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 Rejections - 35 USC § 101
Below is an analysis of the claims with regards to 35 USC 101.
The claims have been considered with the two-prong analysis of 35 USC 101 to determine the subject matter eligibility.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter:
Answer: YES, the claim is drawn to a process for adaptive slope-based denoising of single-photon LIDAR point clouds.
Step 2A Prong 1: Is the claim directed to a law of nature, a natural phenomenon (product of nature), or an abstract idea:
Answer: YES, the claim is drawn to a mathematical process that denoises single photon LIDAR data including specific steps that pertain to a mathematical algorithm.
Step 2A Prong 2: Does the claim recite additional elements that integrate the the judicial exception into a practical application:
Answer: YES, the claim includes a limitation that removes the accessed point cloud whose reachable distance is greater than the reachable distance threshold as a noise point effectively denoising the point cloud.
The claims qualify as eligible subject matter under 35 USC 101.
Pertaining to claim 10:
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.
Claim 10 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because Claim 10 includes the limitation “a computer program stored in memory” however fails to indicate that the memory is non-transitory memory.
Allowable Subject Matter
Claims 1-9 are allowed.
The following is an examiner’s statement of reasons for allowance: The closes prior art is as follows:
Silva, Carlos Alberto, et al. "Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping." Remote Sensing of Environment 253 (2021): 112234.
Gwenzi, David, et al. "Prospects of the ICESat-2 laser altimetry mission for savanna ecosystem structural studies based on airborne simulation data." ISPRS Journal of Photogrammetry and Remote Sensing 118 (2016): 68-82.
Neuenschwander, Amy, and Katherine Pitts. "The ATL08 land and vegetation product for the ICESat-2 Mission." Remote sensing of environment 221 (2019): 247-259.
(newly cited) Yang, Guangning, et al. "IceSat-2 ATLAS photon-counting receiver: initial on-orbit performance." Advanced photon counting techniques XIII. Vol. 10978. SPIE, 2019.
(newly cited) Si, Shuming, et al. "Multiscale feature fusion for the multistage denoising of airborne single photon LiDAR." Remote Sensing 15.1 (2023): 269.
Are considered the closes prior art of record, however, none of the cited prior art show or render obvious
Claim 1,
A method for adaptive slope-based denoising of single-photon LiDAR point clouds, comprising: determining, for any point cloud data in a point cloud data set, an adaptive slope value of the point cloud data based on an elevation and an along-track distance of each piece of point cloud data within two rectangular regions of equal size on two sides of the point cloud data; selecting any point cloud data in the point cloud data set as point cloud data to be accessed; for the point cloud data to be accessed, constructing a quadrilateral denoising kernel of a preset size with the point cloud data to be accessed as a center; deforming the quadrilateral denoising kernel of the point cloud data to be accessed according to an adaptive slope value of the point cloud data to be accessed to obtain a deformed quadrilateral denoising kernel of the point cloud data to be accessed; scaling the deformed quadrilateral denoising kernel of the point cloud data to be accessed in equal proportion and determining an along-track vertex distance of each piece of point cloud data in the point cloud data set; wherein the along-track vertex distance of the point cloud data is a distance value from an along-track vertex to a center point of a newly generated quadrilateral denoising kernel of the point cloud data, which occurs when any edge of the deformed quadrilateral denoising kernel of the point cloud data to be accessed that is scaled in equal proportion collides with the point cloud data; determining a reachable distance of the point cloud data to be accessed and designating the point cloud data to be accessed as accessed point cloud data; designating point cloud data with a smallest along-track vertex distance as new point cloud data to be accessed, and jumping to the above operation including: for the point cloud data to be accessed, constructing a quadrilateral denoising kernel of a preset size with the point cloud data to be accessed as a center; until all point cloud data in the point cloud data set becomes accessed point cloud data; and sorting the reachable distance of each piece of accessed point cloud data, determining a reachable distance threshold using Otsu's Thresholding (OTSU) algorithm, and removing the accessed point cloud data whose reachable distance is greater than the reachable distance threshold as a noise point.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUKE D RATCLIFFE whose telephone number is (571)272-3110. The examiner can normally be reached M-F 9:00AM-5:00PM EST.
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/LUKE D RATCLIFFE/Primary Examiner, Art Unit 3645