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 .
Status of the Claims
This first final action is in response to applicant's amendment on Aug. 13, 2025. Claims 2-3 have been cancelled. Claims 1, 4-19 are pending and have been considered as follows.
Response to Arguments
Applicant’s amendments/arguments with respect to claim(s) under 35 U.S.C 102/103 have been fully considered but are moot because the new ground of rejection does not rely on any reference for any teaching or matter specifically challenged in the argument.
Applicant's amendments/arguments with respect to the rejection of claims under 35 U.S.C. 101 have been fully considered and are persuasive. The rejection of claims under 35 U.S.C. 101 has been withdrawn.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1, 4 and 17-18 are rejected under 35 U.S.C. 102(a)(1) as being obvious over Demir (US 20210010814 Al) in view of Zhao (CN114137592A machine translation)
Regarding claim 1, Demir teaches a method for localizing an autonomous vehicle by fusing a plurality of localization technologies, which is performed by a computing device ( abstract, Fig. 1 and corresponding paragraphs including at least [0003], [0027], [0035] ), the method comprising:
calculating a plurality of localization values by performing localization for a vehicle located in a predetermined region using a plurality of localization technologies for performing localization according to different localization methods ([0042], a predetermined region: around vehicle or certain road [0046]; [0050], [0054], Fig.1 and corresponding paragraphs); and
determining a position and orientation of the vehicle by fusing the plurality of calculated localization values as a result of localizing the vehicle, driving the vehicle based on the determined position and orientation, ( a positioning method for an autonomous vehicle performed by a computer device, the method comprising the steps in which: a plurality of sensors mounted on a vehicle (a positioning method for an autonomous vehicle performed by a computer device, the method comprising the steps in which: a plurality of sensors mounted on a vehicle collect sensor data to perform positioning; a scan matcher using an NDT algorithm for positioning determines scan confidence; and a map-to-odometer conversion result is generated on the basis of GPS data and the scan confidence, wherein the map-to-odometer conversion result means a vehicle position, and the matching result can be used to update an initial pose guess value; [0003], and claims 1 and 6, Fig1 and corresponding paragraphs),
wherein the calculating of the plurality of localization values includes: the calculating of the plurality of localization values includes calculating a first localization value for the vehicle using a first localization technology for performing localization according to a GNSS/INS-based localization method; and calculating a second localization value for the vehicle using a second localization technology for performing localization according to a normal distribution transform (NDT) map-based localization method, the NDT map being generated by post-processing a point cloud for the predetermined region, and the determining of the position and orientation of the vehicle includes deriving position information of the vehicle and orientation information of the vehicle by fusing the calculated first localization value and the calculated second localization value (a plurality of sensors mounted on a vehicle collect sensor data to perform positioning; a scan matcher using an NDT algorithm for positioning determines scan confidence; and a map-to-odometer conversion result is generated on the basis of GPS data and scan confidence; [0003], and claims 1 and 6, Fig1 and corresponding paragraphs).
Demir does not explicitly teach but Zhao teaches wherein the deriving of the position information of the vehicle and the orientation information of the vehicle includes determining a localization technology-specific weight for each of a plurality of regions based on regional characteristics of each of the plurality of regions, and generating a localization technology-specific weight map using the determined localization technology-specific weight; assigning a first weight corresponding to the first localization technology to the calculated first localization value and a second weight corresponding to the second localization technology to the calculated second localization value based on the generated localization technology-specific weight map; and deriving the position information of the vehicle and the orientation information of the vehicle by fusing the first localization value to which the first weight is assigned and the second localization value to which the second weight is assigned (switching multi-source sensor fusion positioning. The method includes satellite navigation and positioning raw data, the velocity raw data, the attitude raw data and the laser radar positioning raw data at the kth time. The data are preprocessed respectively to obtain satellite navigation and positioning processing data, … and lidar positioning processing data; The data is fed into the error filtering model for correction, and the switching strategy between GNSS and lidar (Weight is 0 or 1) is determined. The handover strategy disclosed by the invention can be applied to complex areas such as areas without GPS and multiple vegetation (regional characteristics of the plurality of regions) and solves the problem of unstable positioning at present) The switch module is used to switch the GNSS if the GNSS is normal, let k=k+1, and return to the "acquisition module"; if the GNSS is abnormal and the lidar is normal, switch the lidar, let k=k+1, and return "Acquisition module"; if both GNSS and lidar are abnormal, end.)
It would have been obvious to one of ordinary skill in the art before the effective date of the present invention to modify, vehicle localization, as taught by Demir, generating a localization technology-specific weight map based on the regional characteristics of the plurality of regions, as taught by Zhao, as Demir and Zhao are directed to fusion positioning (same field of endeavor), and one of ordinary skill in the art would have recognized the established utility using generating a localization technology-specific weight map based on the regional characteristics of the plurality of regions and predictably applied it to Demir’s teaching to provide stable positioning when encountering complex scenes.
Regarding claims 18 and 19, please see the rejection above regarding claim 1, which is commensurate in scope to claims 18 and 19. Demir teaches a network interface, a memory, and a computer program loaded into the memory and executed by the processor ([0028]- [0029]) in claim
Regarding claim 4, Demir teaches calculating a third localization value for the vehicle using a third localization technology for performing localization according to a lane matching-based localization method ([0011]-[0016]), and wherein the determining of the position and the orientation of the vehicle includes deriving the position information of the vehicle and orientation information of the vehicle by fusing the calculated first localization value, the calculated second localization value, and the calculated third localization value (Fig. 1 and corresponding paragraphs).
Claims 5-17 are rejected under 35 U.S.C. 103 as being obvious over by Demir (US 20210010814 Al) Zhao (CN114137592A machine translation) in view of Kim (US 20230194268 A1)
Regarding claim 5, Demir in view of Zhao does not explicitly teach but Kim teaches wherein the calculating of the third localization value includes generating a lane precision map for the predetermined region; generating real-time lane information using a real-time point cloud acquired from the vehicle; and matching the generated lane precision map with the generated real-time lane information to calculate the third localization value for the vehicle ([0014] Generally, localization of the AV with respect to lane line features are performed by matching LIDAR point cloud data to lane markers that may be dashed, solid, double, or unmarked. Localization to a lane line feature can be performed using point-to-point iterative closet point (ICP), or GICP, Fig 5 and corresponding paragraphs).
It would have been obvious to one of ordinary skill in the art before the effective date of the present invention to modify, vehicle localization, as taught by Demir in view of Zhao, matching the generated lane precision map with the generated real-time lane information to calculate the third localization value for the vehicle, as taught by Kim, as Demir and Kim are directed to vehicle localization (same field of endeavor), and one of ordinary skill in the art would have recognized the established utility using matching the generated lane precision map with the generated real-time lane information to calculate a localization value for the vehicle and predictably applied it to Demir’s teaching to maneuver safely between lanes.
Regarding claim 6, Demir in view of Zhao does not explicitly teach but Kim teaches wherein the matching of the generated lane precision map with the generated real-time lane information includes deriving the position information of the vehicle and the orientation information of the vehicle by matching information included in the generated lane precision map with information included in the generated real-time lane information based on a vehicle coordinate system with a point in the vehicle as an origin ( [0014], Fig 5 and corresponding paragraphs).
The same motivation to combine as the parent claim applies here.
Regarding claim 7, Demir in view of Zhao teaches wherein the generating of the lane precision map includes extracting only points corresponding to a ground surface from the point cloud acquired by scanning the predetermined region to generate a ground surface point cloud for the predetermined region ([0041]-[0043]);
defining a range of interest (ROI) corresponding to the lane in the point cloud acquired by scanning the predetermined region to generate an ROI map for the predetermined region ( [0042] The map tile point cloud data may be represented as a voxel associated with a mean and a covariance. For example, map tiles 114 may be ‘tiles’ around the vehicle which may be loaded at runtime (e.g., as the vehicle is passing through the operating environment). These ‘tiles’ may be of a predetermined size (e.g., 1 km2 ) and stored and loaded in a NDT compatible forma; ROI: around vehicle ); and
extracting only points matched with points included in the generated ROI map and having an intensity equal to or greater than a threshold value from among a plurality of points included in the generated ground surface point cloud to generate the lane precision map for the predetermined region (Fig. 1 and corresponding paragraphs).
Regarding claim 8, Demir teaches the defining of ROI map includes: extracting only points corresponding to a ground surface from the point cloud acquired by scanning the predetermined region to generate a ground surface point cloud for the predetermined region; defining a range of interest (ROI) corresponding to the lane in the point cloud acquired by scanning the predetermined region to generate an ROI map for the predetermined region; and extracting only points matched with points included in the generated ROI map and having an intensity equal to or greater than a threshold value from among a plurality of points included in the generated ground surface point cloud to generate the lane precision map for the predetermined region (Fig 1 and corresponding paragraphs, [0041] The scan matcher 104 may receive map tile point cloud data from a map tile server 112 in addition to the sensor scan point cloud output from the scan accumulator 102 . The map tile point cloud data may be indicative of transformed point cloud data associated with a coarse vehicle location and be built using a reference set of sensors 110 . The point cloud data 116 from the map tile server 112 (e.g., map tile point cloud data) may be represented in a local coordinate frame, but the transformation from local to UTM coordinate frame may also be available. The map tile point cloud data may include static features, ground surfaces, features of the environment, layout of the environment, etc. from the real-world environment or operating environment to be localized. The map tile point cloud data will generally exclude dynamic objects from the real-world environment or operating environment to be localized. In other words, during generation or creation of the map tile point cloud data, dynamic objects may be filtered from this dataset; [0042] The map tile point cloud data may be represented as a voxel associated with a mean and a covariance. For example, map tiles 114 may be ‘tiles’ around the vehicle (ROI) which may be loaded at runtime (e.g., as the vehicle is passing through the operating environment).
Regarding claim 9, Demir in view of Zhao does not explicitly teach but Kim teaches wherein the defining of the ROI map includes labeling lanes on the point cloud acquired by scanning the predetermined region to define a road structure for the predetermined region, thereby generating a road network map for the predetermined region; and setting an area having a predetermined size including lanes labeled on the generated road network map as the ROI to generate the ROI map for the predetermined region (Fig. 5 and corresponding paragraphs).
The same motivation to combine as the parent claim applies here.
Regarding claim 10, Demir teaches wherein the generating of the lane precision map includes extracting only points corresponding to a ground surface from the point cloud acquired by scanning the predetermined region to generate a ground surface point cloud for the predetermined region; labeling lanes in the point cloud acquired by scanning the predetermined region to define a road structure for the predetermined region, thereby generating a road network map for the predetermined region; and extracting only points located on the lane labeled on the generated road network map from among the plurality of points included in the generated ground surface point cloud to generate the lane precision map for the predetermined region (claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 11, Demir teaches wherein the generating of the lane precision map includes: extracting a plurality of points corresponding to the lane from the point cloud acquired by scanning the predetermined region; approximating the plurality of extracted points into a line shape to acquire direction information of each of the plurality of extracted points; and generating a lane precision map including position information of each of the plurality of extracted points and the direction information of each of the plurality of extracted points (claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 12, Demir teaches wherein the generating of the real-time lane information includes acquiring a point cloud collected in real time through a sensor included in the vehicle; setting a range of interest (ROI) on the acquired point cloud; extracting a plurality of points included in a predefined range from among the points included in the set ROI, the predefined range including a longitudinal range, a lateral range, a height range, and an intensity range; and connecting the plurality of extracted points based on a gradient between the plurality of extracted points to generate the real-time lane information(claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 13, Demir teaches wherein the setting of the ROI includes setting the ROI in a three-dimensional space shape having a predetermined size in the acquired point cloud with reference to any one of a position of a center point of the vehicle and a position of the sensor included in the vehicle(claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 14, Demir teaches wherein the setting of the ROI includes setting the ROI in the three-dimensional space shape having a predetermined size at a position corresponding to a direction in which the vehicle travels in the acquired point cloud with reference to the direction in which the vehicle travels (claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 15, Demir teaches wherein the setting of the ROI includes acquiring video data generated by filming a region in the direction in which the vehicle travels through a camera sensor included in the vehicle; analyzing the acquired video data to identify a lane ([0015]); and determining a position relative to the identified lane with reference to the vehicle, determining a position at which the ROI is set based on the determined relative position, and setting the ROI in the three-dimensional space shape having the predetermined size at the determined position at which the ROI is set in the acquired point cloud (claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 16, Demir teaches wherein the generating of the real-time lane information includes acquiring a point cloud collected in real time through a sensor included in the vehicle; setting a range of interest (ROI) on the acquired point cloud; defining a ground surface within the set ROI by approximating points included in the set ROI into a plane shape; extracting a plurality of points included in a predefined range from among points located on the defined ground surface, the predefined range including a longitudinal range, a lateral range, a height range, and an intensity range; and connecting the plurality of extracted points based on a gradient between the plurality of extracted points to generate the real-time lane information(claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 17, Demir teaches wherein the generating of the real-time lane information includes acquiring the point cloud collected in real time through a sensor included in the vehicle; setting a range of interest (ROI) in the acquired point cloud; extracting a plurality of points included in a predefined range from among the points included in the set ROI, the predefined range including a longitudinal range, a lateral range, a height range, and an intensity range; acquiring direction information of each of the plurality of extracted points by approximating the plurality of extracted points into a line shape; and generating real-time lane information including t position information for each of the plurality of extracted points and the direction information of each of the plurality of extracted points (claims 1 and 6, Fig.1 and corresponding paragraphs).
Prior Art
Please refer to form 892 for cited references.
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275,277 (CCPA 1968)).
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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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/J.W./ Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/ Supervisory Patent Examiner, Art Unit 3666
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