CTNF 18/491,604 CTNF 101873 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. The office action is in response to the application filed on October 20, 2023. Claims 1-20 are pending and have been examined. Claims 1-20 are rejected. Information Disclosure Statement Acknowledgment is made of the information disclosure statements filed October 20, 2023, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner. Acknowledgment is made of the information disclosure statements filed October 02, 2024, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner. Priority Applicant's claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The present application claims priority to U.S. Provisional application No. 63/509,500 filed on June 21, 2023. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows: 02-10 The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original non-provisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. V. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994). The disclosure of the prior-filed application, U.S. Provisional application No. 63/509,500 (hereinafter "the '500 provisional application") fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The subject matter of ‘500 provisional application is directed to a fundamentally different invention that discloses LLM inference serving, specifically, a cloud API for hosting LLMs and returning inference results to requesting clients. The instant application is directed to distributed machine learning model training, specifically, fault-tolerant checkpointing of training state across distributed GPU servers during training iterations, with in-memory replica placement, network traffic aware scheduling, and recovery from training disruptions. Not a single limitation of any claim of the instant application finds written description support in the provisional. The provisional does not mention, disclose, describe, or suggest any written description support for the following concepts that appear in every independent claim: training iterations in pertinence to training servers, training accelerators, training state checkpoints, placement plans, replica storage in main memory, prediction of low- communication periods, scheduling of checkpoint transmission, disruptions of training, or resumption of training from checkpoints, distributed training environments, training state information, server groups, network communication analysis, scheduling of checkpoint transmission. The as-filed original specification of the '500 provisional application fails to provide adequate support or enablement for all limitation elements of claims 1-20. Therefore, the effective filing date for claims 1-20 of the instant application is the effective filing date of the instant, non-provisional application, October 20, 2023. Each claim will receive benefit of the earliest filing date above for which a continuous chain of support can be established for the entirety of the claim. Claim Objections 07-29-01 AIA Claim s 1, 2, 3, and 5 are objected to because of the following informalities: "the one or computing" should read "the one or more computing” . Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. According to the USPTO guidelines, a claim is directed to non-statutory subject matter if: Step 1: The claim does not fall within one of the four statutory categories of invention (process, machine, manufacture, or composition of matter), or, Step 2: The claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.04(a)(2)(I) states: "The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations." MPEP 2106.04(a)(2)(III) states: "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions. Further, the MPEP states: "The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g. pen and paper or a slide run) to perform the claim limitation. Using the two-step inquiry, it is clear that Claims 1-20 are each directed to non-statutory subject matter as shown below: Please note the following: The following groups of claims are expressed in different statutory categories: Claims 1-5 are directed to a system comprising of a computing device and a processor configured to carry out a process for providing efficient, distributed checkpointing and fault-recovery system for large machine learning models by optimizing training interruptions, securely saving model states across the main memories of different servers during natural network lulls, and ensuring that training can resume quickly without losing significant progress. Claims 6-15 are directed to a method for providing optimized fault-tolerance system for distributed machine learning. Claims 16-20 are directed to a non-transitory computer-accessible storage media storing a plurality of instructions which, when executed by a processor, cause the processor to carry out a process. With respect to Claim 1 which is an independent claim: Step 1: Claim 1 is directed to a system for an efficient, distributed fault-tolerance system for training large machine learning models, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter. Claim 6 is directed to a method, also known as a process, which is one of the four statutory categories of patentable subject matter. Claim 16 is directed to a non-transitory computer-accessible storage media on which computer-executable instructions are stored, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter. Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “determine a number of training servers of a distributed training environment which is to be used to train a machine learning model, wherein an individual training server includes a main memory and one or more hardware training accelerators,…....”; Determining a number of training servers that comprise of a main memory and one or more training accelerators to train a machine learning model is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). “determine a number of replicas of training state checkpoints of the machine learning model that are to be stored within respective main memories of training servers, wherein an individual training state checkpoint comprises training state information generated at the one or more hardware training accelerators of an individual training server;” ; Determining a number of replicas of training state checkpoints of a ML model (with already pre-generated training state information) to be stored within respective main memories of training servers is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). “generate, based at least in part on the number of replicas and the number of training servers of the distributed training environment, a placement plan for training state checkpoints, wherein the placement plan divides the training servers of the distributed training environment into groups, wherein an individual group includes a plurality of training servers, and wherein the placement plan indicates, with respect to a first training server within a particular group, one or more other training servers of the particular group at which respective replicas of training state checkpoints of the first training server are to be stored in main memory;” ; Generating based at least in part on replicas and training server counts, a placement plan dividing the distributed environment's training servers into groups, where the plan indicates, for a first server, which other servers in its group will store its replica checkpoints in main memory is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). “obtain a prediction of respective timings of one or more low-training-communication periods during training iterations of the machine learning model, wherein the prediction is based at least in part on analysis of network communications among a plurality of hardware training accelerators of the distributed training environment during selected training iterations of the machine learning model;” ; Obtaining, based at least in part on an analysis of network communications among hardware training accelerators, a prediction of timings of low training periods during training iterations of the ML model is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). “schedule, during predicted low-training-communication periods of one or more training iterations of the machine learning model, transmission of respective portions of replicas of training state checkpoints from the first training server of the particular group of training servers to a second training server of the particular group, wherein the second training server is selected based at least in part on the placement plan;” ; Scheduling, based at least in part on a prediction of low-training comm. periods for the training iterations of the ML model, transmission of the replicas of the training state checkpoints from the first training server to a selected second training server as per the placement plan is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “…........wherein training of the machine learning model comprises a plurality of iterations, and wherein during an individual iteration, respective subsets of training state information of the machine learning model are generated at individual ones of the one or more hardware training accelerators;” ; Training a ML model by running a plurality of iterations to generate respective subsets of training state information of said ML model on a hardware training accelerator only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(2) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). “initiate training iterations of the machine learning model at the distributed training environment;”; Initiating training iterations of the machine learning model at the distributed training environment only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1). in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). “and resume, using at least a first replica of a first training state checkpoint which was generated at the first training server and transmitted to the second training server during the predicted low-training-communication periods, training iterations of the machine learning model after a first event results in a disruption of the training iterations.” ; Resuming training iterations after a first event/training disruption by restoring a backup/first replica of a first training state checkpoint that was generated at a first server and then transmitted to a second server during a predicted low-training comm. period only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Training a ML model by running a plurality of iterations to generate respective subsets of training state information of said ML model on a hardware training accelerator AND resuming training iterations after a first event/training disruption by restoring a backup/first replica of a first training state checkpoint that was generated at a first server and then transmitted to a second server during a predicted low-training comm. period amounts to "apply it" (or an equivalent) and mere instructions to implement an abstract idea on a computer using a generic computer component or merely uses a computer in its ordinary capacity as a tool to perform an existing process. -See MPEP 2106.05(f)(2). Initiating training iterations of the machine learning model at the distributed training environment amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a machine learning model, hardware training accelerator, distributed training environment, and training servers is generally linked to a particular technological environment or field of use (AI/ML/Comp. Hardware) - see MPEP 2106.05(h). Therefore, Claim 1 is directed to non-statutory subject matter and rejected. With respect to Claim 2 which is dependent on Claim 1 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “cause a second replica of a second training state checkpoint which was generated at the first training server to be stored in a main memory of the first training server;” ; Storing the second replica of a second training state checkpoint in a main memory of the first training server (where it was generated) is considered insignificant extra-solution activity (memory storage) - see MPEP 2106.05(g). “and resume, using the second replica, training iterations of the machine learning model at the first training server after a second event results in a disruption of the training iterations.” ; Resuming training iterations of the machine learning model using the second replica/backup at the first training server after a second event disruption of the training iterations has occurred only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1). in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). Step 2B: Storing the second replica of a second training state checkpoint in a main memory of the first training server (where it was generated) constitutes as storing and retrieving information in memory which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. - See MPEP 2106.05(d)(II). Using the second replica/backup to resume training iterations of the machine learning model after a second event disruption of the training iterations has occurred amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a distributed training environment, training servers, and training accelerators is generally linked to a particular technological environment or field of use (AI/ML/IT/Data Infrast.) - see MPEP 2106.05(h). Therefore, Claim 2 is directed to non-statutory subject matter and rejected. With respect to Claim 3 which is dependent on Claim 1 respectively: Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “determine a rate at which replicas of training state checkpoints are to be transmitted from the first training server to a remote persistent storage device external to the first training server;” ; Determining a rate at which replicas of training state checkpoints are transmitted from the training server to a remote storage device external to the first training server is an abstract idea of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C). Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “cause a second replica of a second training state checkpoint to be transmitted from the first training server to the remote persistent storage device in accordance with the rate;” ; Transmitting a second replica of a second training state checkpoint from a first training server to the remote storage device in accordance with the rate is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g) “and resume, using the second replica, training iterations of the machine learning model at the first training server after a second event results in a disruption of the training iterations.” ; Resuming training iterations of the machine learning model using the second replica/backup at the first training server after a second event disruption of the training iterations has occurred only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1). in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Transmitting a second replica of a second training state checkpoint from a first training server to the remote storage device in accordance with the rate constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as “well‐understood, routine, and conventional when they are claimed in a generic manner." - See MPEP 2106.05(d)(II). Using the second replica/backup to resume training iterations of the machine learning model after a second event disruption of the training iterations has occurred amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a distributed training environment, training servers, and training accelerators is generally linked to a particular technological environment or field of use (AI/ML/IT/Data Infrast.) - see MPEP 2106.05(h). Therefore, Claim 3 is directed to non-statutory subject matter and rejected. With respect to Claim 4 which is dependent on Claim 1 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “wherein the first replica of the first training state checkpoint comprises one or more of: (a) respective values of learned parameters of the machine learning model or (b) optimizer states of the machine learning model.” ; The usage of learned parameters of a ML model and optimizer states of said model in regards to a first training state checkpoint generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). Step 2B: The usage of learned parameters of a ML model and optimizer states of said model in regards to a first training state checkpoint generally links the use of the abstract idea to a particular technological environment or field of use (AI/ML) - See MPEP § 2106.05(h). Therefore, Claim 4 is directed to non-statutory subject matter and rejected. With respect to Claim 5 which is dependent on Claim 1 respectively: Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “determine a rate at which replicas of training state checkpoints are to be transmitted between the first training server and the second training server, wherein in accordance with the rate, replicas of training state checkpoints are not transmitted between the first training server and the second training server during a subset of training iterations after the prediction is obtained.” ; Determining a rate for transferring replicas/backups of training checkpoints between two servers (first & second), wherein based on this rate, no checkpoint backups are transferred during certain training steps/iterations after a prediction is made is an abstract idea of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C). Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Therefore, Claim 5 is directed to non-statutory subject matter and rejected. With respect to Claim 6 which is an independent claim: Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “determining a number of replicas of training state checkpoints of a machine learning model that are to be stored within respective main memories of training servers of a distributed training environment during training of the machine learning model, wherein individual training servers of the distributed training environment comprise one or more hardware training accelerators, wherein an individual training state checkpoint comprises training state information generated at the one or more hardware training accelerators of an individual training server, and wherein training of the machine learning model comprises a plurality of training iterations;” ; Determining a number of replicas/backups of training state checkpoints of a machine learning model to be stored in training servers' main memories in a distributed environment during training iterations, where individual servers have hardware accelerators that generate the state information for the checkpoints is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). “generating, based at least in part on the number of replicas, a placement plan for training state checkpoints, wherein the placement plan indicates, with respect to an individual training server, one or more other training servers of the distributed training environment at which respective replicas of training state checkpoints of the individual training server are to be stored in main memory;” ; Generating based at least in part on the number of replicas/backups, a placement plan for training state checkpoints, wherein the plan tells each individual training server which other servers will store its checkpoint replicas in their main memory is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “transmitting, during predicted low-communication periods of one or more of the training iterations, respective portions of replicas of training state checkpoints from a first training server to a second training server, wherein the second training server is selected based at least in part on the placement plan;” ; Transmitting portions of replicas/backups of training state checkpoints between servers (first->second) during predicted low-comm periods of training iterations is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g). “and resuming training iterations of the machine learning model after a disruption of the training iterations, wherein said resuming comprises utilizing at least a first replica of a training state checkpoint which was generated at the first training server and transmitted to the second training server during the predicted low-communication periods.” ; Resuming training iterations after a disruption using a first checkpoint replica/backup generated at the first training server and transmitted to the second training server during predicted low-comm. periods only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Transmitting portions of replicas/backups of training state checkpoints between servers (first->second) during predicted low-comm periods of training iterations constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner." - See MPEP 2106.05(d)(II). Using the first training checkpoint replica/backup to resume training iterations of the machine learning model after a disruption of the training iterations has occurred amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a machine learning models and their training checkpoint backups is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h). Therefore, Claim 6 is directed to non-statutory subject matter and rejected. With respect to Claim 7 which is dependent on Claim 6 respectively: Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “predicting the low-communication periods based at least in part on analysis of network traffic between a plurality of hardware training accelerators of the distributed training environment.” ; Predicting, based at least in part on an analysis of network communications among hardware training accelerators (that can be recorded on pen and paper), a prediction of timings of low-comm. training periods during training iterations of the ML model is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Therefore, Claim 7 is directed to non-statutory subject matter and rejected. With respect to Claim 8 which is dependent on Claim 6 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “storing, within main memory of the first training server, a second replica of a training state checkpoint of the first training server;” ; Storing the second replica/backup of a second training state checkpoint in a main memory of the first training server (where it was generated) is considered insignificant extra-solution activity (memory storage) - see MPEP 2106.05(g). “and utilizing the second replica to resume training iterations of the machine learning model subsequent to another disruption of the training iterations.” ; Resuming training iterations after a disruption using a second training checkpoint replica/backup only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). Step 2B: Storing the second replica/backup of a training state checkpoint in a main memory of the first training server (where it was generated) constitutes as storing and retrieving information in memory which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. - See MPEP 2106.05(d)(II). Using the second training checkpoint replica/backup to resume training iterations of the machine learning model after a disruption of the training iterations has occurred amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a machine learning models and their training checkpoint backups is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h). Therefore, Claim 8 is directed to non-statutory subject matter and rejected. With respect to Claim 9 which is dependent on Claim 6 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “reserving a subset of accelerator memory of a particular hardware training accelerator of the first training server to store training state information which is to be transmitted to the second training server,” ; Reserving parts of the first server's training accelerator's memory to store training state information which is to be transmitted to the second training server only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(2) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). “wherein said transmitting the respective portions comprises transferring training state information from the subset to another hardware training accelerator of the second training server.” ; Transmitting/transferring training state information from the subset of the accelerator memory to another training accelerator of a second training server is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g) Step 2B: Reserving parts of the first server's training accelerator's memory to store training state information which is to be transmitted to the second training server amounts to "apply it" (or an equivalent) and mere instructions to implement an abstract idea on a computer using a generic computer component or merely uses a computer in its ordinary capacity as a tool to perform an existing process. -See MPEP 2106.05(f)(2). The usage of hardware training accelerators and training servers is generally linked to a particular technological environment or field of use (AI/ML/Hardware) - see MPEP 2106.05(h). Transmitting/transferring training state information from the subset of the accelerator memory to another training accelerator of a second training server constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner - See MPEP 2106.05(d)(II). Therefore, Claim 9 is directed to non-statutory subject matter and rejected. With respect to Claim 10 which is dependent on Claim 6 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “transmitting a particular portion of a particular replica of a training state checkpoint from the first training server to the second training server during a time interval which is not a predicted low-communication period.” ; Transmitting a particular portion of a particular training state checkpoint backup between servers (first->second) during a time interval THAT IS NOT a predicted low-comm. training period is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g). Step 2B: Transmitting a particular portion of a particular training state checkpoint backup between servers (first->second) during a time interval THAT IS NOT a predicted low-comm. training period constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner." - See MPEP 2106.05(d)(II). Therefore, Claim 10 is directed to non-statutory subject matter and rejected. With respect to Claim 11 which is dependent on Claim 6 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “storing, in a first portion of accelerator memory of a particular training accelerator of the second training server, a first portion of the first replica of the training state checkpoint which is transmitted from the first training server;” ; Storing a first portion of the first replica/backup of the training state checkpoint in a first portion of the accelerator memory of the second training server after it was transmitted from the first training server is considered insignificant extra-solution activity (memory storage) - see MPEP 2106.05(g). “and copying, from a second portion of accelerator memory of the particular training accelerator, to another memory of the second training server, a second portion of the first replica, wherein said copying is performed at least partly in parallel with said storing the first portion of the first replica in the first portion of the accelerator memory.” ; Concurrently copying a second part of the first replica/backup from the specific accelerator's memory to the second server's memory while storing the first part of the first replica in the accelerator's memory is considered insignificant extra-solution activity (post-solution activity) - see MPEP 2106.05(g). Step 2B: Storing a first portion of the first replica/backup of the training state checkpoint in a first portion of the accelerator memory of the second training server after it was transmitted from the first training server constitutes as storing and retrieving information in memory which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. - See MPEP 2106.05(d)(II). Concurrently copying a second part of the first replica/backup from the specific accelerator's memory to the second server's memory while storing the first part of the first replica in the accelerator's memory constitutes as an insignificant extra-solution activity, specifically a post-solution activity. - see MPEP 2106.05(g). Therefore, Claim 11 is directed to non-statutory subject matter and rejected. With respect to Claim 12 which is dependent on Claim 6 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “storing a collection of training state information of the machine learning model at a storage service separate from the training servers of the distributed training environment, wherein the collection comprises contents of one or more training state checkpoints of one or more the training servers;” ; Storing a collection of training state information (multiple training state checkpoints of one or more training servers) at a storage service separate from the training servers is considered insignificant extra-solution activity (memory storage) - see MPEP 2106.05(g). “and subsequent to a particular failure detected at the distributed training environment, copying, from the storage service to one or more training servers of the distributed training environment, at least a portion of the collection to enable resumption of training iterations of the machine learning model.” ; Copying a portion of the training state information collection from the storage service to a training server to enable resumption of the ML model training iterations is considered insignificant extra-solution activity (post-solution activity) - see MPEP 2106.05(g) Step 2B: Storing a collection of training state information (multiple training state checkpoints of one or more training servers) at a storage service separate from the training servers constitutes as storing and retrieving information in memory which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. - See MPEP 2106.05(d)(II). Copying a portion of the training state information collection from the storage service to a training server to enable resumption of the ML model training iterations constitutes as an insignificant extra-solution activity, specifically a post-solution activity. - see MPEP 2106.05(g). Therefore, Claim 12 is directed to non-statutory subject matter and rejected. With respect to Claim 13 which is dependent on Claim 6 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “obtaining, via one or more programmatic interfaces of a cloud computing environment, a set of parameters pertaining to training of the machine learning model, wherein the set of parameters include the number of training servers in the distributed training environment.” ; Receiving/obtaining a set of training parameters (including # of training servers) via programmatic interfaces of a cloud computing environment is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g). Step 2B: Receiving/obtaining a set of training parameters (including # of training servers) via programmatic interfaces of a cloud computing environment constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when claimed in a generic manner - See MPEP 2106.05(d)(II). Therefore, Claim 13 is directed to non-statutory subject matter and rejected. With respect to Claim 14 which is dependent on Claim 6 respectively: Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “generating, at a cloud computing environment, one or more parameter recommendations pertaining to training of the machine learning model, wherein the one or more parameter recommendations includes the number of replicas;” ; Generating parameter recommendations (including number of replicas/backups) pertaining to ML model training is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “and initiating training iterations of the machine learning model based at least in part on receiving approval of the one or more parameter recommendations.” ; Initiating training iterations of the machine learning model as a result of receiving approval of one or more parameter recommendations only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1). Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Initiating training iterations of the machine learning model as a result of receiving approval of one or more parameter recommendations amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). Therefore, Claim 14 is directed to non-statutory subject matter and rejected. With respect to Claim 15 which is dependent on Claim 6 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “wherein the first training server comprises a compute instance of a virtualized computing service of a cloud computing environment.” ; The usage of a compute instance of a virtualized computing service generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). Step 2B: The usage of a compute instance of a virtualized computing service generally links the use of the abstract idea to a particular technological environment or field of use (cloud computing) - See MPEP § 2106.05(h). Therefore, Claim 15 is directed to non-statutory subject matter and rejected. With respect to Claim 16 which is an independent claim: Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “generate, based at least in part on a number of training servers of a distributed training environment of a machine learning model, a placement plan for training state checkpoints of the machine learning model, wherein the placement plan indicates, with respect to an individual training server, one or more other training servers at which respective replicas of training state checkpoints of the individual training server are to be stored;” ; Generating based at least in part on replicas and training server counts, a placement plan that indicates, for a server, which other servers can store respective checkpoint replicas/backups is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “transmit, during selected time periods of one or more training iterations of the machine learning model, respective portions of replicas of training state checkpoints from a first training server of a particular group of training servers to a second training server of the particular group, wherein the second training server is selected based at least in part on the placement plan;” ; Transmitting a particular portion of a particular training state checkpoint backup between servers (first->second) during selected time periods of training iterations is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g). “and resume training iterations of the machine learning model after a first disruption of the training iterations, wherein resumption of the training iterations comprises retrieval of at least a particular replica of a training state checkpoint which was generated at the first training server and transmitted to the second training server during the selected time periods.” ; Resuming training iterations after a first disruption that involves retrieving a particular replica/backup of a training state checkpoint that was generated at the first server and then transmitted to the second server only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Transmitting a particular portion of a particular training state checkpoint backup between servers (first->second) during selected time periods of training iterations constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when claimed in a generic manner- See MPEP 2106.05(d)(II). Resuming training iterations after a first disruption that involves retrieving a particular replica/backup of a training state checkpoint that was generated at the first server and then transmitted to the second server amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a machine learning models and their training checkpoint backups is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h). Therefore, Claim 16 is directed to non-statutory subject matter and rejected. With respect to Claim 17 which is dependent on Claim 16 respectively: Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “select the time periods of the one or more training iterations based at least in part on analysis of network traffic between a plurality of hardware training accelerators of the distributed training environment.” ; Selecting training iteration time periods based on an analysis of network traffic is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Therefore, Claim 17 is directed to non-statutory subject matter and rejected. With respect to Claim 18 which is dependent on Claim 16 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “store, at the first training server, a local replica of a training state checkpoint of the first training server;” ; Storing a first portion of the first replica/backup of the training state checkpoint in a first portion of the accelerator memory of the second training server after it was transmitted from the first training server is considered insignificant extra-solution activity (memory storage) - see MPEP 2106.05(g). “and utilize the local replica to resume training iterations of the machine learning model after a second disruption of the training iterations.” ; Resuming training iterations after a second disruption by utilizing a local replica/backup only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1). in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). Step 2B: Storing a first portion of the first replica/backup of the training state checkpoint in a first portion of the accelerator memory of the second training server after it was transmitted from the first training server constitutes as storing and retrieving information in memory which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner - See MPEP 2106.05(d)(II). Resuming training iterations after a second disruption by utilizing a local replica/backup amount to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a machine learning models and their training checkpoint backups is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h). Therefore, Claim 18 is directed to non-statutory subject matter and rejected. With respect to Claim 19 which is dependent on Claim 16 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “wherein the machine learning model comprises one or more neural networks.” ; The usage/presence of a neural network generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). Step 2B: The usage/presence of a neural network/machine learning model generally links the use of the abstract idea to a particular technological environment or field of use (AI/ML) - See MPEP § 2106.05(h). Therefore, Claim 19 is directed to non-statutory subject matter and rejected. With respect to Claim 20 which is dependent on Claim 16 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon. Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application: “wherein a particular hardware training accelerator of the first training server comprises one or more of: a graphics processing unit (GPU) or a processor customized for machine learning computations.” ; The usage/presence of a GPU or customized processor for ML computations generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h). Step 2B: The usage/presence of a GPU or customized processor for ML computations generally links the use of the abstract idea to a particular technological environment or field of use (AI/ML/Comp. Hardware) - See MPEP § 2106.05(h). Therefore, Claim 20 is directed to non-statutory subject matter and rejected. Allowable Subject Matter Claims 1-20 are subject to potential allowance. Subject matter ineligibility as a judicial exception under 35 U.S.C. $ 101 still stands. 13-03-01 AIA The following is a statement of reasons for the indication of allowable subject matter: The prior art of record fails to teach or suggest, (determining the required number of training servers and checkpoint replicas, generating a placement plan that assigns specific replica locations across main memories, initiating machine learning training iterations on specialized hardware accelerators, generating critical checkpoint data, including learned parameters and optimizer states, analyzing network communications to predict periods of low training traffic, scheduling/transmitting checkpoint portions to designated backup servers according to the placement plan during these predicted idle low-communication periods, and if any event disruption occurs, resuming training iterations using these stored checkpoints), as a whole . 07-96 AIA The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure : Yu (US20230229905A1, filed January 18, 2022) Checkpoint State Storage for Machine-Learning Model Training : A method for training a machine-learning model. A plurality of nodes are assigned for training the machine-learning model. Nodes include agents comprising at least an agent processing unit and local memory. Each agent manages, via a local network, one or more workers that include a worker processing unit. Shards of a training data set are distributed for parallel processing by workers at different nodes. Each worker processing unit is configured to iteratively train on minibatches of a shard, and to report checkpoint states indicating updated parameters for storage in local memory. Based at least on recognizing a worker processing unit failing, the failed worker processing unit is reassigned and initialized based at least on a checkpoint state stored in local memory. Yu either alone or in-combination fails to disclose the claimed subject matter as a whole. Sivathanu et. Al (US20230236837A1, filed June 30, 2022) Elastically Managing Workers of Multi-Worker Workloads on Accelerator Devices : A computerized method for elastically managing the execution of workers of multi-worker workloads on accelerator devices wherein a first worker of a multi-worker workload is executed on an accelerator device is configured to a second memory state of a second worker, the second worker is executed during a second time interval when the second worker is in a state equivalent to the first worker state, and during the intervals collective communication operations between the workers are accumulated and at the second context switch point the accumulated operations are performed. Sivathanu et. Al either alone or in-combination fails to disclose the claimed subject matter as a whole. Zheng et. Al (US12423578B1, filed March 29, 2022) Distributed Training of Machine Learning Models: A resource set which includes multiple servers with a respective plurality of training computing devices is identified for training a machine learning model. The resource set is subdivided into partition groups, such that each partition group can store a respective replica of state information of the model. The model is trained using the partition groups. The training comprises a multi-stage gathering of a portion of the state information at training computing devices of a particular partition group. Different types of communication channels between training computing devices are used in respective stages of the gathering, including inter-server communication channels in one stage and an intra-server communication channel during another stage. A trained version of the model is stored. Zheng either alone or in combination fails to disclose the claimed subject matter as a whole. Sidorov et. Al (US20240403626A1, filed May 30, 2023) Centralized Architecture for Distributed Data Parallel Training : Systems and techniques are provided for a centralized architecture for distributed data parallel training. An example method can determine, by a centralized process in a distributed data parallel training environment used to train a model via data parallelism, a respective state of each training worker process from a plurality of training worker processes in the distributed data parallel training environment, the model comprising an artificial intelligence (AI) or machine learning (ML) model; determining, by the centralized process based on the respective state of each training worker process, a respective task that one or more training worker processes should perform with respect to a local replica of the model and/or training data associated with the local replica; and sending, by the centralized process to the one or more training worker processes, an instruction to perform the respective task with respect to the local replica of the model and/or the training data. Sidorov either alone or in-combination fails to disclose the claimed subject matter as a whole. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action. It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://;www.uspto.gov/patent/laws-and-regulations/interview-practice. Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e- mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOOR F CHEEMA whose telephone number is (571)272-9642. The examiner can normally be reached Monday-Friday 7:30am-5:00pm alternative Fridays off. 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, Mariela Reyes can be reached at (571) 270-1006. 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. /N.F.C./Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142 Application/Control Number: 18/491,604 Page 2 Art Unit: 2142 Application/Control Number: 18/491,604 Page 3 Art Unit: 2142 Application/Control Number: 18/491,604 Page 4 Art Unit: 2142 Application/Control Number: 18/491,604 Page 5 Art Unit: 2142 Application/Control Number: 18/491,604 Page 6 Art Unit: 2142 Application/Control Number: 18/491,604 Page 7 Art Unit: 2142 Application/Control Number: 18/491,604 Page 8 Art Unit: 2142 Application/Control Number: 18/491,604 Page 9 Art Unit: 2142 Application/Control Number: 18/491,604 Page 10 Art Unit: 2142 Application/Control Number: 18/491,604 Page 11 Art Unit: 2142 Application/Control Number: 18/491,604 Page 12 Art Unit: 2142 Application/Control Number: 18/491,604 Page 13 Art Unit: 2142 Application/Control Number: 18/491,604 Page 14 Art Unit: 2142 Application/Control Number: 18/491,604 Page 15 Art Unit: 2142 Application/Control Number: 18/491,604 Page 16 Art Unit: 2142 Application/Control Number: 18/491,604 Page 17 Art Unit: 2142 Application/Control Number: 18/491,604 Page 18 Art Unit: 2142 Application/Control Number: 18/491,604 Page 19 Art Unit: 2142 Application/Control Number: 18/491,604 Page 20 Art Unit: 2142 Application/Control Number: 18/491,604 Page 21 Art Unit: 2142 Application/Control Number: 18/491,604 Page 22 Art Unit: 2142 Application/Control Number: 18/491,604 Page 23 Art Unit: 2142 Application/Control Number: 18/491,604 Page 24 Art Unit: 2142 Application/Control Number: 18/491,604 Page 25 Art Unit: 2142 Application/Control Number: 18/491,604 Page 26 Art Unit: 2142 Application/Control Number: 18/491,604 Page 27 Art Unit: 2142 Application/Control Number: 18/491,604 Page 28 Art Unit: 2142 Application/Control Number: 18/491,604 Page 29 Art Unit: 2142 Application/Control Number: 18/491,604 Page 30 Art Unit: 2142 Application/Control Number: 18/491,604 Page 31 Art Unit: 2142 Application/Control Number: 18/491,604 Page 32 Art Unit: 2142 Application/Control Number: 18/491,604 Page 33 Art Unit: 2142 Application/Control Number: 18/491,604 Page 34 Art Unit: 2142 Application/Control Number: 18/491,604 Page 35 Art Unit: 2142 Application/Control Number: 18/491,604 Page 36 Art Unit: 2142 Application/Control Number: 18/491,604 Page 37 Art Unit: 2142 Application/Control Number: 18/491,604 Page 38 Art Unit: 2142 Application/Control Number: 18/491,604 Page 39 Art Unit: 2142 Application/Control Number: 18/491,604 Page 40 Art Unit: 2142
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