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 .
Specification
Specification is objected to as failing to provide clear support in the description such that the meaning of the terms “partition equivalent diameter” and “point cloud detection line” as disclosed in claims 3 and 17 may be ascertainable by reference to the description. See 37 CFR 1.75(d)(1).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 3, claim 3 discloses the terms “partition equivalent diameter” and “point cloud detection line”. [0007] of the applicant’s specification states “where a partition equivalent diameter is less than 0.5 times a minimum length of a point cloud detection line”, which provides no further definition for these terms. The terms are not defined in the specification, and the terms do not have an ordinary and customary meaning to those of ordinary skill in the art. For the purposes of applying prior art, “partition equivalent diameter” will be interpreted as the detected distance between two points in a point cloud, and “point cloud detection line” will be interpreted as a threshold distance for grouping or clustering points in a point cloud.
Regarding claim 17, the device claim 17 recites similar limitations to claim 3 and is rejected under similar rationale.
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.
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 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kwon et al (US 10614579 B1, hereinafter Kwon) and Babahajiani et al (US 10049492 B2, hereinafter Babahajiani).
Regarding claim 1, Kwon teaches A power vision dataset augmentation method based on physical system characteristics, comprising: acquiring an initial three-dimensional structure point cloud of a power device (Col 5 Line 16-18 “The system 100 also includes a 3D imaging sensor 116 for capturing a 3D image 118 of the object 104. The 3D image 118 of the object 104 includes a 3D point cloud 120”, Col 11 Line 51-53 “In accordance with an embodiment, the object is a space object, such as an asteroid, spacecraft or other space object.”), preprocessing the initial three-dimensional structure point cloud to obtain a three-dimensional structure point cloud (Col 5 Line 65 – Col 6 Line 2 “The 3D model point cloud 102 is actually an updated 3D model point cloud that is generated by sensor data fusion or combining the 2D image 108 and the 3D image 118 at the same viewpoint 138 using entropy-based upsampling”),
acquiring a captured image of the power device and determining light and shadow information of the power device based on the captured image (Col 6 Line 31-35 “point quantization or subsampling is performed using color information from the 2D image 108 for filtering the registered upsampled 3D point clouds 148 to generate an updated 3D model point cloud or 3D model point cloud 102 of the object 104”, Figure 3A shows that 2D image 108 includes light and shadows);
PNG
media_image1.png
691
347
media_image1.png
Greyscale
matching the three-dimensional structure with the captured image by transforming an angle and a scale of the three-dimensional structure (Col 8 Line 24-29 “the 2D image and the 3D image are aligned. In accordance with an example, the 2D image and the 3D image are aligned using pre-acquired calibration information. The pre-acquired calibration information includes parameters of scale difference, translation offset, and rotation offset.”);
and constructing a dataset based on the three-dimensional true-color structure and the captured image (Col 5 Line 51-54 “the 2D image 108 data and the 3D image 118 data are combined or fused for each viewpoint 138 or location of the sensor platform 136 and the data for each of the viewpoints are combined”, Claim 3 “further comprising repeating the process for each of a set of viewpoints or locations of the sensor platform.”).
Kwon fails to explicitly teach determining point clouds of power device vertices and edges based on the three-dimensional structure point cloud, partitioning point clouds of non-significant vertices and edges in the three-dimensional structure point cloud and replacing partitions with geometric plane primitives to obtain a three-dimensional structure represented by the geometric plane primitives, wherein the point clouds of the non-significant vertices and edges are point clouds with a feature degree lower than a preset percentage, and determining a three-dimensional true-color structure of the power device based on the light and shadow information of the power device after the three-dimensional structure matches the captured image, but in related field of endeavor Babahajiani teaches determining point clouds of power device vertices (Col 4 Line 18-21 “The LiDAR point clouds may be considered a set of vertices in a three-dimensional coordinate system, wherein a vertex may be represented by a planar patch defined by a 3D vector.”) and edges based on the three-dimensional structure point cloud (Col 7 Line 32-36 “contours are extracted to find boundaries of objects. For example, Pavlidis contour-tracing algorithm (“Algorithms for graphics and image processing”, Computer science press, 1982) may be used to identify each contour as a sequence of edge points”);
partitioning point clouds of non-significant vertices and edges in the three-dimensional structure point cloud (Col 5 Line 42-48 “In the method of FIG. 3, a three-dimensional (3D) point cloud about at least one object of interest is obtained (300) as an input for the process. Ground and/or building objects are detected (302) from 3D point cloud data using an unsupervised segmentation method, and the detected ground and/or building objects are removed (304) from the 3D point cloud data”, Col 6 Line 3-9 “Therefore, the original point cloud is divided into ground and vertical object point clouds, as shown in FIG. 4a, where the ground points are shown as rasterized and the vertical points are shown as dark. The 3D point cloud of the scene is first divided into a set of rectangular, non-overlapping tiles along the horizontal x-y plane. The size of the tiles may be e.g. 10 m×10 m.”) and replacing partitions with geometric plane primitives to obtain a three-dimensional structure represented by the geometric plane primitives, wherein the point clouds of the non-significant vertices and edges are point clouds with a feature degree lower than a preset percentage (Col 6 Line 1-9 “The aim of the ground segmentation is to remove points belonging to the scene ground, such as roads and sidewalks. Therefore, the original point cloud is divided into ground and vertical object point clouds, as shown in FIG. 4a, where the ground points are shown as rasterized and the vertical points are shown as dark. The 3D point cloud of the scene is first divided into a set of rectangular, non-overlapping tiles along the horizontal x-y plane. The size of the tiles may be e.g. 10 m×10 m.”, Col 8 Line 44-48 “merging the 3D points into voxels comprising a plurality of 3D points such that for a selected 3D point, all neighboring 3D points within a third predefined threshold from the selected 3D point are merged into a voxel without exceeding a maximum number of 3D points in a voxel”);
and determining a three-dimensional true-color structure of the power device based on the light and shadow information of the power device after the three-dimensional structure matches the captured image (Col 5 Line 6-15 “a plurality of three-dimensional (3D) point clouds are obtained (200) about a plurality of objects of interest, each of said 3D points cloud being labelled to a category of objects of interest. Facades for the objects of interests categorized as buildings are rendered (202) using an ambient occlusion method, where illumination of the point cloud is calculated based on light coming from a theoretical hemisphere or sphere around the object of interest”, Col 11 Line 14-19 “Ambient occlusion, in general, refers to a shading and rendering technique used to calculate how exposed each point in a scene is to ambient lighting. In scenes with open sky this is done by estimating the amount of visible sky for each point, and the methods are also referred to as skydome rendering.”)
It would have been obvious to one of ordinary skill in the art prior to the time of filing to have modified Kwon to include determining point clouds of power device vertices and edges based on the three-dimensional structure point cloud, partitioning point clouds of non-significant vertices and edges in the three-dimensional structure point cloud and replacing partitions with geometric plane primitives to obtain a three-dimensional structure represented by the geometric plane primitives, wherein the point clouds of the non-significant vertices and edges are point clouds with a feature degree lower than a preset percentage, and determining a three-dimensional true-color structure of the power device based on the light and shadow information of the power device after the three-dimensional structure matches the captured image as taught by Babahajiani. Doing so would improve computational efficiency (Col 14 Line 5-11 “by detecting ground and building objects from the 3D point cloud data using an unsupervised segmentation method, huge amount of data (more than 75% of points) are labeled, and only small amount of point cloud which have complex shape remains to be segmented. Thus, the computational efficiency is significantly improved.”)
Regarding claim 9, the device (Kwon Col 5 Line 58-63 “The system 100 also includes an image processing system 142. In at least one example, the memory device 140 is a component of the image processing system 142. The image processing system 142 includes a processor 144. The image processing system 142 or processor 144 is configured for generating a 3D model point cloud 102 of the object 104. ”), claim 9 is similar in scope to the method claim 1, and is rejected under similar rationale.
Regarding claim 10, the non-transitory computer-readable storage medium (Kwon Col 5 Line 58-63 “The system 100 also includes an image processing system 142. In at least one example, the memory device 140 is a component of the image processing system 142. The image processing system 142 includes a processor 144. The image processing system 142 or processor 144 is configured for generating a 3D model point cloud 102 of the object 104. ”) claim 10 is similar in scope to the method claim 1, and is rejected under similar rationale.
Claims 2-3, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kwon and Babahajiani as applied to claim 1 and 9 above, and further in view of Douillard et al (US 20180364717 A1, hereinafter Douillard). Regarding claim 2, Kwon as modified by Babahajiani teaches the power vision dataset augmentation method based on physical system characteristics according to claim 1,
Kwon further teaches wherein acquiring the initial three-dimensional structure point cloud of the power device, preprocessing the initial three-dimensional structure point cloud to obtain the three-dimensional structure point cloud, and determining the point clouds of the power device vertices and edges based on the three-dimensional structure point cloud comprises: acquiring the initial three-dimensional structure point cloud of the power device, performing point cloud registration (Col 6 Line 20-23 “registration of multiple point clouds 148 is performed with only selective points from the point clouds 148 and using entropy based upsampling”) and point cloud filtering (Col 6 Line 31-33 “point quantization or subsampling is performed using color information from the 2D image 108 for filtering the registered upsampled 3D point clouds”) on the initial three-dimensional structure point cloud to obtain the three-dimensional structure point cloud (Col 6 Line 33-35 “to generate an updated 3D model point cloud or 3D model point cloud 102 of the object 104”).
Babahajiani further teaches for edge coordinates of the power device in the three-dimensional structure point cloud, segmenting a point cloud at a position of the edge coordinates based on a clustering algorithm to obtain several cluster subsets, (Col 7 Line 32-39 “Next, contours are extracted to find boundaries of objects. For example, Pavlidis contour-tracing algorithm (“Algorithms for graphics and image processing”, Computer science press, 1982) may be used to identify each contour as a sequence of edge points. The resulting segments are checked on one or more aspects, such as size and diameters (i.e. height and width), to distinguish buildings from other objects.”) and calculating an edge representation of the three-dimensional structure point cloud based on an RANSAC algorithm (Col 6 Line 34-37 “an estimation method, for example a RANSAC (RANdom SAmple Consensus) method, is adopted to fit a plane p to candidate ground points that are collected from all cells.”, Col 10 Line 45-49 “Patch planarity may be defined as the average square distance of all 3D points from the best fitted plane computed by the RANSAC algorithm. This feature may be useful for distinguishing planar objects with smooth surface, such as cars, from non-planar objects, such as trees.”).
It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Kwon and Babahajiani to include for edge coordinates of the power device in the three-dimensional structure point cloud, segmenting a point cloud at a position of the edge coordinates based on a clustering algorithm to obtain several cluster subsets, and calculating an edge representation of the three-dimensional structure point cloud based on an RANSAC algorithm as further taught by Babahajiani. Doing so would improve computational efficiency (Col 14 Line 5-11 “by detecting ground and building objects from the 3D point cloud data using an unsupervised segmentation method, huge amount of data (more than 75% of points) are labeled, and only small amount of point cloud which have complex shape remains to be segmented. Thus, the computational efficiency is significantly improved.”) Kwon and Babahajiani fail to explicitly teach performing a voxel gradient solution on the three-dimensional structure point cloud based on point cloud voxels, and calculating a gradient change rate maximum value to obtain coordinates of the power device vertices and edges in the three-dimensional structure point cloud; and detecting a curvature change and a normal direction change of a local space point set in the several cluster subsets but in related field of endeavor, Douillard teaches performing a voxel gradient solution on the three-dimensional structure point cloud based on point cloud voxels ([0049] “locally flat voxels may not form a single cluster when determining the ground region associated with the autonomous vehicle, in which case, the interpolation module 218 may interpolate between points to determine if a gradient is above or below a threshold gradient for growing the ground plane cluster.”),
and calculating a gradient change rate maximum value to obtain coordinates of the power device vertices and edges in the three-dimensional structure point cloud ([0030] “determining a ground plane in the operation 112 may include determining an inner product between a vector in the height dimension (e.g., a reference direction) of an apparatus carrying such a LIDAR system, and the normal vector 120, expressed in a common coordinate system. In such an example, the inner product exceeding a threshold of 15 degrees may indicate that the voxel 110 does not comprise the ground”, [0049] “the interpolation module 218 may interpolate between points to determine if a gradient is above or below a threshold gradient for growing the ground plane cluster.”); and detecting a curvature change and a normal direction change of a local space point set in the several cluster subsets ([0071] “In some instances, the slope can be determined as a change in height 424 (Δz) divided by a horizontal distance 426 between the points 416 and 418. Thus, in some instances, the slope can be based at least in part on the average <x, y, z> values associated with the first cluster 342 and the average <x, y, z> values associated with the second cluster 344.”), It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Kwon and Babahajiani to include performing a voxel gradient solution on the three-dimensional structure point cloud based on point cloud voxels, and calculating a gradient change rate maximum value to obtain coordinates of the power device vertices and edges in the three-dimensional structure point cloud; and detecting a curvature change and a normal direction change of a local space point set in the several cluster subsets as taught by Douillard. Doing so would improve object tracking and improve processing by reducing an amount of data to be considered ([0020] “The segmentation techniques described herein can improve a functioning of a computing device by providing a framework for efficiently segmenting data for object tracking” [0072] “Further, increasing the size of the ground cluster may increase an amount of data to be disregarded during object segmentation, which improves processing by reducing an amount of voxels to be considered as objects”)
Regarding claim 3, Kwon as modified by Babahajiani teaches The power vision dataset augmentation method based on physical system characteristics according to claim 1, but fails to explicitly teach wherein partitioning the point clouds of the non-significant vertices and edges in the three-dimensional structure point cloud and replacing the partitions with the geometric plane primitives to obtain the three-dimensional structure represented by the geometric plane primitives comprises: performing partition on regions of the point clouds of the non-significant vertices and edges in the three-dimensional structure point cloud based on a clustering algorithm, wherein a partition equivalent diameter is less than 0.5 times a minimum length of a point cloud detection line and replacing partitions obtained by the clustering algorithm with triangle plane primitives to obtain a three-dimensional structure represented by the triangle plane primitives.
In related field of endeavor, Douillard teaches performing partition on regions of the point clouds of the non-significant vertices and edges in the three-dimensional structure point cloud based on a clustering algorithm, wherein a partition equivalent diameter is less than 0.5 times a minimum length of a point cloud detection line ([0066] “a cluster may be grown by determining locally flat voxels that are adjacent to one another, or that are within a threshold distance to another locally flat voxel, to generate a first cluster 342 and a second cluster 344”);
and replacing partitions obtained by the clustering algorithm with triangle plane primitives to obtain a three-dimensional structure represented by the triangle plane primitives ([0047] “the ground determination module 214 may utilize a marching cubes-type algorithm to create a mesh based on average point values associated with voxels to determine triangles including at least three points to create a surface”).
It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Kwon and Babahajiani to include performing partition on regions of the point clouds of the non-significant vertices and edges in the three-dimensional structure point cloud based on a clustering algorithm, wherein a partition equivalent diameter is less than 0.5 times a minimum length of a point cloud detection line, and replacing partitions obtained by the clustering algorithm with triangle plane primitives to obtain a three-dimensional structure represented by the triangle plane primitives as taught by Douillard. Doing so would improve object tracking and improve processing by reducing an amount of data to be considered ([0020] “The segmentation techniques described herein can improve a functioning of a computing device by providing a framework for efficiently segmenting data for object tracking” [0072] “Further, increasing the size of the ground cluster may increase an amount of data to be disregarded during object segmentation, which improves processing by reducing an amount of voxels to be considered as objects”)
Regarding claim 16, the device claim 16 depends on claim 9, and is similar in scope to the method claim 2, and is rejected under similar rationale.
Regarding claim 17, the device claim 17 depends on claim 9, and is similar in scope to the method claim 3, and is rejected under similar rationale.
Claims 7 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Kwon and Babahajiani as applied to claim 1 and 9 above, and further in view of Kang et al (CN 111932673) and Chapman et al (US 20210287453 A1).
Regarding claim 7, Kwon as modified by Babahajiani teaches the power vision dataset augmentation method based on physical system characteristics according to claim 1, but fail to explicitly teach wherein constructing the dataset based on the three-dimensional true-color structure and the captured image comprises: orthogonally projecting the three-dimensional true-color structure at different angles and scales to obtain two-dimensional image data in any direction and size; determining an image set A of the power device with partially occlusion based on angle transformation; adjusting illumination to obtain an image set B under strong light and weak light; conducting angle transformation by a preset amplitude for a location of a specified component or defect of the power device to obtain an image set C of a target at different viewing angles; scaling up and down a specified target of the power device to obtain a local image set D and an image set E with a small proportion of target pixels; and combining the captured image, the image set A, the image set B, the image set C, the image set D, and the image set E into the dataset.
In related field of endeavor, Kang teaches wherein constructing the dataset based on the three-dimensional true-color structure and the captured image comprises: orthogonally projecting the three-dimensional true-color structure at different angles and scales to obtain two-dimensional image data in any direction and size; (Claim 3 “changing the camera viewing angle of the virtual scene, comprising: Camera position fixing, object winding shaft in rotating in the plane; fixing the object position; using the rotating matrix to make the camera around shaft in rotating in the plane.”); adjusting illumination to obtain an image set B under strong light and weak light (Claim 3 “changing the illumination intensity of the virtual scene to the three-dimensional reconstruction. setting intensity variation range of illumination intensity, randomly selecting one illumination intensity in the intensity variation range when performing image rendering each time;”); scaling up and down a specified target of the power device to obtain a local image set D and an image set E with a small proportion of target pixels (Claim 3 “changing the object size of the virtual scene, comprising: setting the size variation range of the object size; randomly selecting one object size in the size variation range when performing image rendering each time;”); and combining the captured image, the image set A, the image set B, the image set C, the image set D, and the image set E into the dataset (Claim 1 “obtaining a plurality of different two-dimensional images by changing the virtual scene; changing the virtual scene comprises changing the illumination intensity of the virtual scene, at least one of object size and camera observation angle.”).
It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Kwon and Babahajiani to include orthogonally projecting the three-dimensional true-color structure at different angles and scales to obtain two-dimensional image data in any direction and size; adjusting illumination to obtain an image set B under strong light and weak light; and scaling up and down a specified target of the power device to obtain a local image set D and an image set E with a small proportion of target pixels as taught by Kang. Doing so would increase the diversity of samples for an object model (Abstract “the defects that a CAD model of an object is difficult to construct and a composite sample with high diversity cannot be obtained in the prior art can be effectively overcome”)
Kwon, Babahajiani and Kang fail to explicitly teach determining an image set A of the power device with partially occlusion based on angle transformation; and conducting angle transformation by a preset amplitude for a location of a specified component or defect of the power device to obtain an image set C of a target at different viewing angles. In related field of endeavor, Chapman teaches determining an image set A of the power device with partially occlusion based on angle transformation ([0062] “diorama integrator 170 determines how rotation 153 impacts visualization of 3D diorama assets 152 and rendered 3D environment objects 162, e.g., if an asset 152 would be positioned in front of an object 162 to partially occlude object 162”); and conducting angle transformation by a preset amplitude for a location of a specified component or defect of the power device to obtain an image set C of a target at different viewing angles ([0062] “diorama integrator 170 receives user input to rotate 3D diorama 150 based an input or control received through a portal 120 page, or is triggered to execute rotation or programmed animation. At 404, diorama integrator 170 determines the requested or programmed degree of 3D diorama 150 movement or rotation 153 (e.g., rotate 3D diorama 30 degrees)”)
It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Kwon, Babahajiani and Kang to include determining an image set A of the power device with partially occlusion based on angle transformation; and conducting angle transformation by a preset amplitude for a location of a specified component or defect of the power device to obtain an image set C of a target at different viewing angles as taught by Chapman. Doing so would provide realistic depictions of 3D assets ([0045] “In this manner, embodiments provide verisimilitude in the resulting rendered spatial environment, or in other words, a truer or more accurate depiction of how the 3D diorama assets and rendered 3D environment appear together including when one may occlude the other and occlusion changes resulting from rotation”)
Regarding claim 21, the device claim 21 is similar in scope to the method claim 7, and is rejected under similar rationale.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kwon, Babahajiani, and Douillard as applied to claims 2-3 above, and further in view of Chapman.
Regarding claim 11, the method claim 11 depends on claim 2 and recites similar limitations to claim 7, and is rejected under similar rationale.
Regarding claim 12, the method claim 12 depends on claim 3 and recites similar limitations to claim 7, and is rejected under similar rationale.
Allowable Subject Matter
Claims 4-6, 13-15, and 18-20 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: Regarding claim 4, the closest prior art of MohammadBagher et al (US 20150379162 A1, hereinafter MohammadBagher) teaches fitting a mathematical model fitted to material types in imaging data for calculating reflection information, including shadow information, texture information, specular and diffuse information, and color information ([0090] “models discussed herein may include three tabulated ID vectors, one each for the shadowing-masking G, microfacet distribution D, and Fresnel F factors, as well as the scalar diffuse and specular coefficients ρ.sub.d and ρ.sub.s”, [0108] “a user may fit to the MERL database which stores measurements for 100 different isotropic materials. Each material measurement is stored in a uniformly-sampled 3D block of (θ.sub.h, θ.sub.d, φ.sub.d) parameter space with angular sampling 90×90×180, for each RGB color channel.”) but fails to explicitly teach the combined limitations as a whole. Furthermore, no prior art of record alone or in combination teaches claim 4 as a whole. Therefore claim 4 is considered to be allowable subject matter.
Claim 5-6 and 13-15 are dependent on claim 4, and are therefore considered allowable. Claim 18 recites similar limitations to claim 4, and is objected to under similar reasoning.
Claim 19-20 are dependent on claim 18, and are therefore considered allowable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Tobenkin (US 11875447 B1) teaches performing color correction on 3D objects formed by point cloud data, including analyzing positions of point cloud data points to determine light, shadows, reflections, and other factors on color values (Col 2 Line 4-12 "methods for color correcting three-dimensional (“3D”) objects formed by point cloud data points by leveraging the 3D positional data associated with the point cloud data points. The automated color correction involves analyzing the positioning of the data points that form different parts or sides of the 3D object to determine the different amounts of light, shadows, reflections, and/or other factors that the data points are exposed to from their respective and relative positions, and determining the impact that these factors have on the color values of the data points").
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN PATRICK GOCO whose telephone number is (571)272-5872. The examiner can normally be reached M-Th, 7:00 am - 5:00 pm.
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, Kee Tung can be reached at (571)272-7794. 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.
/J.P.G./ Examiner, Art Unit 2611
/KEE M TUNG/ Supervisory Patent Examiner, Art Unit 2611
Prosecution Insights