CTFR 18/257,881 CTFR 84295 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. The amendments were received on 5/1/2026. Claims 1-4 and 10-38 are pending where claims 1-4 and 10-38 were previously presented. Specification The applicant provided a replacement title, in view of the new title, the respective objection has been withdrawn. 35 USC § 112 The applicant amended the claims to address the 35 USC 112 rejections. In view of the amendments, the respective 35 USC 112 rejections have been withdrawn. 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 17-19, 21, and 34-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. With regard to claim 17: Step 2A, Prong One: The claim recites the following limitations which are drawn towards an abstract idea: perform calculation of the assigned calculation range (recites mental process steps of evaluating/analysis that can include calculation steps including mathematical calculations); wherein the assigned calculation range is changed based on information regarding the resources such that a sum of a communication time required to transmit the calculation result and a calculation time required to perform the assigned calculation range does not exceed a specific threshold (recites mental process steps involving mathematical calculations and comparisons with a decision on whether less work should be given, stay the same or even more work given, similar to work assignments given out to workers in an office setting). As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below. Step 2A, Prong Two: The following limitations have been identified as being additional elements as discussed below. An information processing device, comprising: a processor configured to (recites generic hardware/computer element at a high-level of generality for performing generic functions, see MPEP 2106.05(f)): receive a part of a series of calculation of a deep neural network as an assigned calculation range (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g)); transmit a calculation result of the assigned calculation range to a designated destination (recites insignificant extrasolution activity of transmitting information over a network, see MPEP 2106.05(g)), acquire information regarding resources including at least one of spare calculation capacity of the information processing device or communication capacity or communication quality of a communication link through which the calculation result is transmitted (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g)); transmit the acquired information to a designation source of the assigned calculation range (recites insignificant extrasolution activity of transmitting information over a network, see MPEP 2106.05(g)); and receive information regarding a change in the assigned calculation range from the designation source (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g)), As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recite generic computer functions that are performed by generic computer hardware elements. Step 2B: Below is the analysis of the claims: An information processing device, comprising: a processor configured to (recites generic hardware/computer element at a high-level of generality for performing generic functions, see MPEP 2106.05(f)): receive a part of a series of calculation of a deep neural network as an assigned calculation range (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d)); transmit a calculation result of the assigned calculation range to a designated destination (recites well-understood, routine, and conventional activity of transmitting information over a network, see MPEP 2106.05(d)), acquire information regarding resources including at least one of spare calculation capacity of the information processing device or communication capacity or communication quality of a communication link through which the calculation result is transmitted (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d)); transmit the acquired information to a designation source of the assigned calculation range (recites well-understood, routine, and conventional activity of transmitting information over a network, see MPEP 2106.05(d)); and receive information regarding a change in the assigned calculation range from the designation source (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d)), As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite generic computer functions that are performed by generic computer hardware elements With regard to claim 18, this claim recites wherein the information regarding the change in the calculation range identifies one splitting mode selected from a plurality of splitting modes (recites mental process steps of evaluating and forming a decision including assignment ranges, similar to setting up queues/booths for particular letter ranges for some event registration such as running event, e.g. 1 st booth is for last names beginning with A through D; 2 nd booth is last names beginning with E through I; et cetera), and the plurality of splitting modes define respective distributions of the series of calculation of the deep neural network among a communication terminal, a server, and communication nodes (recites field of use limitations describing the computer hardware components and their intended device, see MPEP 2106.05(f)). With regard to claim 19, this claim recites wherein the processor is further configured to transmit (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d)), in a case where the calculation result satisfies a condition for ending the series of calculation in the middle, the calculation result to a final reception destination of the series of calculation rather than the designated destination (recites mental process steps of determining/evaluating how confident is the result and judging step on whether to use the result or keep evaluating/computing information). With regard to claim 21: Step 2A, Prong One: The claim recites the following limitations which are drawn towards an abstract idea: estimate a communication time and a calculation time based on information regarding the resources (recites mental process steps involving mathematical calculations based on known or received/collected information); and determine a plurality of entities selected from the communication terminal, the server, and the communication nodes to which respective parts of the series of calculation of the deep neural network are assigned such that a sum of the communication time and the calculation time does not exceed a specific threshold (recites mental process steps of evaluating/analyzing data and mapping/identifying which component is meant to perform some task, e.g. similar to knowing what jobs/tasks are assigned to subordinates similar to task assignments while trying to minimize the length of time for each entity). As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below. Step 2A, Prong Two: The following limitations have been identified as being additional elements as discussed below. An information processing system, comprising: a plurality of communication nodes (recites generic computer elements to merely apply the judicial exception in a computer, see MPEP 2106.05(f)) included in a communication network that relays communication between a communication terminal and a server, wherein the communication terminal transmits an input to a deep neural network or performs at least a part of a series of calculation of the deep neural network and transmits a result of the calculation, …, the plurality of communication nodes are configured to transmit information regarding resources of the communication network to a specific communication node (recites insignificant extrasolution activity of receiving/transmitting information over a network, see MPEP 2106.05(g)), the server performs at least part of the series of calculation of the deep neural network (recites apply-it limitations of a computer hardware element recited at a high-level of generality performing the judicial exception, see MPEP 2106.05(f)), the specific communication node comprises a processor configured to: (recites apply-it limitations of a computer hardware element recited at a high-level of generality performing the judicial exception, see MPEP 2106.05(f)). As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recite generic computer functions such as receiving or transmitting information that are performed by generic computer hardware elements. Step 2B: Below is the analysis of the claims: An information processing system, comprising: a plurality of communication nodes (recites generic computer elements to merely apply the judicial exception in a computer, see MPEP 2106.05(f)) included in a communication network that relays communication between a communication terminal and a server, wherein the communication terminal transmits an input to a deep neural network or performs at least a part of a series of calculation of the deep neural network and transmits a result of the calculation, …, the plurality of communication nodes are configured to transmit information regarding resources of the communication network to a specific communication node (recites well-understood, routine, and conventional activity of receiving/transmitting information over a network, see MPEP 2106.05(d)), the server performs at least part of the series of calculation of the deep neural network (recites apply-it limitations of a computer hardware element recited at a high-level of generality performing the judicial exception, see MPEP 2106.05(f)), the specific communication node comprises a processor configured to: (recites apply-it limitations of a computer hardware element recited at a high-level of generality performing the judicial exception, see MPEP 2106.05(f)). As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite generic computer functions such as receiving/transmitting information that are performed by generic computer hardware elements. With regard to claim 34: Step 2A, Prong One: The claim recites the following limitations which are drawn towards an abstract idea: estimate a communication time required to receive the first information and transmit a result of calculation through the communication link using the communication capacity or the communication quality (recites mental process steps involving mathematical calculations based on known or received/collected information); estimate a calculation time required to execute a remaining part of the series of calculation or a second assignment range using the spare calculation capacity (recites mental process steps involving mathematical calculations based on known or received/collected information); identify a node to which the output value included in the first information is to be input based on the identification information (recites mental process step of evaluating information to determine what do with the result; similar to delegating tasks to subordinates); and execute the remaining part of the series of calculation of the deep neural network or the second assignment range by inputting the output value included in the identified node (recites mental process step of performing a calculation/computation). As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below. Step 2A, Prong Two: The following limitations have been identified as being additional elements as discussed below. An information processing device, comprising: a processor configured to: (recites generic hardware element at a high-level of generality to perform the judicial exception, see MPEP 2106.05(f)) receive first information including identification information and an output value of a node included in a final layer in a first assignment range in a series of calculation of a deep neural network from a first entity that executed a preceding part of the series of calculation of the deep neural network (recites insignificant extrasolution activity of receiving/transmitting information over a network, see MPEP 2106.05(g)), receive information regarding resources of a communication network that relays communication between the information processing device and at least a second entity configured to perform the series of calculation (recites insignificant extrasolution activity of receiving/transmitting information over a network, see MPEP 2106.05(g)), wherein the resources include at least one of communication capacity or communication quality of a communication link and spare calculation capacity of the information process device (recites field of use limitations describing the intended meaning of the data that is received, see MPEP 2106.05(f)); As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recite generic computer functions such as receiving or transmitting information that are performed by generic computer hardware elements. Step 2B: Below is the analysis of the claims: An information processing device, comprising: a processor configured to: (recites generic hardware element at a high-level of generality to perform the judicial exception, see MPEP 2106.05(f)) receive first information including identification information and an output value of a node included in a final layer in a first assignment range in a series of calculation of a deep neural network from a first entity that executed a preceding part of the series of calculation of the deep neural network (recites well-understood, routine, and conventional activity of receiving/transmitting information over a network, see MPEP 2106.05(d)), receive information regarding resources of a communication network that relays communication between the information processing device and at least a second entity configured to perform the series of calculation (recites well-understood, routine, and conventional activity of receiving/transmitting information over a network, see MPEP 2106.05(d)), wherein the resources include at least one of communication capacity or communication quality of a communication link and spare calculation capacity of the information process device (recites field of use limitations describing the intended meaning of the data that is received, see MPEP 2106.05(f)); As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite generic computer functions such as receiving/transmitting information that are performed by generic computer hardware elements. With regard to claim 35, this claim recites wherein the processor is further configured to: transmit a result of the remaining part of the series of calculation of the deep neural network or the second assignment range to a transmission source of the first information such that a sum of a communication time required to transmit the result and a calculation time required to execute the remaining part of the series of calculation satisfies a specific threshold (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d)). With regard to claim 36, this claim recites wherein the processor is further configured to determine based on the space calculation capacity of the information process device to estimate the calculation time required to execute the second assignment range (recites mental process steps of forming a determination based on received/monitored observations; also recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)). With regard to claim 37, this claim recites wherein the processor is further configured to determine the second assignment range based communication capacity of the communication quality of the communication link to estimate the communication time required to transmit the result of the calculation (recites mental process steps of forming a determination based on received/monitored observations; (recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)). With regard to claim 38, this claim recites wherein the communication quality is determined based on at least one of a delay time, a data rate, or a channel occupancy ratio (recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)). Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim 34 is rejected under 35 U.S.C. 103 as being unpatentable over Sridharan et al [US 2019/0205745 A1] in view of Che et al [US 2020/0175361 A1] . With regard to claim 34, Sridharan teaches an information processing device, comprising: a processor configured to (see paragraph [0381]): receive first information including identification information and an output value of a node included in a final layer in a first assignment range in a series of calculation of a deep neural network from a first entity that executed a preceding part of the series of calculation of the deep neural network (see paragraphs [0222]-[0223]; see Figure 22; the system can perform calculations at a first group of nodes and be able to send the information from a particular node to another device that will feed the input into the next layer to continue the processing) receive information regarding resources of a communication network that relays communication between the information processing device and at least a second entity configured to perform the series of calculation (see paragraphs [0191], [0253], and [0216], [0273], [0224]; the system can include device that can receiving information about the resources of the network include various nodes that can perform calculations and transmit results); identify a node to which the output value included in the first information is to be input based on the identification information; and execute the remaining part of the series of calculation of the deep neural network or the second assignment range by inputting the output value included to the identified node (see paragraphs [0222]-[0223]; see Figure 22; the system can perform calculations at a first group of nodes and be able to send the information from a particular node to another device that will feed the input into the next layer to continue the processing). Sridharan does not appear to explicitly teach: an output value of a node included in a final layer in a first assignment range in a series of calculation of a deep neural network from a first entity that executed a preceding part of the series of calculation of the deep neural network, wherein the resources include at least one of communication capacity or communication quality of a communication link and spare calculation capacity of the information processing device; estimate a communication time required to receive the first information and transmit a result of calculation through the communication link using the communication capacity or the communication quality; estimate a calculation time required to execute a remaining part of the series of calculation or a second assignment range using the space calculation capacity; and execute the remaining part of the series of calculation of the deep neural network or the second assignment range by inputting the output value included to the identified node. Che teaches an output value of a node included in a final layer in a first assignment range in a series of calculation of a deep neural network from a first entity that executed a preceding part of the series of calculation of the deep neural network (see paragraphs [0034] and [0035] and Figure 5; the system can evaluate device capabilities and determine the respective portion of the calculations will be assigned to the respective node/device). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan by including means to assign particular calculations/operations as taught by Che in order to not overload or overburden particular nodes in the system while also ensuring the system knows which operations are meant to be performed at particular nodes or node groups while maintaining the ability to adjust the assignments later based on observed performance of the devices thereby helping to maximize the efficiency of the system of nodes/devices when performing the various calculations/operations. Sridharan in view of Che teach: wherein the resources include at least one of communication capacity or communication quality of a communication link and spare calculation capacity of the information processing device; estimate a communication time required to receive the first information and transmit a result of calculation through the communication link using the communication capacity or the communication quality; estimate a calculation time required to execute a remaining part of the series of calculation or a second assignment range using the space calculation capacity; and execute the remaining part of the series of calculation of the deep neural network or the second assignment range by inputting the output value included to the identified node. Sridharan in view of Che teach wherein the resources include at least one of communication capacity or communication quality of a communication link and spare calculation capacity of the information processing device (see Che, paragraph [0034]; see Sridharan, paragraph [0216]; the resource can include the communication capacity for the communication link/path as well as have the ability for the system to determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency), estimate a communication time required to receive the first information and transmit a result of calculation through the communication link using the communication capacity or the communication quality; estimate a calculation time required to execute a remaining part of the series of calculation or a second assignment range using the space calculation capacity (see Sridharan, paragraphs [0216] and [0230]; see Che, paragraphs [0024]-[0025], [0028], and [0034]-[0035]; the system can determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency where the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network), executes remaining calculation of the deep neural network or calculation of a second assignment range by inputting the output value included in the first information to the identified node (see Che, paragraph [0034]; see Sridharan, paragraphs [0222]-[0223]; see Figure 22; the system can perform calculations at a first group of nodes and be able to send the information from a particular node to another device that will feed the input into the next layer to continue the processing where the groups can be assigned particular calculations) . 07-21-aia AIA Claim s 1-4, 11, 12, 14-18, 20-33, and 35-38 are rejected under 35 U.S.C. 103 as being unpatentable over Sridharan et al [US 2019/0205745 A1] in view of Che et al [US 2020/0175361 A1] and Zhou et al [US 2018/0276031 A1] . With regard to claim 1, Sridharan teaches an information processing device, comprising: a processor configured to (see paragraph [0381]): receive information regarding resources of a communication network that relays communication between a communication terminal and a server, wherein the communication terminal transmits an input to a deep neural network or is in charge of at least a part of a series of calculation of the deep neural network and transmits a result of the calculation, the server is able to be in charge of at least part of the series of calculation of the deep neural network (see paragraphs [0191], [0253], and [0216], [0273], [0224]; the system can include device that can receiving information about the resources of the network include various nodes that can perform calculations and transmit results); determine at least one entity from among the communication terminal, the server, and the communication nodes (see paragraphs [0222]-[0225]; the system can utilize the information about the resources to make a determination/selection that affects the operations or intended operation of the machine learning process). Sridharan does not appear to explicitly teach: the resources include at least one of communication capacity or communication quality of a communication link in the communication network and spare calculation capacity of communication nodes in the communication network; estimate a communication time required to transmit a calculation result through the communication link based on the communication capacity or the communication quality; estimate a calculation time required for at least one communication node to perform the at least part of the series of calculations based on the spare calculation capacity of the communication nodes; determine at least one entity from among the communication terminal, the server, and the communication nodes to which at least part of the series of calculation of the deep neural network is assigned based on a condition that a sum of the communication time and the calculation time does not exceed a specific threshold; and assign the at least part of the series of calculation to the determined at least one entity. Che teaches the resources include at least one of communication capacity or communication quality of a communication link in the communication network, … estimate a communication time required to transmit a calculation result through the communication link based on the communication capacity or the communication quality (see paragraphs [0024]-[0025], [0028], and [0034]-[0035]; the resource can include the communication capacity for the communication link/path and be able to calculate or estimate a communication time for transmitting information); determine at least one entity from among the communication terminal, the server, and the communication nodes to which at least part of the series of calculation of the deep neural network is assigned based on a condition that a sum of the communication time and the calculation time does not exceed a specific threshold (see paragraphs [0034] and [0035]; the system can evaluate device capabilities and determine the respective portion of the calculations will be assigned to the respective node/device). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan by including means to assign particular calculations/operations as taught by Che in order to not overload or overburden particular nodes in the system while also ensuring the system knows which operations are meant to be performed at particular nodes or node groups while maintaining the ability to adjust the assignments later based on observed performance of the devices thereby helping to maximize the efficiency of the system of nodes/devices when performing the various calculations/operations. Sridharan in view of Che teach the resources include at least one of communication capacity or communication quality of a communication link in the communication network and spare calculation capacity of communication nodes in the communication network (see Che, paragraph [0034]; see Sridharan, paragraph [0216]; the resource can include the communication capacity for the communication link/path as well as have the ability for the system to determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency), estimate a communication time required to transmit a calculation result through the communication link based on the communication capacity or the communication quality; estimate a calculation time required for at least one communication node to perform the at least part of the series of calculations based on the spare calculation capacity of the communication nodes (see Sridharan, paragraphs [0216] and [0230]; see Che, paragraphs [0024]-[0025], [0028], and [0034]-[0035]; the system can determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency where the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network), determine at least one entity from among the communication terminal, the server, and the communication nodes to which at least part of the series of calculation of the deep neural network is assigned based on a condition that a sum of the communication time and the calculation time does not exceed a specific threshold (see Che, paragraph [0034] and [0035]; see Sridharan, paragraphs [0222]-[0225]; the system can assign particular calculations or ranges to particular devices). Sridharan in view of Che do not appear to explicitly teach determine at least one entity from among the communication terminal, the server, and the communication nodes to which at least part of the series of calculation of the deep neural network is assigned based on a condition that a sum of the communication time and the calculation time does not exceed a specific threshold ; and assign the at least part of the series of calculation to the determined at least one entity. Zhou teaches determine at least one entity from among the communication terminal, the server, and the communication nodes to which at least part of the series of calculation of the deep neural network is assigned based on a condition that a sum of the communication time and the calculation time does not exceed a specific threshold (see paragraphs [0093]-[0095] and [0042], [0054], and [0081]; various metrics can be analyzed together to determine how well the node is expected to perform or is performing and be able to make a determination/selection of a node based on that information). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan in view of Che by analyzing/utilizing performance metrics that combine different metrics such as network metrics and computation/processor metrics as taught by Zhou in order to be able to make determinations/selections of nodes based on their capabilities and whether they meet expected performance constraints for respective tasks/operations while considering all aspects of how a networked system interacts rather than just one component (e.g. I/O cost) thus allowing the system to make more informed decisions to help better optimize the networked system to perform the operations. Sridharan in view of Che and Zhou teach determine at least one entity from among the communication terminal, the server, and the communication nodes to which at least part of the series of calculation of the deep neural network is assigned based on a condition that a sum of the communication time and the calculation time does not exceed a specific threshold ; and assign the at least part of the series of calculation to the determined at least one entity (see Zhou, paragraphs [0093]-[0095] and [0042], [0054], and [0081]; Che, paragraph [0034] and [0035]; see Sridharan, paragraphs [0222]-[0225]; the system can utilize the information about the resources to make a determination/selection that affects the operations or intended operation of the machine learning process where various metrics can be analyzed together to determine how well the node is expected to perform or is performing and be able to make a determination/selection of a node based on that information). With regard to claim 2, Sridharan in view of Che and Zhou teach wherein the processor is further configured to determine the at least one of the communication nodes as the entity to which the at least part of the series of calculation is assigned (see Sridharan, paragraph [0226]; see Che, paragraphs [0023] and [0047]; various different types of system nodes can be used in the networked system). With regard to claim 3, Sridharan in view of Che and Zhou teach wherein the processor is further configured to determine a calculation range for which the entity assigned with at least part of the series of calculation performs the at least part of the series of calculation, and the calculation range is determined based on the information regarding the resources (see Che, paragraph [0034]; the range of calculations/operations is determined based on the information about the resources). With regard to claim 4, Sridharan in view of Che and Zhou teach wherein the processor is further configured to determine the at least one of the communication nodes that are present on a communication route between the communication terminal and the server as the entity to which the at least part of the series of calculation is assigned (see Sridharan, paragraphs [0226] and [0254] and [0235]; the system can utilize a network with the communication nodes on a route/path to the server and can select devices on that path for any further configuration/adjustment as needed). With regard to claim 11, Sridharan in view of Che and Zhou teach wherein the processor is further configured to: receive information regarding a topology of the communication network, change the entity to which the at least part of the series of calculation is assigned based on a change in the communication route accompanying a change in the topology (see Sridharan, paragraphs [0222], [0264], and [0224]; the system can receive information about the topology of the network and can change information accordingly based on changes in the topology). With regard to claim 12, Sridharan in view of Che and Zhou teach wherein the processor is further configured to determine the calculation range by selection of one of a plurality of splitting modes based on the information regarding the resources (see Sridharan, paragraphs [0234] and [0216]; see Che, paragraph [0024]; the system can perform various optimizations to determine what type of splitting/grouping or assignments the system would want to do). With regard to claim 14, Sridharan in view of Che and Zhou teach wherein the processor is further configured to change the calculation range by increasing or decreasing the calculation range based on of variations in the resources (see Che, paragraphs [0034] and [0035]; see Sridharan, paragraphs [0216] [0148]; the system can adjust the range accordingly based on the device/node characteristics as well as the respective capabilities of other nodes in the system). With regard to claim 15, Sridharan in view of Che and Zhou teach wherein the processor is further configured to transmit the calculation range to the communication node determined as the entity to which the at least part of the series of calculation is assigned (see Che, paragraph [0034]; the system is able to transmit the respective range of information needed to the respective node). With regard to claim 16, Sridharan in view of Che and Zhou teach wherein the processor is further configured to: determine a setting value for increasing communication quality of a wireless communication link on a communication route, and transmit the setting value to the communication nodes that are present on the communication route (see Sridharan, paragraph [0220] and [0227]; see Che, paragraph [0050]; the system can adjust various settings to help improve the quality of service of the system including its communication quality). With regard to claim 17, Sridharan teaches an information processing device, comprising: a processor configured to: receive a part of a series of calculation of a deep neural network as an assigned calculation range (see paragraphs [0198], [0204]; the system can perform various calculations/operations as part of the neural network); perform calculation of the assigned calculation range, transmit a calculation result of the assigned calculation range to a designated destination (see paragraphs [0204]-[0205]; the respective device/node can perform computations and transmit the result to a designated or known destination),; acquire information regarding resources including at least one of spare calculation capacity of the information processing device or communication capacity or communication quality of a communication link through which the calculation result is transmitted (see paragraphs [0224] and [0222] and [0227] and [0234]; the system can acquire/monitor information about the quality/metrics of the system); transmits the acquired information to a designation source of the assigned calculation range (see paragraph [0233]-[0234]; the various information that is monitored can be transmitted to a particular location where it can be aggregated with other data). Sridharan does not appear to explicitly teach: receive a part of a series of calculation of a deep neural network as an assigned calculation range ; transmits the acquired information to a designation source of the assigned calculation range , and receive information regarding a change in the assigned calculation range, wherein the assigned calculation range is changed based on information regarding the resources such that a sum of the communication time required to transmit the calculation result and a calculation time required to perform the assigned calculation does not exceed a specific threshold. Che teaches receive a part of a series of calculation of a deep neural network as an assigned calculation range (see paragraphs [0034] and [0035]; the system can evaluate device capabilities and determine the respective portion of the calculations will be assigned to the respective node/device). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan by including means to assign particular calculations/operations as taught by Che in order to not overload or overburden particular nodes in the system while also ensuring the system knows which operations are meant to be performed at particular nodes or node groups while maintaining the ability to adjust the assignments later based on observed performance of the devices thereby helping to maximize the efficiency of the system of nodes/devices when performing the various calculations/operations. Sridharan in view of Che teach transmits the acquired information to a designation source of the assigned calculation range , and receive information regarding a change in the assigned calculation range (see Sridharan, paragraphs [0224] and [0222] and [0227] and [0234]; see Che, paragraphs [0023]-[0025] and [0034]; the system can send information about the various performance counters and utilize that to make any adjustments as needed to the configuration or grouping of the various network components including the respective range for a node/device). Sridharan in view of Che do not appear to explicitly teach wherein the assigned calculation range is changed based on information regarding the resources such that a sum of the communication time required to transmit the calculation result and a calculation time required to perform the assigned calculation does not exceed a specific threshold. Zhou teaches a sum of the communication time required to transmit the calculation result and a calculation time required to perform the assigned calculation does not exceed a specific threshold (see paragraphs [0093]-[0095] and [0042], [0054], and [0081]; various metrics can be analyzed together to determine how well the node is expected to perform or is performing and be able to make a determination/selection of a node based on that information). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan in view of Che by analyzing/utilizing performance metrics that combine different metrics such as network metrics and computation/processor metrics as taught by Zhou in order to be able to make determinations/selections of nodes based on their capabilities and whether they meet expected performance constraints for respective tasks/operations while considering all aspects of how a networked system interacts rather than just one component (e.g. I/O cost) thus allowing the system to make more informed decisions to help better optimize the networked system to perform the operations. Sridharan in view of Che and Zhou teach wherein the assigned calculation range is changed based on information regarding the resources such that a sum of the communication time required to transmit the calculation result and a calculation time required to perform the assigned calculation does not exceed a specific threshold (see Zhou, paragraphs [0093]-[0095] and [0042], [0054], and [0081]; Che, paragraph [0034] and [0035]; see Sridharan, paragraphs [0222]-[0225]; the system can utilize the information about the resources to make a determination/selection that affects the operations or intended operation of the machine learning process where various metrics can be analyzed together to determine how well the node is expected to perform or is performing and be able to make a determination/selection of a node based on that information). With regard to claim 18, Sridharan in view of Che and Zhou teach wherein the information regarding the change in the assigned calculation range identifies one splitting mode selected from a plurality of splitting modes, and the plurality of splitting modes define respective distributions of the series of calculation of the deep neural network among a communication terminal, a server, and communication nodes (see Sridharan, paragraphs [0234] and [0216]; see Che, paragraph [0024]; the system can perform various optimizations to determine what type of splitting/grouping or assignments the system would want to do). With regard to claim 20, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above. With regard to claim 21, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above. The main difference between claims 1 and 21 is that claim 21 recites: “the plurality of communication nodes are configured to transmit information regarding resources of the communication network to a specific communication node” (Sridharan, see paragraphs [0222]-[0225] and [0234]; see Che, paragraph [0034]; the system can utilize have a designated node the monitors/receives information about the various entities on the network to perform calculations to estimate/predict the performance of the various entities of the network so that the system can adjust assignments/groupings as appropriate). With regard to claim 22, this claim is substantially similar to claim 1 and is rejected for similar reasons as claim 1 as discussed above. Claim 22 recites additional limitations that are mapped below: Sridharan in view of Che and Zhou teach executing calculation of the first assignment range ; transmitting first information including identification information and the output value of a node included in a final layer in the first assignment range; receiving the first information; identifying a node to which the output value included in the first information is input based on the identification information included in the first information; and executing remaining calculation of the deep neural network or calculation of a second assignment range by inputting the output value included in the first information to the identified node (see paragraphs [0222]-[0223]; see Figure 22; see Che, paragraphs [0034] and [0035]; the system can perform calculations at a first group of nodes and be able to send the information from a particular node to another device that will feed the input into the next layer to continue the processing where the groups can be assigned particular calculations). With regard to claim 23, Sridharan in view of Che and Zhou teach transmitting a result of the remaining calculation of the deep neural network or the calculation of the second assignment range to a transmission source of the first information (see Sridharan, paragraphs [0223], [0161], and [0181]; see Che, paragraph [0027]; the system has means of replying or transmitting a result of the calculation), wherein the remaining calculation or the second assignment range is executed such that a sum of a communication time required to transmit the result and a calculation time required to execute the remaining calculation satisfies the specific threshold (see Zhou, paragraphs [0093]-[0095] and [0042], [0054], and [0081]; Che, paragraph [0034] and [0035]; see Sridharan, paragraphs [0222]-[0225]; the system can utilize the information about the resources to make a determination/selection that affects the operations or intended operation of the machine learning process where various metrics can be analyzed together to determine how well the node is expected to perform or is performing and be able to make a determination/selection of a node based on that information including nodes in later stages of the calculation process). With regard to claim 24, Sridharan in view of Che and Zhou teach receiving conditions for determining the first assignment range, wherein the conditions include at least one of the communication capacity, the communication quality, or spare calculation capacity of an entity configured to execute the first assignment range (see Che, paragraphs [0023] and [0047]; various different types of system nodes can be used in the networked system and be assigned loads/ranges based on the conditions/capabilities of the device); estimating the communication time and the calculation time using the conditions (see Sridharan, paragraphs [0216] and [0230]; see Che, paragraphs [0024]-[0025], [0028], and [0034]-[0035]; the system can determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency where the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network); determining the first assignment range using the communication time and the calculation time such that the sum of the communication time and the calculation time does not exceed the specific threshold (see Zhou, paragraphs [0093]-[0095] and [0042], [0054], and [0081]; Che, paragraph [0034] and [0035]; see Sridharan, paragraphs [0222]-[0225]; the system can utilize the information about the resources to make a determination/selection that affects the operations or intended operation of the machine learning process where various metrics can be analyzed together to determine how well the node is expected to perform or is performing and be able to make a determination/selection of a node based on that information). With regard to claim 25, Sridharan in view of Che and Zhou teach wherein the spare calculation capacity is used to determine the calculation time required to execute the first assignment range (see Sridharan, paragraph [0216]; the system can determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency). With regard to claim 26, Sridharan in view of Che and Zhou teach wherein the communication capacity or the communication quality is used to determine the communication time required to transmit the output value (see Sridharan, paragraphs [0216] and [0230]; see Che, paragraphs [0024]-[0025], [0028], and [0034]-[0035]; the resource can include the communication capacity for the communication link/path and be able to calculate or estimate a communication time for transmitting information with the system being able to take into account the network/communication latency or time when analyzing/selecting particular devices in the network). With regard to claim 27, Sridharan in view of Che and Zhou teach wherein the communication quality is determined based on of at least one of a delay time, a data rate, or a channel occupancy ratio (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network). With regard to claim 28, Sridharan in view of Che and Zhou teach wherein an entity to execute the remaining calculation of the deep neural network and the calculation of the second assignment range is selected such that a sum of a communication time required for transmitting the output value and a calculation time required to execute the remaining calculation satisfies the specific threshold (see Zhou, paragraphs [0093]-[0095] and [0042], [0054], and [0081]; Che, paragraph [0034] and [0035]; see Sridharan, paragraphs [0222]-[0225]; the system can utilize the information about the resources to make a determination/selection that affects the operations or intended operation of the machine learning process where various metrics can be analyzed together to determine how well the node is expected to perform or is performing and be able to make a determination/selection of a node based on that information including nodes in later stages of the calculation process). With regard to claim 29, this claim is substantially similar to claim 1 and is rejected for similar reasons as claim 1 as discussed above. Claim 29 recites additional limitations that are mapped below: Sridharan in view of Che and Zhou teach executes calculation of the first assignment range ; and transmit first information including identification information and the output value of a node included in a final layer in the first assignment range to a second entity configured to execute a remaining part of the series of calculation of the deep neural network (see Che, paragraph [0034]; see Sridharan, paragraphs [0222]-[0223]; see Figure 22; the system can perform calculations at a first group of nodes and be able to send the information from a particular node and will be the input at another device that represents the next layer to continue the processing where the groups can be assigned particular calculations). With regard to claim 30, Sridharan in view of Che teach wherein processor is further configured to: transmit the first information to the second entity that performs a remaining part of the series of calculation of the deep neural network, and receive a result of the remaining part of the series of calculation of the deep neural network (see Sridharan, paragraphs [0222]-[0223]; see Figure 22; the system can perform calculations at a first group of nodes and be able to send the information from a particular node to another device that will feed the input into the next layer to continue the processing) such that a sum of a communication time required for transmitting the first information and receiving the result and a calculation time required to execute the first assignment range and the remaining part of the series of calculation does not exceed a specific threshold (see Zhou, paragraphs [0093]-[0095] and [0042], [0054], and [0081]; Che, paragraph [0034] and [0035]; see Sridharan, paragraphs [0222]-[0225]; the system can utilize the information about the resources to make a determination/selection that affects the operations or intended operation of the machine learning process where various metrics can be analyzed together to determine how well the node is expected to perform or is performing and be able to make a determination/selection of a node based on that information including nodes in later stages of the calculation process). With regard to claim 31, Sridharan in view of Che teach wherein the spare calculation capacity of the information processing device is used to determine the calculation time required to execute the first assignment range (see Sridharan, paragraph [0216]; the system can determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency). With regard to claim 32, Sridharan in view of Che teach wherein the communication capacity or the communication quality of the communication link is used to determine the communication time required to transmit the first information (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network). With regard to claim 33, Sridharan in view of Che teach wherein the communication quality is determined based on at least one of a delay time, a data rate, or a channel occupancy ratio (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network). With regard to claim 35, Sridharan in view of Che teach all the claim limitations of claim 34 as discussed above. Sridharan in view of Che teach transmit a result of the remaining part of the series of calculation of the deep neural network or the second assignment range to a transmission source of the first information (see Sridharan, paragraphs [0223], [0161], and [0181]; see Che, paragraph [0027]; the system has means of replying or transmitting a result of the calculation). Sridharan in view of Che do not appear to explicitly teach: such that a sum of a communication time required to transmit the result and a calculation time required to execute the remaining part of the series of calculation satisfies a specific threshold. Zhou teaches such that a sum of a communication time required to transmit the result and a calculation time required to execute the remaining part of the series of calculation satisfies a specific threshold (see paragraphs [0093]-[0095] and [0042], [0054], and [0081]; various metrics can be analyzed together to determine how well the node is expected to perform or is performing and be able to make a determination/selection of a node based on that information). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan in view of Che by analyzing/utilizing performance metrics that combine different metrics such as network metrics and computation/processor metrics as taught by Zhou in order to be able to make determinations/selections of nodes based on their capabilities and whether they meet expected performance constraints for respective tasks/operations while considering all aspects of how a networked system interacts rather than just one component (e.g. I/O cost) thus allowing the system to make more informed decisions to help better optimize the networked system to perform the operations. Sridharan in view of Che in further view of Zhou teach transmit a result of the remaining part of the series of calculation of the deep neural network or the second assignment range to a transmission source of the first information such that a sum of a communication time required to transmit the result and a calculation time required to execute the remaining part of the series of calculation satisfies a specific threshold (see Sridharan, paragraphs [0223], [0161], and [0181]; see Che, paragraph [0027]; see Zhou, paragraphs [0093]-[0095] and [0042], [0054], and [0081]; various metrics can be analyzed together to determine how well the node is expected to perform or is performing and be able to make a determination/selection of a node based on that information including being able to transmit results to the next stage/layer for further processing). With regard to claim 36, Sridharan in view of Che in further view of Zhou teach wherein the processor is further configured to determine the second assignment range based on the spare calculation capacity of the information processing device to estimate the calculation time required to execute the second assignment range (see Sridharan, paragraph [0216]; the system can determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency). With regard to claim 37, Sridharan in view of Che in further view of Zhou teach wherein the processor is further configured to determine the second assignment range based on communication capacity or the communication quality of the communication link to estimate the communication time required to transmit the result of the calculation (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network). With regard to claim 38, Sridharan in view of Che in further view of Zhou teach wherein the communication quality is determined based on at least one of a delay time, a data rate, or a channel occupancy ratio (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network) . 07-21-aia AIA Claim s 10 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sridharan et al [US 2019/0205745 A1] in view of Che et al [US 2020/0175361 A1] and Zhou et al [US 2018/0276031 A1] in further view of Nolan et al [US 2019/0349733 A1] . With regard to claim 10, Sridharan in view of Che teach all the claim limitations of claims 1, 2, and 4 as discussed above. Sridharan in view of Che and Zhou do not appear to explicitly teach wherein the processor is further configured to: receive information regarding a position of the communication terminal, and change the entity to which the at least part of the series of calculation is assigned is based on a change in the communication route accompanying movement of the communication terminal. Nolan teaches a change in the communication route accompanying movement of the communication terminal (see Nolan, paragraph [0151] and [0222]; detects that other devices are now in the network, i.e. moved). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan in view of Che and Zhou by including means to identify local devices and try to preserve the locality of calculations as taught by Nolan in order to be able to detect when changes occur and respective nodes are now local to other sets of nodes so that the system can dynamically adjust its groupings to help ensure overall quality of service by maintaining low latency and high-throughput and not degrading the quality by having nodes communicate with each other that are at large distances away from each other. Sridharan in view of Che and Zhou in further view of Nolan teach wherein the information processing device further receives information regarding a position of the communication terminal, and the entity to which the series of calculation is assigned is changed in response to a change in the communication route accompanying movement of the communication terminal (see Sridharan, paragraphs [0225]-[0226]; see Che, paragraph [0035]; see Nolan, paragraph [0151] and [0222]; changes in the network including movement of devices to determine which sets of devices are local to each other and can adjust the grouping of devices accordingly). With regard to claim 13, Sridharan in view of Che teach all the claim limitations of claims 1, 3, and 12 as discussed above. Sridharan in view of Che and Zhou teach the process is further configured to recreate the plurality of splitting modes in a case where a specific communication node of the communication nodes is absent from a communication route changed based on movement of the communication terminal (see Sridharan, paragraphs [0226] and [0264]; the system can determine when no nodes are present/available on a route or path and can do adjustments as needed including adjusting the paths as necessary). Sridharan in view of Che and Zhou do not appear to explicitly teach wherein the resources include a position of the communication terminal. Nolan teaches a change in the communication route accompanying movement of the communication terminal (see Nolan, paragraphs [0221]-[0222] and [0151]; the devices can be associated with locations where the devices can be small enough to be moved). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan in view of Che and Zhou by including means to identify local devices and try to preserve the locality of calculations as taught by Nolan in order to be able to detect when changes occur and respective nodes are now local to other sets of nodes so that the system can dynamically adjust its groupings to help ensure overall quality of service by maintaining low latency and high-throughput and not degrading the quality by having nodes communicate with each other that are at large distances away from each other . 07-21-aia AIA Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Sridharan et al [US 2019/0205745 A1] in view of Che et al [US 2020/0175361 A1] and Zhou et al [US 2018/0276031 A1] in further view of Grokop et al [US 2014/0143579 A1] . With regard to claim 19, Sridharan in view of Che and Zhou teach all the claim limitations of claim 17 as discussed above. Sridharan in view of Che and Zhou do not appear to explicitly teach transmit, in a case where the calculation result satisfies a condition for ending the series of calculation in the middle, the calculation result to a final reception destination of the series of calculation rather than the designated destination. Grokop teaches in a case where the calculation result satisfies a condition for ending the series of calculation in the middle (see paragraphs [0032] and [0035]; the system can perform a series of calculations and be able to, upon determining high confidence, end/skip the other calculations in the middle). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan in view of Che and Zhou by including means to determine when the evaluation/computations are above a threshold level of confidence even when in the middle of the computations as taught by Grokop in order to save processing time and network communication costs by not having to continue various calculations/computations when the confidence of the output can already be determined with a high-level of confidence. Sridharan in view of Che and Zhou in further view of Grokop transmit, in a case where the calculation result satisfies a condition for ending the series of calculation in the middle, the calculation result to a final reception destination of the series of calculation rather than the designated destination (see Grokop, paragraphs [0032] and [0035]; see Che, paragraph [0023]; see Sridharan, Figures 19, 20E, and 22; the system can end the series of calculations/computations even before reaching the end stage/layer and be able to output the result). Response to Arguments Applicant’s arguments (see the third paragraph on page 20) with respect to the objection to the specification have been fully considered and are persuasive. The applicant amended the title and the respective objection has been withdrawn. Applicant’s arguments (see the last paragraph on page 20) with respect to 35 USC 112 rejection of claim 16 have been fully considered and are persuasive. The applicant amended the claims to address the 35 USC 112 rejections. In view of the amendments, the respective 35 USC 112 rejections have been withdrawn. Applicant’s arguments (see the first paragraph on page 21 through the last paragraph on page 28) with respect to the 35 USC 101 rejections have been fully considered and are persuasive. The 35 USC 101 rejections of claim 1 and respective dependent claims have been withdrawn. The applicant amended claim 1 to include additional limitations wherein the combination, when viewed as a whole, direct the claims to a practical application including the assigning of the calculations to determined entities. Applicant's arguments (see the first and second whole paragraphs on page 29) have been fully considered but they are not persuasive. The applicant argues that the other independent and dependent claims should have their rejections withdrawn for reasons discussed with claim 1. The Examiner respectfully disagrees. As illustrated in the 35 USC 101 rejections above, each of the independent claims have slight variations of the limitations and claim scope for which they cover. Accordingly, some of the independent claims (and subsequently the respective dependent claims too) still recite and are directed towards an abstract idea as discussed above. Applicant's arguments (see the last paragraph on page 29 through the last paragraph on page 33) have been fully considered but they are not persuasive. The applicant argues that the cited prior art do not teach the amended claim limitations as recited, in particular because Zhou doesn’t teach those limitations. The Examiner respectfully disagrees. The applicant indicates that Zhou doesn’t teach the assignment of the calculations of a neural work based on the conditions; however, as noted in the 35 USC 101 rejections, Zhou was not relied upon to solely teach that limitation but rather based on the combination of references. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller , 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARC S SOMERS whose telephone number is (571)270-3567. The examiner can normally be reached M-F 11-8 EST. 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, Ann Lo can be reached at 5712729767. 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. /MARC S SOMERS/Primary Examiner, Art Unit 2159 6/2/2026 Application/Control Number: 18/257,881 Page 2 Art Unit: 2159 Application/Control Number: 18/257,881 Page 3 Art Unit: 2159 Application/Control Number: 18/257,881 Page 4 Art Unit: 2159 Application/Control Number: 18/257,881 Page 5 Art Unit: 2159 Application/Control Number: 18/257,881 Page 6 Art Unit: 2159 Application/Control Number: 18/257,881 Page 7 Art Unit: 2159 Application/Control Number: 18/257,881 Page 8 Art Unit: 2159 Application/Control Number: 18/257,881 Page 9 Art Unit: 2159 Application/Control Number: 18/257,881 Page 10 Art Unit: 2159 Application/Control Number: 18/257,881 Page 11 Art Unit: 2159 Application/Control Number: 18/257,881 Page 12 Art Unit: 2159 Application/Control Number: 18/257,881 Page 13 Art Unit: 2159 Application/Control Number: 18/257,881 Page 14 Art Unit: 2159 Application/Control Number: 18/257,881 Page 16 Art Unit: 2159 Application/Control Number: 18/257,881 Page 17 Art Unit: 2159 Application/Control Number: 18/257,881 Page 18 Art Unit: 2159 Application/Control Number: 18/257,881 Page 19 Art Unit: 2159 Application/Control Number: 18/257,881 Page 20 Art Unit: 2159 Application/Control Number: 18/257,881 Page 21 Art Unit: 2159 Application/Control Number: 18/257,881 Page 22 Art Unit: 2159 Application/Control Number: 18/257,881 Page 23 Art Unit: 2159 Application/Control Number: 18/257,881 Page 24 Art Unit: 2159 Application/Control Number: 18/257,881 Page 27 Art Unit: 2159 Application/Control Number: 18/257,881 Page 28 Art Unit: 2159 Application/Control Number: 18/257,881 Page 29 Art Unit: 2159 Application/Control Number: 18/257,881 Page 30 Art Unit: 2159 Application/Control Number: 18/257,881 Page 31 Art Unit: 2159 Application/Control Number: 18/257,881 Page 32 Art Unit: 2159 Application/Control Number: 18/257,881 Page 33 Art Unit: 2159 Application/Control Number: 18/257,881 Page 34 Art Unit: 2159 Application/Control Number: 18/257,881 Page 35 Art Unit: 2159 Application/Control Number: 18/257,881 Page 36 Art Unit: 2159 Application/Control Number: 18/257,881 Page 37 Art Unit: 2159 Application/Control Number: 18/257,881 Page 39 Art Unit: 2159 Application/Control Number: 18/257,881 Page 40 Art Unit: 2159 Application/Control Number: 18/257,881 Page 41 Art Unit: 2159 Application/Control Number: 18/257,881 Page 42 Art Unit: 2159 Application/Control Number: 18/257,881 Page 43 Art Unit: 2159 Application/Control Number: 18/257,881 Page 44 Art Unit: 2159 Application/Control Number: 18/257,881 Page 45 Art Unit: 2159
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