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 statements (IDS) were filed on 11/15/2023, 05/22/2025 and 05/11/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant first argues the 112(f) interpretation of the limitations present in claims 1, 4, 6 and 8, by indicating that the corresponding limitations have been modified and fixed, examiner agrees and the 112(f) interpretation has been rescinded. Applicant then mentions the rejections under 103, the first argument here being that the following newly amended claim limitation in independent claim 1 is not taught under the cited reference Hepp in view of Martins to: “control movement of the range image sensor to N imaging positions and obtain N range images of the teaching object at the N imaging positions, where N > M ....”, examiner agrees that the underlined part of the newly amended limitation is not taught under the cited references, however examiner includes a new cited reference, Kalra, that does teach the specified limitation. The second argued limitation is that the following limitation is not taught under the cited references: "generate a plurality of composite data pieces from a plurality of different combinations of the N range images; and calculate an accuracy of each of the plurality of composite data pieces indicating a degree of matching between the composite data piece and the teaching object ....", examiner disagrees and shows reasoning. Hepp clearly states in page 5 section 3, that each voxel is computed in a volumetric occupancy map which utilizes images captured by the sensor, each image contains the voxels observed and each observed voxel contains a measure of observation quality to use in a dense point cloud. Therefore this dense point cloud combines the data of these composite pieces of data from the images and the observed qualities of the voxels to be used in equations 1-8. Equations 1-8 are used for stereo matching as seen in page 6 equations 1-6, most importantly 4-6 which show the represented voxel information (point in a point cloud) which is used for stereo matching. Then see page 8 section 4.4.1 Viewpoint information along with equations 7 and 8. See also page 5 column 1 penultimate paragraph and final paragraph. The occupied voxels are measured from 1 to 0, this represents the degree of matching (a value close to 0.5 is an unknown voxel and therefore inaccurate) and those voxels need to be accurate (known) for a full scan of the object. The equations are used to finally obtain an optimized subset of viewpoints (most accurate viewpoints).
The second presented argument of the applicant concerning the 103 rejections is that Hepp and Martins would not have been combined in view of one of ordinary skill in the art, the reasoning, according to the applicant, being that “Hepp is directed to aerial multi-view stereo reconstruction of outdoor scenes using a quadrotor, with viewpoint and trajectory optimization constrained by flight time and unknown space exploration. (Abstract, Figs. 1-2, §§ 3-4). Martins is directed to automated industrial surface scanning based on a priori CAD model information using optical range sensors, with viewpoint planning, scanning path generation, and surface scanning for inspection of manufactured parts. (Abstract, § 1)”, to teach the specified limitation (which has already been argued above). Examiner disagrees, both inventions have the purpose of scanning an object/scene (the scenes in Hepp are directed to houses which can be considered a whole object (See fig. 1-4 Hepps)), they both also have the purpose of optimizing viewpoints to scan a surface, in addition to both being imaging systems. Specifically, Martins has a clear improvement to the cited reference Hepp as seen in the cited rejection, that improvement being a guarantee to measure all of the surface voxels through the use of fast and accurate range sensors to scan free form surfaces. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Therefore independent claim 1 stays rejected.
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.
Claims 1-5, 7, 11 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hepp et. al., hereafter Hepp (Hepp, Benjamin, Matthias Nießner, and Otmar Hilliges. "Plan3d: Viewpoint and trajectory optimization for aerial multi-view stereo reconstruction." ACM Transactions on Graphics (TOG) 38.1 (2018): 1-17.) in view of Martins et. al., hereafter Martins (Martins, Fernando António Rodrigues, et al. "Automated 3D surface scanning based on CAD model." Mechatronics 15.7 (2005): 837-857.) and further in view of Kalra et. al. (US Pub. No. 20220044441 A1).
As per claim 1, Hepp teaches “an information processing system for a measurement apparatus configured to
perform measurement of an object to be inspected three-dimensionally using M range images obtained at M imaging positions by a.. image sensor configured to obtain a range image using a principle of triangulation, the information processing system being a system for determining, prior to measurement of an object to be inspected that is different from and of the same type as the teaching object, the M imaging positions by using the teaching object, the information processing system comprising… (See page 2 fig. 1 and page 5 fig. 2, it shows an object (house or building) being reconstructed three dimensionally. See also page 2 column 1 paragraph 1 “Images from a commodity RGB camera, mounted on an autonomously navigated quadcopter, are fed into a multi-view stereo reconstruction pipeline that produces high-quality results but is computationally expensive.” The multi view stereo reconstruction is a use of a principle of triangulation. See page 6 section 4.1 “The high-level goal is to find an optimized subset of viewpoints from a larger set of candidate views that maximizes the information gained about the 3D surface of the scene… Each viewpoint v ∈ C has an associated position and orientation denoted as v.p and v.q. ” The viewpoints are interpreted as the imaging positions and the optimized subset of viewpoints (as also seen in the figures) is used for taking M range images at M imaging positions in order to reconstruct the object. See also fig. 1, fig. 2, fig. 3 and fig. 4, it shows structures of the same type (housing) but are different.) “…a memory, and a processor configured to execute instructions stored in the memory to cause the information processing system to:”” (See page 15 section recursive greedy method, the system utilizes storage “To speed up this computation, we keep a separate list that stores for each viewpoint a tuple of the viewpoint index and the corresponding information gain given the current viewpoint set.” This shows that the aerial robot has the ability to store the algorithms and data offline (see page 6 section 4 Method “One difficulty that presents itself is that the final objective of 3D reconstruction quality cannot be measured directly due to the absence of ground-truth data and the offline nature of the SfM and MVS pipeline.”) In addition it is well known in the art that computers and robots contain storage to perform actions and processes. Therefore at least implicitly a storage is taught. Hepp)
…obtain N range images of the teaching object at the N imaging positions, where N > M; (See page 6 section 4.1 “The high-level goal is to find an optimized subset of viewpoints from a larger set of candidate views that maximizes the information gained about the 3D surface of the scene. We assume that we are given a graph G = (C,M) of viewpoint candidates C alongside observed voxels and motions M between viewpoints as edges. Each viewpoint v ∈ C has an associated position and orientation denoted as v.p and v.q… ” The optimized viewpoints are M and the larger set of candidate viewpoints are N viewpoints. See also fig. 2 on page 2 (B) shows the candidate viewpoints and (C) shows the optimized set of viewpoints. See also page 7-8 section 4.4 Viewpoint Candidate graph. Since each viewpoint contains the observed voxels, it implicitly teaches obtaining N range images at N imaging positions. Hepp)
generate a plurality of composite data pieces from a plurality of different combinations of the… range images; and (See page 5 section 3 System overview paragraph 2 “..The quadrotor flies this regular pattern and records an initial set of images. These recordings are then processed via a state-of-the-art SfM and MVSpipeline bySchönberger et al. (2016a, 2016b) to attain camera poses together with depth and normal maps for each viewpoint. To generate a 3D surface reconstruction, the depth maps are fused into a dense point cloud, and, utilizing the Poisson Surface Reconstruction method (Kazhdan and Hoppe 2013), a mesh is extracted (Figure 2(a))… In addition to the initial reconstruction, we compute a volumetric occupancy map containing occupied, free-space, and unobserved voxels. Each voxel also carries with it a measure of observation quality.” A dense point cloud (contains a plurality of composite data pieces) is created from a set of initial images which utilizes voxels. A voxel can represent a point in the dense point cloud. It is also used in equations 1-8. Hepp)
calculate an accuracy of each of the plurality of composite data pieces indicating a degree of matching between the composite data piece and the teaching object (See page 6 equations 1-6, most importantly 4-6 which show the represent voxel information (point in a point cloud) which is used for stereo matching. Then see page 8 section 4.4.1 Viewpoint information along with equations 7 and 8. See also page 5 column 1 penultimate paragraph and final paragraph “In addition to the initial reconstruction, we compute a volumetric occupancy map containing occupied, free-space, and unobserved voxels. Each voxel also carries with it a measure of observation quality. The occupancy map (Figure 2(b)) is used during planning to reason about free-space and collision freedom as well as approximation of the observable surface area from any given viewpoint and the (remaining) uncertainty about the scene. The main objective of our optimization formulation is to maximize total information (i.e., certainty about voxels in the region of interest)… ” See also page 5 column 2 last paragraph along with the paragraph at the beginning of the following page “The occupancy map OM is essential in distinguishing between occupied, free, and unobserved space. This is encoded by an occupancy value oc(τ) ∈ [0,1] for each voxel τ ∈ OM. Here, we overload the term occupancy to encompass both a known occupancy and an unknown occupancy (i.e., a value close to 0 encodes a known empty voxel, a value close to 1 encodes a known occupied voxel, and a value close to 0.5 encodes an unknown voxel”. On page 6 equations 2-3 show that the occupancy value is utilized to determine when a voxel is occupied and this occupation value represents the accuracy of the current voxel in the point cloud representing different surfaces of the reconstruction model. Therefore this represents a degree of matching (variation from 0 to 1) (matched = known occupied = 1, the degree of matching being from 0 to 1. ) Hepp) “and determine the M imaging positions from the N imaging positions based on the accuracy of each of the plurality of composite data pieces.” (See section 4.1 on page 6 paragraph 1. “The high-level goal is to find an optimized subset of viewpoints from a larger set of candidate views that maximizes the information gained about the 3D surface of the scene.” The previous stated equations (1-8) are used to gain a smaller subset of viewpoints from a larger set of candidate viewpoints. See also page 7 section 4.3 Algorithm 1 and page 16 algorithms 2 and 3. See also fig. 1 and fig. 2. Hepp), however Hepp does not teach “a range image sensor”, “N range images” and “control movement of the range image sensor to N imaging positions and obtain N range images of the teaching object at the N imaging positions”. In addition, the following limitation is implicitly already taught, but not explicitly “a memory: and a processor configured to execute instructions stored in the memory to cause the information processing system to:”
Martins teaches “a range image sensor” (See page 4 paragraph 1 “Each viewpoint is completely defined by the optical range sensor orientation (view direction) and the associated volume to be scanned.”) and “N range images” (See page 8 paragraphs 2-4 “This viewpoint set searching process may not generate an optimum viewpoint set (in terms of the number of viewpoints), but guarantees that the viewpoint set is complete in the sense that, all possible measurable surface voxels will be covered by the viewpoint set.” And pages 8-10 section 4 Scanning path generation and surface scanning, on page 10 paragraph 2 “The process of scanning path generation for each viewpoint is executed off-line and is then used to drive the surface scanning process. In this last phase, the object surface is scanned according to the viewpoints and trajectories previously defined.” See also equations 1-8 on pages 5-7. On page 7 paragraph 4 “The function G(j) is computed to each viewpoint j ∈ V, where xs and xsc are weighting coefficients used to set the weight of each partial function and can range between 0.0 and 1.0 considering that xs + xsc =1;” Martins )
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Hepp with the teachings of Martins to utilize a range image sensor and obtain a first set of images. The modification would have been motivated by the desire to have the ability to scan free form surfaces and be able to acquire thousands of points per second, in addition, it guarantees all measurable surface voxels are complete, therefore it is an improvement, as suggested by Martins (See page 2 paragraph 1 “The recent development of fast and accurate non-contact optical 3D range sensors [15] offers the possibility to overcome some of the contact technology problems. The capability to acquire thousands of points per second and the ability to scan free form surfaces are the main advantages.” See also page 8 paragraphs 3-4 “This viewpoint set searching process may not generate an optimum viewpoint set (in terms of the number of viewpoints), but guarantees that the viewpoint set is complete in the sense that, all possible measurable surface voxels will be covered by the viewpoint set.” Martins)
Kalra teaches “control movement of the range image sensor to N imaging positions and obtain N range images of the teaching object at the N imaging positions” (See abstract and paragraphs 5, 29 and 204 along with fig. 10. “[0204] An imaging system is used to capture images of the surface 1002 of the object from multiple poses with respect to the normal direction of the surface. In the embodiment shown in FIG. 10, a polarization camera system 1010 is used as the imaging system to capture images of the surface 1002, including the portions illuminated by the physical illumination source 1004 (e.g., including the particular point 1008). The polarization camera system 1010 captures images the surface 1002 from different poses 1012, such as by moving the polarization camera system 1010 from one pose to the next, and capturing polarization raw frames from each pose.” Kalra), Kalra also teaches “a memory: and a processor configured to execute instructions stored in the memory to cause the information processing system to…”(See paragraph 29 “[0029] According to one embodiment of the present disclosure, a system includes: a main camera; one or more support cameras spaced apart from the main camera, the main camera and the one or more support cameras having overlapping fields of view; and a pose estimator including a processor and memory, the memory storing instructions that, when executed by the processor, cause the processor to control the main camera, the one or more support cameras and to perform the methods of any of the above.” Kalra)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Hepp with the teachings of Martins and Kalra to take control of the movement of the image sensor to obtain an image in all of the viewpoints in accordance with a processor and memory storing instructions. The modification would have been motivated by the desire to have high accuracy, precise boundaries of an object, real-time feedback to improve accuracy and approach scanning the object with a correct pose/viewpoint, therefore it is an improvement, as suggested by Kalra (See paragraphs 85, 65, 161, 199 and 165 “[0085] Some aspects of embodiments of the present disclosure relate to computing a high accuracy pose estimate of objects 22 in a scene based on a joint estimate of the poses the objects across the main camera 10 and the support cameras 30. Some aspects of embodiments of the present disclosure also relate to the calibration of camera systems based on computing joint estimates of poses of objects 22 within a scene.” “[0165]… By training such a deep learning model using labeled polarization information (e.g., polarization raw frames and/or features in polarization representation spaces, such as intensity, AOLP ϕ, and DOLP ρ), embodiments of the present disclosure enable the accurate detection of boundaries of instances of optically challenging objects, in addition to objects that might otherwise be detectable by images captured by comparative color cameras.” “[0258]… These predictions are provided as real-time feedback to the robotic controller to improve the accuracy and repeatability of the movement of the end-effector of the robotic arm to the Desired Position.” “[0065]…As another example, in the case of robotics for use in manufacturing, pose estimation may be used by robots to detect the position and orientation of physical manufacturing components, such that a robot arm can approach the component from the correct angle to obtain a proper grip on the part for assembly with other components of a manufactured product (e.g., gripping the head of a screw and threading the screw into a hole, whereas gripping a screw by the tip would make it difficult to insert into a hole).” Kalra)
Claims 14 and 15 are rejected under the same analysis as claim 1. (See page 15 section recursive greedy method, the system utilizes storage “To speed up this computation, we keep a separate list that stores for each viewpoint a tuple of the viewpoint index and the corresponding information gain given the current viewpoint set.” This shows that the aerial robot has the ability to store the algorithms and data offline (see page 6 section 4 Method “One difficulty that presents itself is that the final objective of 3D reconstruction quality cannot be measured directly due to the absence of ground-truth data and the offline nature of the SfM and MVS pipeline.”) In addition it is well known in the art that computers and robots contain storage to perform actions and processes. Therefore at least implicitly a storage is taught. Hepp)
As per claim 2, Hepp in view of Martins and Kalra teaches “The information processing system according to claim 1, wherein each of the plurality of composite data pieces is point cloud data representing the object in a three-dimensional space using a point cloud, and the accuracy is a value in accordance to a variation in the point cloud with respect to the teaching object. (See also page 5 column 2 last paragraph along with the paragraph at the beginning of the following page “The occupancy map OM is essential in distinguishing between occupied, free, and unobserved space. This is encoded by an occupancy value oc(τ) ∈ [0,1] for each voxel τ ∈ OM. Here, we overload the term occupancy to encompass both a known occupancy and an unknown occupancy (i.e., a value close to 0 encodes a known empty voxel, a value close to 1 encodes a known occupied voxel, and a value close to 0.5 encodes an unknown voxel”. On page 6 equations 2-3 show that the occupancy value is utilized to determine when a voxel is occupied and this occupation value represents the accuracy of the current voxel in the point cloud representing different surfaces of the reconstruction model. Therefore this represents a degree of matching (variation from 0 to 1) (matched = known occupied = 1, the degree of matching being from 0 to 1. ). See also page 5 section 3 System overview paragraph 2 “..The quadrotor flies this regular pattern and records an initial set of images. These recordings are then processed via a state-of-the-art SfM and MVSpipeline bySchönberger et al. (2016a, 2016b) to attain camera poses together with depth and normal maps for each viewpoint. To generate a 3D surface reconstruction, the depth maps are fused into a dense point cloud, and, utilizing the Poisson Surface Reconstruction method (Kazhdan and Hoppe 2013), a mesh is extracted (Figure 2(a))… In addition to the initial reconstruction, we compute a volumetric occupancy map containing occupied, free-space, and unobserved voxels. Each voxel also carries with it a measure of observation quality. See also fig. 1 and fig. 2. Hepp)
As per claim 3, Hepp in view of Martins and Kalra teaches “The information processing system according to claim 2, wherein the variation in the point cloud is based on variations of points in the plurality of composite data pieces with respect to a predetermined reference surface and on variations of points in the plurality of composite data pieces with respect to a surface of the teaching object.” (See also page 5 column 2 last paragraph along with the paragraph at the beginning of the following page “The occupancy map OM is essential in distinguishing between occupied, free, and unobserved space. This is encoded by an occupancy value oc(τ) ∈ [0,1] for each voxel τ ∈ OM. Here, we overload the term occupancy to encompass both a known occupancy and an unknown occupancy (i.e., a value close to 0 encodes a known empty voxel, a value close to 1 encodes a known occupied voxel, and a value close to 0.5 encodes an unknown voxel”. As seen on page 5 column 1 paragraphs 3-4 “First, a user defines a Region of Interest (ROI) and specifies a simple and safe overhead pattern via a map-based interface to ac quire an initial set of images… To generate a 3D surface reconstruction, the depth maps are fused into a dense point cloud, and, utilizing the Poisson Surface Reconstruction method (Kazhdan and Hoppe 2013), a mesh is extracted (Figure 2(a)). It is important to note that this initial reconstruction is highly inaccurate and incomplete since the viewpoints stem from a simple, regular pattern flown at relatively high altitude to avoid collisions. ” See also fig. 2. This initial reconstruction is considered a predetermined reference surface using variations (from the initial occupancy) and then variations (occupancy from different viewpoints) are also used to determine the final occupancy of the voxel. Therefore the final variation of the point cloud is based on the initial and following occupancy values. Hepp )
As per claim 4, Hepp in view of Martins and Kalra teaches “The information processing system according to claim 1, wherein the processor is configured to execute instructions stored in the memory to further cause the information processing system to generate a movement path for changing a position of the range image sensor to the M imaging positions to allow the range image sensor to complete imaging the object to be inspected at the M imaging positions in a shortest time.” (See fig. 2 along with page 6 section 4.1 Optimizing Viewpoint Trajectories paragraphs 1-5 “The high-level goal is to find an optimized subset of viewpoints from a larger set of candidate views that maximizes the information gained about the 3D surface of the scene. We assume that we are given a graph G = (C,M) of viewpoint candidates C alongside observed voxels and motions M between viewpoints as edges… The goal of the method is to generate a trajectory (i.e., a path through a subset of the nodes in the candidate graph) for the quadcopter that yields good reconstruction quality and fulfills robot constraints. ” See also page 5 column 1 paragraph 1 “For reasonable runtimes of 10 minutes, we observe that our approach consistently outperforms this method.” See also page 3 column 1 paragraph 2 “Given the emergence of small and affordable aerial robots (MAVs), equipped with high-resolution cameras, it is a natural choice to leverage these for image acquisition… Moreover,current MAVs are battery constrained to 10- to 15-minute flight times, making intelligent viewpoint selection an even more pressing issue.” As can be seen, 10 minutes is the shortest time of the presented battery constraints, therefore it falls within the BRI of “a shortest time”. Hepp)
As per claim 5, Hepp in view of Martins and Kalra teaches “The information processing system according to claim 1, wherein the M imaging positions are determined based on the accuracy and time for imaging used by the range image sensor.” (See fig. 2 along with page 6 section 4.1 Optimizing Viewpoint Trajectories paragraphs 1-5 “The high-level goal is to find an optimized subset of viewpoints from a larger set of candidate views that maximizes the information gained about the 3D surface of the scene. We assume that we are given a graph G = (C,M) of viewpoint candidates C alongside observed voxels and motions M between viewpoints as edges… The goal of the method is to generate a trajectory (i.e., a path through a subset of the nodes in the candidate graph) for the quadcopter that yields good reconstruction quality and fulfills robot constraints. ” See also page 5 column 1 paragraph 1 “For reasonable runtimes of 10 minutes, we observe that our approach consistently outperforms this method.” Since it uses equations 1-8, it is also based on the accuracy by the occupancy of the voxels in the point cloud. Hepp)
As per claim 7, Hepp in view of Martins and Kalra teaches “The information processing system according to claim 1, wherein the range image sensor has a predetermined upper limit for time for imaging, and the M is a greatest number of range images to be captured by the range image sensor until the time for imaging used by the range image sensor reaches the predetermined upper limit.” (See page 11 column 2 section 5.5.1 Church “Church. Figure 7 shows results for the church scene, acquired with a total of 160 images. The initial flight pattern uses 20 viewpoints arranged in an ellipse. Based on the initial reconstruction, a viewpoint path with 140 viewpoints and a maximum flight time of 10 minutes was planned (see Figure 1) and flown.” The 140 viewpoints is the variable M (greatest number of range images) since it accounts for M images and in this experiment the upper limit is 10 minutes. Hepp )
As per claim 11, Hepp in view of Martins and Kalra teaches “The information processing system according to claim 1, wherein range images of the teaching object captured with the range image sensor are obtained at adjacent positions that are in a constant positional relationship to each of the N imaging positions, and the plurality of composite data pieces are generated from a plurality of different combinations of N image sets, and each of the N image sets includes a range image captured at one of the N imaging positions and a range image captured at an adjacent position of the adjacent positions for the one of the N imaging positions.” (See page 8 column 1 paragraph 2-5 “To sample new 3D candidate positions, we take the first 3D position from the exploration queue and generate six new positions by adding an offset in the −x, +x, −y, +y, −z, and +z directions, respectively. The resulting positions are discarded if they are too close to existing viewpoint candidates or do not lie in free space; otherwise they are added to C and to the exploration queue.” The system adds viewpoints used for acquiring images and these are adjacent and based on a constant positional relationship. See page 5 section 3 System overview paragraph 2 “In addition to the initial reconstruction, we compute a volumetric occupancy map containing occupied, free-space, and unobserved voxels. Each voxel also carries with it a measure of observation quality.” A dense point cloud (contains a plurality of composite data pieces) is created from a set of initial images which utilizes voxels. A voxel can represent a point in the dense point cloud. It is also used in equations 1-8 which show the use of calculated viewpoints with added viewpoints. See also page 8 section 4.4.1 most importantly equation 7 and column 2 paragraph 1 along with fig. 2. Hepp )
As per claim 13, Hepp in view of Martins and Kalra teaches “The information processing system according claim 1, wherein the information processing system includes the measurement apparatus, and the measurement apparatus obtains, as a result of the measurement of the object to be expected, composite data of the M range images of the object to be inspected captured by the range image sensor at the M imaging positions.” (See fig. 2 and page 5 section 3 System Overview column 1 penultimate paragraph “In addition to the initial reconstruction, we compute a volumetric occupancy map containing occupied, free-space, and un observed voxels. Each voxel also carries with it a measure of observation quality.” and column 2 paragraph 3 “Figure 2(c) shows the output of our planning method, where viewpoints that were added due to their contributed information are rendered in blue. Additional viewpoints that were added to ensure that the SfM & MVS backend can register all images into a single reconstruction are rendered in cyan. The edges are color coded to signal MAV progress along the path. The plan is then executed by the drone, and the acquired images are used to update the 3Dmodel(Figure 2(d)).” Each viewpoint represents an imaging position used to acquire an image. Fig. 2d shows the final 3d reconstructed model which uses the SfM and MVS pipeline. See also page 6 section 4.1 Optimizing Viewpoint Trajectories equations 1-6 and page 8 equation 7. Examiner interprets “composite data” as any data used to represent a voxel/point in the 3d model. This is all used for the optimized subset of viewpoints as seen on page 6 section 4.1 Optimizing viewpoint trajectories. Hepp )
Pertinent Prior Art
Yoshikawa et. al. (US 20190318498 A1), discloses finding optimal viewpoints to measure an object surface (Paragraphs 102, 103 and 108), but does not disclose the dependent claims.
Tomomi et. al. (JP2015178984A), discloses measurement accuracy for a 3d dimensional model. (See abstract and page 2 paragraphs 1-4), but does not disclose the dependent claims.
Allowable Subject Matter
Claims 6, 8-9 and 12 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. Claim 10 is allowable.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DYLAN JOHN MENDEZ MUNIZ/Examiner, Art Unit 2675
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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