DETAILED ACTION
Remarks
Claims 1-20 are pending in the application and are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/03/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed 04/07/2026 have been fully considered but they are not persuasive. Regarding the applicant’s argument for Claim 1 and 11’s rejections for “an object segmentation system”, Penghui discloses (Page 3, “The depth sensor 30 is arranged at the front end of the operating table 11, and the depth sensor 30 is located between the lidar 20 on both sides in the left and right direction, and is used to collect static objects or physical environment around the forklift. Moving objects in the for visual obstacle avoidance.”) and (Page 4, “The rear depth camera can simultaneously use the RBG information and the depth information to identify and estimate the position and pose of the target to be inserted by the forklift, so as to perform precise insertion actions on goods with uncertain positions.”) meaning that although Penghui doesn’t explicitly disclose the exact wording of segmentation process of detected objects into obstructions and allowed objects, the forklift is still clearly able to differentiate between obstacles and the target itself. Therefore this rejection for claim 1 and 11, and their dependent claims still hold.
Regarding the applicant’s argument for Claim 1’s rejection for “a processer” and “memory device”, Penghui discloses (Page 5, “Specifically: the visual inertial odometer data output by the front follower camera 50 is time-synchronized with the roulette encoder odometer data and lidar data, and then the visual inertial odometer data and roulette encoder odometer data synchronized with the elapsed time are time-synchronized. Kalman filtering is performed with the lidar data to obtain the optimal state estimation.”) and (Page 2, “In this embodiment, the lidar adopts a measurement type lidar, which can obtain the measurement data of the original point cloud of the object, and solves the obstacle avoidance type laser used in the background technology to avoid obstacles in the low-position plane.”) meaning that although Penghui doesn’t explicitly disclose a processer and memory device, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application to have a processor and memory device in the forklift because they would allow the forklift’s Kalman filter to process and store data from multiple types of sensors on the fly to avoid obstacles in its environment. Therefore this rejection for claim 1, and its dependent claims still hold.
Regarding the applicant’s argument for Claim 1 and 11’s rejections for “semantic data”, the newly added IDS reference Ichinose covers the limitation for “semantic data” not explicitly disclosed in Penghui.
Claim Rejections - 35 USC § 103
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.
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, 9-11, 12-13, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Penghui (CN212669124U provided translation) in view of Ichinose (US 20170285644 A1).
Re Claim 1, Penghui discloses an autonomous mobile robot (AMR) (Fig. 1-2), comprising:
at least one sensor configured to acquire point cloud data; (Page 1, “A plurality of the lidars are respectively arranged on both sides of the front end of the operation platform, and the lidar has a predetermined height from the ground, and is used to scan the environment in front of and on both sides of the forklift within a preset scanning range;”; Page 2, “In this embodiment, the lidar adopts a measurement type lidar, which can obtain the measurement data of the original point cloud of the object, and solves the obstacle avoidance type laser used in the background technology.”)
a pallet detection system configured to provide a pose of a payload; (Page 2, “The rear depth camera can simultaneously use the RBG information and the depth information to identify and estimate the position and pose of the target to be inserted by the forklift, so as to perform precise insertion actions on goods with uncertain positions.”)
and an object segmentation system comprising computer program code executable by the at least one processor to segment detected objects into obstructions and allowed objects based on the point cloud data, the pose of the payload, and (Page 1, “A plurality of the lidars are respectively arranged on both sides of the front end of the operation platform, and the lidar has a predetermined height from the ground, and is used to scan the environment in front of and on both sides of the forklift within a preset scanning range; The depth sensor is arranged at the front end of the console, and the depth sensor is located between the lidars on both sides in the left and right direction, and is used to collect static objects around the forklift or moving objects in the physical environment For visual obstacle avoidance; The rear depth camera is arranged at the middle position of the rear end of the operating table, and is used for the forklift to insert and extract the target and accurately locate the position of the inserting target.”)
Penghui does not explicitly disclose at least one processor in communication with at least one computer memory device. However, it is stated that the forklift’s systems perform Kalman filtering on the LiDAR data (Page 5, “Specifically: the visual inertial odometer data output by the front follower camera 50 is time-synchronized with the roulette encoder odometer data and lidar data, and then the visual inertial odometer data and roulette encoder odometer data synchronized with the elapsed time are time-synchronized. Kalman filtering is performed with the lidar data to obtain the optimal state estimation.”) to allow the forklift to avoid obstacles (Page 2, “In this embodiment, the lidar adopts a measurement type lidar, which can obtain the measurement data of the original point cloud of the object, and solves the obstacle avoidance type laser used in the background technology to avoid obstacles in the low-position plane.”). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application to have a processor and memory device in the forklift because they would allow the forklift to process and store data, and avoid obstacles.
Additionally, while Penghui teaches depth detection, it does not explicitly disclose semantic data about the payload however, Ichinose discloses (Paragraph 0020, "Specifically, as shown in FIG 5, for example, if a load 130 is placed on the pallet 100 and the pallet 100 is placed on a base 120, the region where there may be the pallet 100 is determined by the dimensions of the base 120 and the pallet 100." Applicant states in specification that semantic data about the payload can include the dimensions of the payload).
Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application to combine Penghui and Ichinose’s forklifts because the inclusion of object dimensions determined by Ichinose’s forklift would further improve the ability of Penghui’s forklift to contact or avoid them without collisions.
Re Claim 2, Penghui discloses wherein the at least one processor provides an expected pose of the payload (Page 2, “The rear depth camera can simultaneously use the RBG information and the depth information to identify and estimate the position and pose of the target to be inserted by the forklift, so as to perform precise insertion actions on goods with uncertain positions.”).
Re Claim 3, Penghui does not explicitly disclose wherein the object segmentation system generates at least one first region around the payload based on the pose of the payload and the expected pose of the payload. However, it is stated that the pose of the payload is the first thing the forklift’s object segmentation system acquirees once it gets in the near vicinity of the target (Page 4, “In this embodiment, after the forklift reaches the vicinity of the predetermined position, the depth sensor 30 performs preliminary positioning of the insertion target (for example, a pallet), and confirms the coordinate position of the pallet. At this time, the forklift makes a U-turn, and the rear depth camera 40 is used to accurately position the pallet.” Preliminary position is equivalent to the expected position and the confirmation of the coordinate position is equivalent to the actual pose). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application that the at least one first region of the forklifts path planning would be based around the payload.
Re Claim 9, Penghui discloses wherein the at least one sensor comprises at least one of a LiDAR scanner and a 3D camera (Page 1, “A plurality of the lidars are respectively arranged on both sides of the front end of the operation platform, and the lidar has a predetermined height from the ground, and is used to scan the environment in front of and on both sides of the forklift within a preset scanning range… The rear depth camera is arranged at the middle position of the rear end of the operating table, and is used for the forklift to insert and extract the target and accurately locate the position of the inserting target.”).
Re Claim 10, Penghui discloses wherein the AMR includes a pair of forks and the payload is a palletized payload (Page 4, “In this embodiment, after the forklift reaches the vicinity of the predetermined position, the depth sensor 30 has preliminarily confirmed the coordinate position of the insertion target (for example, a pallet).”; Fig. 2)
Re Claim 11, Penghui discloses an object segmentation method for use by autonomous mobile robot (AMR) (Page 8, “It should be noted that the foregoing method embodiment and the forklift embodiment belong to the same concept, and the specific implementation process is detailed in the forklift embodiment, and the technical features in the forklift embodiment are correspondingly applicable to the method embodiment, and will not be repeated here.”) the method comprising:
acquiring point cloud data using at least one sensor; (Page 1, “A plurality of the lidars are respectively arranged on both sides of the front end of the operation platform, and the lidar has a predetermined height from the ground, and is used to scan the environment in front of and on both sides of the forklift within a preset scanning range”; Page 2, “In this embodiment, the lidar adopts a measurement type lidar, which can obtain the measurement data of the original point cloud of the object, and solves the obstacle avoidance type laser used in the background technology to avoid obstacles in the low-position plane.”)
providing a pose of a payload using a pallet detection system; (Page 2, “The rear depth camera can simultaneously use the RBG information and the depth information to identify and estimate the position and pose of the target to be inserted by the forklift, so as to perform precise insertion actions on goods with uncertain positions.” Target is interpreted to be payload. )
and an object segmentation system segmenting detected objects into obstructions and allowed objects based on the point cloud data, the pose of the payload, (Page 1, “A plurality of the lidars are respectively arranged on both sides of the front end of the operation platform, and the lidar has a predetermined height from the ground, and is used to scan the environment in front of and on both sides of the forklift within a preset scanning range; The depth sensor is arranged at the front end of the console, and the depth sensor is located between the lidars on both sides in the left and right direction, and is used to collect static objects around the forklift or moving objects in the physical environment For visual obstacle avoidance; The rear depth camera is arranged at the middle position of the rear end of the operating table, and is used for the forklift to insert and extract the target and accurately locate the position of the inserting target.”)
While Penghui teaches depth detection, it also does not explicitly disclose semantic data about the payload however, Ichinose discloses (Paragraph 0020, "Specifically, as shown in FIG 5, for example, if a load 130 is placed on the pallet 100 and the pallet 100 is placed on a base 120, the region where there may be the pallet 100 is determined by the dimensions of the base 120 and the pallet 100." Applicant states in specification that semantic data about the payload can include the dimensions of the payload).
Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application to combine Penghui and Ichinose’s forklifts because the inclusion of object dimensions determined by Ichinose’s forklift would further improve the ability of Penghui’s forklift to contact or avoid them without collisions.
Re Claim 12, Penghui discloses providing an expected pose of the payload (Page 2, “The rear depth camera can simultaneously use the RBG information and the depth information to identify and estimate the position and pose of the target to be inserted by the forklift, so as to perform precise insertion actions on goods with uncertain positions.”), but does not explicitly disclose using at least one processer. However, it is stated that the forklift’s systems perform Kalman filtering on the LiDAR data (Page 5, “Specifically: the visual inertial odometer data output by the front follower camera 50 is time-synchronized with the roulette encoder odometer data and lidar data, and then the visual inertial odometer data and roulette encoder odometer data synchronized with the elapsed time are time-synchronized. Kalman filtering is performed with the lidar data to obtain the optimal state estimation.”) to allow the forklift to avoid obstacles (Page 2, “In this embodiment, the lidar adopts a measurement type lidar, which can obtain the measurement data of the original point cloud of the object, and solves the obstacle avoidance type laser used in the background technology to avoid obstacles in the low-position plane.”). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application to have a processor in the forklift because they would allow the forklift to process and store the expected pose data, and avoid obstacles.
Re Claim 13, Penghui does not explicitly generating at least one first region around the payload based on the pose of the payload and the expected pose of the payload using the object segmentation system. However, it is stated that the pose of the payload is the first thing the forklift’s object segmentation system acquirees once it gets in the near vicinity of the target (Page 4, “In this embodiment, after the forklift reaches the vicinity of the predetermined position, the depth sensor 30 has preliminarily confirmed the coordinate position of the insertion target (for example, a pallet). At this time, the forklift turns around, and the rear depth camera 40 recognizes the pallet and accurately positions the pallet. Specifically, the rear depth camera 40 is used to accurately align the slot of the pallet, so that the vehicle body The fork 12 can be precisely aligned and inserted into the slot of the pallet.” Preliminary position is equivalent to the expected position and the confirmation of the coordinate position is equivalent to the actual pose). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application that the at least one first region of the forklifts path planning would be based around the payload.
Re Claim 17, Penghui does not explicitly disclose filtering out points from the point cloud data based on the at least one first region and the at least one second region using the object segmentation system. However, it is stated that the forklift’s systems perform Kalman filtering on the LiDAR data (Page 5, “Specifically: the visual inertial odometer data output by the front follower camera 50 is time-synchronized with the roulette encoder odometer data and lidar data, and then the visual inertial odometer data and roulette encoder odometer data synchronized with the elapsed time are time-synchronized. Kalman filtering is performed with the lidar data to obtain the optimal state estimation.”). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application that so long as the system is being fed point cloud data found by the LiDARs, the Kalman filter will be able to filter out the points of these scanned regions.
Re Claim 18, Penghui does not explicitly disclose excluding the filtered out points for obstruction detection using a processor. However, it is stated that system cross checks the data from both LiDAR sensors and calculates the error between the two (Page 7, “In an embodiment, in the step S2, the scan match processing of the left and right lidar data respectively obtains the state estimation between the respective frames and the frame, and at the same time obtains the error estimation and the variance of the respective state estimation”). Thus, it would be obvious to since a person of ordinary skill in the art at the time of the effective filing of the application that the Kalman filter would filter out this data error in order to obtain optimal state estimation for the forklift’s obstruction detection.
Re Claim 19, Penghui discloses wherein the at least one sensor comprises at least one of a LiDAR scanner and a 3D camera (Page 1, “A plurality of the lidars are respectively arranged on both sides of the front end of the operation platform, and the lidar has a predetermined height from the ground, and is used to scan the environment in front of and on both sides of the forklift within a preset scanning range; The depth sensor is arranged at the front end of the console, and the depth sensor is located between the lidars on both sides in the left and right direction, and is used to collect static objects around the forklift or moving objects in the physical environment For visual obstacle avoidance; The rear depth camera is arranged at the middle position of the rear end of the operating table, and is used for the forklift to insert and extract the target and accurately locate the position of the inserting target.”).
Re Claim 20, Penghui discloses wherein the AMR includes a pair of forks and the payload is a palletized payload (Page 4, “In this embodiment, after the forklift reaches the vicinity of the predetermined position, the depth sensor 30 has preliminarily confirmed the coordinate position of the insertion target (for example, a pallet).”; Fig. 2).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Penghui (CN212669124U provided translation) in view of Ichinose (US20170285644A1) and Wei (CN111126269A provided translation).
Re Claim 4, Modified Penghui teaches that the loaded pallet his robot collects is square from a top view (Fig. 4 and Page 3, “4 is a schematic diagram of the scanning range of a two-dimensional single-line lidar when a forklift is fully loaded with a pallet according to an embodiment of the present invention”) and that the rear depth camera determines the position and pose of the loaded pallet using depth (Page 2, “The rear depth camera can simultaneously use the RBG information and the depth information to identify and estimate the position and pose of the target to be inserted by the forklift, so as to perform precise insertion actions on goods with uncertain positions”), but does not explicitly disclose wherein the at least one first region is at least one three-dimensional box. However, Wei teaches a method of creating a three-dimensional box region about a targeted object (Page 1, “…a three-dimensional target detection method is provided, including: setting a first coordinate center of a target object in a monocular image as a second coordinate center of a 3D bounding box of the target object; acquiring an acquisition location”). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application to modify Modified Penghui’s robot with Wei’s teaching of target box bounding because it would provide the robot with a more efficient manner of path planning for a three-dimensional pallet or any target object.
Re Claim 14, Modified Penghui teaches that the loaded pallet his robot collects is square from a top view (Fig. 4 and Page 3, “4 is a schematic diagram of the scanning range of a two-dimensional single-line lidar when a forklift is fully loaded with a pallet according to an embodiment of the present invention”) and that the rear depth camera determines the position and pose of the loaded pallet using depth (Page 2, “The rear depth camera can simultaneously use the RBG information and the depth information to identify and estimate the position and pose of the target to be inserted by the forklift, so as to perform precise insertion actions on goods with uncertain positions”), but does not explicitly disclose wherein the at least one first region is at least one three-dimensional box. However, Wei teaches a method of creating a three-dimensional box region about a targeted object (Page 1, “…a three-dimensional target detection method is provided, including: setting a first coordinate center of a target object in a monocular image as a second coordinate center of a 3D bounding box of the target object; acquiring an acquisition location”). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application to modify Modified Penghui’s robot with Wei’s teaching of target box bounding because it would provide the robot with a more efficient manner of path planning for a three-dimensional pallet or any target object.
Claims 5, 7-8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Penghui (CN212669124U provided translation) in view of Ichinose (US20170285644A1) and Dammeyer (US 5738187).
Re Claim 5, Modified Penghui discloses the expected pose of the target/payload and forklift/robot (Page 6, “The radar data is processed by Scan Match to obtain the state estimation between the respective frame and the frame, and the error estimate and variance of the respective state estimation are obtained at the same time; the lidar data is time synchronized with the roulette encoder odometer data, and the elapsed time is synchronized Kalman filtering is performed on the data to obtain the optimal state estimation, which makes the estimation of the forklift's pose and state more accurate.”) but does not explicitly disclose wherein the object segmentation system generates at least one second region between forks of the robot and outriggers of the robot based on the expected pose of the payload and an expected pose of the robot. However, Dammeyer teaches a forklift with outriggers and a camera that can position itself to see the area below the forks when they are elevated (Col 4, Line 55-57, “Forward of the body 20 are outriggers 35 carrying front support wheels 37.”; Col. 1, Line 56-64, “This invention also includes a camera, which is equipped with a horizontal plane reticle and mounted on a vertically movable carriage assembly and which is protected from damage and contact with the floor when the forks are in their lowermost position. The camera is lowered to a first predetermined position below the forks and load when the forks are raised, which provides the camera with a view that is optimum for viewing a target for vertical height position of the forks or load.”). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application to modify Modified Penghui’s robot with Dammeyer’s teachings of outriggers and cameras to improve its stability and to allow observation of the space beneath the forks when they are elevated and/or loaded with a pallet in order to establish a second region for the forklift’s object segmentation system, and prevent Penghui’s depth camera from being obstructed by a loaded pallet or from being damaged when near the floor.
Re Claim 7, Modified Penghui does not explicitly disclose wherein the object segmentation system is configured to filter out points from the point cloud data based on the at least one first region and the at least one second region. However, it is stated that the forklift’s systems perform Kalman filtering on the LiDAR data (Page 5, “Specifically: the visual inertial odometer data output by the front follower camera 50 is time-synchronized with the roulette encoder odometer data and lidar data, and then the visual inertial odometer data and roulette encoder odometer data synchronized with the elapsed time are time-synchronized. Kalman filtering is performed with the lidar data to obtain the optimal state estimation.”). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application that so long as the system is being fed point cloud data found by the LiDARs, the Kalman filter will be able to filter out the points of these scanned regions.
Re Claim 8, Modified Penghui does not explicitly disclose wherein the processor is configured to not use the filtered out points for obstruction detection. However, it is stated that system cross checks the data from both LiDAR sensors and calculates the error between the two (Page 7, “In an embodiment, in the step S2, the scan match processing of the left and right lidar data respectively obtains the state estimation between the respective frames and the frame, and at the same time obtains the error estimation and the variance of the respective state estimation”). Thus, it would be obvious to since a person of ordinary skill in the art at the time of the effective filing of the application that the Kalman filter would filter out this data error in order to obtain optimal state estimation for the forklift’s obstruction detection.
Re Claim 15, Modified Penghui discloses the expected pose of the target/payload and forklift/robot (Page 6, “The radar data is processed by Scan Match to obtain the state estimation between the respective frame and the frame, and the error estimate and variance of the respective state estimation are obtained at the same time; the lidar data is time synchronized with the roulette encoder odometer data, and the elapsed time is synchronized Kalman filtering is performed on the data to obtain the optimal state estimation, which makes the estimation of the forklift's pose and state more accurate.”) but does not explicitly disclose generating at least one second region between forks of the robot and outriggers of the robot based on the expected pose of the payload and an expected pose of the robot using the object segmentation system. However, Dammeyer teaches a forklift with outriggers and a camera that can position itself to see the area below the forks when they are elevated (Col 4, Line 55-57, “Forward of the body 20 are outriggers 35 carrying front support wheels 37.”; Col. 1, Line 56-64, “This invention also includes a camera, which is equipped with a horizontal plane reticle and mounted on a vertically movable carriage assembly and which is protected from damage and contact with the floor when the forks are in their lowermost position. The camera is lowered to a first predetermined position below the forks and load when the forks are raised, which provides the camera with a view that is optimum for viewing a target for vertical height position of the forks or load.”). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application to modify Modified Penghui’s method with Dammeyer’s teachings of outriggers and cameras to improve its stability and to allow observation of the space beneath the forks when they are elevated and/or loaded with a pallet in order to establish a second region for the forklift’s object segmentation system, and prevent Penghui’s depth camera from being obstructed by a loaded pallet or from being damaged when near the floor.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Penghui (CN212669124U provided translation) in view of Ichinose (US20170285644A1), Wei (CN111126269A provided translation), and Dammeyer (US 5738187).
Re Claim 6, Modified Penghui does not explicitly disclose wherein the at least one second region is an at least one three-dimensional box. However, Wei teaches a method of creating a three-dimensional box region about a targeted object (Page 1, “…a three-dimensional target detection method is provided, including: setting a first coordinate center of a target object in a monocular image as a second coordinate center of a 3D bounding box of the target object; acquiring an acquisition location”). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application to modify Modified Penghui’s robot with Wei’s teaching of target box bounding because it would provide the robot with a more efficient manner of path planning for a three-dimensional pallet or any target object.
Re Claim 16, Modified Penghui does not explicitly disclose wherein the at least one second region is an at least one three-dimensional box. However, Wei teaches a method of creating a three-dimensional box region about a targeted object (Page 1, “…a three-dimensional target detection method is provided, including: setting a first coordinate center of a target object in a monocular image as a second coordinate center of a 3D bounding box of the target object; acquiring an acquisition location”). Thus, it would be obvious to a person of ordinary skill in the art at the time of the effective filing of the application to modify Modified Penghui’s robot with Wei’s teaching of target box bounding because it would provide the robot with a more efficient manner of path planning for a three-dimensional pallet or target object.
Conclusion
Applicant's submission of an information disclosure statement under 37 CFR 1.97(c) with the timing fee set forth in 37 CFR 1.17(p) on 02/03/2026 prompted the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 609.04(b). 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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/A.K.P./Examiner, Art Unit 3657 /ABBY LIN/Supervisory Patent Examiner, Art Unit 3657
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