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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/22/2026 has been entered.
Response to Arguments
Applicant's arguments filed June 22, 2026 with respect to claims 1, 3, 4, 6 – 8, 10 – 12, 14, 15, 17, 18 and 20 – 22 have been considered but are moot because the new grounds of rejection do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 (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 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.
Claims 1, 3, 4, 11– 12, 14, 15, 17 and 18 are rejected under 35 U.S.C 103 as being unpatentable over by Hildebrand et al. ‘SIMULATING LIDAR TO CREATE TRAINING DATA FOR MACHINE LEARNING ON 3D POINT CLOUDS’ (hereinafter Hildebrand) in view of Frank ‘Distance-Field Based Skeletons for Virtual Navigation’ (hereinafter Frank), Qian ‘Straight Skeleton Based Automatic Generation of Hierarchical Topological Map in Indoor Environment’ (hereinafter Qian) and Huang Patent Application Publication No. WO-2021022615-A1 (hereinafter Huang).
Regarding claim 1, Hildebrand discloses about a computer-implemented method for generating a training dataset having training patterns each including a 3D point cloud of a respective travelable environment (Hildebrand in [Introduction; Paragraph – 2] discloses about training dataset, “In this paper, we present a method to automate the process of generating semantically enriched 3D point clouds that are suitable for training”. Additionally, Hildebrand in [Section – 3.1; Paragraph – 1] discloses about travelable environment, “The 3D model is created in Blender. The general structures – walls, windows, doors, stairs – are modeled”), the method comprising, for each 3D point cloud: obtaining a 3D surface representation of the respective travelable environment (Hildebrand in [Section – 3.1; Paragraph – 1] discloses, “The 3D model is created in Blender. The general structures – walls, windows, doors, stairs – are modeled”); determining a traveling path inside the respective travelable environment (Hildebrand in [Section – 2.3; Paragraph – 2] discloses, “All scanner types can be attached to a user-defined path to define the movement of the scanner during the scan simulation. This movement, as well as the right rotational patterns...”); and generating a virtual scan of the respective travelable environment along the traveling path, thereby obtaining the 3D point cloud (Hildebrand in [Section – 2.1; Paragraph – 1] discloses, “After the ray cast, the vLiDAR scanner is rotated and moved according to the scanner’s rotation and movement speeds, before conducting the next ray cast. By repeatedly sampling the scene in this manner, a realistic 3D point cloud representing the scene is created”), circulation area (Hildebrand in [Introduction; Paragraph – 4] discloses, “we consider DFB values only for those voxels within a navigable region inside the virtual environment, where we can navigate by manipulating the virtual camera”).
Hildebrand doesn’t disclose about the following limitation as further recited in the claim.
Frank discloses about the determining of the traveling path further including: determining a circulation area of the respective travelable environment (Frank in [Introduction; Paragraph – 4] discloses, “we consider DFB values only for those voxels within a navigable region inside the virtual environment, where we can navigate by manipulating the virtual camera” wherein the ‘navigable area’ is the circulation area), determining a positioned graph inside the circulation area, the positioned graph representing a topological skeleton of the circulation area (Frank in [Section – 2.2; Paragraph 1 – 3] discloses, “The first step of our centerline algorithm can be separated into two stages. First, convert the volumetric DFB field into a 3D directed weighted graph ... A minimum-cost spanning tree of our directed graph is defined as a tree that connects all the voxels in the navigable region at the minimum DFB cost”), wherein the positioned graph represents a portion of a medial axis graph of the (Frank in [Abstract] discloses, “our automatic path planning algorithm rapidly generates centered flight paths, a skeleton, in the navigable region of the virtual environment”), and identifying a browsing sequence of points on the positioned graph (Frank in [Section – 2.3; Paragraph 1 – 3] discloses, “we can quickly find the sequence of voxels on the centerline by tracing back from E to S according to the MCS connectivity”);
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Frank into the system of Hildebrand because it would allow the system to determine where a person can actually move distinguishing navigable zones from obstacles.
Hildebrand and Frank doesn’t disclose about the following limitation as further recited in the claim.
Qian discloses the medial axis graph including at least one isolated leaf, the isolated leaf including an end node connected through one or more successive nodes to a crossing node (Qian in [Section – IV (A), Paragraph – 1] discloses, “The degree dvi of the vertex vi(vi ∈ V ) is defined as the number of edges associated with it. The vertex satisfying dvi = 1 or dvi > 2 is defined as an intersection point. E in G is rearranged by combining all the edges between two intersection points into a skeleton segment”. Furthermore, Qian in[Section – V (B), Paragraph – 1] discloses about end node, “branchpoint (degree dn>2) or endpoint (degree dn=1)”. The edges between the endpoint and branch point equates to successive nodes between them), and wherein the determining of the positioned graph includes removing the at least one isolated leaf from the medial axis graph, the removed isolated leaf representing a walkable area of the circulation area (Qian in [Section – IV (A), Paragraph – 1] and [Section – IV (B), Paragraph – 8] discloses about removing isolated leaf, “the skeleton segment si(si ∈ S) whose endpoints coincide with the original polygon is supposed to be pruned by deleting all the edges in si and associated isolated vertices with dv = 0(v ∈ e,e ∈ si)” and “After connection between the endpoint in Gsw linked to the region and the closest point in Gso inside the region, a short dead-end skeleton with with dvi = 1 is pruned. Finally, an HTM M = {Gsw,Gso, Pr} can be obtained”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Qian into the system of Hildebrand in view of Frank because it would allow the system to eliminates irrelevant and filter out unnecessary walkable branches.
Hildebrand, Frank and Qian doesn’t disclose about the following limitation as further recited in the claim.
Huang the identifying of the browsing sequence further including minimizing a traveling distance, such that the path visits each node of the positioned graph at least once, thereby obtaining the traveling path (Huang in [Page – 4, Paragraph – 10] discloses, “after obtaining multiple access nodes, a certain exploration sequence needs to be planned to traverse the multiple access nodes. Optionally, by inputting the position coordinates of multiple visiting nodes into the traveling salesman problem algorithm, and solving them to obtain the optimal exploration path. Optionally, by setting different exploration weights for each access node”. Huang in [Page – 5, Paragraph – 1] discloses about visit each node, “The generated exploration path includes the traversal sequence of each access node”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Huang into the system of Hildebrand in view of Frank and Qian because minimizing travelling distance prevents the scanner from takin unnecessary long routes between those positions.
Summary of Citations (Huang)
[Page – 4, Paragraph – 10]; “after obtaining multiple access nodes, a certain exploration sequence needs to be planned to traverse the multiple access nodes. Optionally, by inputting the position coordinates of multiple visiting nodes into the traveling salesman problem algorithm, and solving them to obtain the optimal exploration path. Optionally, by setting different exploration weights for each access node”.
[Page – 5, Paragraph – 1]; “The generated exploration path includes the traversal sequence of each access node”.
Summary of Citations (Qian)
[Section – IV (A), Paragraph – 1]; “The degree dvi of the vertex vi(vi ∈ V ) is defined as the number of edges associated with it. The vertex satisfying dvi = 1 or dvi > 2 is defined as an intersection point. E in G is rearranged by combining all the edges between two intersection points into a skeleton segment”.
[Section – IV (A), Paragraph – 1]; “the skeleton segment si(si ∈ S) whose endpoints coincide with the original polygon is supposed to be pruned by deleting all the edges in si and associated isolated vertices with dv = 0(v ∈ e,e ∈ si)”.
[Section – IV (B), Paragraph – 8]; “After connection between the endpoint in Gsw linked to the region and the closest point in Gso inside the region, a short dead-end skeleton with with dvi = 1 is pruned. Finally, an HTM M = {Gsw,Gso, Pr} can be obtained”.
[Section – V (B), Paragraph – 1]; “branchpoint (degree dn>2) or endpoint (degree dn=1)”.
Summary of Citations (Frank)
[Abstract]; “our automatic path planning algorithm rapidly generates centered flight paths, a skeleton, in the navigable region of the virtual environment”.
[Introduction; Paragraph – 4]; “we consider DFB values only for those voxels within a navigable region inside the virtual environment, where we can navigate by manipulating the virtual camera”.
[Section – 2.2; Paragraph 1 – 3]; “The first step of our centerline algorithm can be separated into two stages. First, convert the volumetric DFB field into a 3D directed weighted graph ... A minimum-cost spanning tree of our directed graph is defined as a tree that connects all the voxels in the navigable region at the minimum DFB cost”.
[Section – 2.3; Paragraph 1 – 3]; “we can quickly find the sequence of voxels on the centerline by tracing back from E to S according to the MCS connectivity”.
Summary of Citations (Hildebrand)
[Introduction; Paragraph – 2]; “In this paper, we present a method to automate the process of generating semantically enriched 3D point clouds that are suitable for training. 3D models with semantics information are used to simulate a LiDAR scan to produce 3D point clouds that have almost identical properties as in real acquisition campaigns ... be used to classify 3D point clouds of real environments”.
[Introduction; Paragraph – 4]; “we consider DFB values only for those voxels within a navigable region inside the virtual environment, where we can navigate by manipulating the virtual camera”.
[Section – 2; Paragraph – 1]; “a new method to synthesize realistic 3D point clouds containing extensive semantic information”.
[Section – 2.1; Paragraph – 1]; “After the ray cast, the vLiDAR scanner is rotated and moved according to the scanner’s rotation and movement speeds, before conducting the next ray cast. By repeatedly sampling the scene in this manner, a realistic 3D point cloud representing the scene is created”.
[Section – 2.3; Paragraph – 2]; “All scanner types can be attached to a user-defined path to define the movement of the scanner during the scan simulation. This movement, as well as the right rotational patterns...”.
[Section – 3.1; Paragraph – 1]; “The 3D model is created in Blender. The general structures – walls, windows, doors, stairs – are modeled”.
Regarding claims 3 and 4, the combination of Hildebrand, Frank, Qian and Huang as a whole teaches claim 1, and Frank teaches claim 3 and 4 for the same grounds of rejection from the Final Office Action of 04/20/2026.
Regarding claim 11, the combination of Hildebrand, Frank, Qian and Huang as a whole teaches claim 1, and Frank teaches claim 11 for the same grounds of rejection from the Non- Final Office Action of 04/20/2026.
Regarding claim 12, claim 12 is claim 1 except for a method of applying a training dataset for training a neural network taking as input a scan of a real-world environment thus the rejection of claim 1 is incorporated herein. With respect to the addition limitation, reference Hildebrand in [Introduction, Paragraph – 2] discloses, “we present a method to automate the process of generating semantically enriched 3D point clouds that are suitable for training ... Hence, the output data can be used directly for training neural networks. This in turn can be used to classify 3D point clouds of real environments”.
Summary of Citations (Hildebrand)
[Introduction, Paragraph – 2]; “we present a method to automate the process of
generating semantically enriched 3D point clouds that are suitable for training ... Hence, the output data can be used directly for training neural networks. This in turn can be used to classify 3D point clouds of real environments”.
Regarding claim 14, is essentially claim 3 with different dependency therefore rejected under same analysis of claim 3.
Regarding claim 15, apparatus claim 15 corresponds to method claim 12. Therefore, the rejection analysis and motivation to combine of claim 12 is applicable to claim 15.
Regarding claim 17, apparatus claim 17 corresponds to method claim 3. Therefore, the rejection analysis and motivation to combine of claim 3 is applicable to claim 17.
Regarding claim 18, Hildebrand discloses the device of claim 15, wherein the processor is coupled to the computer readable storage medium (Hildebrand in [Section – 2.3, Paragraph – 3] discloses, “Scan results are stored in CSV files with the raw XYZ, semantic class, normal, and object ID data as columns to allow for compatibility with most state-of-the-art 3D point cloud processing software”).
Summary of Citations (Hildebrand)
[Section – 2.3, Paragraph – 3]; “Scan results are stored in CSV files with the raw XYZ, semantic class, normal, and object ID data as columns to allow for compatibility with most state-of-the-art 3D point cloud processing software”.
Claims 6 – 8, 10 and 20 – 22 are rejected under 35 U.S.C 103 as being unpatentable over by Hildebrand in view of Frank, Qian, Huang and Petrovskaya US Patent Application Publication No. US-20160148433-A1 (hereinafter Petrovskaya).
Regarding claims 6 – 8, 10 and 20 – 22 the combination of Hildebrand, Frank, Qian and Huang as a whole teaches claim 1 but fails to teach the further limitations as recited in claims 6 – 8, 10 and 20 – 22. Petrovskaya teaches claims 6 – 8, 10 and 20 – 22 for the same grounds of rejection and motivation established in the Final Office Action of 04/20/2026.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to ZAID MUHAMMAD SALEH whose telephone number is (703)756-1684.
The examiner can normally be reached M-F 8 am - 5 pm ET. 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, Vu Le can be
reached on (571)272-7332. The fax phone number for the organization where this application or
proceeding is assigned is 571-273-8300. Information regarding the status of published or
unpublished applications may be obtained from Patent Center. Unpublished application
information in Patent Center is available to registered users. To file and manage patent
submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit
https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center
and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For
additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
If you would like assistance from a USPTO Customer Service Representative, call 800-786
9199 (IN USA OR CANADA) or 571-272-1000.
/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
08/18/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
Prosecution Insights