Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
The following action is in response to the communication(s) received on 07/06/2026.
As of the claims filed 07/06/2026:
Claims 1, 9, and 17 have been amended.
Claims 1, 3-9, and 11-20 are pending.
Claims 1, 9, and 17 are independent claims.
Response to Arguments
Applicant’s arguments filed 07/06/2026 have been fully considered, but are not fully persuasive.
With respect to the prior art rejections under 35 USC § 103:
Applicant asserts that the prior art does not teach the amended limitations, as Sun does not teach the last-executed DNN task computational layer, transmitting the output of the last executed computational layer, or subsequent execution of the computational layer without re-executing the last executed computational layer (p.12-13). This argument has been carefully considered but is rendered moot by the new prior art further in view of Li: Li teaches that the offloading task transfer method between devices can comprise receiving a status indicating a last-executed computational layer (p.5 fig.5 right; the last layer executed before the partition point before sending to the mobile device corresponds to the status). Additionally, Li further teaches transmitting the output of the last executed computational layer (p.5 fig.5 right; sending the last layer to the mobile device corresponds to transmitting). Lastly, Li further teaches the execution of the remaining part of the DNN tasks comprises executing the subsequent computational layer without re-executing the last executed computational layer (p.5 fig.5 right; p.6 ¶2; “the rest will run on the mobile device”; the rest of the layers executed by the mobile device does not restart the model layer task and thus corresponds to the remaining part of the first part of the DNN task).
Applicant further asserts that the rest of the cited references do not remedies the deficiencies of Sun (p.13 last ¶). Examiner respectfully submits that the above reasoning shows how Li, not Sun, teaches these amended limitations.
Applicant further asserts that Independent claims 9 and 17 are patentable for the arguments provided above (p.14 ¶2). Examiner respectfully submits that these claims remain rejected for at least the same reasons given above.
Applicant further asserts that the dependent claims are patentable at least by virtue of dependency to their respective alleged patentable parent claims (p.14 ¶2). Examiner respectfully submits that these claims remain rejected at least by virtue of dependency to their parent claims which remain rejected for the reasons given above.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1, 3-9, and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al., “Task Offloading Optimization for UAV-Assisted Fog-Enabled Internet of Things Networks” (hereinafter Huang), in view of Sun et al., US 20220248122 A1 (hereinafter Sun), further in view of StackExchange, "How do you distinguish between a complex and a simple model in machine learning?" (hereinafter Stack), further in view of Herbas et al., "Deep Learning Neural Network: Complex vs. Simple Model" (hereinafter Herbas), further in view of CentralCommand, “Alert when devices have low battery” (hereinafter CentralCommand), further in view of Li et al., "Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy" (hereinafter Li).
Regarding Claim 1, Huang teaches: A method of task management in an internet of things (IoT)-edge network, the edge network including a first device and a plurality of second devices, and the method comprising: (Huang [Abst] Recently, unmanned aerial vehicles (UAVs) have been considered as an efficient way to provide enhanced coverage or relaying services to Internet of Things devices (IDs) in wireless systems with limited or no infrastructure. In this article, a UAVs-assisted fog-enabled Internet of Things (IoT) network is studied, in which moving UAVs are equipped with computing capabilities to offer task offloading opportunities to IDs…
[0014] The task assigning device 10 can analyze a detection parameter generated by at least one of the first electronic device 12 and the second electronic device 14, and determine which electronic device can afford partial simple or partial complex operation loading in accordance with an analysis result of the detection parameter, so as to adaptively assign operation tasks for the first electronic device 12 and the second electronic device 14.)
Huang does not teach, but Sun further teaches:
splitting computational layers of a deep neural network (DNN) task into a first part of the DNN task to be executed by at least one second device among the plurality of second devices and a second part of the DNN task to be executed by the first device, (Sun [0014] The task assigning device 10 can analyze a detection parameter generated by at least one of the first electronic device 12 and the second electronic device 14, and determine which electronic device can afford partial simple or partial complex operation loading in accordance with an analysis result of the detection parameter, so as to adaptively assign operation tasks for the first electronic device 12 and the second electronic device 14.
[provision p.4] we can use larger or better neural net architecture…) (Note: the tasks comprising neural net architecture adaptively assigned into partial simple and complex operations correspond to splitting the computational layers of the DNN task into respective parts of DNN tasks.)
Sun and Huang are analogous to the present invention because both are from the same field of endeavor of methods of assigning tasks to edge devices. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the task transfer method from Sun into Huang’s IoT task offloading method. The motivation would be to “analyze the battery level of the first electronic device 12 to accordingly decide assignment of the unprocessed task between the first electronic device 12 and the second electronic device” (Sun [0015]).
Huang/Sun does not teach, but Stack further teaches:
wherein the first part of the DNN task comprises a first number of computational layers, wherein the second part of the DNN task comprises a second number of computational layers, (Stack [p.2 3rd point]
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) (Note: the larger layer count corresponds to the second number of computational layers; the smaller layer count corresponds to the first number of computational layers)
Stack and Huang/Sun are analogous to the present invention because both are from the same field of endeavor of deep neural network models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the association of higher complexity with more computational layers from Stack into Huang/Sun’s IoT task offloading method. The motivation would be to “distinguish between a complex and a simple model in machine learning” (Stack, title).
Huang/Sun/Stack does not teach, but Herbas further teaches:
and wherein the first number of computational layers is determined based on a minimum theoretical inference time for completion of the DNN task; (Herbas [p.9 ¶1] the difference that Model_15 processing time is about three times longer to that of Model_5, which is mainly due to the dual convolutional layers in each of the 5 convolutional blocks, compared to 1 that Model_5 has per block) (Note: the model which takes longer in processing time corresponds to the computational layers being based on the theoretical inference time for completion of the DNN task)
Herbas and Huang/Sun/Stack are analogous to the present invention because both are from the same field of endeavor of properties of DNN models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the recognition that higher layer count associates with the processing time from Vidhya into Huang/Sun/Stack’s IoT task offloading method. The motivation would be to “compare…a complex to a simple model” (Herbas, p.2 2nd ¶).
Sun, via Huang/Sun/Stack/Herbas, further teaches:
assigning the first part of the DNN task from the first device to the at least one second device, while the second part of the DNN task is set to be executed by the first device; (Sun [0005] According to the claimed invention, a task assigning device of adaptively assigning at least one operation task between different electronic devices includes a first electronic device, a second electronic device and a central host. [0028] In the present invention, the first electronic device 12 and the second electronic device 14 can have neural net computation function; the first electronic device 12 has simply function of the neural net computation due to a small size and less power storage, and the second electronic device 14 has advanced function of the neural net computation due to a large size and preferred power storage.) (Note: supported by provisional p.3: “the first part is running on device 1 and second part is running on device 2…”; p.4: “the task partition…”)
triggering an alarm based on …determining whether the at least one second device satisfies one of a first predetermined criteria and a second predetermined criteria during execution of the first part of the DNN task; (Sun [0015] When the first electronic device 12 has an unprocessed task, the task assigning device 10 can analyze the battery level of the first electronic device 12 to accordingly decide assignment of the unprocessed task between the first electronic device 12 and the second electronic device 14… If the battery level of the first electronic device 12 is in a low level, the task assigning device 10 may control the first electronic device 12 to process few or none of the unprocessed task; the processed task and most of the unprocessed task can be transmitted to the second electronic device 14 for accomplishing the whole task…) (Note: supported by provisional p.4)
Huang/Sun/Stack/Herbas does not teach, but CentralCommand further teaches:
triggering an alarm to the IoT device based on the determination recited above: (CentralCommand [p.1 4th ¶] A template sensor which collects all sensors with a device_class of battery and sets it state equal to the number of those sensors with a state <= 10
A threshold sensor which is true any time that template sensor’s value is above 0
An alert which notifies me every 12 hours with a count of the number of devices with low battery (if any have low battery)
CentralCommand and Huang/Sun/Stack/Herbas are analogous to the present invention because both are from the same field of endeavor of managing IOT devices. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the alerting method from CentralCommand into Huang/Sun/Stack/Herbas’s IoT task offloading method. The motivation would be to “Since your phone’s battery gets drained daily, if you get a notification about low battery you’re probably going to go plug it in pretty quickly. But with IOT devices most of them take months, a year or even longer to go from 100% to 0. So a low battery alert probably isn’t urgent at all. It’s something you have to take care of but definitely not an immediate problem. (CentralCommand [p1 2nd ¶]).
Sun, via Huang/Sun/Stack/Herbas/CentralCommand/Li, further teaches:
identifying another second device among the plurality of second devices, subsequent to the triggering of the alarm; (Sun [0015] When the first electronic device 12 has an unprocessed task, the task assigning device 10 can analyze the battery level of the first electronic device 12 to accordingly decide assignment of the unprocessed task between the first electronic device 12 and the second electronic device 14. If the battery level of the first electronic device 12 is in a high level, the task assigning device 10 can control the first electronic device 12 to process most of the unprocessed task; the processed task and few of the unprocessed task can be transmitted from the first electronic device 12 to the second electronic device 14 for accomplishing the whole task.) (Note: supported by provisional p.4)
and based on the determining that the at least one second device satisfies the second predetermined criteria, transferring a remaining part of the first part of the DNN task to the another second device to be executed by the another second device, (Sun [0015] When the first electronic device 12 has an unprocessed task, the task assigning device 10 can analyze the battery level of the first electronic device 12 to accordingly decide assignment of the unprocessed task between the first electronic device 12 and the second electronic device 14. If the battery level of the first electronic device 12 is in a high level, the task assigning device 10 can control the first electronic device 12 to process most of the unprocessed task; the processed task and few of the unprocessed task can be transmitted from the first electronic device 12 to the second electronic device 14 for accomplishing the whole task.) (Note: supported by provisional p.4, p.9; the processed task corresponds to the output of a computational layer; both the finished and unfinished tasks transmitted to the second electronic device corresponds to transmitting remaining parts of the DNN tasks which also comprises an output of a computational layer)
Huang/Sun/Stack/Herbas/CentralCommand does not explicitly teach that the first part of the DNN task produces a status indicating a last executed computational layer of the first part of the DNN task executed by the at least one second device and that the another second device executes the remaining part of the first part of the DNN task beginning from a computational layer subsequent to the last executed computational layer, without re-starting the last executed computational layer. However, Li further teaches:
wherein transferring the remaining part of the first part of the DNN task to the another second device comprises: receiving, by the first device, a status of the first part of the DNN task from the at least one second device, the status indicating a last executed computational layer of the first part of the DNN task executed by the at least one second device;
(Li [p.5 fig.5 right]
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[p.6 ¶2] At co-inference stage, according to the partition and early-exit plan, the edge server will execute the layer before the partition point and the rest will run on the mobile device.) (Note: the last layer executed before the partition point before sending to the mobile device corresponds to the status that indicates the last executed computational layer)
and transmitting, to the another second device, an output of the last executed computational layer, such that the another second device executes the remaining part of the first part of the DNN task beginning from a computational layer subsequent to the last executed computational layer, without re-starting the last executed computational layer.
(Li [p.5 fig.5 right]
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[p.6 ¶2] At co-inference stage, according to the partition and early-exit plan, the edge server will execute the layer before the partition point and the rest will run on the mobile device.) (Note: the rest of the layers executed by the mobile device does not restart the model layer task and thus corresponds to the remaining part of the first part of the DNN task)
Li and Huang/Sun/Stack/Herbas/CentralCommand are analogous to the present invention because both are from the same field of endeavor of offloading tasks between devices. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the layer partition execution by Li into Huang/Sun/Stack/Herbas/CentralCommand’s IoT task offloading method. The motivation would be to “the runtime and output data size of different layers exhibit great heterogeneities, and layers with a long runtime do not necessarily have a large output data size. Then, an intuitive idea is DNN partitioning, i.e., partitioning the DNN into two parts and offloading the computational intensive one to the server at a low transmission overhead, and thus to reduce the end-to-end latency” (Li p.4 ¶1).
Regarding Claim 3, Huang/Sun/Stack/Herbas/CentralCommand/Li respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Huang, via Huang/Sun/Stack/Herbas/CentralCommand/Li, further teaches:
The method of claim 1, wherein prior to assigning the first part of the DNN task, the method comprises: identifying a nearest second device as the at least one second device to assign the first part of the DNN task, using a path loss method. (Huang [Abst] Recently, unmanned aerial vehicles (UAVs) have been considered as an efficient way to provide enhanced coverage or relaying services to Internet of Things devices (IDs) in wireless systems with limited or no infrastructure. In this article, a UAVs-assisted fog-enabled Internet of Things (IoT) network is studied, in which moving UAVs are equipped with computing capabilities to offer task offloading opportunities to IDs.
[p.3 1st col 4th ¶] Consider a two-layer UAVs-assisted fog-enabled IoT networks, including the air network layer, which is composed of UAVs, and the ground network layer, which is composed of IoT devices. The IoT devices could communicate by D2D communications link, and the orthogonal multiple access technology is used for the data transmission between the UAVs and R-IDs. Assume there are M R-IDs and X F-IDs, such as smartphones, tablets, client TDs, etc., denoted as M={1,2,…,M} and D={1,2,…,D} , respectively. UAVs are considered as the flying FNs with a fixed circular trajectory, denoted as N={1,2,…,N} , and device groups are randomly allocated in the network, which can dynamically join or leave the coverage of UAVs…
[p.3 2nd col last ¶] In time slot q, assume that all IDs are located at the xy -plane, as shown in Fig. 3. In the 3-D Cartesian coordinate scenario, the UAV n flies along the trajectory defined as Zn(q)=[xn(q),yn(q),hn(q)] , where the altitude hn(q) is chosen to guarantee the safety for both UAVs and IDs, and the initial position Zn(0) is predetermined. In the scenario, hn(q) of UAV n is fixed [22], [23], [28], [29]; therefore, the trajectory of UAV n on the xy -plane could be optimized. The location of UAV n , R-ID m , and F-ID d is denoted as Zn(q)=[xn(q),yn(q)] , Zm(q)=[xm(q),ym(q)] , and Zd(q)=[xd(q),yd(q)] , respectively. In time slot q , the velocity Vn(q) and the acceleration ψn(q) of UAV n are denoted as
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Regarding Claim 4, Huang/Sun/Stack/Herbas/CentralCommand/Li respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Sun, via Huang/Sun/Stack/Herbas/CentralCommand/Li, further teaches:
The method of claim 1, wherein the first predetermined criteria is based on a battery level of the at least one second device, and wherein determining whether the at least one second device satisfies the first predetermined criteria comprises: receiving the battery level of the at least one second device…; and determining that the battery level of the at least one second device is below a predefined threshold. (Sun [0015] When the first electronic device 12 has an unprocessed task, the task assigning device 10 can analyze the battery level of the first electronic device 12 to accordingly decide assignment of the unprocessed task between the first electronic device 12 and the second electronic device 14… If the battery level of the first electronic device 12 is in a low level, the task assigning device 10 may control the first electronic device 12 to process few or none of the unprocessed task; the processed task and most of the unprocessed task can be transmitted to the second electronic device 14 for accomplishing the whole task.)
CentralCommand, via Huang/Sun/Stack/Herbas/CentralCommand/Li, further teaches:
at predetermined time intervals (CentralCommand [p.1 4th ¶] An alert which notifies me every 12 hours with a count of the number of devices with low battery (if any have low battery)) (Note: every 12 hours corresponds to the predetermined time interval.)
Regarding Claim 5, Huang/Sun/Stack/Herbas/CentralCommand/Li respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Sun, via Huang/Sun/Stack/Herbas/CentralCommand/Li, further teaches:
The method of claim 1, wherein the second predetermined criteria is based on a priority task, and wherein determining whether that the at least one second device satisfies the second predetermined criteria comprises: receiving an indication that the priority task having a higher priority than the first part of the DNN task is assigned to the at least one second device for execution. (Sun [0015] When the first electronic device 12 has an unprocessed task, the task assigning device 10 can analyze the battery level of the first electronic device 12 to accordingly decide assignment of the unprocessed task between the first electronic device 12 and the second electronic device 14. If the battery level of the first electronic device 12 is in a high level, the task assigning device 10 can control the first electronic device 12 to process most of the unprocessed task; the processed task and few of the unprocessed task can be transmitted from the first electronic device 12 to the second electronic device 14 for accomplishing the whole task.) (Note: the first electronic device having the unprocessed task corresponds to the indication that the priority task having a higher priority than the first part of the DNN task is assigned to the at least one second device.)
Regarding Claim 6, Huang/Sun/Stack/Herbas/CentralCommand/Li respectively teaches and incorporates the claimed limitations and rejections of Claim 5. Huang, via Huang/Sun/Stack/Herbas/CentralCommand/Li, further teaches:
The method of claim 5, wherein transferring the remaining part of the first part of the DNN task to the another second device comprises:
transferring the remaining part of the first part of the DNN task to the another second device for execution while executing the priority task having the higher priority at the at least one second device. (Huang [p.2 1st col 2nd ¶] Besides, in a computation offloading-enabled network, IDs could partially execute tasks locally or offload to the mobile-edge computing (MEC) to further improve the network performance [13]. Therefore, a computation task can be divided into small parts with different data size, and processed at both the local terminal and the remote MEC parallelly.) (Note: the processing done parallelly corresponds to the tasks being executed while transferring the remaining part)
Regarding Claim 7, Huang/Sun/Stack/Herbas/CentralCommand/Li respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Huang, via Huang/Sun/Stack/Herbas/CentralCommand/Li, further teaches:
The method of claim 1, wherein identifying the second edge device comprises: identifying a second nearest second device as the another second device, using a path loss method. (Huang [p.4 1st col middle ¶]
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Regarding Claim 8, Huang/Sun/Stack/Herbas/CentralCommand/Li respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Sun, via Huang/Sun/Stack/Herbas/CentralCommand/Li, further teaches:
The method of claim 1, wherein the first device has a first computing capability and each of the plurality of second devices have a respective second computing capability, the respective second computing capability being higher than the first computing capability. (Sun [0028] In the present invention, the first electronic device 12 and the second electronic device 14 can have neural net computation function; the first electronic device 12 has simply function of the neural net computation due to a small size and less power storage, and the second electronic device 14 has advanced function of the neural net computation due to a large size and preferred power storage.) (Note: supported by provisional p.5)
Independent Claim 9 recites A system of task management in an internet of things (IoT)-edge network, the IoT-edge network including an IoT device and a plurality of edge devices, and the system comprising (Huang [Abst] Recently, unmanned aerial vehicles (UAVs) have been considered as an efficient way to provide enhanced coverage or relaying services to Internet of Things devices (IDs) in wireless systems with limited or no infrastructure. In this article, a UAVs-assisted fog-enabled Internet of Things (IoT) network is studied, in which moving UAVs are equipped with computing capabilities to offer task offloading opportunities to IDs.)to perform precisely the methods of Claim 1. Thus, Claim 9 is rejected for reasons set forth in Claim 1.
Claim(s) 10-16, dependent on Claim 9, also recite the system configured to perform precisely the methods of Claims 2-8, respectively, and thus are rejected for reasons set forth in these claims.
Regarding Claim 18, Huang/Sun/Stack/Herbas/CentralCommand/Li respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Huang, via Huang/Sun/Stack/Herbas/CentralCommand/Li, further teaches:
The method according to claim 1, wherein subsequent to the second predetermined criteria being satisfied, the method further comprises:
transferring the remaining part of the first part of the DNN task to the another second device for execution while executing a priority task having a higher priority at the at least one second device. (Huang [p.2 1st col 2nd ¶] Besides, in a computation offloading-enabled network, IDs could partially execute tasks locally or offload to the mobile-edge computing (MEC) to further improve the network performance [13]. Therefore, a computation task can be divided into small parts with different data size, and processed at both the local terminal and the remote MEC parallelly.)
Regarding Claim 19, Huang/Sun/Stack/Herbas/CentralCommand/Li respectively teaches and incorporates the claimed limitations and rejections of Claim 18. Huang, via Huang/Sun/Stack/Herbas/CentralCommand/Li, further teaches:
The method according to claim 18, wherein the first part of the DNN task is transferred to the another second device for execution without restarting layers already executed by the at least one second device. (Huang [p.2 1st col 2nd ¶] Besides, in a computation offloading-enabled network, IDs could partially execute tasks locally or offload to the mobile-edge computing (MEC) to further improve the network performance [13]. Therefore, a computation task can be divided into small parts with different data size, and processed at both the local terminal and the remote MEC parallelly.) (Note: IDs partially executing the tasks corresponds to not restarting the layers already executed by the at least one second device)
Independent Claim 9 recites A system of task management in an internet of things (IoT)-edge network, the IoT-edge network including an IoT device and a plurality of edge devices, and the system comprising (Huang [Abst] Recently, unmanned aerial vehicles (UAVs) have been considered as an efficient way to provide enhanced coverage or relaying services to Internet of Things devices (IDs) in wireless systems with limited or no infrastructure. In this article, a UAVs-assisted fog-enabled Internet of Things (IoT) network is studied, in which moving UAVs are equipped with computing capabilities to offer task offloading opportunities to IDs.)to perform precisely the methods of Claim 1. Thus, Claim 9 is rejected for reasons set forth in Claim 1.
Claim(s) 10-16, dependent on Claim 9, also recite the system configured to perform precisely the methods of Claims 2-8, respectively, and thus are rejected for reasons set forth in these claims.
Independent Claim 17 recites precisely the methods of Claim 1. Thus, Claim 17 is rejected for reasons set forth in Claim 1. (Note: The amendments to the claims recite claim 17 to have precisely the methods of claim 1. “identifying another second device among the plurality of second devices, subsequent to the triggering of the alarm” in claim 1 corresponds to “subsequent to the triggering of the alarm, determining another second device from the plurality of second devices,” as “identifying” and “determining” have no functional difference.)
Claim(s) 20, dependent on Claim 17, also recite the system configured to perform precisely the methods of Claims 5, respectively, and thus are rejected for reasons set forth in these claims.
Conclusion
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.
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/J.H./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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