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 .
Claims 1-13 are currently pending and examined below.
Response to amendment
This is a final Office action in response to applicant's remarks/arguments filed on 09/21/2026.
Status of the claims:
Claims 1-9, 11-12 have been amended.
The objection to the drawings and the specification has been withdrawn.
The rejection of claims 2-4, 11 under 35 U.S.C. 112 (b) is withdrawn in response to Applicant's amendment filed on 09/21/2026.
Applicants’ arguments, see Remarks page 10, filed on 09/21/2026, with respect to the rejection(s) of claim(s) 1-13 under 103 have been fully considered and are not persuasive. Therefore, the rejection is maintained.
Applicant argues that Mahata fails to teach or suggest “the road structural rule being associated with the acquired position information of the vehicle.”
However, as previously discussed with respect to claim 3, Mahata teaches using the current vehicle position to identify/select the applicable road segment and thereafter applying standard road width information to the selected road segment. See Mahata [0151]-[0153] and Figs. 5 and 6A-6D. In particular, Mahata [0151] teaches that the current vehicle position is known and is used to select a parent road segment, and Mahata [0152] teaches using a standard road width (rw) to determine whether that selected parent road segment is complete. Thus, the road width information is applied to the road segment identified based on the acquired vehicle position information. Moreover, as set forth in the previous Office action with respect to claim 3, it would have been obvious to one of ordinary skill in the art to associate the standard road width with the particular road section/segment corresponding to the current vehicle position because Mahata selects the road segment using the vehicle position and uses the standard road width to determine whether that selected road segment is complete. Such a modification would have predictably improved road extraction where different road sections have different widths. Accordingly, Applicant's argument does not overcome the rejection.
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 1-5, 9, 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Mahata et al. (US 20230186647 A1, “Mahata”).
Regarding claim 1, Mahata teaches a travelable area extraction apparatus comprising:
at least one memory (storing instructions), and at least one processor (Mahata [0106] states that the invention may be carried out on a suitable hardware system, using processing and memory capacity, and may run on a local system if desired. Mahata then discloses algorithmic processing steps in [0109]- [0157] and Figs. 2, 5, 6A–6D, and 7A–7B.) configured to execute the instructions to;
input three-dimensional data from a three-dimensional data acquisition unit mounted in a vehicle (Mahata [0107] states that the basic input is derived from a vehicle-mounted sensor system that produces a LiDAR-derived point cloud data set, camera images, and vehicle location/azimuth information. Mahata [0109] further explains that a large block of point cloud data is sliced into smaller blocks associated with camera snapshots. Mahata [0110] and Fig. 2 show input point-cloud data and image data being preprocessed, including downsampling, rotating, and slicing the point cloud.);
acquire position information of the vehicle (Mahata [0107] discloses that the input includes the location and azimuth angle of the vehicle when images are taken. Mahata [0112] refers to GPS locations of the surveillance car. Mahata [0114] states that the imagery datasets store the current location and azimuth angle of the surveillance car, and that the point cloud is sliced starting from the correct vehicle position. Mahata [0151] states that the vehicle position is known from the image dataset and that the car must be on the road.); and
extract (a travelable area) from the three-dimensional data (Mahata teaches extracting the road/road segment from the 3D point cloud. Mahata [0116] states that the road part creates one or more large plane surfaces in the point cloud. Mahata [0117]- [0120] teaches primary road extraction using RANSAC, where the largest extracted point-cloud plane is treated as the road plane. Mahata [0139]- [0140] teach fine-level segmentation using constrained planar cuts. Mahata [0150] -[0155] teach extracting the true road parts from over-segmented point cloud data and outputting the road segment. See also Figs. 2, 5, and 6A–6D.) on the basis of the position information of the vehicle (Mahata [0151] directly uses vehicle position to identify the road segment: because the vehicle position is known and the car must be on the road, a small circle is drawn around the car position, and the largest segment touching the circle is selected as the parent road segment. Mahata [0153] and Figs. 6A–6D show the surveillance car location, the selected parent segment, and merging with adjacent segments.) and (a road structural rule) indicating a distance from one end to the other end of a traveling area in a road (Mahata [0152] teaches using standard road width (r₍w₎) information, described as a known nominal value for a particular road system. If the width of the parent segment is smaller than the standard road width, Mahata searches fragmented parts in an expected direction to complete the full road segment. Mahata [0156] further explains estimating segment width using maximum and minimum x-axis bounds, i.e., a distance across the road segment. Figs. 5 and 7A–7B also show standard road width and segment-width estimation.).
Mahata fails to explicitly disclose at least one memory storing instructions, a travelable area and a road structural rule, the road structural rule being associated with the acquired position information of the vehicle.
Mahata discloses computerized processing steps for extracting road segments from vehicle-generated LiDAR point-cloud data (Figs. 1, 2, 5, 6A–6D, 7A–7B, and 13). Although Mahata does not disclose “memory storing instructions,” it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mahata’s computerized road-segment extraction system so that the disclosed point-cloud processing algorithms are stored in memory as executable instructions and executed by a processor, because storing software instructions in memory for processor execution was a known and predictable way to perform computerized point-cloud data processing.
Mahata still fails to explicitly disclose a travelable area and a road structural rule, the road structural rule being associated with the acquired position information of the vehicle.
Mahata instead uses road segment, road part, true road parts, and standard road width. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mahata’s vehicle-based road-segment extraction system so that Mahata’s extracted road segment/true road part corresponds to the claimed travelable area, and Mahata’s standard road width corresponds to the claimed road structural rule. Mahata teaches using the standard road width to determine whether the detected road segment is complete and to merge adjacent fragmented point-cloud segments to complete the full road segment ([0150]-[0157], Figs. 5, 6A-6D, 7A-7B). Such modification would have predictably improved the reliability of extracting the correct road/travelable area from vehicle-mounted three-dimensional data.
Mahata [0151]-[0153], Figs. 5 and 6A-6D also teach using the current vehicle position to identify/select the applicable parent road segment and applying the standard road width (rw) to determine whether the selected parent road segment is complete. Thus, Mahata applies the road structural rule/standard road width information to the road segment identified based on the acquired vehicle-position information. It would have been obvious to one of ordinary skill in the art to associate the standard road width with the particular road section/segment corresponding to the current vehicle position because Mahata selects the road segment using vehicle position and uses the standard road width to determine whether that selected segment is complete, thereby predictably improving road extraction where different road sections have different widths
Regarding claim 2, Mahata teaches the travelable area extraction apparatus according to claim 1, wherein the at least one processor configured to execute the instructions to: extract a specific range as a road area from a position in three-dimensional data corresponding to an installation position of the three- dimensional data acquisition unit mounted in the vehicle in the input three-dimensional data ( Mahata discloses extracting a local/specific range of three-dimensional point-cloud data around the GPS car position and then extracting the road area from that local point cloud. In particular, Mahata’s Fig. 39 discloses inputting point-cloud data and the GPS location of a car position, downsampling the point cloud, rotating and slicing the point cloud into small parts around the car location, and creating a small local point cloud. Mahata then performs road-plane detection/classification on the local point cloud to produce a primary road mask. Mahata’s Fig. 2 similarly shows input point-cloud data, preprocessing by downsampling, rotating, and slicing, and road detection by extracting plane parts from the point cloud using RANSAC. Also, since the installation position (for example, a laser emission position of the LiDAR sensor) of the three-dimensional data acquisition unit in the vehicle is fixed, the position in the three- dimensional data corresponding to the installation position as the current position of the vehicle can be determined.), and
extract a travelable area from the extracted road area on the basis of the position information of the vehicle and the road structural rule ( Mahata’s Fig. 5 uses the current coordinate of the surveillance car and standard width of road (rw) as inputs, draws a small circle around the car position, selects the largest segment touching the circle as the parent segment, and uses the standard road width to determine whether additional adjacent segments should be merged to complete the road segment. Figs. 6A- 6D show the surveillance-car location and the merging of adjacent segments, and Figs. 7A- 7B show calculating road-segment width using x-axis bounds).
Regarding claim 3, Mahata teaches the travelable area extraction apparatus according to claim 1, wherein the road structural rule (is defined for each road section), and is associated with the position information of the vehicle (Mahata teaches using GPS/current vehicle position and road-width information together. Mahata [0112] discloses using GPS locations of the surveillance car. Mahata [0114] states that the imagery datasets store the current location and azimuth angle of the surveillance car, and the point cloud is sliced starting from the correct vehicle position. Mahata [0151] uses the known vehicle position to select the parent road segment because “the car must be on the road.” Mahata [0152] then uses “standard road width (rw) information,” described as a known nominal value for a particular road system, to determine whether the selected parent segment is complete. Mahata [0156]- [0157] further calculate segment width using x-axis bounds and fragmented segment centroids. See also Figs. 5, 6A–6D, and 7A–7B. See also, the rejection of claim 1).
Mahata teaches using vehicle position with standard road-width information to complete the road segment. Mahata does not explicitly state that the standard road width is defined for each road section. However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mahata’s road-segment extraction system to associate the standard road width with the particular road section/segment corresponding to the current vehicle position. Mahata already selects the road segment using the vehicle position and uses the standard road width to determine whether that segment is complete. The modification would predictably improve road extraction where different road sections have different widths.
Regarding claim 4, Mahata teaches the travelable area extraction apparatus according to claim 2, wherein the at least one processor configured to execute the instructions to;
extract the road area from the input three-dimensional data on the basis of a traveling direction of the vehicle and a height from the installation position of the three- dimensional data acquisition unit to the road (Mahata [0114] discloses rotating the point cloud around the z-axis so the point cloud is aligned with the car azimuth angle and slicing the point cloud in the y-direction starting from the correct vehicle position. Mahata [0115] states that the z-axis is vertical, the y-axis is parallel to the road axis and direction of travel, and the x-axis is perpendicular to the direction of travel. Mahata [0116]- [0120] teach extracting the primary road plane from the point cloud using RANSAC. Mahata Fig. 39 ([0344]- [0345]) also shows using GPS location of the car position, rotating/slicing the point cloud around the car location, RANSAC plane search, height-based filtering of extracted planes, surface-normal calculation, and extracting a primary road part.).
Mahata does not explicitly recite “height from the installation position,” it would have been obvious to modify Mahata’s vehicle-mounted LiDAR road-extraction process to use the known vertical relation between the mounted LiDAR/vehicle position and the road plane, because Mahata already aligns the point cloud using the vehicle travel direction, uses the vertical z-axis, extracts the road plane, and applies height-based filtering. This would predictably improve separation of road-surface points from non-road points.
Regarding claim 5, Mahata teaches the travelable area extraction apparatus according to claim 1, wherein the at least one processor configured to execute the instructions to;
acquire three-dimensional data for a traveling direction of the vehicle (Mahata [0114] discloses slicing point-cloud data in the y-direction starting from the correct vehicle position. Mahata [0115] explains that the y-axis is parallel to the road axis and the direction of travel of the vehicle. Thus, Mahata acquires/processes the point-cloud data in the traveling direction of the vehicle. See Fig. 15, [0206].).
Regarding claim 9, Mahata teaches a travelable area extraction system comprising:
a three-dimensional data acquisition unit mounted in a vehicle (Mahata teaches that the input is derived from a vehicle-mounted sensor system that produces a LiDAR-derived point-cloud data set, images, vehicle location, and vehicle azimuth information. The LiDAR/mobile mapping sensor system corresponds to the claimed three-dimensional data acquisition unit mounted in the vehicle. See Mahata [0107] and Fig. 1.);
at least one memory (storing instructions), and at least one processor (Mahata [0106] states that the invention may be carried out on a suitable hardware system, using processing and memory capacity, and may run on a local system if desired. Mahata then discloses algorithmic processing steps in [0109]- [0157] and Figs. 2, 5, 6A–6D, and 7A–7B) configured to execute the instructions to;
input three-dimensional data from a three-dimensional data acquisition unit mounted in a vehicle (Mahata [0107] states that the basic input is derived from a vehicle-mounted sensor system that produces a LiDAR-derived point cloud data set, camera images, and vehicle location/azimuth information. Mahata [0109] further explains that a large block of point cloud data is sliced into smaller blocks associated with camera snapshots. Mahata [0110] and Fig. 2 show input point-cloud data and image data being preprocessed, including downsampling, rotating, and slicing the point cloud.);
acquire position information of the vehicle (Mahata [0107] discloses that the input includes the location and azimuth angle of the vehicle when images are taken. Mahata [0112] refers to GPS locations of the surveillance car. Mahata [0114] states that the imagery datasets store the current location and azimuth angle of the surveillance car, and that the point cloud is sliced starting from the correct vehicle position. Mahata [0151] states that the vehicle position is known from the image dataset and that the car must be on the road.); and
extract (a travelable area) from the three-dimensional data (Mahata teaches extracting the road/road segment from the 3D point cloud. Mahata [0116] states that the road part creates one or more large plane surfaces in the point cloud. Mahata [0117]- [0120] teaches primary road extraction using RANSAC, where the largest extracted point-cloud plane is treated as the road plane. Mahata [0139]- [0140] teach fine-level segmentation using constrained planar cuts. Mahata [0150] - [0155] teach extracting the true road parts from over-segmented point cloud data and outputting the road segment. See also Figs. 2, 5, and 6A–6D.) on the basis of the position information of the vehicle (Mahata [0151] directly uses vehicle position to identify the road segment: because the vehicle position is known and the car must be on the road, a small circle is drawn around the car position, and the largest segment touching the circle is selected as the parent road segment. Mahata [0153] and Figs. 6A–6D show the surveillance car location, the selected parent segment, and merging with adjacent segments.) and (a road structural rule) indicating a distance from one end to the other end of a traveling area in a road (Mahata [0152] teaches using standard road width (r₍w₎) information, described as a known nominal value for a particular road system. If the width of the parent segment is smaller than the standard road width, Mahata searches fragmented parts in an expected direction to complete the full road segment. Mahata [0156] further explains estimating segment width using maximum and minimum x-axis bounds, i.e., a distance across the road segment. Figs. 5 and 7A–7B also show standard road width and segment-width estimation.).
Mahata fails to explicitly disclose at least one memory storing instructions, a travelable area and a road structural rule , the road structural rule being associated with the acquired position information of the vehicle.
Mahata discloses computerized processing steps for extracting road segments from vehicle-generated LiDAR point-cloud data (Figs. 1, 2, 5, 6A–6D, 7A–7B, and 13). Although Mahata does not disclose “memory storing instructions,” it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mahata’s computerized road-segment extraction system so that the disclosed point-cloud processing algorithms are stored in memory as executable instructions and executed by a processor, because storing software instructions in memory for processor execution was a known and predictable way to perform computerized point-cloud data processing.
Mahata still fails to explicitly disclose a travelable area and a road structural rule, the road structural rule being associated with the acquired position information of the vehicle.
. Mahata instead uses road segment, road part, true road parts, and standard road width.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mahata’s vehicle-based road-segment extraction system so that Mahata’s extracted road segment/true road part corresponds to the claimed travelable area, and Mahata’s standard road width corresponds to the claimed road structural rule. Mahata teaches using the standard road width to determine whether the detected road segment is complete and to merge adjacent fragmented point-cloud segments to complete the full road segment ([0150]- [0157], Figs. 5, 6A-6D, 7A-7B). Such modification would have predictably improved the reliability of extracting the correct road/travelable area from vehicle-mounted three-dimensional data.
Mahata [0151]-[0153], Figs. 5 and 6A-6D also teach using the current vehicle position to identify/select the applicable parent road segment and applying the standard road width (rw) to determine whether the selected parent road segment is complete. Thus, Mahata applies the road structural rule/standard road width information to the road segment identified based on the acquired vehicle-position information. It would have been obvious to one of ordinary skill in the art to associate the standard road width with the particular road section/segment corresponding to the current vehicle position because Mahata selects the road segment using vehicle position and uses the standard road width to determine whether that selected segment is complete, thereby predictably improving road extraction where different road sections have different widths.
Claim 12 is method claim corresponding to apparatus claim 1. It is rejected for the same reasons.
Claim 13 recites a non-transitory computer-readable medium storing a program for causing a computer to execute the method of claim 12. As discussed above with respect to claim 1 (claim 12 is rejected for the same reasons), Mahata teaches the claimed method by inputting vehicle-mounted LiDAR point-cloud data, acquiring GPS/current vehicle position information, and extracting a road segment/true road part from the point-cloud data based on the vehicle position and standard road width information. Mahata further teaches that the invention may be carried out on a suitable hardware system using processing and memory capacity, and Mahata discloses specific computerized algorithmic steps for downsampling, rotating, slicing, RANSAC road-plane extraction, point-cloud segmentation, parent-segment selection based on vehicle position, width calculation, and merging fragmented segments. See Mahata [0106]- [0116], [0139]- [0140], [0150]- [0157] and Figs. 2, 5, 6A–6D, and 7A–7B.
Mahata does not expressly disclose “non-transitory computer-readable medium storing a program,” it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mahata’s computerized road-segment extraction system so that Mahata’s disclosed point-cloud road-extraction algorithms are stored as a program on a non-transitory computer-readable medium and executed by a computer, because Mahata’s road-extraction process is computerized, uses processing and memory capacity, and storing executable program instructions in memory was a known and predictable way to perform such computerized LiDAR point-cloud processing.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Mahata in view of Peake et al. (US 20200074266 A1, “Peake”).
Regarding claim 6, Mahata fails to explicitly teach the travelable area extraction apparatus according to claim 1, wherein the at least one processor configured to execute the instructions to: acquire three-dimensional data for all directions around the vehicle.
However, Peake teaches multiple LiDAR sensor heads positioned on or around a vehicle, where the sensor heads together provide a complete 360-degree view around the vehicle ([0150]). It further teaches combining or stitching data from multiple sensor heads to generate a point cloud or 3D image covering a 360-degree horizontal view around the vehicle ([0151]-[0152]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mahata’s vehicle-mounted point-cloud road extraction system to acquire 3D point-cloud data in all directions around the vehicle using a 360-degree LiDAR sensor arrangement, as taught by Peake, because surrounding 3D sensing would provide more complete environmental/road data around the vehicle and improve road-area extraction when relevant road portions are not only forward of the vehicle.
Claims 7, 11 are rejected under 35 U.S.C. 103 as being unpatentable over Mahata in view of Kovarik et al. (US 20160132705 A1, “Kovarik”).
Regarding claim 7, Mahata fails to explicitly teach the travelable area extraction apparatus according to claim 1, wherein the at least one processor configured to execute the instructions to: acquire information from a position information provision unit installed on a road, and calculate position information of a vehicle on the road.
However, Kovarik teaches a vehicle position recognition system including a magnetic marker forming a magnetic field at a predetermined position on the road surface, a magnetic sensor detecting the marker field, and an on-vehicle detector performing vehicle-position operation based on the detected magnetic field ([0094]-[0095], [0103]- [0104], claim 1). It also teaches location beacons installed in or near roadway surfaces to provide accurate vehicle-position information ([036], [0106]- [0107]), and RFID tags at stationary locations along a traffic lane detected by a vehicle-mounted RFID reader ([0100], claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mahata’s GPS/current-position-based road extraction system to calculate or correct the vehicle position using road-installed markers, beacons, or RFID tags, as taught by Kovarik, because road-installed position units provide accurate local vehicle-position information and would improve selection of the correct road segment in Mahata’s point-cloud road extraction process.
Regarding claim 11, Mahata fails to explicitly teach the travelable area extraction system according to claim 9, further comprising a position information provision unit installed on a road,
wherein the at least one processor configured to execute the instructions to: calculate a position of the vehicle by detecting information from the position information provision unit.
However, Kovarik teaches a vehicle position recognition system including a magnetic marker forming a magnetic field at a predetermined position on the road surface, a magnetic sensor detecting the marker field, and an on-vehicle detector performing vehicle-position operation based on the detected magnetic field ([0094]-[0095], [0103]- [0104], claim 1). It also teaches location beacons installed in or near roadway surfaces to provide accurate vehicle-position information ([036], [0106]- [0107]), and RFID tags at stationary locations along a traffic lane detected by a vehicle-mounted RFID reader ([0100], claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the Mahata vehicle-based road extraction system to include road-installed position-information units, as taught by Kovarik, because Mahata’s selection of the correct road segment depends on the vehicle position, and road-installed markers/beacons/RFID tags would improve local vehicle-position accuracy.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Mahata in view of Zhu et al. (US 20190164018 A1, “Zhu”).
Regarding claim 8, Mahata teaches the travelable area extraction apparatus according to claim 1, wherein the at least one processor configured to execute the instructions to:
detect a traveling lane by detecting a line on a road on which a vehicle travels (Mahata [0158] teaches extracting road components from 360-degree panoramic images. Mahata [0159] explains that a top-view road image can show road boundary texture or boundary median lines as straight lines. Mahata [0160] teaches detecting groups of vertical lines using Hough transform or deep learning. Mahata [0161] (see also, claim 1) teaches detecting and classifying road markings into categories such as median line, road boundary, center-line, and turning markings. Mahata [0165] uses the current vehicle location to identify the source road segment because the segment belonging to the vehicle is called the source road. See Figs. 9A–9C, 10A–10C, and 11.).
Mahata fails to explicitly teach the detect an opposite lane by detecting the line on the road and information indicating that the road is the opposite lane.
However, Zhu teaches that false-positive drivable areas may be produced by “roadways in the opposite direction of travel,” and that the processing performed by the drivable road surface detection module can eliminate or minimize those false-positive areas [0046].
It would have been obvious to modify Mahata’s road-line classification process to use Zhu’s opposite-direction-roadway information to distinguish the host traveling lane from an opposite lane, because Mahata already detects and classifies road lines/markings, and Zhu teaches that roadways in the opposite direction of travel can be false-positive drivable areas that should be eliminated or minimized.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Mahata in view of Kawahara et al. (US 20160272199 A1 “Kawahara”).
Regarding claim 10, Mahata still fails to explicitly teach the travelable area extraction system according to claim 9, further comprising a traveling support control apparatus configured to support traveling by controlling an operation of the vehicle, on the basis of the travelable area extracted from the three- dimensional data.
However, Kawahara teaches a travel control system including a control unit, vehicle control unit, engine control ECU, brake control ECU, and steering angle control ECU. Kawahara [0035] teaches that the control unit autonomously controls the vehicle travel state by outputting commands to the engine control ECU, brake control ECU, and steering angle control ECU. Kawahara [0059] teaches commanding the engine, brake, or steering ECU to adjust speed and steer the host vehicle into a low-potential field in an adjacent lane.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mahata’s vehicle-based road/travelable-area extraction system to use the extracted travelable area for vehicle travel support/control, as taught by Kawahara, because Mahata identifies the road/travelable area from vehicle-mounted 3D data, and Kawahara teaches controlling vehicle operation using travel-control information to support vehicle travel. Such modification would have predictably allowed the vehicle to use the extracted road/travelable-area information to control steering, braking, or speed and thereby improve travel support.
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 extension fee 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 date of this final action.
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/JEMPSON NOEL/Examiner, Art Unit 3645
/YUQING XIAO/Supervisory Patent Examiner, Art Unit 3645
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