DETAILED ACTION
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 § 101
2. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In view of the new 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register Vol. 84, No. 4, January 7, 2019), the Examiner has considered the claims and has determined that under step 1, claims 1-14 are to a process, claims 15-16 are to a machine, claims 17-18 are to a machine, and claims 19-20 are to an article of manufacture. Next under the new step 2A prong 1 analysis, the claims are considered to determine if they recite an abstract idea (judicial exception) under the following groupings: (a) mathematical concepts, (b) certain methods of organizing human activity, or (c) mental processes. The independent claims contain at least the following bolded limitations (see representative independent claims) that fall into the grouping of mathematical concepts:
1. A method of calibration for determining a relative position and orientation relationship between a LIDAR and a vehicle, the method comprising:
receiving a point cloud determined by the LiDAR from a field of view, the point cloud being in a LIDAR coordinate system;
receiving a set of survey points determined by a survey device from the field of view, the set of survey points being in a survey coordinate system;
determining, based on the point cloud and the set of survey points, a first transformation parameter between the LiDAR coordinate system and the survey coordinate system; and
determining, based on the first transformation parameter and position and orientation information of the vehicle in the survey coordinate system, a calibration parameter between the LiDAR coordinate system and a vehicle coordinate system.
15. A device of calibration for determining a relative position and orientation relationship between a LIDAR and a vehicle, the device comprising:
a data receiver configured to receive a point cloud determined by the LiDAR from a field of view and a set of survey points determined by a survey device from the field of view, wherein the point cloud is in a LIDAR coordinate system, the set of survey points are in a survey coordinate system, and the device is connected to the LIDAR and the survey device; and
a data processor configured to:
determine a first transformation parameter between the LiDAR coordinate system and the survey coordinate system based on the point cloud and the set of survey points, and
determine a calibration parameter between the LiDAR coordinate system and a vehicle coordinate system based on the first transformation parameter and position and orientation information of the vehicle in the survey coordinate system.
17. A system, comprising:
a LiDAR configured to determine a point cloud corresponding to a field of view, the point cloud being in a LIDAR coordinate system;
a survey device configured to determine a set of survey points corresponding to the field of view, the set of survey points being in a survey coordinate system; and
a calibrator device connected to the survey device and the LiDAR, and configured to determine a relative position and orientation relationship between the LiDAR and a vehicle, wherein the calibrator device is further configured to:
receive the point cloud and the set of survey points, to determine a first transformation parameter between the LiDAR coordinate system and the survey coordinate system based on the point cloud and the set of survey points; and
determine a calibration parameter between the LiDAR coordinate system and a vehicle coordinate system based on the first transformation parameter and position and orientation information of the vehicle in the survey coordinate system.
19. A non-transitory computer-readable storage medium storing instructions that when executed by a processor, cause the processor to perform a method comprising:
receiving a point cloud determined by a LiDAR from a field of view, the point cloud being in a LiDAR coordinate system;
receiving a set of survey points determined by a survey device from the field of view, the set of survey points being in a survey coordinate system;
determining, based on the point cloud and the set of survey points, a first transformation parameter between the LiDAR coordinate system and the survey coordinate system; and
determining, based on the first transformation parameter and position and orientation information of a vehicle in the survey coordinate system, a calibration parameter between the LiDAR coordinate system and a vehicle coordinate system.
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula."(see MPEP 2106.04(a)(2) I.). Thus the limitations of “determining a relative position and orientation relationship between a LiDAR and a vehicle”, “determining, based on the point cloud and the set of survey points, a transformation parameter between the LIDAR coordinate system and the survey coordinate system” and “determining, based on the first transformation parameter and position and orientation information of the vehicle in the survey coordinate system, a calibration parameter between the LiDAR coordinate system and a vehicle coordinate system” amount to a description in words of performing mathematical calculations to calculate output variables of relative position and orientation, a transformation parameter, and a calibration parameter. The determining of a relative position and orientation relationship between a LiDAR and a vehicle amounts to solving for numerical coordinate position and angular orientation values between a LiDAR and vehicle. The determination of the first transformation parameter between the LiDAR coordinate system to a survey coordinate system amounts to geometrical processing to convert one set of position data to a different frame of reference, which is just mathematical calculations. The determination of a calibration parameter amounts to further geometrical processing to obtain correction numerical positional values or differences between the LiDAR coordinate system and vehicle coordinate system.
Next in step 2A prong 2, the independent claims are analyzed to determine whether there are additional elements or combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception such that it is more than a drafting effort designed to monopolize the exception, in order to integrate the judicial exception into a practical application. These limitations have been identified and underlined above, and are not indicative of integration into a practical application because: (1) “a device of calibration...the device comprising:”, “the device is connected to the LiDAR and the survey device,” “a data processor configured to:”, “a system, comprising: a calibrator device connected to the survey device and the LiDAR, and configured to determine”, and “a non-transitory computer-readable storage medium storing instructions that when executed by a processor, cause the processor to perform a method comprising”, are limitations that amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); and (2) “receiving a point cloud determined by the LiDAR from a field of view, the point cloud being in a LiDAR coordinate system”, “receiving a set of survey points determined by a survey device from the field of view, the set of survey points being in a survey coordinate system”, “ a data receiver configured to receive a point cloud determined by the LiDAR from a field of view and a set of survey points determined by a survey device from the field of view, wherein the point cloud is in a LiDAR coordinate system, the set of survey points are in a survey coordinate system,” “a LiDAR configured to determine a point cloud corresponding to a field of view, the point cloud being in a LIDAR coordinate system,” and “a survey device configured to determine a set of survey points corresponding to the field of view, the set of survey points being in a survey coordinate system” are limitations that amount to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)).
Next in step 2B, the independent claims are considered to determine if they recite additional elements that amount to an inventive concept (“significantly more”) than the recited judicial exception. The recitations for “a device of calibration...the device comprising:”, “the device is connected to the LiDAR and the survey device,” “a data processor configured to:”, “a system, comprising: a calibrator device connected to the survey device and the LiDAR, and configured to determine”, and “a non-transitory computer-readable storage medium storing instructions that when executed by a processor, cause the processor to perform a method comprising” do not add significantly more because they
amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). The use of generic connected computer equipment is considered insignificant additional elements. As recited in the MPEP, 2106.07(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection (see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94). The limitations of “receiving a point cloud determined by the LiDAR from a field of view, the point cloud being in a LiDAR coordinate system”, “receiving a set of survey points determined by a survey device from the field of view, the set of survey points being in a survey coordinate system”, “ a data receiver configured to receive a point cloud determined by the LiDAR from a field of view and a set of survey points determined by a survey device from the field of view, wherein the point cloud is in a LiDAR coordinate system, the set of survey points are in a survey coordinate system,” “a LiDAR configured to determine a point cloud corresponding to a field of view, the point cloud being in a LIDAR coordinate system,” and “a survey device configured to determine a set of survey points corresponding to the field of view, the set of survey points being in a survey coordinate system” do not add significantly more because they describe necessary data gathering, and do not describe any gathering of data in an unconventional physical measurement arrangement.
Dependent claims 2-14, 16, and 20 contain additional limitations that fall under the abstract idea grouping of mathematical concepts to describe additional calculations and numerical processing techniques to calculate additional variables and data. Dependent claim 18 describes the general environment of which the abstract idea data calculations pertain to, and does not provide an integration into a practical application or significantly more because such limitations amount to generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)) without affecting any subsequent change in the technological environment or field of use.
3. An invention is not rendered ineligible for patent simply because it involves an abstract concept. Applications of such concepts "to a new and useful end" remain eligible for patent protection (see Alice Corp., 134 S. Ct. at 2354 (quoting Benson, 409 U.S. at 67)). However, "a claim for a new abstract idea is still an abstract idea" (see Synopsys v. Mentor Graphics Corp. _F.3d_, 120 U.S.P.Q. 2d1473 (Fed. Cir. 2016)). There needs to be additional elements or combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception or render the claim as a whole to be significantly more than the exception itself in order to demonstrate “integration into a practical application” or an “inventive concept.” For instance, particular physical arrangements for actively obtaining the sensor data, or further physical applications using the calculated calibration parameter to drive a physical transformation, change in physical operation, or repair/maintenance of a technology or technical process could provide integration into a practical application to demonstrate an improvement to the technology or technical field.
Claim Rejections - 35 USC § 102
4. 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)(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.
5. Claim(s) 1-7 and 15-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang et al. (US Pat. Pub. 2022/0194412, hereinafter “Zhang”).
In regards to claim 1, Zhang teaches a method of calibration for determining a relative position and orientation relationship between a LIDAR and a vehicle (Zhang paragraph [0037] and Fig. 2 teach a method for determining a calibration between two different types of sensors which may in turn provide an indication of whether one of the sensors has moved with respect to the vehicle reference frame, where one of the sensors may be a LiDAR; Zhang paragraph [0028] teaches calibrating a LiDAR sensor’s on-vehicle position and orientation with respect to the vehicle’s coordinate frame), the method comprising:
receiving a point cloud determined by the LiDAR from a field of view, the point cloud being in a LIDAR coordinate system (Zhang paragraph [0052] teaches receiving point cloud data from a first sensor of a LiDAR in a first map layer’s coordinate frame, and paragraph [0040] teaches capturing a pose of the LiDAR unit within the coordinate frame of the LiDAR map layer 204 (in a LiDAR coordinate system));
receiving a set of survey points determined by a survey device from the field of view, the set of survey points being in a survey coordinate system (Zhang paragraphs [0040] and [0053] teach receiving a set of camera image data (survey points) determined by a second sensor camera (survey device), where the image data is in the coordinate frame of a visual map layer (survey coordinate system); Zhang paragraph [0092] teaches where the camera may be a camera array that captures a 360° view (survey) of the surrounding environment);
determining, based on the point cloud and the set of survey points, a first transformation parameter between the LiDAR coordinate system and the survey coordinate system (Zhang Fig. 2 Item 206 and paragraph [0037] teach aligning the two layers of map data (LiDAR + visual map) to determine a first known transformation between the coordinate frames of the two layers of map data); and
determining, based on the first transformation parameter and position and orientation information of the vehicle in the survey coordinate system, a calibration parameter between the LiDAR coordinate system and a vehicle coordinate system (Zhang abstract and paragraph [0002] teach based on the known transformation, determining respective poses for the first sensor and the second sensor in a common coordinate frame, where paragraph [0028] defines the common coordinate frame to be a common vehicle coordinate frame for the purposes of calibration; Zhang paragraph [0055] teaches determining a calibration in the translation and rotation of the first sensor (including the LiDAR sensor) and second sensor with respect to the vehicle’s coordinate frame).
In regards to claim 2, Zhang teaches the method further comprising:
determining the position and orientation information of the vehicle in the survey coordinate system by transformation through a second transformation parameter between the survey coordinate system and the vehicle coordinate system (Zhang paragraph [0031] teaches determining the on-vehicle position and orientation in the camera survey coordinate system by a translation and rotation of each camera (including a second transformation parameter of a second camera) with respect to the vehicle’s coordinate frame); or
predetermining the position and orientation information of the vehicle in the survey coordinate system (Zhang paragraph [0055] teaches where the vehicle’s coordinate frame is known (predetermined)).
In regards to claim 3, Zhang teaches the method wherein determining, based on the point cloud and the set of survey points, the first transformation parameter between the LiDAR coordinate system and the survey coordinate system (Zhang Fig. 2 Item 206 and paragraph [0037] teach aligning the two layers of map data (LiDAR + visual map) to determine a first known transformation between the coordinate frames of the two layers of map data) comprises:
updating the first transformation parameter and determining parameter evaluation information based on a current value of the first transformation parameter, the set of survey points, and a matching point in the point cloud (Zhang paragraphs [0005]-[0006], [0062], and [0078] teach updating the transformation parameters based on comparing a current value of transformation parameters of translation and rotation to a previous determined translation and rotation to incorporate any differences between the set of survey points from the camera to the matching point in the LiDAR point cloud).
In regards to claim 4, Zhang teaches the method wherein updating the first transformation parameter and determining parameter evaluation information based on the current value of the first transformation parameter, the set of survey points, and the matching point in the point cloud (Zhang paragraphs [0005]-[0006], [0062], and [0078] teach updating the transformation parameters based on comparing a current value of transformation parameters of translation and rotation to a previous determined translation and rotation to incorporate any differences between the set of survey points from the camera to the matching point in the LiDAR point cloud) comprises:
determining position and orientation information of the matching point in the survey coordinate system based on the current value of the first transformation parameter and position and orientation information of the matching point in the LiDAR coordinate system (Zhang paragraphs [0004]-[0005] teach performing a comparison (matching) of the respective poses (position and orientation) between the first and second sensors (i.e., between the survey coordinate system to the LiDAR coordinate system)); and
updating the first transformation parameter and determining the parameter evaluation information based on the position and orientation information of the matching point in the survey coordinate system and the set of survey points (Zhang paragraphs [0006], [0062], and [0078] teach performing an updating of the transformation parameters based on any differences in the comparison between the set of survey points from the camera to the matching point in the LiDAR point cloud).
In regards to claim 5, Zhang teaches the wherein determining, based on the point cloud and the set of survey points, the first transformation parameter between the LiDAR coordinate system and the survey coordinate system (Zhang Fig. 2 Item 206 and paragraph [0037] teach aligning the two layers of map data (LiDAR + visual map) to determine a first known transformation between the coordinate frames of the two layers of map data) comprises:
based on a determination that the parameter evaluation information meets an iteration condition, continuing to update the first transformation parameter and the parameter evaluation information until the parameter evaluation information does not meet the iteration condition (Zhang paragraph [0084] teaches continuing to perform refinements in the calibration while an iteration condition of an error in alignment between the two sensors is detected, until the overall error is minimized).
In regards to claim 6, Zhang teaches the calibration further comprising:
determining that the parameter evaluation information meets the iteration condition based on at least one of that a number of updates of the first transformation parameter reaches a first threshold (Zhang paragraph [0077] teaches where the iteration condition of calibration may continue until the series of calibrations occurs over a predetermined number of frames (i.e., number of updates reaches a number of frames)) or that a matching error with an adjacent survey point in the set of survey points is not greater than a second threshold after the matching point is transformed to the survey coordinate system based on the updated first transformation parameter (Zhang paragraph [0084] also teaches an iteration condition of determining whether a matching error is sufficiently minimized (i.e., not greater than a second threshold)).
In regards to claim 7, Zhang teaches the calibration further comprising:
prior to updating the first transformation parameter and determining the parameter evaluation information based on the current value of the first transformation parameter, the set of survey points, and the matching point in the point cloud (Zhang paragraphs [0005]-[0006], [0062], and [0078] teach updating the transformation parameters based on comparing a current value of transformation parameters of translation and rotation to a previous determined translation and rotation to incorporate any differences between the set of survey points from the camera to the matching point in the LiDAR point cloud), filtering the point cloud based on a target region to determine the matching point (Zhang paragraph [0052] teaches performing a scan-matching technique to filter the point cloud data to only localize the pose information of the first sensor as a matching point, where the scan-matching occurs prior to any updating of the first transformation parameter (based on a comparison that occurs later in paragraph [0055]).
In regards to claim 15, Zhang teaches a device of calibration for determining a relative position and orientation relationship between a LIDAR and a vehicle (Zhang abstract, Fig. 2, and paragraph [0037] teach a computing system (device) for implementing a calibration between two different types of sensors which may in turn provide an indication of whether one of the sensors has moved with respect to the vehicle reference frame, where one of the sensors is a LiDAR; Zhang paragraph [0028] teaches calibrating a LiDAR sensor’s on-vehicle position and orientation with respect to the vehicle’s coordinate frame), the device comprising:
a data receiver configured to receive (Zhang paragraph [0049] teaches a computing platform as a data receiver configured to obtain and process captured sensor-data) a point cloud determined by the LiDAR from a field of view and a set of survey points determined by a survey device from the field of view, wherein the point cloud is in a LIDAR coordinate system, the set of survey points are in a survey coordinate system (Zhang paragraph [0052] teaches receiving point cloud data from a first sensor of a LiDAR in a first map layer’s coordinate frame and paragraph [0040] teaches capturing a pose of the LiDAR unit within the coordinate frame of the LiDAR map layer 204 (in a LiDAR coordinate system); Zhang paragraphs [0040] and [0053] teach also receiving a set of camera image data (survey points) determined by a second sensor of a camera (survey device) where the image data is in the coordinate frame of a visual map layer (survey coordinate system)), and Zhang paragraph [0092] teaches where the camera may be a camera array that captures a 360° view (survey) of the surrounding environment), and the device is connected to the LIDAR and the survey device (Zhang Fig. 5 teaches where the computing system is connected to the sensor system 501 including the LiDAR and survey device); and
a data processor configured (Zhang paragraph [0012] teaches a processor as part of the computing system that executes program instructions) to:
determine a first transformation parameter between the LiDAR coordinate system and the survey coordinate system based on the point cloud and the set of survey points (Zhang Fig. 2 Item 206 and paragraph [0037] teach aligning the two layers of map data (LiDAR + visual map) to determine a first known transformation between the coordinate frames of the point cloud LiDAR layer data and camera survey layer map data), and
determine a calibration parameter between the LiDAR coordinate system and a vehicle coordinate system based on the first transformation parameter and position and orientation information of the vehicle in the survey coordinate system (Zhang abstract and paragraph [0002] teach based on the known transformation, determining respective poses for the first sensor and the second sensor in a common coordinate frame, where paragraph [0028] defines the common coordinate frame to be a common vehicle coordinate frame for the purposes of calibration; Zhang paragraph [0055] teaches determining a calibration in the translation and rotation of the first sensor (including the LiDAR sensor) and second sensor with respect to the vehicle’s coordinate frame).
In regards to claim 16, Zhang teaches the device wherein the data processor is further configured to:
determine the position and orientation information of the vehicle in the survey coordinate system by transformation through a second transformation parameter between the survey coordinate system and the vehicle coordinate system (Zhang paragraph [0031] teaches determining the on-vehicle position and orientation in the camera survey coordinate system by a translation and rotation of each camera (including a second transformation parameter of a second camera) with respect to the vehicle’s coordinate frame); or
predetermine the position and orientation information of the vehicle in the survey coordinate system (Zhang paragraph [0055] teaches where the vehicle’s coordinate frame is known (predetermined)).
In regards to claim 17, Zhang teaches a system (Zhang abstract and Fig. 2 teach a computing system), comprising:
a LiDAR configured to determine a point cloud corresponding to a field of view, the point cloud being in a LIDAR coordinate system (Zhang paragraph [0052] teaches a first sensor of a LiDAR for capturing point cloud data corresponding to a field of view, where the point cloud data is localized in a first map layer’s coordinate frame, and paragraph [0040] teaches capturing a pose of the LiDAR unit within the coordinate frame of the LiDAR map layer 204 (in a LiDAR coordinate system)) ;
a survey device configured to determine a set of survey points corresponding to the field of view, the set of survey points being in a survey coordinate system (Zhang paragraphs [0040] and [0053] teach a second sensor of a camera (survey device) configured to determine a set of camera image data (survey points) corresponding to the field of view, where the image data is in the coordinate frame of a visual map layer (survey coordinate system); Zhang paragraph [0092] teaches where the camera may be a camera array that captures a 360° view (survey) of the surrounding environment); and
a calibrator device connected to the survey device and the LiDAR (Zhang Fig. 5 teaches a calibrator on-board computing system connected to the sensor system (including the camera survey device and LiDAR), and configured to determine a relative position and orientation relationship between the LiDAR and a vehicle (Zhang abstract, Fig. 2, and paragraph [0037] teach the computing system implementing a calibration between two different types of sensors which may in turn provide an indication of whether one of the sensors has moved with respect to the vehicle reference frame, where one of the sensors is a LiDAR; Zhang paragraph [0028] teaches calibrating a LiDAR sensor’s on-vehicle position and orientation with respect to the vehicle’s coordinate frame), wherein the calibrator device is further configured to:
receive the point cloud and the set of survey points, to determine a first transformation parameter between the LiDAR coordinate system and the survey coordinate system based on the point cloud and the set of survey points (Zhang Fig. 2 Item 206 and paragraph [0037] teach aligning the two layers of map data (LiDAR + visual map) to determine a first known transformation between the coordinate frames of the point cloud LiDAR layer data and camera survey layer map data); and
determine a calibration parameter between the LiDAR coordinate system and a vehicle coordinate system based on the first transformation parameter and position and orientation information of the vehicle in the survey coordinate system (Zhang abstract and paragraph [0002] teach based on the known transformation, determining respective poses for the first sensor and the second sensor in a common coordinate frame, where paragraph [0028] defines the common coordinate frame to be a common vehicle coordinate frame for the purposes of calibration; Zhang paragraph [0055] teaches determining a calibration in the translation and rotation of the first sensor (including the LiDAR sensor) and second sensor with respect to the vehicle’s coordinate frame).
In regards to claim 18, Zhang teaches the system wherein a reference object is in the field of view, a surface of the reference object comprises a first region and a second region, and a reflectivity of the first region is different from a reflectivity of the second region (Zhang Fig. 1B and paragraph [0033] teaches where a reference object of a checkerboard target is in the field of view, where a checkerboard pattern comprises alternating first region and second regions having different reflectivities).
In regards to claim 19, Zhang teaches a non-transitory computer-readable storage medium storing instructions that when executed by a processor, cause the processor to perform a method (Zhang paragraph [0012] teaches a non-transitory computer-readable medium storing program instructions that are executed ty a processor to perform a method) comprising:
receiving a point cloud determined by a LiDAR from a field of view, the point cloud being in a LiDAR coordinate system (Zhang paragraph [0052] teaches receiving point cloud data from a first sensor of a LiDAR in a first map layer’s coordinate frame, and paragraph [0040] teaches capturing a pose of the LiDAR unit within the coordinate frame of the LiDAR map layer 204 (in a LiDAR coordinate system));
receiving a set of survey points determined by a survey device from the field of view, the set of survey points being in a survey coordinate system (Zhang paragraphs [0040] and [0053] teach receiving a set of camera image data (survey points) determined by a second sensor of a camera (survey device), where the image data is in the coordinate frame of a visual map layer (survey coordinate system); Zhang paragraph [0092] teaches where the camera may be a camera array that captures a 360° view (survey) of the surrounding environment);
determining, based on the point cloud and the set of survey points, a first transformation parameter between the LiDAR coordinate system and the survey coordinate system (Zhang Fig. 2 Item 206 and paragraph [0037] teach aligning the two layers of map data (LiDAR + visual map) to determine a first known transformation between the coordinate frames of the two layers of map data); and
determining, based on the first transformation parameter and position and orientation information of a vehicle in the survey coordinate system, a calibration parameter between the LiDAR coordinate system and a vehicle coordinate system (Zhang abstract and paragraph [0002] teach based on the known transformation, determining respective poses for the first sensor and the second sensor in a common coordinate frame, where paragraph [0028] defines the common coordinate frame to be a common vehicle coordinate frame for the purposes of calibration; Zhang paragraph [0055] teaches determining a calibration in the translation and rotation of the first sensor (including the LiDAR sensor) and second sensor with respect to the vehicle’s coordinate frame).
In regards to claim 20, Zhang teaches the non-transitory computer-readable storage medium wherein the method further comprises:
determining the position and orientation information of the vehicle in the survey coordinate system by transformation through a second transformation parameter between the survey coordinate system and the vehicle coordinate system (Zhang paragraph [0031] teaches determining the on-vehicle position and orientation in the camera survey coordinate system by a translation and rotation of each camera (including a second transformation parameter of a second camera) with respect to the vehicle’s coordinate frame); or
predetermining the position and orientation information of the vehicle in the survey coordinate system (Zhang paragraph [0055] teaches where the vehicle’s coordinate frame is known (predetermined)).
Allowable Subject Matter
6. Claims 8-14 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
7. The following is a statement of reasons for the indication of allowable subject matter:
Claim 8 contains allowable subject matter because the closest prior art, Zhang et al. (US Pat. Pub. 2022/0194412) fails to anticipate or render obvious the method wherein filtering the point cloud based on the target region to determine the matching point comprises: filtering the point cloud based on a first target region to determine a reference plane; filtering the point cloud based on the reference plane to determine a filtered point cloud; and filtering the filtered point cloud based on a second target region to determine the matching point, in combination with the rest of the claim limitations as claimed and defined by the Applicant.
8. Dependent claims 9-14 depend from claim 8 and contain allowable subject matter for at least the same reasons as given for claim 8.
Pertinent Art
9. Applicants are directed to consider additional pertinent prior art included on the Notice of References Cited (PTOL 892) attached herewith. The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the of the passage as taught by the prior art or disclosed by the Examiner. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
B. Nabatchian (US Pat. No. 10,859,684) discloses Method and System for Camera-LiDAR Calibration.
C. Shotan et al. (US Pat. No. 12,085,678) discloses Methods for Localizing Light Detection and Ranging (LIDAR) Calibration Targets.
D. Mou (US Pat. Pub. 2019/0012808) discloses Calibration Verification Methods for Autonomous Vehicle Operations.
E. Zhu et al. (US Pat. Pub. 2019/0392228) discloses Integrated Sensor Calibration in Natural Scenes.
F. Liao et al. (US Pat. Pub. 2021/0109204) discloses Calibration of LiDAR Sensors.
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL D LEE whose telephone number is (571)270-1598. The examiner can normally be reached on M to F, 9:30 am to 6 pm.
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/PAUL D LEE/Primary Examiner, Art Unit 2857 10/14/2026
