms generate a suggested set of actions based on data collected from environments; ML algorithms can also be an action prediction model. The action predicted by the actor network is deployed to the target UE], selecting one of the Agents [i.e. UE-101, Section 0049: A UE is configured as an Agent] on the basis of the prediction; and initiating execution [Sections 0107, 0499: The ML algorithms generate a suggested set of actions; ML algorithms can also be an action prediction model. Apparatus comprising means for executing the instructions] by the selected Agent of the action selected by the selected Agent [Sections 0061, 0065, 0150, 0382: Only one target UE-101 (i.e. Agent) is triggered to get an action update, use policy gradient framework, derive an action and update for the target UE. Various observations and measurements used to detect the UE-101 (Agent) which is done at the edge compute node-140 (i.e. orchestration node) to trigger management policy update for the selected UE-101 (i.e. Agent). The action predicted by the actor network is deployed to the target UE. Policy Action shall be a result of execution of multiple policy conditions]. As to Claim 31. (New) Yeh discloses a method as claimed in claim 30, wherein generating the prediction of which of the Agents is based on an indication of which of the Agents was selected during a previous iteration of the method [Fig. 5, Sections 0037, 0065, 0117, 0150: Each participating edge compute nodes can share their observation data for iterations of the RL learning. The edge compute node-140 to trigger management policy update for the selected UE (i.e. Agent). The RL training agent keeps at least one copy of at least one previously trained AI/ML model. The action predicted by the actor network is deployed to the target UE (Agent)]. As to Claim 32. (New) Yeh discloses a method as claimed in claim 30, wherein generating the prediction comprises, using an ML model [Section 0107: The ML algorithms generate a suggested set of actions; ML algorithms can also be an action prediction model], predicting respective expected values of the performance measure if the respective Agents are allowed to execute within the environment respective actions selected by the respective Agent for management of the respective operational parameters [Figs. 1C, 3, Sections 0036, 0060, 0298: For RL, the agent obtain observation data based on various measurements and determines operational parameters. Actions based on the states x, where x is a number (value), determines a quality value, the Q value is a measure of the overall expected reward r (integer/value), state s and performs action a, and used for deep learning. The environment where the ML model instance is running monitors the running ML model and provides a scaling mechanism that include numbers (i.e. values) or percentage]. As to Claim 33. (New) Yeh discloses a method as claimed in claim 30, wherein predicting respective expected values of the performance measure is based on the following: the obtained state representation as input to the ML model; and current values of trainable parameters of the ML model [Sections 0049, 0058, 0060-0061, 0107: The network parameters used may be various performance indicators. The LSTM layer in representation network includes 1 to N (where N is a number/value), each of which takes a state as an input. Actions based on the states x, where x is a number (value), determines a quality value, the Q value is a measure of the overall expected reward r (integer) and perform actions, a (integer), used for deep learning. Observation information of UEs are used in the LSTM layer to learn a representation of the current environment dynamics. The ML algorithms can also be an action prediction model]. As to Claim 34. (New) Yeh discloses a method as claimed in claim 32, wherein the ML model comprises at least one of the following: a Deep Neural Network (DNN); and a Recurrent Neural Network (RNN) [Fig. 1C, Sections 0042, 0570: A deep neural network (DNN) may be used as the artificial brain of the agents. Artificial neural network/neural network or NN refer to an ML technique comprising a collection of NNs usually used for supervised learning; Examples of NNs include deep NN (DNN) and recurrent NN (RNN)]. As to Claim 35. (New) Yeh discloses a method as claimed in claim 30, wherein generating the prediction is further based on a representation of a state of the environment obtained during a previous iteration of the method [Sections 0037, 0061, 0107, 0125: Each participating edge compute nodes can share their observation data for iterations of the RL learning. Observation information of UEs are used to learn a representation of the current environment. The ML algorithms generate a suggested set of actions; ML algorithms can also be an action prediction model. A deep contextual RL architecture comprises three components, including: (1) representation learning, where features are extracted from observation from a wireless environment]. As to Claim 36. (New) Yeh discloses a method as claimed in claim 35, wherein: the RL [i.e. DRL] process includes a Deep Neural Network (DNN); and generating the prediction comprises, using the DNN, the obtained state representation [Sections 0037, 0044, 0107, 0125: Each participating edge compute nodes can share their observation data for iterations of the RL learning. In one example DRL agent use DNN that is used as one or more function approximators. The ML algorithms generate a suggested set of actions; ML algorithms can also be an action prediction model. A deep contextual RL architecture comprises (1) representation learning, where features are extracted from observation from a wireless environment] and the state representation obtained during the previous iteration of the method [Sections 0037, 0117: Each participating edge compute nodes can share their observation data for iterations of the RL learning. The RL training agent keeps at least one copy of at least one previously trained AI/ML model], predicting respective expected values of the performance measure if the respective Agents are allowed to execute within the environment the respective actions selected by the respective Agents for management of the respective operational parameters [Fig. 5, Sections 0036, 0049, 0060, 0065: For RL, the agent obtain observation data based on various measurements and determines operational parameters. The network parameters may be various performance indicators. Actions based on the states, determines a quality value, the Q value is a measure of the overall expected reward r, state s and performs action a, and used for deep learning. The edge compute node-140 to trigger management policy update for the selected UE (i.e. Agent))]. As to Claim 38. (New) Yeh discloses a method as claimed in claim 30, wherein: the ML process is a Reinforcement Learning (RL) process; and generating the prediction comprises, using a single ML model and a single inference [Sections 0032, 0046, 0062: In the present disclosure, a deep reinforcement learning (DRL) architecture is provided and include a single DRL with long-short term memory (LSTM). The output variables may be in the form of inferences and predictions containing the relevant data observations and ML features. Additionally, the RL model used for distributed training and inference determination], predicting respective expected values of the performance measure if the respective Agents are allowed to execute within the environment respective actions selected by the respective Agents for management of the respective operational parameters [Fig. 5, Sections 0036, 0049, 0060, 0065: For RL, the agent obtain observation data based on various measurements and determines operational parameters. The network parameters may be various performance indicators. Actions based on the states, determines a quality value, the Q value is a measure of the overall expected reward r, state s and performs action a, and used for deep learning. The edge compute node-140 to trigger management policy update for the selected UE (i.e. Agent))]. As to Claim 39. (New) Yeh discloses a method as claimed in claim 38, wherein predicting respective expected values of the performance measure is based on the following: the obtained state representation as input to the single ML model; and current values of trainable parameters of the single ML model [Sections 0032, 0058, 0060, 0107: A deep reinforcement learning (DRL) architecture is provided and include a single DRL. The LSTM layer in representation network includes 1 to N (where N is a number/value), each of which takes a state as an input. Actions based on the states x, where x is a number (value), determines a quality value, the Q value is a measure of the overall expected reward r (integer) and perform actions, a (integer), used for deep learning. The ML algorithms can also be an action prediction model]. As to Claim 40. (New) Yeh discloses a method as claimed in claim 30, wherein the ML process is a Reinforcement Learning (RL) process and the method further comprises [Sections 0079: The present disclosure considers reinforcement learning (RL) approaches that learn policies and parameters through interacting with the environment; provides artificial intelligence (AI) and/or machine learning (ML) techniques while applying RL for multi-access traffic management]: obtaining a value of the performance measure for the communication network; adding the obtained state representation, the selected Agent, and the obtained value of the performance parameter to an experience buffer; and based on the experience buffer, updating trainable parameters of an ML model used to generate the prediction [Sections 0038, 0050-0051, 0138, 0561: Additionally, a collection of one or more states, one or more actions, and one or more rewards may be referred to as experience data; and data structure to contain the experience data in an experience buffer or replay buffer. The central training engine train the model and update the weights (i.e. values). For RL model update, a centralized reward is calculated by the coordinator based on the collected observations; deploy the updated AI/ML model; strategy computed by the AI/ML model based on current observation(s). The actor network updates the parameters; the replay buffer store experience data structures such as training process and an inference process for the system architecture. ML uses statistics to build mathematical modeling order to make predictions or decisions based on training data]. As to Claim 41. (New) Yeh discloses a method as claimed in claim 30, wherein: the ML process is a Supervised Learning (SL) process and generating the prediction comprises, using dedicated ML models for the respective Agents [Section 0107, 0150, 0561: The ML algorithms generate a suggested set of actions based on data collected from environments; ML algorithms can also be an action prediction model trained through supervised learning. The action predicted by the actor network is deployed to the target UE (i.e. agents). The term “ML model,” or ML techniques generally fall into the following main types of learning problem categories: supervised learning] ; predicting respective expected values of the performance measure if the respective Agents are allowed to execute within the environment respective actions selected by the respective Agents for management of the respective operational parameters [Fig. 5, Sections 0036, 0049, 0060, 0065: For RL, the agent obtain observation data based on various measurements and determines operational parameters. The network parameters may be various performance indicators. Actions based on the states, determines a quality value, the Q value is a measure of the overall expected reward r, state s and performs action a, and used for deep learning. The edge compute node-140 to trigger management policy update for the selected UE (i.e. Agent))]. As to Claim 42. (New) Yeh discloses a method as claimed in claim 41, wherein predicting respective expected values of the performance measure is based on the following: the obtained state representation as input to the respective dedicated ML models; and current values of trainable parameters of the respective ML models [Sections 0049, 0058, 0060-0061, 0107: The network parameters used may be various performance indicators. The LSTM layer in representation network includes 1 to N (where N is a number/value), each of which takes a state as an input. Actions based on the states x, where x is a number (value), determines a quality value, the Q value is a measure of the overall expected reward r (integer) and perform actions, a (integer), used for deep learning. Observation information of UEs are used in the LSTM layer to learn a representation of the current environment dynamics. The ML algorithms can also be an action prediction model]. As to Claim 43. (New) Yeh discloses A method as claimed in claim 30, wherein: the ML process is a Supervised Learning (SL) process and generating the prediction comprises obtaining, from at least one of the Agents [Section 0107, 0150, 0561: The ML algorithms generate a suggested set of actions based on data collected from environments; ML algorithms can also be an action prediction model trained through supervised learning. The action predicted by the actor network is deployed to the target UE (i.e. agents). The term “ML model,” or ML techniques generally fall into the following main types of learning problem categories: supervised learning] ; respective expected values of the performance measure if the at least one Agent is allowed to execute within the environment respective actions selected by the at least one Agent for management of respective operational parameters [Fig. 5, Sections 0036, 0049, 0060, 0065: For RL, the agent obtain observation data based on various measurements and determines operational parameters. The network parameters may be various performance indicators. Actions based on the states, determines a quality value, the Q value is a measure of the overall expected reward r, state s and performs action a, and used for deep learning. The edge compute node-140 to trigger management policy update for the selected UE (i.e. Agent))]. As to Claim 44. (New) Yeh discloses a method as claimed in claim 30, wherein selecting one of the Agents on the basis of the prediction comprises selecting the Agent predicted to result in a greatest increase of the performance measure, unless a precondition for an alternative selection is fulfilled, wherein the precondition comprises a maximum or minimum limit on the number of times an Agent may be selected consecutively [Figs. 1A-B, 2, 5, Sections 0065, 0100, 0107: Various observations and measurements used to detect the UE-101 (Agent) which is done at the edge compute node-140 to trigger management policy update for the selected UE-101 (i.e. Agent). Additionally, if performance measurements indicate that the action are drifting towards sub-optimal (i.e. moving towards above optimal) performance outcomes, in response a back-up model Agent (i.e. selected Agent) is selected. The ML algorithms generate a suggested set of actions based on data collected from environments; ML algorithms can also be an action prediction model]. As to Claim 45. (New) Yeh discloses a method as claimed in claim 30, wherein the performance measure comprises a weighted combination of performance parameters for the communication network [Sections 0045, 0059: The neurons and edges have weights that adjust as learning proceeds; the weight increases or decreases the strength of the signal at a connection. Additional RNN layer(s) can be added to learn the measurement sequence correlation and incorporate it for decision making; methods provide a scheme for estimating which direction to shift one or more weights in order to make the agent better at its task(s)]. As to Claim 46. (New) Yeh discloses a method as claimed in claim 30, wherein one or more of the following applies: at least one of the operational parameters is managed at cell level, each cell having a dedicated managing Agent for the parameter within the cell; and at least one of the operational parameters is managed at environment level [Sections 0051, 0064, 0171: Each UE collects observations from the environment. Determines a state based on observations from the UEs 101/multi-agents; examples of the observations include operational parameters. As examples, the measurements collected by the UEs include one or more of the following: network or cell load]. As to Claim 47. (New) Yeh discloses a method as claimed in claim 30, wherein one or more of the following applies: the environment comprises a cluster of cells; and the plurality of operational parameters include remote electronic tilt and maximum downlink transmission power [Sections 0051, 0064, 0171: Each UE collects observations from the environment. Determines a state based on observations from the UEs 101/multi-agents; examples of the observations include operational parameters. As examples, the measurements collected by the UEs include one or more of the following: network or cell load]. As to Claim 48. (New) Yeh discloses an orchestration node [i.e. Coordinator-210 or Edge Compute Node/Agent-140] configured to orchestrate management of a plurality of operational parameters in an environment of a communication network, wherein each of the operational parameters is managed by a respective Agent, wherein at least one performance parameter of the communication network is operable to be impacted by each of the operational parameters, and [Figs. 1A-B, 2, Sections 0049, 0064, 0155: FIG. 2 shows Collaborative Multi-Agent DRL architecture, each multi-access UE is configured as agent/multi-agents-101 interact with the environment to collect data, statistics and determine a state of the environment which are sent to a coordinator entity-210 that determines the state of the environment 100x; coordinator entity 210 is employed as a centralized training engine or edge compute node; network parameters also used by the coordinator for the state of the environment; the network parameters may be various performance indicators. Determines a state based on observations from the UEs 101/multi-agents; examples of the observations may include operational parameters. Edge compute node provide orchestration of multiple applications and are coordinated with orchestration functions]: wherein the orchestration node comprises: processing circuitry [Processor-2604] configured to [Fig. 26, Sections 0155, 0410: Edge compute node provide orchestration of multiple applications and are coordinated with orchestration functions. The compute node includes a processor 2604]: obtain a representation of a state of the environment [Figs. 1B, 2-3, 6, 8, Sections 0036, 0051, 0061: The Agent-140 obtains a state based on observations from the UEs about environment-100x, and the Agent-140 obtain observation data. Each UE collects observations from the environment, which are used for the state input and UEs report their observations to the central coordinator. Observation information of UEs are used to learn a representation of the current environment dynamics]; using a Machine Learning (ML) process and the obtained state representation [Fig. 2, Sections 0036, 0051, 0125: The Agent-140 obtains a state based on observations from the UEs about environment-100x. Collaborative Multi-Agent DRL procedure where each UE collects observations from the environment, which are used for the state input for RL agent; report their observations to the central coordinator; the coordinator deploy the updated AI/ML (artificial intelligence-AI)/machine learning-ML) model. A deep contextual RL architecture comprises three components, including: (1) representation learning, where features are extracted from observation from a wireless environment], generate a prediction of which of the Agents [i.e. UE-101, Section 0049: A UE is configured as an Agent] , if allowed to execute within the environment respective actions selected by the respective Agent for management of the respective operational parameters, will result in the greatest increase of a performance measure for the communication network [Figs. 1A-B, 2, 5, Sections 0064, 0065, 0100, 0107, 0150: Determines a state based on observations from the UEs 101/multi-agents; examples of the observations include operational parameters. Various observations and measurements used to detect the UE-101 (Agent) which is done at the edge compute node-140 to trigger management policy update for the selected UE-101 (i.e. Agent). Additionally, if performance measurements indicate that the action are drifting towards sub-optimal (i.e. moving towards above optimal) performance outcomes, in response a back-up model Agent (i.e. selected Agent) is selected. The ML algorithms generate a suggested set of actions based on data collected from environments; ML algorithms can also be an action prediction model. The action predicted by the actor network is deployed to the target UE]; select one of the Agents [i.e. UE-101, Section 0049: A UE is configured as an Agent] on the basis of the prediction; and initiate execution [Sections 0107, 0499: The ML algorithms generate a suggested set of actions; ML algorithms can also be an action prediction model. Apparatus comprising means for executing the instructions] by the selected Agent of the action selected by the selected Agent [Sections 0061, 0065, 0150, 0382: Only one target UE-101 (i.e. Agent) is triggered to get an action update, use policy gradient framework, derive an action and update for the target UE. Various observations and measurements used to detect the UE-101 (Agent) which is done at the edge compute node-140 (i.e. orchestration node) to trigger management policy update for the selected UE-101 (i.e. Agent). The action predicted by the actor network is deployed to the target UE. Policy Action shall be a result of execution of multiple policy conditions]. As to Claim 49. (New) Yeh discloses the orchestration node of claim 48, wherein the processing circuitry is configured to [Fig. 26, Sections 0155, 0410: Edge compute node provide orchestration of multiple applications and are coordinated with orchestration functions. The compute node includes a processor 2604]: generate the prediction based on predicting, using an ML model, respective expected values of the performance measure if the respective Agents are allowed to execute within the environment respective actions selected by the respective Agent for management of the respective operational parameters [See Claim 32 because both claims have similar subject matter therefore similar rejection applies herein] . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Guo et al. US 20230083486. In particular, the title “LEARNING ENVIRONMENT REPRESENTATIONS FOR AGENT CONTROL USING PREDICTIONS” Furthermore, each additional prior arts cited on PTO-892 but not applied in rejection contains a disclosed description related to the claimed subject matter found either in the Figures, description summary and/or disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAEL M ULYSSE whose telephone number is (571)272-1228. The examiner can normally be reached Monday-Friday 9am-5pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chirag G. Shah can be reached at (571)272-3144. 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. May 21, 2026 /JAEL M ULYSSE/Primary Examiner, Art Unit 2477 Application/Control Number: 18/717,824 Page 2 Art Unit: 2477 Application/Control Number: 18/717,824 Page 3 Art Unit: 2477 Application/Control Number: 18/717,824 Page 4 Art Unit: 2477 Application/Control Number: 18/717,824 Page 5 Art Unit: 2477 Application/Control Number: 18/717,824 Page 6 Art Unit: 2477 Application/Control Number: 18/717,824 Page 7 Art Unit: 2477 Application/Control Number: 18/717,824 Page 8 Art Unit: 2477 Application/Control Number: 18/717,824 Page 9 Art Unit: 2477 Application/Control Number: 18/717,824 Page 10 Art Unit: 2477 Application/Control Number: 18/717,824 Page 11 Art Unit: 2477 Application/Control Number: 18/717,824 Page 12 Art Unit: 2477 Application/Control Number: 18/717,824 Page 13 Art Unit: 2477 Application/Control Number: 18/717,824 Page 14 Art Unit: 2477 Application/Control Number: 18/717,824 Page 15 Art Unit: 2477 Application/Control Number: 18/717,824 Page 16 Art Unit: 2477 Application/Control Number: 18/717,824 Page 17 Art Unit: 2477 Application/Control Number: 18/717,824 Page 18 Art Unit: 2477 Application/Control Number: 18/717,824 Page 19 Art Unit: 2477 Application/Control Number: 18/717,824 Page 20 Art Unit: 2477
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