DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “point cloud data acquisition module”, “spatial smoothing module”, “pose adjustment module “, “convergence determination module” in claims 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 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.
Claim 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al (T. Ma, B. Xia, Y. Ou, J. Wang and S. Xu, "Robust Second-Order LiDAR Bundle Adjustment Algorithm Using Mean Squared Group Metric," in IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 12831-12844, 2025, doi: 10.1109/TASE.2025.3541199. ) in view of Liu et al (Z. Liu and F. Zhang, "BALM: Bundle Adjustment for Lidar Mapping," in IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3184-3191, April 2021, doi: 10.1109/LRA.2021.3062815)
Regarding claim 1, Ma discloses A system for LiDAR point cloud processing (Section 1- Introduction, Light detection and ranging (LiDAR) is a three dimensional scanning sensor with the continuous-time sampling characteristic.), comprising:
a point cloud data acquisition module configured to receive point cloud data (Section 1 – Introduction, the point cloud will be transformed into the map coordinate system and used to update the point-based map (relative to the feature based map);
a spatial smoothing module configured to generate a fitted surface from the point cloud data using a predetermined kernel size, wherein the fitted surface provides an estimation of locations of points in the point cloud data (Section 2B - Bundle Adjustment, The bundle adjustment algorithm is widely used in 3D reconstruction and SLAM It leverages the co-view constraints of landmarks to construct a joint estimation model for both landmarks and multi-frame poses, allowing for the refinement of the local map.);
a convergence determination module configured to assess a convergence of the pose adjustment by comparing a change in pose adjustment to a threshold value (Section 5B -Dataset experiment, If the initial value provided by the front-end pose is poor, the landmark parameters may not be able to converge to a better state, leading to divergent estimation results.),
wherein if the change exceeds the threshold value, the kernel size is updated, and the system is configured to repeat the spatial smoothing and pose adjustment modules with the updated kernel size (Section 5B – Dataset experiment, RSO-BA algorithm’s robust kernel function which reweights all measurements. The measurements corresponding to flatter plane landmarks have larger weights in the optimization process, which improves estimation accuracy.)
Liu disclsoes a pose adjustment module configured to perform pose adjustment on a LiDAR frame of the point cloud data by calculating an error between points on the fitted surface and corresponding data points in the point cloud data (Section 3A - Direct BA Formulation, This agrees well to our intuition that the 3D point-cloud map (hence the plane or edge features) are determined once the scan poses are known. Moreover, the optimization on the poses T reduces to minimizing the eigen values of the matrix A), and
adjusting a pose of the LiDAR frame to minimize the error (Section 3A - Direct BA Formulation, in case of plane feature, the direct BA formulation is to minimize the summed squared distance from each plane feature point pi,which depends on the pose T)
Ma and Liu are combinable because they are from the same field of invention.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify bundle adjustment of Ma to include a pose adjustment module configured to perform pose adjustment on a LiDAR frame of the point cloud data by calculating an error between points on the fitted surface and corresponding data points in the point cloud data, and adjusting a pose of the LiDAR frame to minimize the error as described by Liu.
The motivation for doing so would have been to lidar bundle adjustment as minimizing the distance from a feature point to its matched edge or plane to greatly reduces the optimization scale and allows large-scale dense plane and edge features to be used. (Liu, Abstract).
Therefore, it would have been obvious to combine Ma and Liu to obtain the invention as specified in claim 1.
Regarding claim 2, Ma discloses wherein the spatial smoothing module is further configured to sample the point cloud data using Principal Component Analysis (PCA) to calculate an initial point normal of the point cloud data (Section 2A – Points cloud registration, Chen and Medioni proposed a point-to-plane registration algorithm. This approach derives surface features by fitting the local point set in the target model using principal component analysis (PCA))
Regarding claim 3, Ma discloses wherein the spatial smoothing module is further configured to obtain an estimation of initial point normals in relation to neighborhood point normals, wherein neighborhood points are located within a distance defined by the kernel size from the initial points (Section 2C – Problem formulation, where the plane landmark is represented by πj= nj,dj T , with nj = nj,x,nj,y,nj,z T as normal vector, dj as the distance from the origin to the plane.)
Regarding claim 4, Ma discloses wherein the spatial smoothing module is further configured to project neighborhood points to a tangent space of a kernel (Section 2C – Explicit Second-Order Estimator, ti ∈ R3 is the translation component of Ti. j = j,1, j,2, j,3 T is the closest point representation[16]of plane landmark πj).
Regarding claim 5, Ma discloses wherein the spatial smoothing module is further configured to determine parameters for a kernel using the projected neighborhood points (Section 2C – Explicit Second-Order Estimator Initially, we ignore the effect of ρ(·) and parameterize the estimated values of the mean square group metric c′ i,j).
Regarding claim 6, Ma discloses wherein the spatial smoothing module is further configured to determine optimal parameters for a polynomial smoothing kernel using least squares estimation, wherein the estimation is based on a Gaussian radial weight function (Section 2B – Bundle Adjustment, The BA problem is solved using an approximate second-order method based on Gauss-Newton.).
Regarding claim 7, Ma discloses wherein the pose adjustment module is further configured to determine a Jacobian of the error (Section 2C – Problem formulation, By calculating the first-order partial derivative of πj with respect to yj,Jk j=∂πj ∂yk can be obtained).
Regarding claim 8, Ma discloses wherein the convergence determination module is configured to update the kernel size by reducing the kernel size (Section 2B – Bundle Adjustment, the error metrics ci,j and cj in (4) compromise scale interpretability due to the use of the group matrix, making it difficult to determine the appropriate threshold for the robust kernel function.).
Regarding claim 9, Ma discloses wherein the convergence determination module is configured to update the kernel size by reducing the kernel size in steps according to a predefined reduction factor or a reduction function (Section 5A – Experimental setup, this algorithm uses QR decomposition to calculate the reduction error vector and the reduction Jacobian matrix.)
Regarding claim 10, Ma discloses wherein the convergence determination module is configured to update the kernel size with a constant (section 6 – Conclusion and Future work, Leveraging the mean square group metric with scale interpretability, the robust BA model can be built using a robust kernel function.).
Regarding claim 11, Ma discloses a method for LiDAR point cloud processing (Section 1- Introduction, Light detection and ranging (LiDAR) is a three dimensional scanning sensor with the continuous-time sampling characteristic.), comprising:
receiving point cloud data (Section 1 – Introduction, the point cloud will be transformed into the map coordinate system and used to update the point-based map (relative to the feature based map);;
generating a fitted surface from the point cloud data using a predetermined kernel size, wherein the fitted surface provides an estimation of locations of points in the point cloud data (Section 2B - Bundle Adjustment, The bundle adjustment algorithm is widely used in 3D reconstruction and SLAM It leverages the co-view constraints of landmarks to construct a joint estimation model for both landmarks and multi-frame poses, allowing for the refinement of the local map.);
performing pose adjustment on a LiDAR frame of the point cloud data by calculating an error between points on the fitted surface and corresponding data points in the point cloud data, and adjusting a pose of the LiDAR frame to minimize the error;
assessing a convergence of the pose adjustment by comparing a change in pose adjustment to a threshold value; and
when the change exceeds the threshold value (Section 5B -Dataset experiment, If the initial value provided by the front-end pose is poor, the landmark parameters may not be able to converge to a better state, leading to divergent estimation results.),
updating the kernel size and re- iterating the fitted surface generation and pose adjustment steps with the updated kernel size (Section 5B – Dataset experiment, RSO-BA algorithm’s robust kernel function which reweights all measurements. The measurements corresponding to flatter plane landmarks have larger weights in the optimization process, which improves estimation accuracy.)
Liu discloses performing pose adjustment on a LiDAR frame of the point cloud data by calculating an error between points on the fitted surface and corresponding data points in the point cloud data, and adjusting a pose of the LiDAR frame to minimize the error (Section 3A - Direct BA Formulation, This agrees well to our intuition that the 3D point-cloud map (hence the plane or edge features) are determined once the scan poses are known. Moreover, the optimization on the poses T reduces to minimizing the eigen values of the matrix A), and
assessing a convergence of the pose adjustment by comparing a change in pose adjustment to a threshold value (Section 3A - Direct BA Formulation, in case of plane feature, the direct BA formulation is to minimize the summed squared distance from each plane feature point pi,which depends on the pose T)
Ma and Liu are combinable because they are from the same field of invention.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify bundle adjustment of Ma to include performing pose adjustment on a LiDAR frame of the point cloud data by calculating an error between points on the fitted surface and corresponding data points in the point cloud data, and adjusting a pose of the LiDAR frame to minimize the error; assessing a convergence of the pose adjustment by comparing a change in pose adjustment to a threshold value as described by Liu.
The motivation for doing so would have been to lidar bundle adjustment as minimizing the distance from a feature point to its matched edge or plane to greatly reduces the optimization scale and allows large-scale dense plane and edge features to be used. (Liu, Abstract).
Therefore, it would have been obvious to combine Ma and Liu to obtain the invention as specified in claim 11.
Regarding claim 12, Ma discloses wherein the step of generating the fitted surface further comprises sampling the point cloud data using Principal Component Analysis (PCA) to calculate an initial point normal of the point cloud data (Section 2A – Points cloud registration, Chen and Medioni proposed a point-to-plane registration algorithm. This approach derives surface features by fitting the local point set in the target model using principal component analysis (PCA))
Regarding claim 13, Ma discloses wherein the step of generating the fitted surface further comprises obtaining an estimation of initial point normals in relation to neighborhood point normals, wherein neighborhood points are located within a distance defined by the kernel size from the initial points (Section 2C – Problem formulation, where the plane landmark is represented by πj= nj,dj T , with nj = nj,x,nj,y,nj,z T as normal vector, dj as the distance from the origin to the plane.)
Regarding claim 14, Ma discloses wherein the step of generating the fitted surface further comprises projecting neighborhood points to a tangent space of a kernel (Section 2C – Explicit Second-Order Estimator, ti ∈ R3 is the translation component of Ti. j = j,1, j,2, j,3 T is the closest point representation[16]of plane landmark πj).
Regarding claim 15, Ma discloses wherein the step of generating the fitted surface further comprises determining parameters for a kernel using the projected neighborhood points (Section 2C – Explicit Second-Order Estimator Initially, we ignore the effect of ρ(·) and parameterize the estimated values of the mean square group metric c′ i,j).
Regarding claim 16, Ma discloses wherein the step of generating the fitted surface further comprises determining optimal parameters for a polynomial smoothing kernel using least squares estimation, wherein the estimation is based on a Gaussian radial weight function (Section 2B – Bundle Adjustment, The BA problem is solved using an approximate second-order method based on Gauss-Newton.).
Regarding claim 17, Ma discloses wherein the step of performing pose adjustment further comprises determining a Jacobian of the error between points on the fitted surface and corresponding data points in the point cloud data (Section 2C – Problem formulation, By calculating the first-order partial derivative of πj with respect to yj,Jk j=∂πj ∂yk can be obtained).
Regarding claim 18, Ma discloses wherein the step of assessing convergence of the pose adjustment further comprises updating the kernel size by reducing the kernel size (Section 2B – Bundle Adjustment, the error metrics ci,j and cj in (4) compromise scale interpretability due to the use of the group matrix, making it difficult to determine the appropriate threshold for the robust kernel function.).
Regarding claim 19, Ma discloses wherein the step of assessing convergence of the pose adjustment further comprises updating the kernel size by reducing the kernel size in steps according to a predefined reduction factor or a reduction function (Section 5A – Experimental setup, this algorithm uses QR decomposition to calculate the reduction error vector and the reduction Jacobian matrix.)
Regarding claim 20, Ma discloses wherein the step of assessing convergence of the pose adjustment further comprises updating the kernel size with a constant (section 6 – Conclusion and Future work, Leveraging the mean square group metric with scale interpretability, the robust BA model can be built using a robust kernel function.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIVANG I PATEL whose telephone number is (571)272-8964. The examiner can normally be reached on M-F 9am-5pm.
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, Alicia Harrington can be reached on (571) 272-2330. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SHIVANG I PATEL/Primary Examiner, Art Unit 2615
