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 .
Claim Status
Claims 1-13 were pending for examination for Application No. 18/640,990, filed April 19th, 2024. In the remarks and amendments received on July 30th, 2026, claims 1, 3-4, 8-9, and 12-13 are amended, no claims are added, and claims 2 and 6-7 are cancelled. Claims 4-5 were previously withdrawn from consideration due to Restriction Election. Accordingly, claims 1, 3-5, and 8-13 are pending for examination in the application.
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.
Claim(s) 1, 8, and 10-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schroeter (US-20200401817-A1) in view of Huang et al. (US-20210004610-A1) and Hu et al. (US-20230137464-A1).
Regarding claim 1, Schroeter teaches:
A method of matching scan data based on driving environment features of an autonomous vehicle that is performed by a computing device (Fig. 10), the method comprising:
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extracting features from a plurality of pieces of scan data in consideration of driving environment features of an autonomous vehicle (“a second point cloud may be obtained from one or more sensors of a vehicle (e.g., the vehicle 905) traveling through the region… one or more clusters of second points of the second point cloud may be identified,” Paras. [0111-0112]);
and performing matching on the plurality of pieces of scan data using the extracted features (“At block 1008, correspondences may be determined. The correspondences may be between first points of the first point cloud and cluster points of the one or more clusters of the second point cloud,” Para [0113]),
wherein the extracting of the features includes:
extracting a first feature from the generated ground data (“LIDAR segmentation provides clusters with different types, including but not limited to, ground point; thin, vertical structures (e.g., poles); lane markings,” Para [0121]);
and extracting a second feature from the generated non-ground data (“LIDAR segmentation provides clusters with different types, including but not limited to, ground point; thin, vertical structures (e.g., poles); lane markings,” Para [0121]).
wherein the extracting of the second feature includes: generating a plurality of clusters by clustering a plurality of scan points included in the generated non-ground data (“The system 1100 may determine one or more geometric features of an object corresponding to the one or more segments, clusters of points, or sets of points (e.g., determined via segmentation),” Para [0127]);
selecting a cluster including a predetermined number or more of scan points from among the plurality of generated clusters (“The geometric features may be determined based on how the cluster of points may be distributed in the three dimensions (for example, the x, y, and z dimensions),” Para [0127]);
and when scan points included in the selected cluster are distributed in a form of a pole, extracting the selected cluster as the second feature (“the vehicle computing system 1120 may determine that the shape of the object and/or the geometric features of the object may be a thin vertical structure such as a pole or a tree trunk,” Para [0128]).
Schroeter is not relied upon to teach the following limitations. Huang, however, further teaches:
wherein the extracting of the features includes:
generating ground data and non-ground data by dividing scan points corresponding to a ground surface among a plurality of scan points included in specific scan data (“At every moving frame, the system may separate ground points from non-ground points, and may update a histogram of ground point intensity,” Para [0183]).
Schroeter and Huang are not relied upon to teach the following limitations. Hu, however, further teaches:
wherein the extracting of the selected cluster as the second feature includes:
calculating a covariance matrix for the scan points included in the selected cluster (“calculating eigenvalues [λ.sub.i0, λ.sub.i1, λ.sub.i2] of a covariance matrix of neighbor points N.sub.i,” Para [0040]), wherein the calculated covariance matrix includes three eigenvalues corresponding to each of three mutually perpendicular axis directions (“eigenvalues [λ.sub.i0, λ.sub.i1, λ.sub.i2] represent the variance in magnitude in the direction of the largest spread of the neighbor points N… the eigenvalue λ.sub.i0 (i.e., the one with the highest magnitude) may pertain to the x, y, or z axis, whichever represents the direction of largest spread,” Para [0040]);
and when a size of one of the three eigenvalues is greater than or equal to a first threshold value and sizes of the remaining two eigenvalues are less than a second threshold value smaller than the first threshold value, extracting an edge generated by the scan points included in the selected cluster as the second feature (“a check is done of whether all of the following are true: λ.sub.i0 (the highest eigenvalue)>threshold1 and both λ.sub.i1 and λ.sub.i2<threshold2. If the check at block 230 indicates that the conditions are met, then the point p.sub.i is identified as an edge point E at block 235,” Para [0040], where an “edge point E” is a “e.g… a tree, lamp post” (Para [0034]).
Huang is considered to be analogous to the claimed invention because they are both in the field of autonomous vehicles that use LIDAR. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Huang into Schroeter for the benefit of fewer false positive ground or non-ground feature detections.
Hu is considered to be analogous to the claimed invention because they are both in the field of autonomous vehicles that use LIDAR. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Hu into Schroeter and Huang for the benefit of more accurate pole/tree/vertical object detections.
Regarding claim 8, the rejection of claim 1 is incorporated herein. Schroeter in view of Huang and Hu teach the method of claim 1, and Hu further teaches:
wherein the extracting of the selected cluster as the second feature includes, when a z-axis component value of a unit vector in a long-axis direction of the distribution of the scan points included in the selected cluster is greater than or equal to a preset value (“a check is done of whether all of the following are true: λ.sub.i0 (the highest eigenvalue)>threshold1,” Para [0040]), extracting an edge generated by the scan points included in the selected cluster as the second feature (“If the check at block 230 indicates that the conditions are met, then the point p.sub.i is identified as an edge point E at block 235,” Para [0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Hu into Schroeter and Huang for the benefit of more accurate pole/tree/vertical object detections.
Regarding claim 10, the rejection of claim 1 is incorporated herein. Schroeter in view of Huang and Hu teach the method of claim 1, and Schroeter further teaches:
wherein the plurality of pieces of scan data include first scan data collected at a first time point (Fig. 10, 1002, “Obtain a first point cloud”) and second scan data collected at a second time point after the first time point (Fig. 10, 1004, “Obtain a second point cloud”),
and in the performing of the matching, a relative transformation between a coordinate system of the first scan data and a coordinate system of the second scan data is derived (“The ICP technique may iteratively revise the transformation (e.g., combination of translation and rotation) needed to minimize an error metric, usually a distance from the source point cloud to the reference point cloud,” Para [0092]) by matching a feature extracted from the first scan data with a feature extracted from the second scan data (“Some embodiments may employ an ICP technique for performing localization. For example, when determining correspondences between point clouds,” Para [0092]),
wherein the feature extracted from the first scan data and the feature extracted from the second scan data include features extracted in consideration of the driving environment features of the autonomous vehicle (“The alignment of the first point cloud and the second point cloud may be based on the respective weights of various clusters of the first point cloud. The respective weights may be based on respective geometric features that correspond to the respective clusters, such as one or more edges of a thin vertical structure such as a pole or a tree trunk,” Para [0138]).
Regarding claim 11, the rejection of claim 1 is incorporated herein. Schroeter in view of Huang and Hu teach the method of claim 1, and Schroeter further teaches:
wherein the plurality of pieces of scan data include first scan data and second scan data, and the performing of the matching includes:
finding correspondences between a plurality of features extracted from the first scan data and a plurality of features extracted from the second scan data on the basis of distances between the plurality of features extracted from the first scan data and the plurality of features extracted from the second scan data (“The system 920 finds correspondences of points of the point cloud obtained from the vehicle's 950 LIDAR scan with points of the point cloud of the HD map (which was obtained by combining data from several LIDAR scans). The correspondences may be determined using a process such as ICP,” Para [0100]);
and matching the first scan data with the second scan data using the distances between the plurality of features extracted from the first scan data and the plurality of features extracted from the second scan data that correspond to each other as a cost function so that a cost of the cost function has a minimum value (“The ICP technique may iteratively revise the transformation (e.g., combination of translation and rotation) needed to minimize an error metric, usually a distance from the source point cloud to the reference point cloud,” Para [0092]).
Claims 12 and 13 are considered to be corresponding device and non-transitory recording medium claims that correspond to method claim 1. Therefore, the rejection of claim 1 applies to claims 12 and 13.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schroeter in view of Huang and Hu as applied to claim 1 above, and further in view of Guo et al. (US-20190163989-A1).
Regarding claim 3, the rejection of claim 1 is incorporated herein. Schroeter in view of Huang and Hu teach the method of claim 1, and Schroeter further teaches:
wherein the extracting of the first feature includes: setting a feature extraction area on the generated ground data on the basis of a type of sensor that collects the specific scan data and a position of the sensor (“The vehicle sensors 105 may include one or more cameras that may capture images of the surroundings of the vehicle. A LIDAR may survey the surroundings of the vehicle by measuring distance to a target by illuminating that target with a laser light pulses,” Para [0047]), in other words, a LIDAR sensor is limited to viewing the surroundings of the vehicle due to the inherent scope of its machinery).
Schroeter is not relied upon to teach the following limitations. Guo, however, further teaches:
extracting at least one scan point whose intensity is greater than or equal to a predetermined value from among a plurality of scan points included in the set feature extraction area (“image/LIDAR fusion module 250 can determine the total number of points in the set of candidate lane marking LIDAR points for which the intensity of the point is greater than the threshold th,” Para [0059]);
and extracting at least one of the extracted at least one scan point and an edge (“candidate lane marking”) generated by the extracted at least one scan point as the first feature (“Once the candidate lane marking LIDAR points are retained as described above, the image/LIDAR fusion module 250 can pass the retained lane marking LIDAR points to the post-processor module 255,” Para [0061]).
Guo is considered to be analogous to the claimed invention because they are both in the field of detecting lane markings using LIDAR. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Guo into Schroeter and Huang and Hu for the benefit of a safer autonomous vehicle.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schroeter in view of Huang and Hu as applied to claim 1 above, and further in view of Omar et al. (US-20230306621-A1).
Regarding claim 1, the rejection of claim 1 is incorporated herein. Schroeter in view of Huang and Hu teach the method of claim 1, but are not relied upon to teach the following limitations. Omar, however, further teaches:
wherein the extracting of the selected cluster as the second feature includes, when distances between two or more clusters extracted as the second feature are less than or equal to a predetermined distance, removing remaining clusters except for any one of the two or more clusters (“remaining foreground points are then processed using a clustering method (e.g., density based spatial clustering of applications with noise (DB Scan), or other method) followed by a filtering method… noise clusters are removed through a proximity filter,” Paras [0049-0050]).
Omar is considered to be analogous to the claimed invention because they are both in the field of detecting poles in a driving environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Omar into Schroeter, Huang, and Hu for the benefit of more accurate pole/tree/vertical object detections.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al. (US-20220043152-A1) teaches a method for object detection and tracking using LIDAR.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHEL A OMETZ whose telephone number is (571)272-2535. The examiner can normally be reached 8:30am-5:30pm ET Monday-Thursday, 7:30am-3:30pm ET every other Friday.
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/Rachel Anne Ometz/Examiner, Art Unit 2668 8/28/26
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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