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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 8-14 and, 17-18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hu et al. (20230266451).
Regarding claim 1, Hu teaches:
A fault-detection system (#105 of Fig. 1, autonomous driving module) for detecting fault in a light detection and ranging (LiDAR) system (#180 of Fig. 1, LIDAR sensors) mounted on a vehicle (#102 of Fig. 1, vehicle), the LiDAR system being configured to provide point cloud data of an external environment of the vehicle in accordance with a LiDAR coordinate system (#504 of Fig. 5A, point cloud), the fault- detection system comprising:
one or more processors (#104 of Fig. 1, alignment module, [73] states that modules can include processors),
a memory device (#192 of Fig. 1, memory), and
processor-executable instructions stored in the memory device, the processor-executable instructions (#104 of Fig. 1, alignment module, [73] states that modules can include memory stores code executed by the processor) comprising instructions for:
obtaining a vehicle speed [55];
obtaining conversion parameters used for converting from the LiDAR coordinate system to a vehicle coordinate system (#224 of Fig. 2, transformation matrix buffer);
determining whether the vehicle speed exceeds a vehicle speed threshold [55];
in accordance with a determination that the vehicle speed exceeds the vehicle speed threshold, obtaining a representation of a road surface plane expressed in the vehicle coordinate system [52];
obtaining a representation of a native horizontal plane provided by the vehicle [64]; and
determining whether a fault in the LiDAR system has occurred based on the representation of the road surface plane and the representation of the native horizontal plane [45].
Regarding claim 2, Hu also teaches: the fault-detection system of claim 1, wherein the vehicle is configured to provide map data of the external environment of the vehicle in accordance with the vehicle coordinate system [64], the map data comprising the representation of the native horizontal plane of the external environment [64].
Regarding claim 3, Hu also teaches:
The fault-detection system of claim 1, wherein obtaining the conversion parameters used for converting from the LiDAR coordinate system to the vehicle coordinate system comprises:
obtaining a reference rotation vector and a rotation angle from the vehicle [65]; and
converting the reference rotation vector to a rotation matrix [65].
Regarding claim 4, Hu also teaches:
The fault-detection system of claim 1, wherein obtaining the representation of the road surface plane expressed in the vehicle coordinate system comprises:
obtaining the point cloud data provided by the LiDAR system [58];
deriving the road surface plane based on the point cloud data [63];
obtaining the representation of the road surface plane [63]; and
converting the representation of the road surface plane from the LiDAR coordinate system to the vehicle coordinate system using the conversion parameters [64].
Regarding claim 5, Hu also teaches:
The fault-detection system of claim 4, wherein deriving the road surface plane from the point cloud data comprises:
selecting a plurality of reference points on a road surface from the point cloud data [63]; and
deriving the road surface plane based on the plurality of reference points on the road surface [63].
Regarding claim 8, Hu also teaches:
The fault-detection system of claim 1, wherein determining whether the fault in the LiDAR system has occurred comprises:
calculating a deviation angle between the representation of the road surface plane and the representation of the native horizontal plane [45]; and
determining whether the deviation angle exceeds a deviation angle threshold [45].
Regarding claim 9, Hu also teaches:
The fault-detection system of claim 1, wherein the processor-executable instructions comprise further instructions for:
based on the determination that the fault in the LiDAR system has occurred, sending information of the fault to the vehicle [47 and 65].
Hu states in [47] that the autonomous driving module (which includes the alignment module) can send error messages, and states in [65] that the alignment module updates the transformation matrix.
Claim 10 is identical in scope to claim 1 and is rejected for the reasons stated above.
Claim 11 is identical in scope to claim 2 and is rejected for the reasons stated above.
Claim 12 is identical in scope to claim 3 and is rejected for the reasons stated above.
Claim 13 is identical in scope to claim 4 and is rejected for the reasons stated above.
Claim 14 is identical in scope to claim 5 and is rejected for the reasons stated above.
Claim 17 is identical in scope to claim 8 and is rejected for the reasons stated above.
Claim 18 is identical in scope to claim 9 and is rejected for the reasons stated above.
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) 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hu in view of Oliveira Almeida et al. (US 20200074370).
Regarding claim 6, Hu teaches: The fault-detection system of claim 5
Hu does not teach, but Oliveira does teach:
processor-executable instructions comprising instructions for: determining whether a total number of the plurality of reference points satisfies a condition for determining a road surface plane [78, does not run analysis if there are not enough points in the dataset]
Hu also teaches:
Using the RANSAC algorithm to identify a plane [63]
Wikipedia (Random sample consensus
[online]. Wikipedia, 2016 [retrieved on 2016-10-18]. Retrieved from the Internet: <URL: https://web.archive.org/web/20161018124411/https://en.wikipedia.org/wiki/Random_sample_consensus>) teaches:
The RANSAC algorithm requires at least a certain number of points [Section titled “Algorithm”].
It would have been obvious to a person having ordinary skill in the art to modify the LIDAR alignment system of Hu to use a dataset checking method similar to Oliveira with a reasonable expectation of success. This would have the predictable result of allowing the RANSAC algorithm to function properly, as the RANSAC algorithm requires a certain number of points.
Claim 15 is identical in scope to claim 6 and is rejected for the reasons stated above.
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hu in view of Dins et al. (US 20220121836).
Regarding claim 7,Hu teaches: The fault-detection system of claim 5.
Hu does not teach, but Dins does teach:
wherein the processor-executable instructions comprise further instructions for: calculating a variance between the derived road surface plane and the road surface; and determining whether the variance exceeds a variance threshold [30, checks for flatness using a variance threshold].
Hu also teaches:
Not running the correction system when the road surface is not flat [55]
It would have been obvious to a person having ordinary skill in the art to modify the LIDAR alignment system of Hu to use variance to check flatness similar to Dins with a reasonable expectation of success. This would have the predictable result of preventing the system from aligning itself using inaccurate data.
Claim 16 is identical in scope to claim 7 and is rejected for the reasons stated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Matsui (US 11243308) teaches a similar method for detecting misalignment of a LIDAR system but does not use vehicle speed to make reliability determinations. Kato (US 20230010175) teaches a similar method for detecting pitch and roll but uses it to align the entire vehicle and not as a fault detection method.
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/A.D.S./Examiner, Art Unit 3645
/ISAM A ALSOMIRI/Supervisory Patent Examiner, Art Unit 3645