DETAILED ACTION
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 .
Status of the claims
Claims 1 and 13 are amended.
Claims 1-22 are pending.
Response to Arguments/Remarks
With respect to Applicant’s remarks filed on 03/02/2026; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
Applicant remarks:
Qian does not teach “the LDM further representing at least one of the remote devices”.
35 U.S.C. 112(a) rejection of claims 8 and 19 is overcome because the specification paras[0031],[0034]-[0046] mentioned mathematical optimization steps and paras[0035]-[0049] mentioned a number of computations need for tasks which one of ordinary skill in the art of a classifier for determining a destination for performing a computation.
Office Response:
Qian teaches in para[0120] that the sensing device provides data regarding the environment surrounding the mobile vehicle. After that information of the target object is acquired, the information of the target object transmitted to the remote terminal and identification subtask is performed and eventually the processing task includes a map update task.
The remark overcomes 35 U.S.C. 112(a) rejection of claims 8 and 19.
Applicant further argues that the other independent claims which recite similar features are allowable and the dependent claims are also allowable since they depend on allowable subject and the Office respectfully disagrees. It is the Office's stance that all of the claimed subject matter has been properly rejected; therefore, the Office's respectfully disagrees with applicant’s arguments.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1,2,8, 11, 13, 14, 19 and 22 are rejected under 35 U.S.C. 102 (a)(1) and (2) as being anticipated by US20210018938A1 to Qian et al. (herein after “Qian”).
Regarding claim 1, Qian teaches A system for managing operation of a vehicle, comprising:
a wireless network interface configured to communicate with remote devices (See Qian remote terminal 2, figure 1, server 4, figure 1) via at least a first wireless channel and a second wireless channel (See Qian figure 1, para[0065] In addition, a server may be used as a relay station for forwarding data between the remote terminal and the mobile vehicle. such that the remote terminal may communicate with the mobile vehicle to facilitate data exchange with the mobile vehicle. The transmission link may be a direct transmission link or an indirect transmission link);
a computing module configured to maintain a local dynamic map (LDM) representing the vehicle and a surrounding environment (See Qian computation load distribution 600,700,800, figure 6-8, map updating module 60, figure 8,para[0055]) the LDM further representing at least one of the remote devices ( see Qian para[0120] In some embodiments, the sensing device may be used to provide data regarding the environment surrounding the mobile vehicle, such as weather conditions, proximity to potential obstacles, location of geographical features, location of manmade structures, and/or the like. After the information of the target object is acquired, the information of the target object can be transmitted to the remote terminal and the object identification subtask can be performed, e.g., at the remote terminal, to identify the target object based on the information of the target object; para[0123] the processing task includes a map update task,)
and a controller configured to: (See a remote controller, a ground control device)
generate a navigation task from sensor data corresponding to the surrounding environment (See Qian figure 3, para[0043] FIG. 3 is a flowchart of an application of the computation load distribution method according to an embodiment of the present disclosure. In this embodiment, the payload of the mobile vehicle may be a camera, and the processing task may be an image processing task, para[0063] the processing task associated with the mobile vehicle may include one or more of an image acquisition task, an image processing task, an image recognition task, a route query task, a trajectory planning task, and an obstacle avoidance task),
communicate with the computing module to determine a status of on- board computational resources (See Qian figure 2, 6-8, see processing resource determination module 20);
determine, based on the LDM and the status of on-board computational resources, a destination to process the navigation task, the destination being one of a set of resources including the computing module and at least one of the remote devices (See Qian para[0126] the assigning module 1220 may be configured to assign the plurality of subtasks to at least one of a mobile vehicle or a remote terminal for processing based on at least a characteristic of the plurality of subtasks. In some embodiment, the plurality of subtasks may include one or more of an image acquisition subtask, an image recognition subtask, an information acquisition subtask, an object identification subtask, a related-information acquisition subtask, a planning subtask, a change acquisition subtask, an updating subtask, a location identification subtask, and a route determination subtask) ; and
communicate the navigation task to the destination via at least one of an on-board channel and the first and second wireless channels (See Qian figure 2, S203-204).
Regarding claim 2, Qian teaches wherein the navigation task includes processing the sensor data to determine an update to the LDM. (See Qian para[0112] a map update task, para[0134] As such, the assigning module 1220 can assign the updating subtask to the remote terminal to update the map based on an amount of change of the map acquired by the mobile vehicle.)
Regarding claim 8, Qian teaches wherein the controller is further configured to:
generate a first feature set representing the set of resources;
generate a second feature set representing the navigation task (See Qian abstract Determining the one or more processing resources includes determining whether to perform the processing task locally at the mobile vehicle and/or remotely at a remote terminal.);
apply the first and second feature sets to a classifier to determine the destination. (See Qian para[0053] After the location of the mobile vehicle is identified, whether the location of the mobile vehicle is subject to a route query task can be determined. For example, the location of the mobile vehicle may be used to identify whether the mobile vehicle is moving along the planned route. When it is determined that the mobile vehicle is moving along the planned route, the mobile vehicle may continue to move along a planned path until the mobile vehicle reaches the destination. In some embodiments, if it is determined that the mobile vehicle is not moving along the planned route, the mobile vehicle may need to be re-routed to the planned path, such that the mobile vehicle may reach the destination.)
Regarding claim 11, Qian teaches wherein the computing module is further configured to control movement of the vehicle based on the LDM, the movement including at least one of collision avoidance and self-driving operation. (see Qian para0034] The processing task associated with the mobile vehicle may be a task that is associated with a component or a payload of the mobile vehicle. Para[0044]In some embodiments, the camera carried by the mobile vehicle may be used to acquire an image of, e.g., the surrounding of the mobile vehicle. In some embodiments, the image may be subject to an image processing task. For example, the image may be used to determine the position of the mobile vehicle to achieve, e.g., obstacle avoidance and/or route planning. Para[0060] In particular, the trajectory planning may include obstacle avoidance.)
Regarding claim 13, Qian teaches A method of managing operation of a vehicle, comprising:
communicating with remote devices via at least a first wireless channel and a second wireless channel(See Qian figure 1, para[0065] In addition, a server may be used as a relay station for forwarding data between the remote terminal and the mobile vehicle. such that the remote terminal may communicate with the mobile vehicle to facilitate data exchange with the mobile vehicle. The transmission link may be a direct transmission link or an indirect transmission link. );
maintaining a local dynamic map (LDM) representing the vehicle and a surrounding environment (See Qian computation load distribution 600,700,800, figure 6-8, map updating module 60, figure 8,para[0055]) the LDM further representing at least one of the remote devices ( see Qian para[0120] In some embodiments, the sensing device may be used to provide data regarding the environment surrounding the mobile vehicle, such as weather conditions, proximity to potential obstacles, location of geographical features, location of manmade structures, and/or the like. After the information of the target object is acquired, the information of the target object can be transmitted to the remote terminal and the object identification subtask can be performed, e.g., at the remote terminal, to identify the target object based on the information of the target object; para[0123] the processing task includes a map update task,);
generating a navigation task from sensor data corresponding to the surrounding environment (See Qian figure 3, para[0043] FIG. 3 is a flowchart of an application of the computation load distribution method according to an embodiment of the present disclosure. In this embodiment, the payload of the mobile vehicle may be a camera, and the processing task may be an image processing task, para[0063] the processing task associated with the mobile vehicle may include one or more of an image acquisition task, an image processing task, an image recognition task, a route query task, a trajectory planning task, and an obstacle avoidance task);
communicating with an on-board computing module to determine a status of on- board computational resources(See Qian figure 2, 6-8, see processing resource determination module 20);;
determining, based on the LDM and the status of on-board computational resources, a destination to process the navigation task, the destination being one of a set of resources including the on-board computing module and at least one of the remote devices(See Qian para[0126] the assigning module 1220 may be configured to assign the plurality of subtasks to at least one of a mobile vehicle or a remote terminal for processing based on at least a characteristic of the plurality of subtasks. In some embodiment, the plurality of subtasks may include one or more of an image acquisition subtask, an image recognition subtask, an information acquisition subtask, an object identification subtask, a related-information acquisition subtask, a planning subtask, a change acquisition subtask, an updating subtask, a location identification subtask, and a route determination subtask); and
communicating the navigation task to the destination via at least one of an on- board channel and the first and second wireless channels(See Qian figure 2, S203-204).
Regarding claim 14, Qian teaches wherein the navigation task includes processing the sensor data to determine an update to the LDM. (See Qian para[0112] a map update task,para[0134] As such, the assigning module 1220 can assign the updating subtask to the remote terminal to update the map based on an amount of change of the map acquired by the mobile vehicle.)
Regarding claim 19, Qian teaches further comprising:
generating a first feature set representing the set of resources;
generating a second feature set representing the navigation task (See Qian abstract Determining the one or more processing resources includes determining whether to perform the processing task locally at the mobile vehicle and/or remotely at a remote terminal.);
apply the first and second feature sets to a classifier to determine the destination. (See Qian para[0053] After the location of the mobile vehicle is identified, whether the location of the mobile vehicle is subject to a route query task can be determined. For example, the location of the mobile vehicle may be used to identify whether the mobile vehicle is moving along the planned route. When it is determined that the mobile vehicle is moving along the planned route, the mobile vehicle may continue to move along a planned path until the mobile vehicle reaches the destination. In some embodiments, if it is determined that the mobile vehicle is not moving along the planned route, the mobile vehicle may need to be re-routed to the planned path, such that the mobile vehicle may reach the destination.)
Regarding claim 22, Qian teaches further comprising controlling movement of the vehicle based on the LDM, the movement including at least one of collision avoidance and self-driving operation. (see Qian para0034] The processing task associated with the mobile vehicle may be a task that is associated with a component or a payload of the mobile vehicle. Para[0044]In some embodiments, the camera carried by the mobile vehicle may be used to acquire an image of, e.g., the surrounding of the mobile vehicle. In some embodiments, the image may be subject to an image processing task. For example, the image may be used to determine the position of the mobile vehicle to achieve, e.g., obstacle avoidance and/or route planning. Para[0060] In particular, the trajectory planning may include obstacle avoidance.)
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.
Claims 3-7, 9, 15-18 and 20 are rejected under 35 USC 103 as being unpatentable over US20210018938A1 to Qian et al. (herein after “Qian”) in view of WO2021071621A1 to Hall et al. (herein after “Hall”).
Regarding claim 3, Qian remains applied as claim 1. However, Qian does not expressly disclose or otherwise teach wherein the navigation task is a deep learning (DL) task. Nevertheless, in a related field of invention, Hall teaches wherein the navigation task is a deep learning (DL) task (see Hall para[0075] The radar perception layer 202 may include use of neural network processing and artificial intelligence methods to recognize objects and vehicles, and pass such information on to the sensor fusion and RWM management layer 212.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Qian’s computation load distribution method associated with a mobile device with Hall’s edge system for providing local dynamic map data in order to allow to reduce suffering from latency due to the distance network elements by reflect highly dynamic environmental condition (See Hall para[0002]).
Regarding claim 4, Qian remains applied as claim 1. However, Qian does not expressly disclose or otherwise teach wherein the first wireless channel is a dedicated short-range communications (DSRC) channel and the second wireless channel is a point-to-point millimeter wave (mmWave) channel. Nevertheless, in a related field of invention, Hall teaches wherein the first wireless channel is a dedicated short-range communications (DSRC) channel and the second wireless channel is a point-to-point millimeter wave (mmWave) channel (See Hall paras[0049] [0049] The wired communication link 126 may use a variety of wired networks (e.g., Ethernet, TV cable, telephony, fiber optic and other forms of physical network connections) that may use one or more wired communication protocols, such as Ethernet, Point-To-Point protocol,para[0055] LAA, MuLTEfne, and relatively short range RATs such as ZigBee, Bluetooth, and Bluetooth Low Energy (LE).).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Qian’s computation load distribution method associated with a mobile device with Hall’s edge system for providing local dynamic map data in order to allow to reduce suffering from latency due to the distance network elements by reflect highly dynamic environmental condition (See Hall para[0002]).
Regarding claim 5, Qian and Hall remain applied as claim 4. Qian teach wherein the computing module is further configured to update the LDM based on communications from the remote devices via the DSRC channel (see Qian para[0077] the map updating module 60 may be used to update the map stored in the mobile vehicle using a change in a depth map.).
Regarding claim 6, Qian remains applied as claim 1. Qian teaches wherein the controller is further configured to communicate the navigation task (See Qian para[0112] the processing task may include one or more of an image processing task, a trajectory planning task, a map update task, and a route query task. ). However, Qian does not expressly disclose or otherwise teach destination via the mmWave channel, the destination being one of a remote vehicle and a road side unit (RSU). Nevertheless, in a related field of invention, Hall teaches to the destination via the mmWave channel (See Hall para[0055] LAA, MuLTEfne, and relatively short range RATs such as ZigBee, Bluetooth, and Bluetooth Low Energy (LE).), the destination being one of a remote vehicle and a road side unit (RSU). (see Hall page [0039] In various embodiments, an Edge computing device may receive new or updated (referred to as “first”) LDM data for a service area of the Edge computing device. In some embodiments, the Edge computing device may receive the first LDM data from one or more data sources other than vehicles and mobile devices, such as other vehicles and mobile devices, roadside units (RSUs)).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Qian’s computation load distribution method associated with a mobile device with Hall’s edge system for providing local dynamic map data in order to allow to reduce suffering from latency due to the distance network elements by reflect highly dynamic environmental condition (See Hall para[0002]).
Regarding claim 7, Qian remains applied as claim 1. However, Qian teach wherein the RSU is further configured to communicate the navigation task (See Qian para[0112] the processing task may include one or more of an image processing task, a trajectory planning task, a map update task, and a route query task. ) to a cloud network resource for performing the navigation task (see Qian para[0031] [0031]The remote terminal 2 may be any device that can communicate with the mobile vehicle 1. In some embodiments, the remote terminal 2 may be a handheld device (e.g., a smartphone or a tablet), a remote controller, a ground control device, or a cloud computation platform).
Regarding claim 9, Qian remains applied as claim 1. However, Qian does not expressly disclose or otherwise teach incorporate a representation of the set of resources and a representation of the navigation task into a mathematical model; and process the mathematical model to determine the destination. Nevertheless, in a related field of invention, Hall teaches wherein the controller is further configured to: incorporate a representation of the set of resources and a representation of the navigation task into a mathematical model; and process the mathematical model to determine the destination. (see Hall para[0004] Various aspects include methods performed by an Edge computing device for generating LDM data by receiving first LDM data for a service area of the Edge computing device, integrating the first LDM data into an LDM data model, determining second LDM data of the LDM data model that is relevant to a mobile device, and providing the determined second LDM data to the mobile device. In some aspects, the LDM data model may be maintained in the Edge computing device and includes LDM data of the service area of the Edge computing device. ,para[0005] In some aspects, the mobile device is a computing device in a vehicle. In such aspects, providing the determined second LDM data to the mobile device may include generating a digital map encompassing an area within a predetermined distance of the vehicle, and transmitting the digital map to the vehicle, in which the digital map may be generated and transmitted in a format suitable for use in autonomous navigation of the vehicle.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Qian’s computation load distribution method associated with a mobile device with Hall’s edge system for providing local dynamic map data in order to allow to reduce suffering from latency due to the distance network elements by reflect highly dynamic environmental condition (See Hall para[0002]).
Regarding claim 15, Qian remains applied as claim 1. However, Qian does not expressly disclose or otherwise teach wherein the navigation task is a deep learning (DL) task. Nevertheless, in a related field of invention, Hall teaches wherein the navigation task is a deep learning (DL) task (see Hall para[0075] The radar perception layer 202 may include use of neural network processing and artificial intelligence methods to recognize objects and vehicles, and pass such information on to the sensor fusion and RWM management layer 212.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Qian’s computation load distribution method associated with a mobile device with Hall’s edge system for providing local dynamic map data in order to allow to reduce suffering from latency due to the distance network elements by reflect highly dynamic environmental condition (See Hall para[0002]).
Regarding claim 16, Qian remains applied as claim 1. However, Qian does not expressly disclose or otherwise teach wherein the first wireless channel is a dedicated short-range communications (DSRC) channel and the second wireless channel is a point-to-point millimeter wave (mmWave) channel. Nevertheless, in a related field of invention, Hall teaches wherein the first wireless channel is a dedicated short-range communications (DSRC) channel and the second wireless channel is a point-to-point millimeter wave (mmWave) channel (See Hall paras[0049] [0049] The wired communication link 126 may use a variety of wired networks (e.g., Ethernet, TV cable, telephony, fiber optic and other forms of physical network connections) that may use one or more wired communication protocols, such as Ethernet, Point-To-Point protocol,para[0055] LAA, MuLTEfne, and relatively short range RATs such as ZigBee, Bluetooth, and Bluetooth Low Energy (LE).).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Qian’s computation load distribution method associated with a mobile device with Hall’s edge system for providing local dynamic map data in order to allow to reduce suffering from latency due to the distance network elements by reflect highly dynamic environmental condition (See Hall para[0002]).
Regarding claim 17, Qian remains applied as claim 1. However, Qian teach further comprising updating the LDM based on communications from the remote devices via the DSRC channel. (see Qian para[0077] the map updating module 60 may be used to update the map stored in the mobile vehicle using a change in a depth map.).
Regarding claim 18, Qian remains applied as claim 1. Qian teaches further comprising communicating the navigation task (See Qian para[0112] the processing task may include one or more of an image processing task, a trajectory planning task, a map update task, and a route query task. ). However, Qian does not expressly disclose or otherwise teach to the destination via the mmWave channel, the destination being one of a remote vehicle and a road side unit (RSU). Nevertheless, in a related field of invention, Hall teaches to the destination via the mmWave channel (See Hall para[0055] LAA, MuLTEfne, and relatively short range RATs such as ZigBee, Bluetooth, and Bluetooth Low Energy (LE).) , the destination being one of a remote vehicle and a road side unit (RSU). (see Hall page [0039] In various embodiments, an Edge computing device may receive new or updated (referred to as “first”) LDM data for a service area of the Edge computing device. In some embodiments, the Edge computing device may receive the first LDM data from one or more data sources other than vehicles and mobile devices, such as other vehicles and mobile devices, roadside units (RSUs))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Qian’s computation load distribution method associated with a mobile device with Hall’s edge system for providing local dynamic map data in order to allow to reduce suffering from latency due to the distance network elements by reflect highly dynamic environmental condition (See Hall para[0002]).
Regarding claim 20, Qian remains applied as claim 1. However, Qian does not expressly disclose or otherwise teach incorporating a representation of the set of resources and a representation of the navigation task into a mathematical model; and processing the mathematical model to determine the destination. Nevertheless, in a related field of invention, Hall teaches incorporating a representation of the set of resources and a representation of the navigation task into a mathematical model; and processing the mathematical model to determine the destination (see Hall para[0004] Various aspects include methods performed by an Edge computing device for generating LDM data by receiving first LDM data for a service area of the Edge computing device, integrating the first LDM data into an LDM data model, determining second LDM data of the LDM data model that is relevant to a mobile device, and providing the determined second LDM data to the mobile device. In some aspects, the LDM data model may be maintained in the Edge computing device and includes LDM data of the service area of the Edge computing device, para[0005] In some aspects, the mobile device is a computing device in a vehicle. In such aspects, providing the determined second LDM data to the mobile device may include generating a digital map encompassing an area within a predetermined distance of the vehicle, and transmitting the digital map to the vehicle, in which the digital map may be generated and transmitted in a format suitable for use in autonomous navigation of the vehicle.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Qian’s computation load distribution method associated with a mobile device with Hall’s edge system for providing local dynamic map data in order to allow to reduce suffering from latency due to the distance network elements by reflect highly dynamic environmental condition (See Hall para[0002]).
Claims 10, 12 and 21 are rejected under 35 USC 103 as being unpatentable over US20210018938A1 to Qian et al. (herein after “Qian”) in view of KR 20190003767 A to Manku et al. (herein after “Manku”).
Regarding claim 10, Qian remains applied as claim 1. However, Qian does not expressly disclose or otherwise teach wherein at least one of the distinct spectrum bands is an unlicensed spectrum band. Nevertheless, Manku same field of endeavor teaches wherein at least one of the distinct spectrum bands is an unlicensed spectrum band (See Manku para[0038] For example, each of the above wireless communication channels may occupy a distinct frequency band within the licensed or unlicensed wireless spectrum,).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Qian’s computation load distribution method associated with a mobile device with Manku’s motion detection channel operation using spectrum bands in order to allow to form a single motion detection channel, which may increase the frequency bandwidth of that motion detection channel (See Manku para[0038]).
Regarding claim 12, Qian remains applied as claim 1. Regarding claim 10, Qian remains applied as claim 1. However, Qian does not expressly disclose or otherwise teach wherein the first and second wireless channels have distinct spectrum bands. Nevertheless, Manku same field of endeavor teaches wherein the first and second wireless channels have distinct spectrum bands. (see Manku para[0029] a signal transmitted on a motion detection channel (e.g., transmitted from a first wireless network device) and a signal reflected on the motion detection channel (e.g., received at a second wireless network device))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Qian’s computation load distribution method associated with a mobile device with Manku’s motion detection channel operation using spectrum bands in order to allow to form a single motion detection channel, which may increase the frequency bandwidth of that motion detection channel (See Manku para[0038]).
Regarding claim 21, Qian remains applied as claim 1. However, Qian does not disclose or otherwise teach wherein at least one of the distinct spectrum bands is an unlicensed spectrum band. Nevertheless, Manki same field of endeavor teaches wherein at least one of the distinct spectrum bands is an unlicensed spectrum band. (See Manku para[0038] For example, each of the above wireless communication channels may occupy a distinct frequency band within the licensed or unlicensed wireless spectrum,).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Qian’s computation load distribution method associated with a mobile device with Manku’s motion detection channel operation using spectrum bands in order to allow to form a single motion detection channel, which may increase the frequency bandwidth of that motion detection channel (See Manku para[0038]).
Conclusion
THIS ACTION IS MADE FINAL. 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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/NAZIA AFRIN/Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666
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