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 .
Response to Amendment
The following addresses applicant’s remarks/amendments dated 6/24/2026.
Claims 1, 5-6, and 8-10 were amended. Claim 2-4 were cancelled. Therefore, claims 1and 5-11 are currently pending in the current application and are addressed below.
Response to Arguments
Applicant's arguments filed 6/24/2026 have been fully considered but they are not persuasive. The amendments do not necessitate a new ground of rejection, as the current prior art alone or in combination teaches the amended claims.
The applicant states on pages 8-9 of the remarks that regarding claim 1 and 9-10, the primary reference Zhang does not “reasonably suggest ‘align the low-density point cloud and known three-dimensional shape data of the monitoring target with each other’. However, the applicant failed to consider that in Zhang Para [0130] and [0132] that the first three-dimensional point cloud reconstructed from an image is “superimposed on the surface of the object under test after a preset coded pattern is projected onto the surface of the object“ where the preset coded pattern is the three-dimensional shape data when projected on the object and when the point cloud is superimposed it is aligned with the surface of the object. Additionally, the first three-dimensional point cloud is that of high precision and low density as disclosed in Para [0138].
The applicant states on page 9 of the remarks that Wang does not “reasonably suggest to ‘determine for each point in the high-density point cloud, a deviation amount from the monitoring target defined by the known three-dimensional shape data based on the alignment result, and determine that the foreign body is present when the deviation amount exceeds a predetermined threshold”. However, the applicant failed to consider the ”significantness” disclosed Wang Para [025], [0026] and explained in [0015] is calculated from a difference, and therefore a deviation, with a previously determined frequency spectrum model of a natural image. Therefore, when looking at the significance of pixels exceeding a predetermined threshold in a significance map, the deviation from the threshold via processing pixel significance is also being examined. This is additionally disclosed in Para [0029], where pixels with significance thresholds are converted into voxel data and are matched to form a foreign object map, which are then clustered to find foreign objects.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/28/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, 5-6, and 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (CN114332341A, "Zhou") in view of Wang et al. (WO2020194650Al, "Wang") and Grauer et al. (JP 2015527761 A, "Grauer").
Regarding claim 1, Zhou teaches a foreign body detection system comprising
at least one memory storing computer-executable instructions (Zhou, Para [0120], Fig 3, where memory 32 can store information and instructions that are executable by computer); and
at least one processor configured to access the at least one memory and execute the computer-executable instructions to (Zhou, Para [0121], Fig 3, where processor 31 can call and execute the instructions stored on memory 32):
acquire:
a low-density point cloud by controlling a fixed-point three-dimensional LiDAR scanner to perform scanning including at least a monitoring target (Zhou, Para [0130], [0132], and [0139], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the low density point cloud is the first density point cloud for scanning the object under test);and
a high-density point cloud by controlling the fixed-point three-dimensional LiDAR scanner to perform scanning (Zhou, Para [0130], [0132], and [0139], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the high density point cloud is the second density point cloud for scanning the object under test),
align the low-density point cloud and known three-dimensional shape data of the monitoring target with each other (Zhou, Para [0130] and [0132], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the first point cloud is superimposed on the surface of the object under test after a preset coded pattern);
and an alignment result from aligning the low-density point cloud and the known three- dimensional shape data of the monitoring target with each other (Zhou, Para [0130] and [0132], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the first point cloud is superimposed on the surface of the object under test after a preset coded pattern).
However, Zhou does not teach first scanning range
detect a foreign body in contact with the monitoring target, based on the high-density point cloud, the known three-dimensional shape data,
determine, for each point in the high-density point cloud, a deviation amount from the monitoring target defined by the known three-dimensional shape data based on the alignment result, and determine that the foreign body is present when the deviation amount exceeds a predetermined threshold.
On the other hand, Wang teaches the detection of a foreign object using thresholding of a significance value based on the difference from a natural image and therefore deviation value from a target (Wang, Para [0015], [0025], [0029] Fig 3, where the point cloud extraction unit 155 looks at pixels exceeding a predetermined threshold in the significance map to determine a foreign object from voxel data. which is then registered in a foreign object map as disclosed in Para [0026] Additionally, the significantness is calculated from a difference previously determined frequency spectrum model of a natural image and therefore a deviation when thresholded to create a foreign object map).
.
Accordingly, it would have been obvious of one of ordinary skill in the art, before the effective filing date of the invention to have modified the foreign body detection system of Zhou in view of Wang, by applying the use of foreign object detection unit that uses clusters of voxel data from point cloud data to identify foreign object. See MPEP 2141.111 KSR Rationale D.
However, Zhou in view of Wang still does not teach first scanning range
On the other hand, Grauer teaches the use of multiple fields of view when scanning for an object at different depths for both narrow and wider scanning ranges (Grauer, Para [0044], Fig 18, where the wide FOI 140 and narrow FOI disclosed apply to Zhou, Para [0130] and [0132], Fig 4, first and second point cloud respectively when scanning for a target object).
Accordingly, it would have been obvious of one of ordinary skill in the art, before the effective filing date of the invention to have modified the foreign body detection system of Zhou in view of Wang and Grauer, by applying the wider scanning range for the first point cloud and the narrower point range for the second point cloud to observe objects in a variety of conditions. See MPEP 2141.111 KSR Rationale G.
Regarding claim 5, Zhou in view of Wang and Grauer teaches the foreign body detection system according to claim 1, wherein the at least one processor is further configured to execute the instructions to:
update the known three-dimensional shape data, based on the high-density point cloud (Zhou, Para [0154], Fig 4, where in step Sl022 the second point cloud data, which is a high density point cloud as disclosed in Para [0138], can be mapped on to the preset reference surface to obtain a two dimensional image, which, in combination with the depth information as disclosed in Para [0155] provides data three-dimensions).
Regarding claim 6, Zhou in view of Wang teaches the foreign body detection system according to claim 3, wherein the at least one processor is further configured to execute the instructions to:
align the second point cloud and the three-dimensional shape data with each other (Zhou, Para [0130], [0132], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the second point cloud may be reconstructed based on a densely projected light signal).
Regarding claim 8, Zhou in view of Wang teaches the foreign body detection system according to claim 1, wherein the at least one processor is further configured to execute the instructions to:
estimate the point indicating the foreign body when the deviation amount is equal to or more than a predetermined value, and generate a foreign body candidate point cloud being a group of foreign body candidate points as estimated points (Wang, Para [0015], [0025], [0029] Fig 3, where the point cloud extraction unit 155 looks at pixels exceeding a predetermined threshold in the significance map to determine a foreign object from voxel data. which is then registered in a foreign object map as disclosed in Para [0026] Additionally, the significantness is calculated from a difference previously determined frequency spectrum model of a natural image and therefore a deviation when thresholded to create a foreign object map);
divide the monitoring target into a lattice pattern in a plan view (Zhou, Para [0103], Fig 2 where the first light signal can be projected with a grid pattern), and then count, for each of a plurality of minute ranges generated by division, the foreign body candidate points included in the minute range (Wang, Para [0026], Fig 3, where the foreign object unit 159 takes a multitude of voxels in a cluster to determine the number of foreign objects in a range); and
determine, for each of the plurality of minute ranges, that the foreign body is present within the minute range when a count number of the foreign body candidate points exceeds a predetermined value (Wang, Para [0025], Fig 3, where the point cloud extraction unit 155 looks at pixels exceeding a predetermined threshold in the significance map to determine a foreign object from voxel data, which is then registered in a foreign object map as disclosed in Para [0026]).
Regarding claim 9, Zhou teaches a foreign body detection device comprising
at least one memory storing computer-executable instructions (Zhou, Para [0120], Fig 3, where memory 32 can store information and instructions that are executable by computer); and
at least one processor configured to access the at least one memory and execute the computer-executable instructions to (Zhou, Para [0121], Fig 3, where processor 31 can call and execute the instructions stored on memory 32):
acquire:
a low-density point cloud by controlling a fixed-point three-dimensional LiDAR scanner to perform scanning monitoring target (Zhou, Para [0130], [0132], and [0139], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the low density point cloud is the first density point cloud for scanning the object under test); and
a high-density point cloud by controlling the fixed-point three-dimensional LiDAR scanner to perform scanning (Zhou, Para [0130], [0132], and [0139], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the high density point cloud is the second density point cloud for scanning the object under test),
align the low-density point cloud and known three-dimensional shape data of the monitoring target with each other (Zhou, Para [0130] and [0132], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the first point cloud is superimposed on the surface of the object under test after a preset coded pattern);
(Zhou, Para [0130] and [0132], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the first point cloud is superimposed on the surface of the object under test after a preset coded pattern);
However, Zhou does not teach
detect a foreign body in contact with the monitoring target,
determine, for each point in the high-density point cloud, a deviation amount from the monitoring target defined by the known three-dimensional shape data based on the alignment result; and
determine that the foreign body is present when the deviation amount exceeds a predetermined threshold
On the other hand, Wang teaches the detection of a foreign object using thresholding of a significance value based on the difference from a natural image and therefore deviation value from a target (Wang, Para [0015], [0025], [0029] Fig 3, where the point cloud extraction unit 155 looks at pixels exceeding a predetermined threshold in the significance map to determine a foreign object from voxel data. which is then registered in a foreign object map as disclosed in Para [0026] Additionally, the significantness is calculated from a difference previously determined frequency spectrum model of a natural image and therefore a deviation when thresholded to create a foreign object map).
.
Accordingly, it would have been obvious of one of ordinary skill in the art, before the effective filing date of the invention to have modified the foreign body detection system of Zhou in view of Wang, by applying the use of foreign body detection device that uses clusters of voxel data from point cloud data to identify foreign object. See MPEP 2141.111 KSR Rationale D.
However, Zhou in view of Wang still does not teach
On the other hand, Grauer teaches the use of multiple fields of view when scanning for an object at different depths for both narrow and wider scanning ranges (Grauer, Para [0044], Fig 18, where the wide FOI 140 and narrow FOI disclosed apply to Zhou, Para [0130] and [0132], Fig 4, first and second point cloud respectively when scanning for a target object).
Accordingly, it would have been obvious of one of ordinary skill in the art, before the effective filing date of the invention to have modified the foreign body detection device of Zhou in view of Wang and Grauer, by applying the wider scanning range for the first point cloud and the narrower point range for the second point cloud to observe objects in a variety of conditions. See MPEP 2141.111 KSR Rationale G.
Regarding claim 10, Zhou teaches a computer-implemented foreign body detection method performed by a computer, the foreign body detection method comprising:
acquiring:
a low-density point cloud by controlling a fixed-point three-dimensional LiDAR scanner to perform scanning target (Zhou, Para [0130], [0132], and [0139], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the low density point cloud is the first density point cloud for scanning the object under test); and
a high-density point cloud by controlling the fixed-point three-dimensional LiDAR scanner to perform scanning (Zhou, Para [0130], [0132], and [0139], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the high density point cloud is the second density point cloud for scanning the object under test),
aligning the low-density point cloud and known three-dimensional shape data of the monitoring target with each other (Zhou, Para [0130] and [0132], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the first point cloud is superimposed on the surface of the object under test after a preset coded pattern);
(Zhou, Para [0130] and [0132], Fig 4, where the first and second point cloud are reconstructed from the third and fourth light signal respectively, which, as mentioned in Para [0094] are the first and second images of a depth 3D camera. Additionally, the first point cloud is superimposed on the surface of the object under test after a preset coded pattern); and
However, Zhou does not teach
detecting a foreign body in contact with the monitoring target,
determining, for each point in the high-density point cloud, a deviation amount from the monitoring target defined by the known three-dimensional shape data based on the alignment result; and
determining that the foreign body is present when the deviation amount exceeds a predetermined threshold
On the other hand, Wang teaches the detection of a foreign object using thresholding of a significance value based on the difference from a natural image and therefore deviation value from a target (Wang, Para [0015], [0025], [0029] Fig 3, where the point cloud extraction unit 155 looks at pixels exceeding a predetermined threshold in the significance map to determine a foreign object from voxel data. which is then registered in a foreign object map as disclosed in Para [0026] Additionally, the significantness is calculated from a difference previously determined frequency spectrum model of a natural image and therefore a deviation when thresholded to create a foreign object map).
Accordingly, it would have been obvious of one of ordinary skill in the art, before the effective filing date of the invention to have modified the foreign body detection method of Zhou in view of Wang, by applying the use of foreign body detection device that uses clusters of voxel data from point cloud data to identify foreign object. See MPEP 2141.111 KSR Rationale D.
However, Zhou in view of Wang still does not teach
On the other hand, Grauer teaches the use of multiple fields of view when scanning for an object at different depths for both narrow and wider scanning ranges (Grauer, Para [0044], Fig 18, where the wide FOI 140 and narrow FOI disclosed apply to Zhou, Para [0130] and [0132], Fig 4, first and second point cloud respectively when scanning for a target object).
Accordingly, it would have been obvious of one of ordinary skill in the art, before the effective filing date of the invention to have modified the foreign body detection device of Zhou in view of Wang and Grauer, by applying the wider scanning range for the first point cloud and the narrower point range for the second point cloud to observe objects in a variety of conditions. See MPEP 2141.111 KSR Rationale G.
Regarding claim 11, Zhou in view of Wang teaches a non-transitory computer readable medium storing a program for causing a computer to execute the computer-implemented foreign body detection method according to claim IO (Zhou Para [0225], where instructions can be stored on a computer-readable medium 101, or computer-readable storage medium as disclosed in Para [0227]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Wang, Grauer and Steine (JP 2022510345 A, "Steine").
Regarding claim 7, Zhou in view of Wang teaches the foreign body detection system according to claim 1,
However, Zhou in view of Wang does not teach
On the other hand, Steine teaches the use of a detection device being used on a runway or taxiway for safe landing or aircrafts at airports (Steine, Para [0047], Fig 1, where detection unit 20 can monitor the terrain around device 1 which includes a runway or taxiway as disclosed in Para [0043]).
Accordingly, it would have been obvious of one of ordinary skill in the art, before the effective filing date of the invention to have modified the foreign body detection system of Zhou in view of Wang and Steine, by using the foreign detection system on a runway or taxiway for the safe landing of aircrafts at airports. See MPEP 2141.111 KSR Rationale D.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAKI HAWKINS whose telephone number is (571)272-6595. The examiner can normally be reached Monday-Friday 7:30am-5pm.
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/ZAKI KEHINDE HAWKINS/ Examiner, Art Unit 3645
/YUQING XIAO/ Supervisory Patent Examiner, Art Unit 3645
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