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 .
DETAILED ACTION
This office action is in regards to application # 18/780,474 that was filed on 07/23/2024. Claims 1-12 are currently pending and are under examination.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 and 11-12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Martínez et al. (doc. “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation”).
Regarding Claim 1, Martínez discloses a LiDAR detection method, comprising:
obtaining point cloud data corresponding to an i-1th ground line and point cloud data corresponding to an i-2th ground line (Fig. 3, section 2: describes taking scanned lines “an perform processing in each scan line sequentially. The method explicitly uses the final spline of one scan line as an input for the next scan line (abstract Section 2). The ordering of increasing distance is inherent in sequential scan line acquisition and is reinforced by the forward then backward passes described in section 2):, wherein i is an integer greater than or equal to 3;
calculating a predicted height range of an ith ground line based on the point cloud data corresponding to the i-1th ground line and the point cloud data corresponding to the i-2th ground line (after the spline is defined on a given scan line, its knots are propagated and used to initialize the spline on the subsequent scan line(abstract Section 2, section 3.2, ‘points are labeled into ground and nonground by analyzing their residuals to the final spline”. This is precisely a predicted height range derived from the preceding (i-1/i-2) ground-line data; Fig. 7);
determining a target point in scanning points by detecting a scanning point of an ith row of a scanning line based on the predicted height range (classification occurs by comparing each point’s residual(height deviation) to the propagated flying model of the preceding line (sections, 2, 3.2 ). Points whose residuals fall within an acceptable tolerance of the predicted surface are labeled ground points (i.e. the claimed ‘target point’), Fig. 3); and
obtaining the ith ground line based on the target point, wherein a distance between the ground line and the LiDAR increases from the i-2th ground line, to the i-1th ground line, and to the ith ground line (Once ground(target points) are identified on the current scan line, they become the refined ground line for the scan line and their knots propagated onward (abstract, section 2, conclusions). The sequential ordering of scan lines inherently satisfies the increasing-distance condition stated in the claim (the farthest scan lines are processed after nearer ones in the forward pass).
Regarding claim 11, the device/apparatus claim 11 is rejected under the same rationale as the rejection of method claim 1 for being an apparatus that is being practiced by the rejected method of claim 1 above.
Regarding claim 12, the LiDAR device/apparatus claim 12 is rejected under the same rationale as the rejection of method claim 1 for being a LiDAR device/apparatus that is being practiced by the rejected method of claim 1 above. Martínez discloses a LiDAR detection method/device with a processor and a memory (Fig. 14), wherein a memory stores a computer program (‘programable logic’, Fig. 14)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Martínez et al. (doc. “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation”).
Regarding Claim 2, Martínez discloses a LiDAR detection method as claimed in claim 1 above.
Claim 2 further requires: traversing a scanning point of the i-1th ground line and a scanning point of the i-2th ground line to obtain K pairs of points, wherein one pair of points comprises a first reference point and a second reference point, the first reference point is a scanning point of the i-1th ground line, the second reference point is a scanning point of the i-2th ground line, and a difference between pitch angles of the first reference point and the second reference point is less than or equal to a threshold, wherein K is a positive integer; and based on heights of the first reference point and the second reference point in the pairs of points, calculating a predicted height range of a first scanning point, wherein the first scanning point is a scanning point of the ith row of the scanning line, and pitch angle differences of the K pairs of points are less than or equal to the threshold, and a scanning point of the ith row of the scanning line other than the first scanning point is a second scanning point; and based on the predicted height range of the first scanning point, calculating a predicted height range of the second scanning point.
Martinez expressly teaches sequential traversal of points across adjacent scan lines and the use of information from prior lines to predict the surface of the current line (abstract, section 2, section 2.4, Fig. 3). Neighbor calculation across adjacent scan lines is described and illustrated in Fig. 4. The propagated spline knots constitute a predicted height surface derived from the heights of corresponding points on prior lines. Selecting pairs of corresponding points whose angular (pitch) difference is small is it an obvious way to identify corresponding reference points on successive scan lines.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to recognized that points with similar pitch angles lie on approximately the same local surface segment. Using the heights of such paired points to compute local predicted high(or height range) for the corresponding points on the next line is a straightforward implementation of the knot-propagation and residual framework of Martinez. Extending the prediction from a ‘first’ scanning point to adjacent ‘second’ scanning points follows directly from the sequential and local nature of the processing.
Regarding Claim 3, modified Martínez discloses a LiDAR detection method as claimed in claim 2 above. Claim 3 further requires:
obtaining a height fluctuation value of K first reference points in the K pairs of points and a gradient value of a point pair corresponding to the first scanning point; and based on the height fluctuation value and the gradient value, calculating the predicted height range of the first scanning point.
Martinez’s iterative spline already incorporates local height variations and slope/gradient information: seed selection, push-down and push-up stages, and residual thresholds inherently account for local height statistics and gradients along the scan line and propagated knots (section 2, Fig. 6 illustrating successive refinement of the spline). Computing an explicit fluctuation (e.g. range of heights) and a gradient from a paired points, then deriving a height range, is a conventional numerical alternative to the spline residual and it would have been an obvious design choice for a person of ordinary skill in the art before the effective filing date of the invention seeking a simple or more local prediction.
Regarding Claim 4, modified Martínez discloses a LiDAR detection method as claimed in claim 2 above. Claim 4 further requires:
determining at least two first scanning points adjacent to the second scanning point on the ith row of the scanning line; and based on the predicted height ranges of the at least two first scanning points adjacent to the second scanning point, calculating the predicted height range of the second scanning point.
Martinez processes points sequentially within each scan line and propagates information from neighboring lines (section 2, Fig. 3, Fig. 4 showing neighbor search). Using the already computed predicted heights of adjacent points on the same line to interpolate or constrain the prediction for the intermediate point is a routine local interpolation step that a person of ordinary skill in the art before the effective filing date of the invention would immediately apply to the sequential framework of Martinez.
Regarding Claim 5, Martínez discloses a LiDAR detection method as claimed in claim 1 above. Claim 5 further requires:
obtaining heights of a plurality of scanning points in a neighborhood window with a scanning point Si,j as a center, wherein j is a positive integer and less than or equal to a total number of the scanning points on the ith row of the scanning line;
comparing the heights of the plurality of scanning points in the neighborhood window with the predicted height range of the scanning point Si,j to determine whether the scanning point Si,j is a ground point; and when the scanning point is the ground point, determining the scanning point as the target point.
Martinez classifies each point by comparing its residual(height deviation) to the predicted spline surface derived from prior line(abstract, section 2). Neighborhood information is already used both within a scan line (iterative refinement) and across adjacent lines (knot propagation and neighbor search, Fig. 4). Evaluating a local window of neighboring heights for consistency within a predicted range is the conventional and obvious implementation of residual based classification. Therefore claim 5 is obvious Martinez.
Regarding Claim 6, modified Martínez discloses a LiDAR detection method as claimed in claim 5 above. Claim 5 further specifies two quantitative conditions:
(i) the height of Si,j lies inside the predicted range and the number of neighboring points also inside the range exceeds the first threshold, and
(ii) the height of Si,j lies outside the range but number of neighboring points inside the range exceeds a second threshold.
This majority count/threshold rules are conventional refinements of residual or consistency checks. Martinez already uses residual thresholds to label points (Section 2). Adding a count of how many neighbors also satisfy the residual criterion is a routine robustness measure a person of ordinary skill in the art before the effective filing date of the invention would have found obvious to improve reliability in noisy data, especially given Martinez’s own emphasis on improving reliability in complex scenes via inter-line information (section 2.4).
Regarding Claim 7, Martínez discloses a LiDAR detection method as claimed in claim 1 above. Claim 7 further requires:
performing fitting on the target point Si,q to obtain a fitted height, and obtaining the ith ground line based on the fitted height.
Martinez explicitly performs iterative fitting(Spline interpolation) on the points of each scanline and uses the refined(fitted) surface to define the ground line for that scan line before propagating it onward (abstract, section 2, Figs. 5-6). Linear or other local fitting of selected target/ground is well-known alternative to spline fitting. A person of ordinary skill in the art before the effective filing date of the invention would have regarded it as an obvious substitution yielding predictable results.
Regarding Claim 8, Martínez discloses a LiDAR detection method as claimed in claim 7 above. Claim 7 further requires:
based on the fitted height of the target point Si,q, determining a fitted height range of the scanning point of the ith row of the scanning line; and
using the fitted height range of the scanning point of the ith row of the scanning line as the predicted height range and counting, and returning to perform the step of detecting the scanning point of the ith row of the scanning line based on the predicted height range and determining the target point in the scanning point until a value of the counting is greater than a preset number.
Martinez already performs iterative refinement of the surface on each scan line (push-down and pus- up stages) and propagates the refined knots to subsequent lines (section 2, Fig. 3, Fig. 6). Re-using newly fitted height range as a predicted range for a subsequent pass, together with simple iteration counter, is a conventional iterative control structure that a person of ordinary skill in the art before the effective filing date of the invention would have found obvious to apply to the sequential framework of Martinez in order to improve accuracy.
Regarding Claim 9, modified Martínez discloses a LiDAR detection method as claimed in claim 7 above. Claim 7 further requires:
obtaining fitted heights of April to have target points in a neighborhood window centered on Si,q, computing height fluctuation value from those fitted heights, and determining fitted height ranges for both the target point and non-target points.
Martinez’s iteratives spline already operates on local neighborhoods of points and accounts for height variation when adding or adjusting knots (section 2, Fig. 6). Computing an explicit fluctuation statistic over a window of fitted heights and deriving local ranges is a conventional post processing step that a person of ordinary skill in the art before the effective filing date of the invention would have regarded an obvious implementation detail.
Regarding Claim 10, modified Martínez discloses a LiDAR detection method as claimed in claim 7 above. Claim 10 further requires:
obtaining heights of a plurality of target points in a neighborhood window centered on Si,q and performing linear fitting on those heights to obtain the fitted height of Si,q.
Martinez performs spline (piecewise polynomial) fitting. Linear fitting over local window is a simpler, well-known alternative that a person of ordinary skill in the before the effective filing date of the invention would have found obvious to substitute when computational simplicity is desired, especially given Martinez’s own emphasis on efficiency and real time performance (abstract, section 2, conclusion)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892.
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Assres H. Woldemaryam
Primary Examiner (Aeronautics and Astronautics)
Art Unit 3642
/ASSRES H WOLDEMARYAM/Primary Examiner, Art Unit 3642
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