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 .
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.
Allowable Subject Matter
Claims 1-3, 5-13, and 15-20 are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
Xu (WO 2023065313) teaches:
An object recognition apparatus comprising (Electronic device #800 of Fig. 8): a Light Detection and Ranging (LiDAR) (vehicle-mounted radar #101A of Fig. 1B, [37] “The vehicle-mounted radar 101A can be a millimeter-wave radar, lidar, or other types of radar”); and a processor (processor #801 of Fig. 8) operatively connected to the LiDAR ([126-127] “the processor 801 can be used to call the occlusion relationship judgment application stored in the memory 805 and specifically perform the following operations: Acquire point cloud data of a target point cloud”), wherein the processor is configured to: identify a first point which is a leftmost point with respect to a host vehicle among a point cloud obtained through the LiDAR and representing a first object, a second point which is a rightmost point with respect to the host vehicle among the point cloud representing the first object ([85] “determining the occlusion status of the target obstacle based on the occlusion point cloud corresponding to each of the at least one set of edge point clouds corresponding to the target obstacle.”); identify an angle between, a line segment connecting the first point and the LiDAR, and a line segment connecting the second point and the LiDAR ([69] “Among the undetermined point clouds, the point clouds whose absolute value of the difference between the angle value and the angle value of the target point cloud is greater than or equal to a second threshold are selected and these point clouds are used as occluded point clouds”); identify, as an occluded object, an object which is one of the first object and the second object, which corresponds to one of the first point and the second point which forms a longer distance among a distance between the first point and the LiDAR, and a distance between the second point and the LiDAR and identify an object different from the object identified as the occluded object among the first object and the second object as an object occluding the occluded object. ([54] ”and the distance value of the occluding point cloud is less than the distance value of the target point cloud. Therefore, the occlusion relationship between the occluding object corresponding to the occluding point cloud and the target obstacle is determined as follows: the obstacle corresponding to the occluding point cloud occludes the target obstacle.”),
Xu does not teach:
a third point which is a leftmost point with respect to the host vehicle among a point cloud representing a second object, and
a fourth point which is a rightmost point with respect to the host vehicle among the point cloud representing the second object
identify a first external point, which is closer to the second object, which is one of the first point and the second point, and a second external point, which is closer to the first object, which is one of the third point and the fourth point;
identify an angle between, a line segment connecting the first external point and the LiDAR, and a line segment connecting the second external point and the LiDAR, based on identifying that the first external point and the second external point do not overlap each other;
in response that the angle is less than or equal to a threshold angle
Xu teaches a similar method for determining occlusions between point clouds. While Xu teaches detecting the edges of a target point cloud, and then determining the angle between points clouds, it uses an opposite relationship from the present invention. Xu teaches that angles above a threshold indicate an occlusion relationship, and the angle relationship is used more for segmentation and neighborhood determinations. Xu does not teach identifying the edges of the second object, identifying the two closest points of the object of the 1st through 4th points, identifying an angle based on identifying that points overlap each other, or identifying an occlusion relationship when the angle between point clouds is less than or equal to a threshold angle.
Ayvaci et al. (US 20170220874) teaches:
An object recognition apparatus (environment #100 of Fig. 1) comprising: a Light Detection and Ranging (LiDAR) (depth sensor #142 of Fig. 1, [45] “The depth sensor 142 (FIG. 1) may be or include any of, but is not limited to, a LIDAR sensor,”); and a processor operatively connected to the LiDAR (processor #120 of Fig. 1), wherein the processor is configured to: identify a first point which is a leftmost point with respect to a host vehicle among a point cloud obtained through the LiDAR and representing a first object a second point which is a rightmost point with respect to the host vehicle among the point cloud representing the first object, ([46], see screenshot below)
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identify, as an occluded object, an object which is one of the first object and the second object, which forms a longer distance among a distance between the first point and the LiDAR, and a distance between the second point and the LiDAR ([47] “If, according to Equation (7), the second verification module 124 determines the occluding object is closer than the extended upper-body bounding box ũ.sub.i to the image input device 132 (FIG. 1), then the detection of the partially occluded object by the part-based body detector 110 (FIG. 1) is verified as accurate at block 414”), and identify an object different from the object identified as the occluded object among the first object and the second object as an object occluding the occluded object ([47] “The second verification module 124 determines that the occluding object is closer to the image input device 132 (FIG. 1) than the extended upper-body bounding box ũ.sub.i when the following condition is satisfied:”)
Ayvaci does not teach:
a third point which is a leftmost point with respect to the host vehicle among a point cloud representing a second object, and
a fourth point which is a rightmost point with respect to the host vehicle among the point cloud representing the second object;
identify a first external point, which is closer to the second object, which is one of the first point and the second point, and
a second external point, which is closer to the first object, which is one of the third point and the fourth point;
identify an angle between, a line segment connecting the first external point and the LiDAR, and a line segment connecting the second external point and the LiDAR, based on identifying that the first external point and the second external point do not overlap each other;
identify, as an occluded object, an object which is one of the first object and the second object, which corresponds to one of the first external point and the second external point
Ayvaci teaches a similar method of identifying and verifying an occluded object. Ayvaci calculates the angle corresponding to the leftmost and rightmost points of the occluded object, and then segments LiDAR data to verify the occlusion using depth measurements. Ayvaci does not identify a third and fourth point corresponding to the leftmost and rightmost points of a second object, identify external points as the closest points of the two objects of the 1st-4th point, identify an angle between the two objects, and make a determination of occlusion based on that angle.
Saleh (BBBD: Bounding Box Based Detector for Occlusion Detection and Order Recovery, 2022) teaches:
identify a first point which is a leftmost point, representing a first object, a second point which is a rightmost point, representing the first object a third point which is a leftmost point representing a second object and a fourth point which is a rightmost point representing the second object (Section 3 Methodology: “Once the bounding boxes and the modal segmentation masks are detected using any off-shelf detector, for any two bounding boxes we find the IA. We can determine that there is no occlusion if any of these cases were true:” a person having ordinary skill in the art would understand that the bounding boxes generated using modal segmentation masks would encompass the leftmost and rightmost points), identify, as an occluded object, an object which is one of the first object and the second object, and identify an object different from the object identified as the occluded object among the first object and the second object as an object occluding the occluded object (Section 3 Methodology: “If one of the bounding boxes is fully contained in another one, and both masks in that area are non-zero, then the object with a bigger bounding box is considered the occludee and the smaller one as the occluder … We count the number of non-zero pixels in each mask inside the IA, the object with a larger mask is the occludee”)
Saleh does not teach:
a Light Detection and Ranging (LiDAR);
and a processor operatively connected to the LiDAR
: identify a first point which is a leftmost point with respect to a host vehicle among a point cloud obtained through the LiDAR
a second point which is a rightmost point with respect to the host vehicle among the point cloud
a third point which is a leftmost point with respect to the host vehicle among a point cloud
a fourth point which is a rightmost point with respect to the host vehicle among the point cloud
identify a first external point, which is closer to the second object, which is one of the first point and the second point, and a second external point, which is closer to the first object, which is one of the third point and the fourth point;
identify an angle between, a line segment connecting the first external point and the LiDAR, and a line segment connecting the second external point and the LiDAR, based on identifying that the first external point and the second external point do not overlap each other;
identify, as an occluded object, an object which is one of the first object and the second object, which corresponds to one of the first external point and the second external point which forms a longer distance among a distance between the first external point and the LiDAR, and a distance between the second external point and the LiDAR, in response that the angle is less than or equal to a threshold angle
While Saleh teaches a similar method for identifying occlusion between objects, it is applied to standard RGB images. Saleh does not teach detecting occluded objects using a LiDAR point cloud. Additionally, Saleh does not teach identifying a set of two closest points between the objects, determining the angle between those two points, or identifying an occluded object using distance values.
Mitchell et al. (US 20230177818) teaches:
An object recognition apparatus comprising (system #100 of Fig. 1): a Light Detection and Ranging (LiDAR) (LIDAR System #108 of Fig. 1); and a processor operatively connected to the LiDAR (processor #222 of Fig. 1, [35]), wherein the processor is configured to: identify, as an occluded object, an object which is one of the first object and the second object, which corresponds to one of the first external point and the second external point which forms a longer distance among a distance between the first external point and the LiDAR, and a distance between the second external point and the LiDAR [37-39]
Mitchell teaches a LiDAR with a host vehicle, and using a combination of 2D and 3D images to create bounding boxes for LiDAR point cloud data. Additionally, Mitchell teaches identifying an occluded object using distance data. However, Mitchell does not teach identifying a first and second external point, identifying an angle between said points, identifying that the points do not overlap, or determining an occluded and occluding object in response to the angle between the points being less than a threshold angle.
The prior art discloses identifying the leftmost and rightmost points of objects with respect to a host vehicle among a point cloud and identifying an occluding and occluded object based on distances. The prior art does not disclose or suggest identifying a first and second external point which are among the closest leftmost or rightmost points of two objects, identifying an angle between such points, and then identifying an occluded object and occluding object using distance values, based on the angle being below a threshold angle and on the points not overlapping each other. Accordingly, claim 1 is allowable. Claims 2-3 and 5-9 are allowable by virtue of dependency. Claim 11 is allowable for the same reasons given for claim 1. Claims 12-13 and 15-20 are allowable by virtue of dependency.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4 and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 4, claim 4 recites “the third points or the fourth points” and “the first points or the second points” in the limitation for assigning reliability. However, claim 1, from which claim 4 depends, introduces individually designated first, second, third, and fourth points without defining corresponding plural groups. It is unclear whether each count concerns qualifying points from the respective pair of previously identified points or from additional, undefined groups. Accordingly, the points to be counted and the scope of the limitation for assigning reliability are unclear.
Regarding claim 14, claim 14 recites “the third points or the fourth points” and “the first points or the second points” in the limitation for assigning reliability. However, claim 11, from which claim 14 depends, introduces individually designated first, second, third, and fourth points without defining corresponding plural groups. It is unclear whether each count concerns qualifying points from the respective pair of previously identified points or from additional, undefined groups. Accordingly, the points to be counted and the scope of the limitation for assigning reliability are unclear.
Conclusion
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/A.D.S./Examiner, Art Unit 3645
/ISAM A ALSOMIRI/Supervisory Patent Examiner, Art Unit 3645
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