Prosecution Insights
Last updated: April 19, 2026
Application No. 18/746,871

BUILDING MODELING METHOD USING AERIAL LIDAR AND COMPUTER PROGRAM RECORDED ON RECORDING MEDIUM TO EXECUTE THE SAME

Non-Final OA §101§102§103
Filed
Jun 18, 2024
Examiner
NGUYEN, HAU H
Art Unit
2611
Tech Center
2600 — Communications
Assignee
Mobiltech
OA Round
1 (Non-Final)
90%
Grant Probability
Favorable
1-2
OA Rounds
2y 9m
To Grant
99%
With Interview

Examiner Intelligence

Grants 90% — above average
90%
Career Allow Rate
807 granted / 892 resolved
+28.5% vs TC avg
Moderate +9% lift
Without
With
+8.9%
Interview Lift
resolved cases with interview
Typical timeline
2y 9m
Avg Prosecution
22 currently pending
Career history
914
Total Applications
across all art units

Statute-Specific Performance

§101
5.5%
-34.5% vs TC avg
§103
58.0%
+18.0% vs TC avg
§102
19.2%
-20.8% vs TC avg
§112
3.8%
-36.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 892 resolved cases

Office Action

§101 §102 §103
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 . Information Disclosure Statement The information disclosure statement (IDS) submitted on 06/18/2024 was filed after the mailing date of the application. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 101 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 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 10 recites: A computer program recorded on a recording medium combined with a computing device including a memory; a transceiver; and a processor processing an instruction loaded in the memory to execute separating, by the processor, a point cloud corresponding to a ground and a point cloud corresponding to a non-ground from aerial point cloud data acquired from a LiDAR mounted on a flight device; classifying, by the processor, a point cloud corresponding to a roof of a building among the point clouds corresponding to the non-ground; and modeling, by the processor, the building based on the classified point cloud. MPEP 2106 section III provide a flowchart for subject matter eligibility analysis below: PNG media_image1.png 930 645 media_image1.png Greyscale As shown in the flowchart, Step 1 relates to the statutory categories and ensures that the first criterion is met by confirming that the claim falls within one of the four statutory categories of invention (See MPEP § 2106.03). Therefore, claim 10, which recites a computer program per se. MPEP 2106.03 states that “products that do not have a physical or tangible form, such as information (often referred to as “data per se”) or a computer program per se (often referred to as “software per se”) when claimed as a product without any structural recitations” are not directed to any of the statutory categories. Therfore, claim 10 fails step 1 for eligible subject matter. Claim Rejections - 35 USC § 102 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)(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. Claims 1, 4, 7, and 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Moreno et al. (US. Patent App. Pub. No. 2019/0279420, “Moreno”, hereinafter). As per claim 1, as shown in Fig. 2, Moreno teaches a building modeling method comprising: separating, by a data generating device, a point cloud corresponding to a ground and a point cloud corresponding to a non-ground from aerial point cloud data acquired from a LiDAR mounted on a flight device (¶ [48], “…the received LiDAR data can be classified LiDAR data in which data points corresponding to buildings are classified as such, allowing to easily distinguish and isolate them from data points corresponding to other objects, for example to obtain isolated data points 350 corresponding to the building as shown in FIG. 3B”, and at ¶ [43], aerial data can be acquired from air plane, drone, satellite, and/or any other flying or orbiting device ; classifying, by the data generating device, a point cloud corresponding to a roof of a building among the point clouds corresponding to the non-ground (¶ [52], classifying point cloud data into building, vegetation or ground, and further classifying further into roof, etc.); and modeling, by the data generating device, the building based on the classified point cloud (Fig. 6-7, ¶ [58-59], and further ¶ [85]). As per claim 4, Moreno also teaches in the classifying of a point cloud, a point cloud corresponding to the roof of the building (as addressed in claim 1) and a point cloud corresponding to a tree (¶ [48], corresponding to vegetation) among the point clouds corresponding to the non-ground (buildings and vegetation point cloud, ¶ [48]) are classified based on a number of reflected signals reflected from one pulse from the LiDAR (¶ [79], based on reflectivity patterns in LiDAR data points). As per claim 7, wherein, in the modeling, an edge of the roof is estimated based on a distribution of the classified point cloud and an outer point of the classified point cloud is fit based on the estimated edge (¶ [64], “For example, the initial values can be selected by identifying critical points near the external border of the LiDAR point cloud to define segments needed to build the exterior boundary of each roof surface. In an embodiment, identifying the critical points can comprise identifying first and second free end points of intersection lines which need to be connected via exterior roof lines to define a closed roof surface, and identifying points in the LiDAR point cloud proximate to a boundary of the point cloud which are positioned between the first and second free end points”, which defines the edge of the roof). Claim 10 is similar in scope to claim 1 as addressed above, with the exception of the memory, processor, transceiver which is also taught by Moreno as shown in Fig. 1. Claim 10 is thus rejected under the same rationale. 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. 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. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Moreno et al. (US. Patent App. Pub. No. 2019/0279420) in view of Gupta et al. (US. Patent App. Pub. No. 2021/0398300, “Gupta”). As per claim 2, Moreno does not expressly teach in the separating of a point cloud, the aerial point cloud data is voxelized to a preset size and the other points, except for a point closest to a center point, among points in a voxel, is deleted to acquire a uniform sample. However, in a similar method of modeling building as shown in Fig. 1, and ¶ [29], Gupta teaches this feature, i.e., the aerial point cloud data is voxelized to a preset size and the other points, except for a point closest to a center point among points in a voxel, is deleted (¶ [43], “In using the voxel filter, the dataset can be broken into cells of a pre-selected size, for example, but not limited to, 0.25 m. Any downsampling filter can be used, if reducing the number of points in the dataset is desired”, implying deleting data point which can be near the center), to acquire a uniform sample (¶ [43]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method as taught by Gupta into the method as taught by Moreno as addressed above, the advantage of which is to obtain a uniform dataset of aerial and ground data (¶ [4]). Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Moreno et al. (US. Patent App. Pub. No. 2019/0279420) in view of Welty (US. Patent App. Pub. No. 2011/0149267). As per claim 5, Moreno does not expressly teach in the classifying of a point cloud, a point cloud in which a number of reflected signals reflected from one pulse of the LiDAR exceeds a preset value is identified as a tree. However, Welty teaches a method for analyzing a canopy of a forest by analyzing the spatial uniformity of LiDAR data point heights in a number of areas surrounding a tree top (see Abstract), using reflected signals from pulse of LiDAR points to identify trees (¶ [19-20]), and further teaches a number of reflected signals reflected from one pulse of the LiDAR exceeds a preset value is identified as a tree (¶ [4], inherently because “Irregularities in the data that are smaller than the expected tree size are smoothed out to make the analysis easier. The result is that the topological features that are smaller than the expected tree size are purposely ignored”; the data points should be greater than the expected tree size). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the method as taught by Welty and apply to the method as taught by Moreno as addressed above, the advantage of which is to recognize the tree from the reflected LiDAR data points. As per claim 6, as addressed in claim 5, the combined teachings of Moreno and Welty does include in the classifying of a point cloud, a point corresponding to a reflected signal having a signal strength smaller than a preset value, among reflected signals reflected from one pulse of the LiDAR is deleted. Thus, claim 6 would have been obvious over the combined references for the reason above. Allowable Subject Matter Claims 3, and 8-9 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. The following is a statement of reasons for the indication of allowable subject matter: The prior art taken singly or in combination does not teach or suggest, a building modeling method, among other things, comprising: … wherein, in the separating of a point cloud, the filtered aerial point cloud data is divided into a grid smaller than an average distance between points to generate row and column indexes for each point, a height value of the grid is set using an interpolation method when there is no point in the grid, an opening calculation is performed using a window having a preset size, cases before and after the opening calculation is performed are compared based on a height threshold value to separate the point cloud corresponding to the ground and the point cloud corresponding to the non-ground from each other (claim 3); or …wherein, in the modeling, a covariance matrix using neighboring points of each point included in the classified point cloud is obtained, an eigenvector is obtained through principal component analysis (PCA) on the covariance matrix, and two eigenvectors in which an angle therebetween is within a threshold value is estimated as the edge of the roof (claim 8). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hau H. Nguyen whose telephone number is: 571-272-7787. The examiner can normally be reached on MON-FRI from 8:30-5:30. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tammy Goddard, can be reached on (571) 272-7773. The fax 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 http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /HAU H NGUYEN/Primary Examiner, Art Unit 2611
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Prosecution Timeline

Jun 18, 2024
Application Filed
Jan 24, 2026
Non-Final Rejection — §101, §102, §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
90%
Grant Probability
99%
With Interview (+8.9%)
2y 9m
Median Time to Grant
Low
PTA Risk
Based on 892 resolved cases by this examiner. Grant probability derived from career allow rate.

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