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 Claims
This Office Action is in response to the application filed on 11/26/2024. Claim(s) 1 – 15 are presently pending and are examined in this first action on the merits (FAOM).
Priority
Examiner acknowledges Applicant’s claim to priority based on Application CN202310636369.0 filed 05/31/2023.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 11/26/2024 and 02/24/2026 have been considered by the Examiner.
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.
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.
Claims 1, 7, 8 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Mitsuhisa Shida US20110301779 (“Shida”) in view of Tingbo Hou et. al. US 20220138896 (“Hou”) and Bin Lv et. al. Revolution and rotation based method for roadside LiDAR data integration, Optics and Laser Technology, May 2019 (“Lv”).
As per Claim 1 and 8,
Shida discloses,
A method for determining a vehicle three-dimensional coordinate true value, wherein,
in an initial state, each of a main acquisition vehicle and an auxiliary acquisition vehicle is located at an initial point, and a vehicle-side sensor of each of the main acquisition vehicle and the auxiliary acquisition vehicle faces front of the vehicle; (Fig. 2, [0050] the formation mode in which an arbitrary number of vehicles travels in a line at a relatively short inter-vehicle distance is achieved by the vehicle formation control system, [0050] as shown in FIG. 2, an n-th (n is a natural number) vehicle from the head of the formation is represented by “Cn”. It is assumed that each vehicle travels in the direction of an arrow Y in FIG. 2 and a total number of vehicles in the formation is m (m is a natural number; m≧n). The distance between the vehicle Cn and a vehicle Cn+1 is represented by “Dn”)
when the auxiliary acquisition vehicle travels forward, (Fig. 2) the main acquisition vehicle is stationary, and the vehicle-side sensors of the main acquisition vehicle and the auxiliary acquisition vehicle simultaneously acquire sensed data packets in a preset duration each time the auxiliary acquisition vehicle travels by a preset interval distance and stops (Fig. 4, Fig. 6 S10, [0065] The motional state acquiring unit 12 has a function of acquiring the motional state or acceleration request value information of the vehicle Cn and the motional state or acceleration request value information of other vehicles);
when the auxiliary acquisition vehicle stops for K times, the auxiliary acquisition vehicle continues to travel to a preset distance position and stops, in this case, the vehicle-side sensors of the main acquisition vehicle and the auxiliary acquisition vehicle simultaneously acquire sensed data packets in the preset duration (Fig. 4, Fig. 5, S10 and S12) and the method comprises:
obtaining K sets of ego vehicle position information acquired by an inertial sensor of the auxiliary acquisition vehicle during K stops and K sets of main sensed data packets acquired by the main acquisition vehicle during K stops, wherein K is greater than a preset number of times (Fig. 3, done multiple times),
each set of main sensed data packets is formed by sensed data with a plurality of timestamp frames, the sensed data of each timestamp frame comprises three-dimensional coordinate information of a sensed vehicle, and the sensed vehicle comprises at least the auxiliary acquisition vehicle (see at least [0076]);
Shida does not disclose,
in an initial state, each of a main acquisition vehicle and an auxiliary acquisition vehicle is located at an initial point,
obtaining K sets of ego vehicle position information acquired by an inertial sensor of the auxiliary acquisition vehicle during K stops and K sets of main sensed data packets acquired by the main acquisition vehicle during K stops, wherein K is greater than a preset number of times
calculating a coordinate transformation matrix between a startup vehicle coordinate system of the main acquisition vehicle and a startup vehicle coordinate system of the auxiliary acquisition vehicle based on the K sets of ego vehicle position information and the K sets of main sensed data packets,
Hou teaches,
in an initial state, each of a main acquisition vehicle and an auxiliary acquisition vehicle is located at an initial point (see at least [0004] The at least one processor may obtain point-cloud data acquired by one or more sensors associated with a subject during a time period, the point-cloud data being associated with an initial position of the subject, [0011] based on the registered point-cloud data, a local map associated with the initial position of the subject, the at least one processor may generate the local map by projecting the registered point-cloud data on a plane in a third coordinate system, )
obtaining K sets of ego vehicle position information acquired by an inertial sensor of the auxiliary acquisition vehicle during K stops and K sets of main sensed data packets acquired by the main acquisition vehicle during K stops, wherein K is greater than a preset number of times (Fig. 5, Fig. 6, Fig. 7, [0005] the each group of the plurality of groups may correspond to a time stamp. To obtain the pose data of the subject corresponding to the each group of the plurality of groups of the point-cloud data, the at least one processor may determine, based on the time stamp, the pose data of the subject corresponding to the each group of the plurality of groups of the point-cloud data., [0036] the systems and methods may obtain point-cloud data associated with an initial position of the subject during a time period from one or more sensors (e.g., a LiDAR, a Global Positioning System (GPS) receiver, one or more (Inertial Measurement Unit) IMU sensors) associated with the vehicle, [0040] The IMU sensor may be configured to sense position and orientation changes of the vehicle(s) 110 based on various inertial sensors. By combining the GPS device and the IMU sensor, the sensor 112 can provide real-time pose information of the vehicle(s) 110 as it travels, including the positions and orientations (e.g., Euler angles) of the vehicle(s) 110 at each time point, and [0100] The processing engine 122 (e.g., the obtaining module 410, the point-cloud data obtaining unit 410-1) may obtain point-cloud data acquired by one or more sensors associated with a subject (e.g., the vehicle(s) 110) via scanning a space one time around a current location of a subject as described in connection with operation 510. The point-cloud data may be associated with the current position of the subject (e.g., the vehicle(s) 110). In some embodiments, the subject may be moving when the one or more sensors (e.g., LiDAR) perform the scan. The current position of the subject may refer to a position that the subject locates when the one or more sensors (e.g., LiDAR) fulfill the scan. Details of operation 710 may be the same as or similar to operation 510 as described in FIG. 5, and [0101] the point-cloud data may be divided into a plurality of packets (or groups), for example, Packet 1, Packet 2, … Packet N. Each of the plurality of packets may correspond to a first time stamp).
calculating a coordinate transformation matrix between a startup vehicle coordinate system of the main acquisition vehicle and a startup vehicle coordinate system of the auxiliary acquisition vehicle based on the K sets of ego vehicle position information and the K sets of main sensed data packets (see at least [0009] to transform, based on the pose data of the subject, the each group of the plurality of groups of the point-cloud data from the first coordinate system associated with the subject into the second coordinate system, the at least one processor may determine, based on the pose data of the subject corresponding to the each group of the plurality of groups of the point-cloud data, one or more transform models, [0010] the one or more transform models may include at least one of a translation transformation model or a rotation transformation model, [0059] For example, the registering module 420 may register the each group of the plurality of groups of the point-cloud data by transforming the each group of the plurality of groups of the point-cloud data into the same coordinate system (i.e., the second coordinate system) based on one or more transform models (e.g., a rotation model (or matrix), a translation model (or matrix), and [0085] the processing engine 122 may register the each group of the plurality of groups of the point-cloud data by transforming the each group of the plurality of groups of the point-cloud data into the same coordinate system (i.e., the second coordinate system) based on one or more transform models.)
Thus, Shida discloses a method for determining a vehicle three-dimensional coordinate true value and Hou teaches creating a local map associated with the initial position of a subject using registered point cloud data.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Shida with the generation based on the registered point-cloud data, a local map associated with the initial position of the subject as taught by Hou, with a reasonable expectation of success, to provide systems and methods for generating the HD map (also referred to as a local map) for positioning the vehicle in real-time more accurately [0003]).
Shida does not disclose,
when the auxiliary acquisition vehicle travels forward, the main acquisition vehicle is stationary
wherein the startup vehicle coordinate system refers to a vehicle coordinate system that uses a position, where an on-board computer of the vehicle is started up, as a coordinate system origin; and
calculating projection coordinate information of the auxiliary acquisition vehicle in the vehicle coordinate system of the main acquisition vehicle based on the coordinate transformation matrix and ego vehicle position information acquired by the inertial sensor of the auxiliary acquisition vehicle at the preset distance position; and
taking the projection coordinate information as a three-dimensional coordinate true value of the auxiliary acquisition vehicle, wherein a distance between the preset distance position and the main acquisition vehicle is greater than a preset distance threshold
Lv teaches,
when the auxiliary acquisition vehicle travels forward, the main acquisition vehicle is stationary (see at least [Page 3, Col. 1] One LiDAR sensor is set as the reference LiDAR (RL), which means the location of this LiDAR is fixed. Another LiDAR sensor is set as the matching LiDAR (ML), which means the location of LiDAR can be adjusted in the space.
wherein the startup vehicle coordinate system refers to a vehicle coordinate system that uses a position, where an on-board computer of the vehicle is started up, as a coordinate system origin; (See at least Figure 4 - the Reference LIDAR is using the GPS as a coordinate system origin (Figure 1 below)).
PNG
media_image1.png
383
421
media_image1.png
Greyscale
Figure 1: Optimization with Ground Surface (Fig. 4 of Lv),
calculating projection coordinate information of the auxiliary acquisition vehicle in the vehicle coordinate system of the main acquisition vehicle based on the coordinate transformation matrix and ego vehicle position information acquired by the inertial sensor of the auxiliary acquisition vehicle at the preset distance position; (Lv teaches point registration method for roadside (stationary) LIDAR and automatic data integration with an optimizing algorithm. See at least [Page 2, Col 1] By deploying several LiDAR sensors on the road (analogous to the stationary LIDAR in the current application), the detection range of the whole roadside LiDAR system can be extended. Since the LiDAR reports the XYZ in Cartesian coordinates (converted from spherical coordinates) with the location of LiDAR as the origin, it is necessary to integrate the point clouds from different LiDAR sensors, Fig. 4, [Page4, Col 2] If there are more than two LiDAR sensors in the space, the algorithm first integrates two LiDAR sensors and considered the integrated two LiDAR sensors as the new RL and pick up another sensor close to RL as ML for further integration.
taking the projection coordinate information as a three-dimensional coordinate true value of the auxiliary acquisition vehicle, wherein a distance between the preset distance position and the main acquisition vehicle is greater than a preset distance threshold (See at least Fig. 2. Lv uses ground point extraction to match and integrate the point cloud. Therefore, for simplicity, Lv describes relative location in 2D. Teaching of Lv is not limited to 2D location (Figure 2 below)).
PNG
media_image2.png
362
412
media_image2.png
Greyscale
Figure 2: Relative Location of RL and ML (Fig. 2 of Lv)
Thus, Shida discloses a method for determining a vehicle three-dimensional coordinate true value and Lv teaches revolution and rotation based method for data integration of LiDAR data using one fixed (stationary).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Shida with the generation based on the registered point-cloud data, a local map associated with the initial position of the subject as taught by Lv, with a reasonable expectation of success, to extend the detection range of the LiDAR system (Page. 2, Col. 1 line 8-10).
As per Claim 7 and 14,
Shida does not disclose,
The method according to claim 1, wherein subsequent to the taking the projection coordinate information as a three-dimensional coordinate true value of the auxiliary acquisition vehicle, the method further comprises:
calculating, based on the projection coordinate information and three-dimensional coordinate information of each sensed vehicle in an auxiliary sensed data packet acquired by the vehicle-side sensor of the auxiliary acquisition vehicle at the preset distance position, a three-dimensional coordinate true value of the sensed vehicle in the vehicle coordinate system of the main acquisition vehicle.
Lv teaches,
wherein subsequent to the taking the projection coordinate information as a three-dimensional coordinate true value of the auxiliary acquisition vehicle, the method further comprises:
calculating, based on the projection coordinate information and three-dimensional coordinate information of each sensed vehicle in an auxiliary sensed data packet acquired by the vehicle-side sensor of the auxiliary acquisition vehicle at the preset distance position, a three-dimensional coordinate true value of the sensed vehicle in the vehicle coordinate system of the main acquisition vehicle ( Lv teaches point registration method for roadside (stationary) LIDAR and automatic data integration with an optimizing algorithm. See at least Figure 2 and [Page 2, Col 1] By deploying several LiDAR sensors on the road (analogous to the stationary LIDAR in the current application), the detection range of the whole roadside LiDAR system can be extended. Since the LiDAR reports the XYZ in Cartesian coordinates (converted from spherical coordinates) with the location of LiDAR as the origin, it is necessary to integrate the point clouds from different LiDAR sensors, Fig. 4, [Page4, Col 2] If there are more than two LiDAR sensors in the space, the algorithm first integrates two LiDAR sensors and considered the integrated two LiDAR sensors as the new RL and pick up another sensor close to RL as ML for further integration ).
Thus, Shida discloses a method for determining a vehicle three-dimensional coordinate true value and Lv teaches revolution and rotation based method for data integration of LiDAR data using one fixed (stationary).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Shida with the generation based on the registered point-cloud data, a local map associated with the initial position of the subject as taught by Lv, with a reasonable expectation of success, to extend the detection range of the LiDAR system (Page. 2, Col. 1 line 8-10).
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Shida, Hou, and Lv, as applied to Claim 1 above, and further in view of Xiaowei Lan et. al. (NPL 3D Point Cloud Stitching for Object Detection with Wide FoV using Roadside LiDAR, Electronics 2023, 12, 703) (“Lan”).
As per Claim 3 and 10,
Shida does not disclose,
wherein the calculating projection coordinate information of the auxiliary acquisition vehicle in the vehicle coordinate system of the main acquisition vehicle based on the coordinate transformation matrix and ego vehicle position information acquired by the inertial sensor of the auxiliary acquisition vehicle at the preset distance position comprises:
calculating the projection coordinate information of the auxiliary acquisition vehicle in the vehicle coordinate system of the main acquisition vehicle by left multiplying the coordinate transformation matrix by the ego vehicle position information acquired by the inertial sensor of the auxiliary acquisition vehicle at the preset distance position
Lan teaches,
wherein the calculating projection coordinate information of the auxiliary acquisition vehicle in the vehicle coordinate system of the main acquisition vehicle based on the coordinate transformation matrix and ego vehicle position information acquired by the inertial sensor of the auxiliary acquisition vehicle at the preset distance position comprises:
calculating the projection coordinate information of the auxiliary acquisition vehicle in the vehicle coordinate system of the main acquisition vehicle by left multiplying the coordinate transformation matrix by the ego vehicle position information acquired by the inertial sensor of the auxiliary acquisition vehicle at the preset distance position (see at least [Page 6, Section 3.2] Forward detection refers to processing the point clouds in front of the LiDAR then inputting the network to obtain results, The formular for the point transformation is as follows:
PNG
media_image3.png
99
393
media_image3.png
Greyscale
where (x, y, z) is the location before transform, (x’, y’, z’) represents the location after the transform, and θ is the angle of rotation around the Z-axis. Due to all the x-coordinate values of points in this range being less than 0, forward detection is not required to develop in duplicate)
Thus, Shida discloses a method for determining a vehicle three-dimensional coordinate true value and Lan teaches 3D point cloud stitching to link and merge different frames based on the sensor motion.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Shida with the stitching the LiDAR data using sensor motion as taught by Lan, with a reasonable expectation of success, to obtain long-term trajectory data of all road users and serve autonomous vehicles (Page 1, Section 1) by linking and merging different frames based on the sensor motion for LIDAR data stitching and mapping to expand the map for vehicle navigation (Page 3, Section 2.2).
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Shida, Hou, and Lv, as applied to Claim 1 above, and further in view of Yao Hu et. al. US20220404506 (“Hu”)
As per Claim 6 and 13,
Shida does not disclose,
wherein subsequent to the taking the projection coordinate information as a three-dimensional coordinate true value of the auxiliary acquisition vehicle, the method further comprises:
receiving an adjustment to a vehicle height in the three-dimensional coordinate true value of the auxiliary acquisition vehicle to obtain an adjusted three-dimensional coordinate true value of the auxiliary acquisition vehicle.
Hu teaches,
wherein subsequent to the taking the projection coordinate information as a three-dimensional coordinate true value of the auxiliary acquisition vehicle, the method further comprises:
receiving an adjustment to a vehicle height in the three-dimensional coordinate true value of the auxiliary acquisition vehicle to obtain an adjusted three-dimensional coordinate true value of the auxiliary acquisition vehicle (see at least [0017] a LIDAR-to-LIDAR alignment validation method is provided and includes: aggregating first points of data and the second points of data in a vehicle coordinate system to provide aggregated LIDAR points, where the first points of data are provided based on an output of a first LIDAR sensor, and the second points of data are provided based on an output of a second LIDAR sensor; based on the aggregated LIDAR points, at least one of (i) performing a first method including determining pitch and roll differences between the first LIDAR sensor and the second LIDAR sensor, (ii) performing a second method including determining a yaw difference between the first LIDAR sensor and the second LIDAR sensor, or (iii) performing point cloud registration to determine rotation and translation differences between the first LIDAR sensor and the second LIDAR sensor; based on results of at least one of the first method, the second method or the point cloud registration, determining whether the one or more alignment conditions are satisfied; and in response to the one or more alignment conditions not being satisfied, recalibrating at least one of the first LIDAR sensor or the second LIDAR sensor, [0049] Each of the LIDAR sensors may be mounted in a respective location on a vehicle and have a respective orientation. Since the LIDAR sensors are in different locations and may be in different orientations, the LIDAR sensors may report different values for a same detected object. The LIDAR alignment module 302 performs a LIDAR-to-vehicle transform for each LIDAR sensor and provides six alignment parameters 308 for one or more LIDAR sensors that represent roll, pitch, yaw, x, y and z transform values. The parameters may be represented as a matrix (e.g., a 4×4 matrix).).
Thus, Shida discloses a method for determining a vehicle three-dimensional coordinate true value and Hu teaches real time LIDAR to LIDAR and LIDAR to Vehicle alignment of point cloud data.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Shida with the alignment method taught by Hu, with a reasonable expectation of success, to provide a resultant six parameter vector based on which a determination of alignment is made (0038).
Allowable Subject Matter
Claims 2, 4-5, 9, 11-12 are 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicants should take note of the prior art in the PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHUTOSH PANDE whose telephone number is (571)272-6269. The examiner can normally be reached Monday -Friday 9:00am -5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey Jabr can be reached at 5712721516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.P./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
Prosecution Insights