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 amendment filed September 2, 2025 has been entered. Claims 1-2, 6-7, and 11-26 remain pending in the application.
Response to Arguments
Applicant's arguments filed September 2, 2025 have been fully considered but they are not persuasive.
[1] Zhang does not appear to disclose as claimed i) performing feature extraction on points of data to detect one or more features of one or more predetermined types of objects having one or more predetermined characteristics, and ii) wherein the one or more features are determined to correspond to one or more targets because the one or more features have the one or more predetermined characteristics. The relied upon paragraph [0074] of Zhang discloses localizing a LIDAR unit based on locations of detected objects. Paragraph [0074] does not refer to features of objects and/or performing feature extraction.
Examiner respectfully disagrees.
As explained in the office action, Fig. 4A, paragraphs [0072] and [0074] of Zhang disclose the limitation. A traffic signal 423 is explained as an example of the target, and features corresponding to the traffic signal are extracted from LiDAR data.
[2] The Office Action states that Zhang fails to disclose the limitations of (a): "wherein one or more of the global positioning system locations are of the one or more targets, determining ground-truth positions of the one or more features, correcting the one or more of the global positioning system locations based on the ground-truth positions". Inasmuch as Zhang fails to disclose these features, Zhang further fails to disclose the limitation of (b): "calculating a LIDAR-to-vehicle transform based on the corrected one or more of the global positioning system locations."
Examiner respectfully disagrees.
As explained in the office action, paragraph [0001] of Zhang discloses use of GPS data and LIDAR data. [0002] and [0060] of Zhang disclose calibration and correction for two sensors, which implies that the two sensors may be GPS and LIDAR. Further, paragraphs [0031], [0116], [0026], [0040] of Zhang are cited to strengthen the rejection of limitation (b).
[3] Applicant argued that Chen fails to teach limitation (a). Chen does not disclose obtaining ground-truth positions of one or more features of predetermined types of objects. The relied upon paragraph of Chen does not refer to features of an object and/or determining ground-truth positions of the features of an object. Inasmuch as Chen does not disclose determining ground-truth positions of the features, Chen further does not disclose correcting the one or more of the global positioning system locations based on the ground-truth positions of the features and calculating a LIDAR-to-vehicle transform based on the corrected one or more of the global positioning system locations.
Examiner respectfully disagrees.
Zhang already discloses that the one or more features are determined to correspond to one or more targets. As explained in the office action, Chen teaches determining ground-truth positions of the one or more features (target equivalent) and correcting the one or more of the global positioning system locations based on the ground-truth positions in [0038], [0085].
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 6, 11, 15, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20220194412 A1) in view of Chen (US 20200021987 A1).
Regarding claim 1, Zhang discloses:
A LIDAR-to-vehicle alignment system comprising: a memory configured to store points of data provided based on an output of a LIDAR sensor and global positioning system locations {paragraph [0001] discloses LIDAR and GPS data. [0052] discloses points of data output by a LIDAR. [0059] discloses a memory to store sensor related data.}; and
an autonomous driving module configured to perform an alignment process comprising obtaining the points of data, performing feature extraction on the points of data to detect one or more features of one or more predetermined types of objects having one or more predetermined characteristics, wherein the one or more features are determined to correspond to one or more targets because the one or more features have the one or more predetermined characteristics {[0028] discloses an alignment process and obtaining the points of data. [0030] discloses an autonomous vehicle module. [0074] discloses feature extraction on the points of data: based on a detected location of various objects within each frame of the LiDAR data, such as the traffic signal, construed as a type of object having predetermined characteristics. Fig. 4A and [0072] disclose predetermined characteristics for the features: a LiDAR map layer may include point cloud data representing the traffic signal and the road network. The LiDAR map provides the features of the traffic signal, that is the target. This limitation describes the alignment process with detecting targets by LIDAR.}, and
calculating a LIDAR-to-vehicle transform based on the corrected one or more of the global positioning system locations, based on results of the alignment process {[0031] discloses the pose of LIDAR with respect to the vehicles coordinate, construed as the LIDAR-to-vehicle transform. [0001] discloses use of GPS data and LIDAR data. [0002] discloses calibration for two sensors, which may be GPS and LIDAR. [0060] discloses calibration between the two sensors and action to correct the calibration. [0116] discloses ground-truth data. [0026] discloses data by an accurately calibrated sensor, which may include corrected global positioning system locations, may be used to localize the vehicle within a map for a given area. [0040] discloses localizing with LIDAR data. That is, Zhang discloses calculating a LIDAR-to-vehicle transform based on the corrected GPS locations and the alignment process},
determining whether one or more alignment conditions are satisfied, and in response to the LIDAR-to-vehicle transform not satisfying the one or more alignment conditions, recalibrating at least one of the LIDAR-to-vehicle transform or recalibrating the LIDAR sensor {[0026] discloses recalibrating if alignment conditions are not satisfied}.
Zhang does not disclose:
wherein one or more of the global positioning system locations are of the one or more targets, determining ground-truth positions of the one or more features, correcting the one or more of the global positioning system locations based on the ground-truth positions
Chen teaches determining ground-truth positions of the one or more features in paragraph [0038], which teaches GPS calibration based on ground-truth positions of the targets having the features, and in [0085], which teaches aligning GPS position to the ground truth positions.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the GPS system correcting feature of Chen
with the described invention of Zhang in order to obtain precise GPS positions that facilitate accurate autonomous driving.
Similar reasoning applies to claim 15.
Regarding claim 6, which depends from claim 1, Zhang discloses: wherein the autonomous driving module is configured to, while performing feature extraction: convert data from the LIDAR sensor to a vehicle coordinate system and then to a world coordinate system; and aggregate resulting world coordinate system data to provide the points of data {[0031] discloses converting LIDAR data to a vehicle coordinate system. [0041] discloses converting to common coordinate frame, construed as the world coordinate system. Examiner notes that lidar data includes points of data, and the converting processes applied to each point results in aggregating resulting world coordinate system data to provide the points of data}.
Regarding claim 11, which depends from claim 1, Zhang discloses: wherein: the LIDAR-to-vehicle alignment system is implemented at a vehicle {[0028]}; the memory stores inertial measurement data {[0025] discloses Inertial Measurement Unit (IMU) data}; and the autonomous driving module is configured to, during the alignment process, based on the inertial measurement data, determine an orientation of the vehicle {[0094] discloses determining vehicle orientation} , and correct the orientation based on the ground-truth data {[0116] discloses ground-truth data}.
Regarding claim 17, which depends from claim 15, Zhang discloses:
further comprising: based on inertial measurement data, determine an orientation of a vehicle; and correct the orientation based on the ground-truth positions {[0025], [0094], [0116]}.
Claim(s) 2, 21, 22, 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Chen and in further view of He et al. (US 20210158547 A1).
Regarding claim 2, which depends from claim 1, Zhang discloses: wherein: the autonomous driving module is configured to, while performing feature extraction, detect at least one of (i) a first object of a first predetermined type, (ii) a second object of a second predetermined type, or (ii) a third object of a third predetermined type; and the first predetermined type is a traffic sign; the second predetermined type is a light pole; the third predetermined type is a building, {[0072}: traffic signal, [0106]: building}.
Modified Zhang does not disclose: the autonomous driving module is configured to, while performing feature extraction, detect an edge or a planar surface of the third object.
He teaches detecting an edge when extracting features in paragraph [0059].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the edge detecting feature of He
with the described invention of modified Zhang in order to facilitate feature extraction.
Regarding claim 21, which depends from claim 1, He teaches: wherein the one or more features include at least one of surfaces, edges, and corners of the one or more predetermined types of objects {[0059]}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the edge detecting feature of He
with the described invention of modified Zhang in order to facilitate feature extraction.
Regarding claim 22, which depends from claim 1, He teaches: wherein the one or more features include a surface, an edge, and a corner of one of the one or more predetermined types of objects and correspond to one of the one or more targets {[0059]: detect features or image edges using a corner detection algorithm. [0061]: reconstruct 2D or 3D surfaces}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the surface, edge and corner detecting feature of He with the described invention of modified Zhang in order to facilitate feature extraction.
Regarding claim 24, which depends from claim 1, He teaches: wherein the autonomous driving module is configured when performing the alignment process to: determine whether there is a potential feature using feature extraction comprising using an edge detection algorithm to detect first features of a first type of object, using a plane detection algorithm to detect second features of the first type of object, determine whether the potential feature is ground-truth data; and in response to the potential feature being ground truth data, correct the one or more global positioning system locations based on the ground-truth data {[0059], [0061]}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the surface and edge detecting feature of He with the described invention of modified Zhang in order to facilitate GPS calibration.
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Chen and in further view of Zhu (US 20210263157 A1).
Regarding claim 23, which depends from claim 1, Modified Zhang does not teach: wherein the autonomous driving module is configured determine the ground-truths positions of a plurality of features of a single object, and ii) correct the one or more of the global positioning system locations based on the ground-truth positions of the plurality of features.
Zhu teaches determining ground-truth position of features of an object in paragraph [0041]: features and metadata describing the objects may be used as ground truth.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Zhu’s feature for the ground-truth positions based on the object features with the described invention of modified Zhang in order to facilitate GPS calibration.
Claim(s) 7, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Chen and in further view of Rounds (US 20210341624 A1) and Magnusson (The Three-Dimensional Normal-Distributions Transform – an Efficient Representation for Registration, Surface Analysis, and Loop Detection), which was cited by Applicant.
Regarding claim 7, which depends from claim 1, modified Zhang does not teach: wherein the autonomous driving module is configured to, while determining the ground-truth positions: based on a vehicle speed, a type of acceleration maneuverer, and a global positioning system signal strength, assign weights to the points of data to indicate confidence levels in the points of data; remove ones of the points of data having weight values less than a predetermined weight; and determining a model of a feature corresponding to remaining ones of the points of data to generate ground-truth data indicative of the ground-truth positions, wherein the model is of a plane or a line, the ground-truth data includes the model, an eigenvector, and a mean vector, and the ground-truth data is determined using principal component analysis.
Rounds teaches that vehicle speed, acceleration maneuver and GPS signal strength are factors for determining accurate GPS locations in paragraph [0005].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the GPS location accuracy affecting factors of Rounds with the described invention of modified Zhang in order to facilitate determining the ground-truth positions.
Magnusson teaches assigning weight to the points of data and removing data having less than a predetermined weight in page 38, lines 7-20, and a model of a feature in page 146, lines 28-37.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the weight and model features of Magnusson with the described invention of modified Zhang in order to facilitate analyzing the points of data.
Magnusson teaches: wherein the model is of a plane or a line {page 123, line 26 – page 124, line 2}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the model geometry feature of Magnusson with the described invention of modified Zhang in order to facilitate composing a model.
Magnusson teaches: wherein the ground-truth data includes the model, an eigenvector, and a mean vector {page 146, lines 28-37 teaches the model. Page 57, lines 14-15 teaches an eigenvector. Page 57, line 9 teaches a mean vector}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the model, eigen vector and mean vector features of Magnusson with the described invention of modified Zhang in order to facilitate performing the model application.
Magnusson teaches: wherein the ground-truth data is determined using principal component analysis {page 51, line 38}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the principal component analysis feature of Magnusson with the described invention of modified Zhang in order to facilitate processing points of data.
Regarding claim 16, which depends from claim 15, Rounds teaches: further comprising, while determining the ground-truth positions: based on a vehicle speed, a type of acceleration maneuverer and a global positioning system signal strength {[0005]}. Magnusson teaches: assigning weights to the points of data to indicate confidence levels in the points of data, removing ones of the points of data having weight values less than a predetermined weight; and determining a model of a feature corresponding to remaining ones of the points of data using principal component analysis to generate ground truth-data indicative of the ground-truth positions, wherein the model is of a plane or a line, wherein the ground-truth data includes the model, an eigenvector, and a mean vector {page 38, lines 7-20, page 146, lines 28-37, page 123, line 26 – page 123, line 2, Page 57, lines 14-15, Page 57, line 9}.
Claim(s) 13, 14, 19, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Chen and Magnusson.
Regarding claim 13, which depends from claim 1, Chen teaches: wherein the autonomous driving module is configured to: correct the one or more global positioning system locations using a ground- truth model for a traffic sign or a light pole {[0033]: a street light, a traffic signal, [0038], [0085]}.
Magnusson teaches: project LIDAR points for the traffic sign or the light pole to a plane or a line {page 123, line 26 – page 124, line 2}; calculate an average global positioning system offset for a plurality of timestamps; apply the average global positioning system offset to provide the corrected one or more of the global positioning system locations; and update a vehicle-to-world transform based on the corrected one or more of the global positioning system locations {page 89, lines 19-27 teaches the average global position system offset}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the position offset feature of Magnusson with the described invention of modified Zhang in order to facilitate aligning the sensor system.
Similar reasoning applies to claim 19.
Regarding claim 14, which depends from claim 1, Magnusson teaches: wherein the autonomous driving module is configured to: correct the one or more global positioning system locations and inertial measurement data using ground-truth point matching including running an iterative closest point algorithm to find a transformation between current data and the ground-truth data {page 37, lines 15-22 teaches the iterative closest point algorithm}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the iterative closest point algorithm feature of Magnusson with the described invention of modified Zhang in order to facilitate processing the points data.
Magnusson further teaches: calculating an average global positioning system offset and a vehicle orientation offset for a plurality of timestamps, and applying the average global positioning system offset and the vehicle orientation offset to generate the corrected one or more of the global positioning system locations and a corrected vehicle orientation {page 89, lines 19-27}.
Zhang discloses: update a vehicle-to-world transform based on the corrected one or more of the global positioning system locations and the corrected inertial measurement data {[0031], [0041]}.
Similar reasoning applies to claim 20,
Claim(s) 12, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Chen and in further view of Vianello et al. (US 20210319059 A1).
Regarding claim 12, which depends from claim 1, modified Zhang does not teach: wherein the autonomous driving module is configured to perform interpolation to correct the one or more of the global positioning system locations based on previously determined corrected global positioning system locations.
Vianello teaches using interpolation for GPS location based on previously determined corrected GPS in [0003]: rely on ground-truth data collected by harvesting GPS-accurate locations … infer the address-geolocation association by interpolation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the GPS location interpolation feature of Vianello
with the described invention of modified Zhang in order to facilitate correcting GPS locations.
Similar reasoning applies to claim 18.
Claim(s) 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Chen and He and in further view of Magnusson.
Regarding claim 25, which depends from claim 24, modified Zhang does not teach: wherein the autonomous driving module is configured, in response to the potential feature not being ground truth data: load feature data of the one or more features; assign weights to the one or more features; determine if the feature data is of a first type; and in response to the feature data being of the first type, filtering out low weighted points and performing a principal components analysis or plane fitting to provide a three-dimensional plane model of points of the potential feature.
Magnusson teaches assigning weight to the features and filtering out low weighted points, performing principal component analysis in page 38, lines 7-20, page 146, lines 28-37, page 51, line 38. Examiner notes that “a first type” may be a plane, line, which is taught by Magnusson: wherein the model is of a plane or a line {page 123, line 26 – page 124, line 2}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the weight and principal component analysis feature of Magnusson with the described invention of modified Zhang in order to facilitate aligning the sensor system.
Allowable Subject Matter
Claim 26 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/C.P./Examiner, Art Unit 3661
/RAMYA P BURGESS/Supervisory Patent Examiner, Art Unit 3661
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