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 Objections
Claim 2 is objected to because of the following informality: On Line 3, the Examiner assumes hat “an amount of sift” should actually be --an amount of [[sift]] shift.--. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-4, and 10-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li et al. (US 2020/0088858, disclosed in IDS 09 September 2024), hereinafter Li.
Claim 1: Li discloses a position estimation apparatus (412, Fig. 4) comprising:
at least one memory (428) configured to store instructions [0057]; and
at least one processor (416) configured to execute the instructions to perform operations [0054] comprising:
acquiring point cloud data at a plurality of timings obtained by a sensor mounted on a moving body (“determining the trajectory of the lidar may include: analyzing the plurality of point clouds acquired by the lidar at different time points” [0032]; S220, Fig. 2);
acquiring movement information of the moving body [0036];
generating overlapping point cloud data in which the point cloud data at the plurality of timings has been overlapped on the basis of the movement information (“the point clouds acquired at different time points may be analyzed to recognize the same area or the same object included in the different point clouds, features such as the edge contour or representative feature points of the same area or the same object in different point clouds may be extracted” [0033]); and
estimating a position of a target object using the overlapping point cloud data (“According to time continuity, the position variation amounts of different point clouds may be correlated, and the trajectory of the lidar can be determined” [0033]).
Claim 3: Li further discloses wherein
the movement information is information related to a movement of the moving body between timings when a plurality of point cloud data items overlapped in the overlapping point cloud data are acquired (“Then, by coordinate alignment, the coordinate variation amount of the feature point B from B1(x1, y1, z1) to B2(x2, y2, z2) can be determined, and the position variation amount between the point clouds acquired at time point A1 and time point A2 can be determined” [0034]).
Claim 4: Li further discloses wherein
the sensor emits light and receives reflected light reflected by the target object to measure a distance to the target object (“When the sensor is a lidar, the trajectory of the lidar may be determined by constructing a three-dimensional (3D) scene based on laser point cloud it acquires” [0019]).
Claim 10: Li further discloses wherein
generating the overlapping point cloud data comprises generating the overlapping point cloud data in which the point cloud data has been overlapped a number of times corresponding to a velocity of the moving body (evident that, for a given timing, a higher velocity corresponds to less overlap, i.e. a smaller “coincident area”, since the point clouds are spatially farther apart: “The adjacent point clouds may refer to two point clouds having a coincident area and the distance between the corresponding spatial coordinate points in the coincident area being less than or equal to a point cloud distance threshold in different point clouds” [0033]).
Claim 11: Li further discloses wherein
the operations further comprise estimating the position of the moving body using an estimation result of the position of the target object (“According to time continuity, the position variation amounts of different point clouds may be correlated, and the trajectory of the lidar can be determined” [0033]).
Claim 12: Li discloses a position estimation method (using apparatus 412, Fig. 4) executed by a computer, the estimation method comprising:
acquiring point cloud data at a plurality of timings obtained by a sensor mounted on a moving body (“determining the trajectory of the lidar may include: analyzing the plurality of point clouds acquired by the lidar at different time points” [0032]; S220, Fig. 2);
acquiring movement information of the moving body [0036];
generating overlapping point cloud data in which the point cloud data at the plurality of timings has been overlapped on the basis of the movement information (“the point clouds acquired at different time points may be analyzed to recognize the same area or the same object included in the different point clouds, features such as the edge contour or representative feature points of the same area or the same object in different point clouds may be extracted” [0033]); and
estimating a position of a target object using the overlapping point cloud data (“According to time continuity, the position variation amounts of different point clouds may be correlated, and the trajectory of the lidar can be determined” [0033]).
Claim 13: Li further discloses a program causing a computer (416) to execute the position estimation method [0054].
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.
Claims 2 and 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Li as applied to claim 1 above, and further in view of Qian et al. (US 2024/0095960), hereinafter Qian.
Claim 2: Li is silent with respect to identifying an amount of shift.
Qian, however, in the same field of endeavor of multi-sensor vehicle calibration, discloses wherein
operations comprise identifying an amount of shift using movement information (“Thus, the previously obtained point cloud map generated from LiDAR data that can provide point cloud information within a region of about 150-200 meters in front of the vehicle. The submap can be extracted to identify a region within a pre-determined distance of a location of the vehicle” [0064]), and
generating overlapping point cloud data comprises generating the overlapping point cloud data using the amount of shift (“the LiDAR frontend module can extract a submap from the previously obtained point cloud map that includes dense point cloud of the road comprising the calibration road segment, where the LiDAR frontend module extracts the submap based on a location of the vehicle that can be obtained from the GNSS+IMU sensor” [0064]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Li’s processor to identify an amount of shift for the purpose of reducing the amount of computation needed “to identify a region within a pre-determined distance of a location of the vehicle” (Qian [0064]).
Claims 7-9: Li is silent with respect to the identity of the target object.
Qian, however, in the same field of endeavor of multi-sensor vehicle calibration, discloses wherein
the target object is a line on a road surface (Fig. 4, “the location information of the lane marker” [0096]),
estimating the position of the target object comprises estimating an end point position of the line on the road surface (“receiving, from a light detection and ranging (LiDAR) sensor located on the vehicle, a frame including point cloud data (PCD) of an area of the road that includes the lane marker; and determining a set of three-dimensional (3D) world coordinates of corners of the lane marker” [0096]), and
estimating the position of the target object comprises estimating the end point position using the overlapping point cloud data in a case where the end point position is within a predetermined region in the overlapping point cloud data (“performing a registration of the transformed point cloud to the submap to obtain a first transformation matrix that describes transformation between the transformed point cloud and the submap, where the registration correlates the transformed point cloud to the submap; determining additional transformation matrixes that describes transformation between a position of the LiDAR sensor, the one or more points of the point cloud map reference, and a reference point associated with an inertial measurement unit-global navigation satellite system (IMU-GNSS) sensor located in the vehicle” [0100]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Li’s position estimation apparatus to estimate a position of a line on a road surface for the purpose of ensuring that an autonomous vehicle navigates properly and safely. Language in an apparatus or product claim directed to the function, operation, intended use, and materials upon which the components of the structure work that does not structurally limit the components or patentably differentiate the claimed apparatus or product from an otherwise identical prior art structure will not support patentability. See, e.g., In re Rishoi, 197 F.2d 342, 344-45 (CCPA 1952); In re Otto, 312 F.2d 937, 939-40 (CCPA 1963); In re Ludtke, 441 F.2d 660, 663-64 (CCPA 1971); In re Yanush, 477 F.2d 958, 959 (CCPA 1973).
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Li as applied to claim 4 above, and further in view of He et al. (US 2021/0323572, disclosed in IDS 29 April 2026), hereinafter He.
Claims 5-6: Li is silent with respect to the intensities of the reflected light received by the sensor.
He, however, in the same field of endeavor of multi-sensor vehicle calibration, discloses wherein
an intensity of reflected light received by a sensor is associated with each data point included in acquired point cloud data (“” [0146]),
estimating a position of a target object comprises estimating the position of the target object using intensities of a plurality of data points in overlapping point cloud data (“a first cluster is a cluster for road surface points with magnitudes above a predetermined threshold, and a second cluster is a cluster for all other points with magnitudes below the predetermined threshold or a second predetermined threshold. Here, the points in the first cluster are road surface points” [0148]),
the operations further comprise correcting the intensity of each data point included in the point cloud data or the overlapping point cloud data using a distance of the data point (“based on the intensity gradient, e.g., the magnitudes, a clustering algorithm, such as a k-means clustering algorithm, can be used to cluster the gradient points into two categories” [0148]), and
estimating the position of a target object comprises estimating the position of the target object using the corrected intensity (“a first cluster is a cluster for road surface points with magnitudes above a predetermined threshold, and a second cluster is a cluster for all other points with magnitudes below the predetermined threshold or a second predetermined threshold. Here, the points in the first cluster are road surface points” [0148]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Li’s processor to estimate the position of the target object using received intensities for the purpose of improving the accuracy of position estimation.
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to HINA F AYUB whose telephone number is (571)270-3171. The Examiner can normally be reached on 9am-5pm ET Mon-Fri.
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If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Tarifur Chowdhury can be reached on 571-272-2287. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Hina F Ayub/
Primary Patent Examiner
Art Unit 2877
Prosecution Insights