DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/14/2026, 01/14/2026, 07/07/2026, and 07/15/2026 are being considered by the examiner.
Claim Objections
Claim 7 are objected to because of the following informalities:
In claim 7, Line 1, the term “further comprisIng finding the hole within the virtual 2D picture;” should be corrected “further comprising finding the hole within the virtual 2D picture” in order to correct the typographical error. Appropriate corrections required.
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 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 of this title, 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 1-2, 10-11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over ECKMAN et al. (US 20220335688 A1), hereinafter referenced as ECKMAN in view of FU et al. (US 20180075285 A1), hereinafter referenced as FU and in further view of LI et al. (US 20200364929 A1), hereinafter referenced as LI.
Regarding claim 1, ECKMAN explicitly teaches a method performed by a processor for locating ceiling and floor within a 3D mesh (Fig. 1. Paragraph [0042]-ECKMAN discloses FIG. 1A depicts an example mapping system, including a physical space 100, a point cloud 102, a computer system 104, and a map of the physical space 106. The physical space 100 can include walls, doorways, doors, tables, support beams, etc. The physical space 100 can be a building, house, other structure, or an outdoor space/landscape. In paragraph [0045]-ECKMAN discloses 3D scans and/or images of the physical space 100 can be captured at a time 1. The point cloud 102 can be generated at a time 2. Processing the point cloud at the computer system 104 can be performed at a time 3. And finally, generating the map of the physical space 106 can be performed at a time 4. Moreover, the process can be performed more than once and can be automatically/periodically performed in order to ensure updated maps and/or blueprints of the physical space 100 are maintained. Please also see claim 18), the method comprising:
locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values (Fig. 1. Paragraph [0051]-ECKMAN discloses first, the computer system receives a point cloud in step 202. The computer system can receive the point cloud from a drone that takes 3D scans of a physical environment (wherein a new point cloud scan includes at least one of walls, floor, ceiling, etc.). In paragraph [0053]-ECKMAN disclose the computer system can detect a bounding box of the physical space that hugs walls of the physical space and then rotate the physical space such that the bounding box is aligned with X, Y, and Z axes. the point cloud data can be aligned to X, Y, and Z axes. In paragraph [0077]-ECKMAN discloses the computer system can voxelize the point cloud into predetermined mesh sizes. In paragraph [0080]-ECKMAN discloses the computer system can weed out horizontal planes, such as floors, ceilings, and walls. The covariance matrix is beneficial to compare points and see how they correlate with each other over all of the X, Y, and Z axes. Please also read paragraph [0042]);
finding a minimum z value of the 3D mesh (Fig. 1. Paragraph [0042]-ECKMAN discloses the computer system can determine size information for the selected cluster in step 1108. This can include finding a bounding box for the selected cluster, identifying a minimum x, y, and z coordinate as well as a maximum x, y, and z coordinate for the bounding box);
finding a maximum z value of the 3D mesh (Fig. 1. Paragraph [0042]-ECKMAN discloses the computer system can determine size information for the selected cluster in step 1108. This can include finding a bounding box for the selected cluster, identifying a minimum x, y, and z coordinate as well as a maximum x, y, and z coordinate for the bounding box);
making a histogram of the z values (Fig. 1. Paragraph [0082]-ECKMAN discloses the computer system can also apply a histogram filter to the cluster (step 822). Using the histogram filter is beneficial to identify repeated points within each cluster. Once vertical point patterns are identified, the computer system can determine those point patterns are indicative of vertical objects. If points are stacked on top of each other along the Z axis, then it is more indicative of a vertical pole);
ECKMAN fails to explicitly teach determining the histogram with most area in an up direction; determining the histogram with most area in a down direction.
However, FU explicitly teaches determining the histogram with most area in an up direction (Fig. 5. Paragraph [0063]-FU discloses to roughly detect the location of the floors and ceilings, peaks are identified from the histogram projected along the Z-axis. In paragraph [0075]-FU discloses each detected rough peak voxel level represents one floor or one ceiling located inside the voxel. A 2D line-sweeping algorithm is applied to detect the density variation and to find the accurate location of the floor or ceiling. In paragraph [0076]-FU discloses two parallel sweep planes with a small interval are instantiated for the X-Y plane and swept along the Z-axis from the bottom to the top of the peak voxel level. The position along the Z-axis with the greatest total number of laser scanning points is selected as a refined level location. Please also see claim 1 and read paragraph [0063-0072, 0085 and 0089-0090]);
determining the histogram with most area in a down direction (Fig. 5A. Paragraph [0063]-FU discloses to roughly detect the location of the floors and ceilings, peaks are identified from the histogram projected along the Z-axis. In paragraph [0075]-FU discloses each detected rough peak voxel level represents one floor or one ceiling located inside the voxel. A 2D line-sweeping algorithm is applied to detect the density variation and to find the accurate location of the floor or ceiling. In paragraph [0076]-FU discloses two parallel sweep planes with a small interval are instantiated for the X-Y plane and swept along the Z-axis from the bottom to the top of the peak voxel level. The position along the Z-axis with the greatest total number of laser scanning points is selected as a refined level location. Please also see claim 1 and read paragraph [0063-0072, 0085 and 0089-0090]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN of having a method performed by a processor for locating ceiling and floor within a 3D mesh, the method comprising: locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; making a histogram of the z values, with the teachings of FU of having determining the histogram with most area in an up direction; determining the histogram with most area in a down direction.
Wherein ECKMAN’s method having determining the histogram with most area in an up direction; determining the histogram with most area in a down direction.
The motivation behind the modification would have been to obtain a method that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and FU both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FU provides systems and methods that improves the ability to locate floors and ceilings. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FU et al. (US 20180075285 A1), Abstract and paragraph [0075].
Although FU explicitly teaches setting the histogram with most area in an up direction (Fig. 5. Paragraph [0063]-FU discloses to roughly detect the location of the floors and ceilings, peaks are identified from the histogram projected along the Z-axis. In paragraph [0075]-FU discloses each detected rough peak voxel level represents one floor or one ceiling located inside the voxel. A 2D line-sweeping algorithm is applied to detect the density variation and to find the accurate location of the floor or ceiling. In paragraph [0076]-FU discloses two parallel sweep planes with a small interval are instantiated for the X-Y plane and swept along the Z-axis from the bottom to the top of the peak voxel level. The position along the Z-axis with the greatest total number of laser scanning points is selected as a refined level location. Please also see claim 1 and read paragraph [0063-0072, 0085 and 0089-0090]); and
setting the histogram with most area in a down direction (Fig. 5. Paragraph [0063]-FU discloses to roughly detect the location of the floors and ceilings, peaks are identified from the histogram projected along the Z-axis. In paragraph [0075]-FU discloses each detected rough peak voxel level represents one floor or one ceiling located inside the voxel. A 2D line-sweeping algorithm is applied to detect the density variation and to find the accurate location of the floor or ceiling. In paragraph [0076]-FU discloses two parallel sweep planes with a small interval are instantiated for the X-Y plane and swept along the Z-axis from the bottom to the top of the peak voxel level. The position along the Z-axis with the greatest total number of laser scanning points is selected as a refined level location. Please also see claim 1 and read paragraph [0063-0072, 0085 and 0089-0090]).
ECKMAN in view of FU fail to teach setting the histogram with most area in an up direction as a ceiling; and setting the histogram with most area in a down direction as a floor.
However, LI explicitly teaches setting the histogram (Fig. 6A. Paragraph [0047]-LI discloses a room is a closed region surrounded by the walls, the ceiling and the floorings, and the rooms (including corridors) are connected by a door on the wall. In the histogram that describes the distribution of points along the gravity direction (i.e., Z-axis), the horizontal structure presents a peak, and the non-horizontal structure presents a trough. The ceiling and the flooring are visible as peaks in the point distribution histogram along gravity vector. Since an inter-story connection region is disposed between a ceiling of a low story and a flooring of a high story and very few horizontal structures exist in the inter-story connection region, the inter-story connection region often presents the trough. Each story and the inter-story connection region between two story may be extracted through a “peak-trough-peak” strategy) with most area in an up direction as a ceiling (Fig. 6A, illustrates a histogram and setting the histogram as a ceiling. Paragraph [0031]. Further in paragraph [0088]-LI discloses for each story, polygonal triangulation is performed for the ceiling, the wall, the door and the flooring to construct a final three-dimensional model of the room and output the model in the form of vector mesh. Please also read paragraph [0051-0053]); and
setting the histogram (Fig. 6A. Paragraph [0047]-LI discloses a room is a closed region surrounded by the walls, the ceiling and the floorings, and the rooms (including corridors) are connected by a door on the wall. In the histogram that describes the distribution of points along the gravity direction (i.e., Z-axis), the horizontal structure presents a peak, and the non-horizontal structure presents a trough. The ceiling and the flooring are visible as peaks in the point distribution histogram along gravity vector. Each story and the inter-story connection region between two story may be extracted through a “peak-trough-peak” strategy) with most area in a down direction as a floor (Fig. 6A, illustrates a histogram and setting the histogram as a floor. Paragraph [0031]. Further in paragraph [0088]-LI discloses for each story, polygonal triangulation is performed for the ceiling, the wall, the door and the flooring to construct a final three-dimensional model of the room and output the model in the form of vector mesh. Please also read paragraph [0051-0053]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU of having a method performed by a processor for locating ceiling and floor within a 3D mesh, the method comprising: locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; making a histogram of the z values, with the teachings of LI of having setting the histogram with most area in an up direction as a ceiling; and setting the histogram with most area in a down direction as a floor.
Wherein ECKMAN’s method having setting the histogram with most area in an up direction as a ceiling; and setting the histogram with most area in a down direction as a floor.
The motivation behind the modification would have been to obtain a method that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while LI provides systems and methods that accelerate data acquisition and improve the accuracy of the reconstructed model. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and LI et al. (US 20200364929 A1), Abstract and paragraph [0003 and 0056].
Regarding claim 2, ECKMAN in view of FU and in further view of LI explicitly teach the method of claim 1, ECKMAN fail to teach wherein the up direction is oriented with respect to gravity.
However, LI explicitly teaches wherein the up direction is oriented with respect to gravity (Fig. 6A. Paragraph [0047]-LI discloses an indoor ceiling and an indoor flooring are both horizontal, and all walls are vertical. A room is a closed region surrounded by the walls, the ceiling and the floorings, and the rooms (including corridors) are connected by a door on the wall. In the histogram that describes the distribution of points along the gravity direction (i.e., Z-axis), the horizontal structure presents a peak, and the non-horizontal structure presents a trough. Thus, the ceiling and the flooring are visible as peaks in the point distribution histogram along gravity vector).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI of having a method performed by a processor for locating ceiling and floor within a 3D mesh, the method comprising: locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; making a histogram of the z values, with the teachings of LI of having wherein the up direction is oriented with respect to gravity.
Wherein ECKMAN’s method having wherein the up direction is oriented with respect to gravity.
The motivation behind the modification would have been to obtain a method that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while LI provides systems and methods that accelerate data acquisition and improve the accuracy of the reconstructed model. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and LI et al. (US 20200364929 A1), Abstract and paragraph [0003 and 0056].
Regarding claim 10, ECKMAN explicitly teaches system (Fig. 1, #104 called a computer system. Paragraph [0044]) for locating features within a 3D mesh (Fig. 1. Paragraph [0046]-ECKMAN discloses FIG. 1B depicts an exemplary system diagram. In paragraph [0042]-ECKMAN discloses FIG. 1A depicts an example mapping system, including a physical space 100, a point cloud 102, a computer system 104, and a map of the physical space 106. In paragraph [0045]-ECKMAN discloses 3D scans and/or images of the physical space 100 can be captured at a time 1. The point cloud 102 can be generated at a time 2. Processing the point cloud at the computer system 104 can be performed at a time 3. And finally, generating the map of the physical space 106 can be performed at a time 4. Moreover, the process can be performed more than once and can be automatically/periodically performed in order to ensure updated maps and/or blueprints of the physical space 100 are maintained. Please also see claim 18), the system comprising:
a processor in communication with a memory storing a 3D mesh (Fig. 1. Paragraph [0046]-ECKMAN discloses FIG. 1B depicts an exemplary system diagram. Further in claim 18-ECKMAN discloses the system comprising: one or more processors; and one or more computer-readable devices including instructions that, when executed by the one or more processors, cause the computerized system to perform operations) the processor being configured to:
locate, within the memory, a 3D mesh representing a surface of a room, the 3D mesh having z values (Fig. 1. Paragraph [0051]-ECKMAN discloses first, the computer system receives a point cloud in step 202. The computer system can receive the point cloud from a drone that takes 3D scans of a physical environment. A new point cloud scan includes at least one of walls, floor, ceiling, etc. In paragraph [0053]-ECKMAN disclose the computer system can detect a bounding box of the physical space (e.g., the warehouse room) that hugs walls of the physical space and then rotate the physical space such that the bounding box is aligned with X, Y, and Z axes. In paragraph [0077]-ECKMAN discloses the computer system can voxelize the point cloud into predetermined mesh sizes. Please also see claim 18 and read paragraph [0080]);
find a minimum z value of the 3D mesh (Fig. 1. Paragraph [0042]-ECKMAN discloses the computer system can determine size information for the selected cluster in step 1108. This can include finding a bounding box for the selected cluster, identifying a minimum x, y, and z coordinate as well as a maximum x, y, and z coordinate for the bounding box);
find a maximum z value of the 3D mesh (Fig. 1. Paragraph [0042]-ECKMAN discloses the computer system can determine size information for the selected cluster in step 1108. This can include finding a bounding box for the selected cluster, identifying a minimum x, y, and z coordinate as well as a maximum x, y, and z coordinate for the bounding box);
use the minimum z value and the maximum z value to make a histogram of the z values (Fig. 1. Paragraph [0082]-ECKMAN discloses the computer system can also apply a histogram filter to the cluster (step 822). Using the histogram filter is beneficial to identify repeated points within each cluster. Once vertical point patterns are identified, the computer system can determine those point patterns are indicative of vertical objects. If points are stacked on top of each other along the Z axis, then it is more indicative of a vertical pole);
ECKMAN fails to explicitly teach determine the histogram with most area in an up direction; determine the histogram with most area in a down direction;
However, FU explicitly teaches determine the histogram with most area in an up direction (Fig. 5. Paragraph [0063]-FU discloses to roughly detect the location of the floors and ceilings, peaks are identified from the histogram projected along the Z-axis. In paragraph [0075]-FU discloses each detected rough peak voxel level represents one floor or one ceiling located inside the voxel. A 2D line-sweeping algorithm is applied to detect the density variation and to find the accurate location of the floor or ceiling. In paragraph [0076]-FU discloses two parallel sweep planes with a small interval are instantiated for the X-Y plane and swept along the Z-axis from the bottom to the top of the peak voxel level. The position along the Z-axis with the greatest total number of laser scanning points is selected as a refined level location. Please also see claim 1 and read paragraph [0063-0072, 0085 and 0089-0090]);
determine the histogram with most area in a down direction (Fig. 5. Paragraph [0063]-FU discloses to roughly detect the location of the floors and ceilings, peaks are identified from the histogram projected along the Z-axis. In paragraph [0075]-FU discloses each detected rough peak voxel level represents one floor or one ceiling located inside the voxel. A 2D line-sweeping algorithm is applied to detect the density variation and to find the accurate location of the floor or ceiling. In paragraph [0076]-FU discloses two parallel sweep planes with a small interval are instantiated for the X-Y plane and swept along the Z-axis from the bottom to the top of the peak voxel level. The position along the Z-axis with the greatest total number of laser scanning points is selected as a refined level location. Please also see claim 1 and read paragraph [0063-0072, 0085 and 0089-0090]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN of having a system for locating features within a 3D mesh, the system comprising: a processor in communication with a memory storing a 3D mesh, the processor being configured to: locate, within the memory, a 3D mesh representing a surface of a room, the 3D mesh having z values; find a minimum z value of the 3D mesh; find a maximum z value of the 3D mesh; use the minimum z value and the maximum z value to make a histogram of the z values, with the teachings of FU of having determine the histogram with most area in an up direction; determine the histogram with most area in a down direction.
Wherein ECKMAN’s system having determine the histogram with most area in an up direction; determine the histogram with most area in a down direction.
The motivation behind the modification would have been to obtain a system that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and FU both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FU provides systems and methods that improves the ability to locate floors and ceilings. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FU et al. (US 20180075285 A1), Abstract and paragraph [0075].
Although FU explicitly teaches set the histogram with most area in an up direction (Fig. 5. Paragraph [0063]-FU discloses to roughly detect the location of the floors and ceilings, peaks are identified from the histogram projected along the Z-axis. In paragraph [0075]-FU discloses each detected rough peak voxel level represents one floor or one ceiling located inside the voxel. A 2D line-sweeping algorithm is applied to detect the density variation and to find the accurate location of the floor or ceiling. In paragraph [0076]-FU discloses two parallel sweep planes with a small interval are instantiated for the X-Y plane and swept along the Z-axis from the bottom to the top of the peak voxel level. The position along the Z-axis with the greatest total number of laser scanning points is selected as a refined level location. Please also see claim 1 and read paragraph [0063-0072, 0085 and 0089-0090]); and
set the histogram with most area in a down direction (Fig. 5. Paragraph [0063]-FU discloses to roughly detect the location of the floors and ceilings, peaks are identified from the histogram projected along the Z-axis. In paragraph [0075]-FU discloses each detected rough peak voxel level represents one floor or one ceiling located inside the voxel. A 2D line-sweeping algorithm is applied to detect the density variation and to find the accurate location of the floor or ceiling. In paragraph [0076]-FU discloses two parallel sweep planes with a small interval are instantiated for the X-Y plane and swept along the Z-axis from the bottom to the top of the peak voxel level. The position along the Z-axis with the greatest total number of laser scanning points is selected as a refined level location. Please also see claim 1 and read paragraph [0063-0072, 0085 and 0089-0090]).
ECKMAN in view of FU fail to teach set the histogram with most area in an up direction as a ceiling; and set the histogram with most area in a down direction as a floor.
However, LI explicitly teaches set the histogram (Fig. 6A. Paragraph [0047]-LI discloses a room is a closed region surrounded by the walls, the ceiling and the floorings, and the rooms (including corridors) are connected by a door on the wall. In the histogram that describes the distribution of points along the gravity direction (i.e., Z-axis), the horizontal structure presents a peak, and the non-horizontal structure presents a trough. The ceiling and the flooring are visible as peaks in the point distribution histogram along gravity vector. Since an inter-story connection region is disposed between a ceiling of a low story and a flooring of a high story and very few horizontal structures exist in the inter-story connection region, the inter-story connection region often presents the trough. Each story and the inter-story connection region between two story may be extracted through a “peak-trough-peak” strategy) with most area in an up direction as a ceiling (Fig. 6A, illustrates a histogram and setting the histogram as a ceiling. Paragraph [0031]. Further in paragraph [0088]-LI discloses for each story, polygonal triangulation is performed for the ceiling, the wall, the door and the flooring to construct a final three-dimensional model of the room and output the model in the form of vector mesh. Please also read paragraph [0051-0053]); and
set the histogram (Fig. 6A. Paragraph [0047]-LI discloses a room is a closed region surrounded by the walls, the ceiling and the floorings, and the rooms (including corridors) are connected by a door on the wall. In the histogram that describes the distribution of points along the gravity direction (i.e., Z-axis), the horizontal structure presents a peak, and the non-horizontal structure presents a trough. The ceiling and the flooring are visible as peaks in the point distribution histogram along gravity vector. Since an inter-story connection region is disposed between a ceiling of a low story and a flooring of a high story and very few horizontal structures exist in the inter-story connection region, the inter-story connection region often presents the trough. Each story and the inter-story connection region between two story may be extracted through a “peak-trough-peak” strategy) with most area in a down direction as a floor (Fig. 6A, illustrates a histogram and setting the histogram as a floor. Paragraph [0031]. Further in paragraph [0088]-LI discloses for each story, polygonal triangulation is performed for the ceiling, the wall, the door and the flooring to construct a final three-dimensional model of the room and output the model in the form of vector mesh. Please also read paragraph [0051-0053]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU of having a system for locating features within a 3D mesh, the system comprising: a processor in communication with a memory storing a 3D mesh, the processor being configured to: locate, within the memory, a 3D mesh representing a surface of a room, the 3D mesh having z values; find a minimum z value of the 3D mesh; find a maximum z value of the 3D mesh; use the minimum z value and the maximum z value to make a histogram of the z values, with the teachings of LI of having set the histogram with most area in an up direction as a ceiling; and set the histogram with most area in a down direction as a floor.
Wherein ECKMAN’s system having set the histogram with most area in an up direction as a ceiling; and set the histogram with most area in a down direction as a floor.
The motivation behind the modification would have been to obtain a system that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while LI provides systems and methods that accelerate data acquisition and improve the accuracy of the reconstructed model. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and LI et al. (US 20200364929 A1), Abstract and paragraph [0003 and 0056].
Regarding claim 11, ECKMAN in view of FU and in further view of LI explicitly teach the system of claim 10, ECKMAN fail to teach wherein the down direction is oriented with respect to gravity.
However, LI explicitly teaches wherein the down direction is oriented with respect to gravity (Fig. 6. Paragraph [0047]-LI discloses an indoor ceiling and an indoor flooring are both horizontal, and all walls are vertical. A room is a closed region surrounded by the walls, the ceiling and the floorings, and the rooms (including corridors) are connected by a door on the wall. In the histogram that describes the distribution of points along the gravity direction (i.e., Z-axis), the horizontal structure presents a peak, and the non-horizontal structure presents a trough. Thus, the ceiling and the flooring are visible as peaks in the point distribution histogram along gravity vector).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI of having a system for locating features within a 3D mesh, the system comprising: a processor in communication with a memory storing a 3D mesh, the processor being configured to: locate, within the memory, a 3D mesh representing a surface of a room, the 3D mesh having z values; find a minimum z value of the 3D mesh; find a maximum z value of the 3D mesh; use the minimum z value and the maximum z value to make a histogram of the z values, with the teachings of LI of having wherein the down direction is oriented with respect to gravity.
Wherein ECKMAN’s system having wherein the down direction is oriented with respect to gravity.
The motivation behind the modification would have been to obtain a system that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while LI provides systems and methods that accelerate data acquisition and improve the accuracy of the reconstructed model. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and LI et al. (US 20200364929 A1), Abstract and paragraph [0003 and 0056].
Regarding claim 17, ECKMAN explicitly teaches a non-transitory machine-readable storage medium encoded with instructions for execution by a processor for locating features within a 3D mesh, the non-transitory machine-readable storage medium (Fig. 1. Paragraph [0042]-ECKMAN discloses FIG. 1A depicts an example mapping system, including a physical space 100, a point cloud 102, a computer system 104, and a map of the physical space 106. In paragraph [0045]-ECKMAN discloses 3D scans and/or images of the physical space 100 can be captured at a time 1. The point cloud 102 can be generated at a time 2. Processing the point cloud at the computer system 104 can be performed at a time 3. And finally, generating the map of the physical space 106 can be performed at a time 4. Moreover, the process can be performed more than once and can be automatically/periodically performed in order to ensure updated maps and/or blueprints of the physical space 100 are maintained. Please also see claim 18) comprising:
instructions for locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values (Fig. 1. Paragraph [0051]-ECKMAN discloses first, the computer system receives a point cloud in step 202. The computer system can receive the point cloud from a drone that takes 3D scans of a physical environment. A a new point cloud scan includes at least one of walls, floor, ceiling, etc. In paragraph [0053]-ECKMAN disclose the computer system can detect a bounding box of the physical space (e.g., the warehouse room) that hugs walls of the physical space and then rotate the physical space such that the bounding box is aligned with X, Y, and Z axes. In paragraph [0077]-ECKMAN discloses the computer system can voxelize the point cloud into predetermined mesh sizes. Please also read paragraph [0080]);
instructions for finding a minimum z value of the 3D mesh (Fig. 1. Paragraph [0042]-ECKMAN discloses the computer system can determine size information for the selected cluster in step 1108. This can include finding a bounding box for the selected cluster, identifying a minimum x, y, and z coordinate as well as a maximum x, y, and z coordinate for the bounding box. Please also see claim 18);
finding a maximum z value of the 3D mesh (Fig. 1. Paragraph [0042]-ECKMAN discloses the computer system can determine size information for the selected cluster in step 1108. This can include finding a bounding box for the selected cluster, identifying a minimum x, y, and z coordinate as well as a maximum x, y, and z coordinate for the bounding box. Please also see claim 18);
instructions for making a histogram of the z values (Fig. 1. Paragraph [0082]-ECKMAN discloses the computer system can also apply a histogram filter to the cluster (step 822). Using the histogram filter is beneficial to identify repeated points within each cluster. Once vertical point patterns are identified, the computer system can determine those point patterns are indicative of vertical objects. If points are stacked on top of each other along the Z axis, then it is more indicative of a vertical pole. Please also see claim 18);
ECKMAN fails to explicitly teach instructions for determining the histogram with highest z values in a positive direction;
However, FU explicitly teaches instructions (Fig. 1, #110 called computer program. Paragraph [0030]. Further in paragraph [0028]-FU discloses FIG. 1 is an exemplary hardware and software environment 100 used to implement one or more embodiments of the invention. The hardware and software environment includes a computer 102 and may include peripherals. The computer 102 comprises a general purpose hardware processor 104A and/or a special purpose hardware processor 104B (hereinafter alternatively collectively referred to as processor 104) and a memory 106. In paragraph [0030]-FU discloses computer 102 operates by the general purpose processor 104A performing instructions defined by the computer program 110 under control of an operating system 108. Please also read paragraph [0033]) for determining the histogram with highest z values in a positive direction (Fig. 5. Paragraph [0063]-FU discloses to roughly detect the location of the floors and ceilings, peaks are identified from the histogram projected along the Z-axis. Please also see claim 1 and read paragraph [0063-0072, 0085 and 0089-0090]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN of having a non-transitory machine-readable storage medium encoded with instructions for execution by a processor for locating features within a 3D mesh, the non-transitory machine-readable storage medium comprising: instructions for locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; instructions for finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; instructions for making a histogram of the z values, with the teachings of FU of having instructions for determining the histogram with highest z values in a positive direction.
Wherein ECKMAN’s non-transitory machine-readable storage medium having instructions for determining the histogram with highest z values in a positive direction.
The motivation behind the modification would have been to obtain a non-transitory machine-readable storage medium that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and FU both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FU provides systems and methods that improves the ability to locate floors and ceilings. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FU et al. (US 20180075285 A1), Abstract and paragraph [0075].
Although FU explicitly teaches instructions (Fig. 1, #110 called computer program. Paragraph [0030]-FU discloses computer 102 operates by the general purpose processor 104A performing instructions defined by the computer program 110 under control of an operating system 108. Please also read paragraph [0028 and 0033]) for setting a histogram with most area in an up direction (Fig. 5. Paragraph [0063]-FU discloses to roughly detect the location of the floors and ceilings, peaks are identified from the histogram projected along the Z-axis. In paragraph [0075]-FU discloses each detected rough peak voxel level represents one floor or one ceiling located inside the voxel. A 2D line-sweeping algorithm is applied to detect the density variation and to find the accurate location of the floor or ceiling. In paragraph [0076]-FU discloses two parallel sweep planes with a small interval are instantiated for the X-Y plane and swept along the Z-axis from the bottom to the top of the peak voxel level. The position along the Z-axis with the greatest total number of laser scanning points is selected as a refined level location. Please also see claim 1 and read paragraph [0063-0072, 0085 and 0089-0090]); and
instructions (Fig. 1, #110 called computer program. Paragraph [0030]-FU discloses computer 102 operates by the general purpose processor 104A performing instructions defined by the computer program 110 under control of an operating system 108. Please also read paragraph [0028 and 0033]) for setting a histogram with most area in a down direction (Fig. 5. Paragraph [0063]-FU discloses to roughly detect the location of the floors and ceilings, peaks are identified from the histogram projected along the Z-axis. In paragraph [0075]-FU discloses each detected rough peak voxel level represents one floor or one ceiling located inside the voxel. A 2D line-sweeping algorithm is applied to detect the density variation and to find the accurate location of the floor or ceiling. In paragraph [0076]-FU discloses two parallel sweep planes with a small interval are instantiated for the X-Y plane and swept along the Z-axis from the bottom to the top of the peak voxel level. The position along the Z-axis with the greatest total number of laser scanning points is selected as a refined level location. Please also see claim 1 and read paragraph [0063-0072, 0085 and 0089-0090]).
ECKMAN in view of FU fail to teach setting a histogram with most area in an up direction as a ceiling; and setting a histogram with most area in a down direction as a floor.
However, LI explicitly teaches setting a histogram (Fig. 6A. Paragraph [0047]-LI discloses a room is a closed region surrounded by the walls, the ceiling and the floorings, and the rooms (including corridors) are connected by a door on the wall. In the histogram that describes the distribution of points along the gravity direction (i.e., Z-axis), the horizontal structure presents a peak, and the non-horizontal structure presents a trough. The ceiling and the flooring are visible as peaks in the point distribution histogram along gravity vector. Each story and the inter-story connection region between two story may be extracted through a “peak-trough-peak” strategy) with most area in an up direction as a ceiling (Fig. 6A, illustrates a histogram and setting the histogram as a ceiling. Paragraph [0031]. Further in paragraph [0088]-LI discloses for each story, polygonal triangulation is performed for the ceiling, the wall, the door and the flooring to construct a final three-dimensional model of the room and output the model in the form of vector mesh. Please also read paragraph [0051-0053]); and
setting a histogram (Fig. 6A. Paragraph [0047]-LI discloses a room is a closed region surrounded by the walls, the ceiling and the floorings, and the rooms (including corridors) are connected by a door on the wall. In the histogram that describes the distribution of points along the gravity direction (i.e., Z-axis), the horizontal structure presents a peak, and the non-horizontal structure presents a trough. The ceiling and the flooring are visible as peaks in the point distribution histogram along gravity vector. Each story and the inter-story connection region between two story may be extracted through a “peak-trough-peak” strategy) with most area in a down direction as a floor (Fig. 6A, illustrates a histogram and setting the histogram as a floor. Paragraph [0031]. Further in paragraph [0088]-LI discloses for each story, polygonal triangulation is performed for the ceiling, the wall, the door and the flooring to construct a final three-dimensional model of the room and output the model in the form of vector mesh. Please also read paragraph [0051-0053]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN of having a non-transitory machine-readable storage medium encoded with instructions for execution by a processor for locating features within a 3D mesh, the non-transitory machine-readable storage medium comprising: instructions for locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; instructions for finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; instructions for making a histogram of the z values, with the teachings of FU of having setting a histogram with most area in an up direction as a ceiling; and setting a histogram with most area in a down direction as a floor.
Wherein ECKMAN’s non-transitory machine-readable storage medium having instructions for setting a histogram with most area in an up direction as a ceiling; and instructions for setting a histogram with most area in a down direction as a floor.
The motivation behind the modification would have been to obtain a non-transitory machine-readable storage medium that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while LI provides systems and methods that accelerate data acquisition and improve the accuracy of the reconstructed model. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and LI et al. (US 20200364929 A1), Abstract and paragraph [0003 and 0056].
Claims 3-9, 12-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over ECKMAN et al. (US 20220335688 A1), hereinafter referenced as ECKMAN in view of FU et al. (US 20180075285 A1), hereinafter referenced as FU and in further view of LI et al. (US 20200364929 A1), hereinafter referenced as LI and in further view of FORD et al. (US 20180144555 A1), hereinafter referenced as FORD.
Regarding claim 3, ECKMAN in view of FU and in further view of LI explicitly teach the method of claim 1, ECKMAN in view of FU and in further view of LI fail to explicitly teach further comprising finding a hole.
However, FORD explicitly teaches further comprising finding a hole (Fig. 1. Paragraph [0032]-FORD discloses the identification component 104 can identify one or more opening areas (e.g., architectural opening areas, architectural window areas, architectural door areas, architectural skylight areas, etc.) associated with 3D data (e.g., 3D-reconstructed data, captured 3D data, mesh data, etc.) and/or a 3D model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI of having a method performed by a processor for locating ceiling and floor within a 3D mesh, the method comprising: locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; making a histogram of the z values, with the teachings of FORD of having further comprising finding a hole.
Wherein ECKMAN’s method having further comprising finding a hole.
The motivation behind the modification would have been to obtain a method that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FORD provides systems and methods that improve 3D models by more accurately finding data associated with an opening area (e.g., windows, doors, skylights, etc.). Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FORD et al. (US 20180144555 A1), Abstract and paragraph [0022-0023].
Regarding claim 4, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teach the method of claim 3, ECKMAN in view of FU and in further view of LI fail to explicitly teach wherein finding a hole further comprises determining a virtual picture bottom and a virtual picture top.
However, FORD explicitly teaches wherein finding a hole (Fig. 1. Paragraph [0032]-FORD discloses the identification component 104 can identify one or more opening areas (e.g., architectural opening areas, architectural window areas, architectural door areas, architectural skylight areas, etc.) associated with 3D data (e.g., 3D-reconstructed data, captured 3D data, mesh data, etc.) and/or a 3D model) further comprises determining a virtual picture bottom and a virtual picture top (Fig. 1. Paragraph [0033]-FORD discloses the identification component 104 can employ predetermined information associated with an architectural opening (e.g., a window, a skylight, a door, etc.) to identify an opening area. A window area generally comprises a height within a certain range (e.g., a height that is less than a door height) and/or generally comprises a lower boundary (e.g., a bottom boundary) that is above (e.g., significantly above) a height of a nearest flat plane associated with a floor. The identification component 104 can identify an architectural opening of a flat surface as a skylight area by employing predetermined information associated with a skylight (e.g., a skylight area is generally located on a flat plane associated with a ceiling, etc.)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI and in further view of FORD of having a method performed by a processor for locating ceiling and floor within a 3D mesh, the method comprising: locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; making a histogram of the z values, with the teachings of FORD of having wherein finding a hole further comprises determining a virtual picture bottom and a virtual picture top.
Wherein ECKMAN’s method having wherein finding a hole further comprises determining a virtual picture bottom and a virtual picture top.
The motivation behind the modification would have been to obtain a method that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FORD provides systems and methods that improve 3D models by more accurately finding data associated with an opening area (e.g., windows, doors, skylights, etc.). Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FORD et al. (US 20180144555 A1), Abstract and paragraph [0022-0023].
Regarding claim 5, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teach the method of claim 4, ECKMAN fails to explicitly teach further comprising locating a 2D segment correlated with the 3D mesh.
However, FU explicitly teaches further comprising locating a 2D segment correlated with the 3D mesh (Fig. 5. Paragraph [0087]-FU discloses reference grid information is extracted from the 3D voxel structure. The floor plan points are projected on 2D space and the reference grids are extracted along both the X-axis and Y-axis. In paragraph [0089]-FU discloses a peak in the Y-axis histogram identifies a rough wall location or location marker of the reference grid. In paragraph [0090]-FU discloses peak in the X-axis histogram identifies a rough wall location or location marker of the reference grid).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI and in further view of FORD of having a method performed by a processor for locating ceiling and floor within a 3D mesh, the method comprising: locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; making a histogram of the z values, with the teachings of FU of having further comprising locating a 2D segment correlated with the 3D mesh.
Wherein ECKMAN’s method having further comprising locating a 2D segment correlated with the 3D mesh.
The motivation behind the modification would have been to obtain a method that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and FU both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FU provides systems and methods that improves the ability to locate floors and ceilings. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FU et al. (US 20180075285 A1), Abstract and paragraph [0075].
Regarding claim 6, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teach the method of claim 5, ECKMAN fails to explicitly teach further comprising using the virtual picture bottom and the virtual picture top, taking a virtual 2D picture of the 3D mesh along the 2D segment.
However, FU explicitly teaches further comprising using the virtual picture bottom and the virtual picture top (Fig. 5. Paragraph [0077]-FU discloses after the locations of the floors and ceilings have been detected, this information is then used to segment the whole facility into several level regions and to handle the points level by level), taking a virtual 2D picture of the 3D mesh along the 2D segment (Fig. 5. Paragraph [0079]-FU discloses the segmented point cloud data of each level are projected directly to the X-Y plane. In paragraph [0087]-FU discloses the reference grid information is extracted from the 3D voxel structure. The floor plan points are projected on 2D space and the reference grids are extracted along both the X-axis and Y-axis).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI and in further view of FORD of having a method performed by a processor for locating ceiling and floor within a 3D mesh, the method comprising: locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; making a histogram of the z values, with the teachings of FU of having further comprising using the virtual picture bottom and the virtual picture top, taking a virtual 2D picture of the 3D mesh along the 2D segment.
Wherein ECKMAN’s method having further comprising using the virtual picture bottom and the virtual picture top, taking a virtual 2D picture of the 3D mesh along the 2D segment.
The motivation behind the modification would have been to obtain a method that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and FU both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FU provides systems and methods that improves the ability to locate floors and ceilings. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FU et al. (US 20180075285 A1), Abstract and paragraph [0075].
Regarding claim 7, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teach the method of claim 6, ECKMAN in view of FU and in further view of LI fails to explicitly teach further comprisIng finding the hole within the virtual 2D picture.
However, FORD explicitly teaches further comprisIng finding the hole (Fig. 1. Paragraph [0032]-FORD discloses the identification component 104 can identify one or more opening areas (e.g., architectural opening areas, architectural window areas, architectural door areas, architectural skylight areas, etc.) associated with 3D data (e.g., 3D-reconstructed data, captured 3D data, mesh data, etc.) and/or a 3D model) within the virtual 2D picture (Fig. 1. Paragraph [0023]-FORD discloses data for an opening area can be determined and/or generated based on the 2D image data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI and in further view of FORD of having a method performed by a processor for locating ceiling and floor within a 3D mesh, the method comprising: locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; making a histogram of the z values, with the teachings of FORD of having further comprisIng finding the hole within the virtual 2D picture.
Wherein ECKMAN’s method having further comprisIng finding the hole within the virtual 2D picture.
The motivation behind the modification would have been to obtain a method that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FORD provides systems and methods that improve 3D models by more accurately finding data associated with an opening area (e.g., windows, doors, skylights, etc.). Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FORD et al. (US 20180144555 A1), Abstract and paragraph [0022-0023].
Regarding claim 8, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teach the method of claim 7, ECKMAN in view of FU and in further view of LI fails to explicitly teach wherein when the hole does not intersect the vertical picture bottom or when the hole is not at least as wide as a predetermined door width, then classifying the hole as a window.
However, FORD explicitly teaches wherein when the hole does not intersect the vertical picture bottom or when the hole is not at least as wide as a predetermined door width, then classifying the hole as a window (Fig. 1. Paragraph [0033]-FORD discloses a window area generally comprises a height within a certain range (e.g., a height that is less than a door height) and/or generally comprises a lower boundary (e.g., a bottom boundary) that is above (e.g., significantly above) a height of a nearest flat plane associated with a floor. The identification component 104 can identify an architectural opening of a flat surface as a window area in response to a determination that a height of the architectural opening is less than a threshold level (e.g., a certain height that is less than a door opening, etc.) and/or based on particular boundary data associated with the architectural opening. The identification component 104 can identify an architectural opening of a flat surface as a door area by employing predetermined information associated with a door. A door area generally comprises a height within a certain range (e.g., a height that is greater than a window height) and/or generally comprises a lower boundary (e.g., a bottom boundary) that corresponds to flat plane associated with a floor).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI and in further view of FORD of having a method performed by a processor for locating ceiling and floor within a 3D mesh, the method comprising: locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; making a histogram of the z values, with the teachings of FORD of having wherein when the hole does not intersect the vertical picture bottom or when the hole is not at least as wide as a predetermined door width, then classifying the hole as a window.
Wherein ECKMAN’s method having wherein when the hole does not intersect the vertical picture bottom or when the hole is not at least as wide as a predetermined door width, then classifying the hole as a window.
The motivation behind the modification would have been to obtain a method that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FORD provides systems and methods that improve 3D models by more accurately finding data associated with an opening area (e.g., windows, doors, skylights, etc.). Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FORD et al. (US 20180144555 A1), Abstract and paragraph [0022-0023].
Regarding claim 9, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teach the method of claim 8, ECKMAN fails to explicitly teach wherein taking a virtual 2D picture of the 3D mesh along the 2D segment comprises taking a virtual picture with a y axis running from the virtual picture bottom to the virtual picture top.
However, FU explicitly teaches wherein taking a virtual 2D picture of the 3D mesh along the 2D segment comprises taking a virtual picture with a y axis running from the virtual picture bottom to the virtual picture top (Fig. 5. Paragraph [0077]-FU discloses after the locations of the floors and ceilings have been detected, this information is then used to segment the whole facility into several level regions and to handle the points level by level. In paragraph [0079]-FU discloses the segmented point cloud data of each level are projected directly to the X-Y plane. In paragraph [0087]-FU discloses the reference grid information is extracted from the 3D voxel structure. The floor plan points are projected on 2D space and the reference grids are extracted along both the X-axis and Y-axis. Please also read paragraph [0054, 0058 and 0060]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI and in further view of FORD of having a method performed by a processor for locating ceiling and floor within a 3D mesh, the method comprising: locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; making a histogram of the z values, with the teachings of FU of having wherein taking a virtual 2D picture of the 3D mesh along the 2D segment comprises taking a virtual picture with a y axis running from the virtual picture bottom to the virtual picture top.
Wherein ECKMAN’s method having wherein taking a virtual 2D picture of the 3D mesh along the 2D segment comprises taking a virtual picture with a y axis running from the virtual picture bottom to the virtual picture top.
The motivation behind the modification would have been to obtain a method that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and FU both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FU provides systems and methods that improves the ability to locate floors and ceilings. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FU et al. (US 20180075285 A1), Abstract and paragraph [0075].
Regarding claim 12, ECKMAN in view of FU and in further view of LI explicitly teach the system of claim 10, ECKMAN in view of FU and in further view of LI fail to explicitly teach the processor being further configured to find a hole.
However, FORD explicitly teaches the processor (Fig. 1, #112 called a processor. Paragraph [0026]-FORD discloses the system 100 can include architectural opening reconstruction component 102. In FIG. 1, the architectural opening reconstruction component 102 includes an identification component 104, a spatial data component 106, a visual data component 108 and/or a rendering component 110. System 100 can include memory 114 for storing computer executable components and instructions. System 100 can further include a processor 112 to facilitate operation of the instructions (e.g., computer executable components and instructions) by system 100) being further configured to find a hole (Fig. 1. Paragraph [0032]-FORD discloses the identification component 104 can identify one or more opening areas (e.g., architectural opening areas, architectural window areas, architectural door areas, architectural skylight areas, etc.) associated with 3D data (e.g., 3D-reconstructed data, captured 3D data, mesh data, etc.) and/or a 3D model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI of having a system for locating features within a 3D mesh, the system comprising: a processor in communication with a memory storing a 3D mesh, the processor being configured to: locate, within the memory, a 3D mesh representing a surface of a room, the 3D mesh having z values; find a minimum z value of the 3D mesh; find a maximum z value of the 3D mesh; use the minimum z value and the maximum z value to make a histogram of the z values, with the teachings of FORD of having the processor being further configured to find a hole.
Wherein ECKMAN’s system having the processor being further configured to find a hole.
The motivation behind the modification would have been to obtain a system that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FORD provides systems and methods that improve 3D models by more accurately finding data associated with an opening area (e.g., windows, doors, skylights, etc.). Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FORD et al. (US 20180144555 A1), Abstract and paragraph [0022-0023].
Regarding claim 13, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teaches the system of claim 12, ECKMAN further teaches the processor being further configured to determine a virtual picture bottom and a virtual picture top (Fig. 1. Paragraph [0051]-ECKMAN discloses first, the computer system receives a point cloud in step 202. The computer system can receive the point cloud from a drone that takes 3D scans of a physical environment. A new point cloud scan includes at least one of walls, floor, ceiling, etc. In paragraph [0053]-ECKMAN disclose the computer system can detect a bounding box of the physical space (e.g., the warehouse room) that hugs walls of the physical space and then rotate the physical space such that the bounding box is aligned with X, Y, and Z axes. In paragraph [0077]-ECKMAN discloses the computer system can voxelize the point cloud into predetermined mesh sizes. Further in paragraph [0042]-ECKMAN discloses the computer system can determine size information for the selected cluster. This can include finding a bounding box for the selected cluster, identifying a minimum x, y, and z coordinate as well as a maximum x, y, and z coordinate for the bounding box Please also see claim 18 and read paragraph [0067-0069 and 0080]).
Regarding claim 14, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teach the system of claim 13, ECKMAN fails to explicitly teach the processor being further configured to locate a 2D segment correlated with the 3D mesh.
However, FU further teaches the processor (Fig. 1, #104A called a processor. Paragraph [0030]-FU discloses computer 102 operates by the general purpose processor 104A performing instructions defined by the computer program 110 under control of an operating system 108. Please also read paragraph [0028 and 0033]) being further configured to locate a 2D segment correlated with the 3D mesh (Fig. 5. Paragraph [0087]-FU discloses reference grid information is extracted from the 3D voxel structure. The floor plan points are projected on 2D space and the reference grids are extracted along both the X-axis and Y-axis. In paragraph [0089]-FU discloses a peak in the Y-axis histogram identifies a rough wall location or location marker of the reference grid. In paragraph [0090]-FU discloses peak in the X-axis histogram identifies a rough wall location or location marker of the reference grid).
Regarding claim 15, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teach the system of claim 14, ECKMAN fails to explicitly teach the processor being further configured to: use the virtual picture bottom and the virtual picture top, taking a virtual 2D picture of the 3D mesh along the 2D segment.
However, FU explicitly teaches the processor (Fig. 1, #104A called a processor. Paragraph [0030]-FU discloses computer 102 operates by the general purpose processor 104A performing instructions defined by the computer program 110 under control of an operating system 108. Please also read paragraph [0028 and 0033]) being further configured to:
use the virtual picture bottom and the virtual picture top (Fig. 5. Paragraph [0077]-FU discloses after the locations of the floors and ceilings have been detected, this information is then used to segment the whole facility into several level regions and to handle the points level by level), taking a virtual 2D picture of the 3D mesh along the 2D segment (Fig. 9. Paragraph [0079]-FU discloses the segmented point cloud data of each level are projected directly to the X-Y plane. In paragraph [0087]-FU discloses the reference grid information is extracted from the 3D voxel structure. The floor plan points are projected on 2D space and the reference grids are extracted along both the X-axis and Y-axis).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI and in further view of FORD of having a system for locating features within a 3D mesh, the system comprising: a processor in communication with a memory storing a 3D mesh, the processor being configured to: locate, within the memory, a 3D mesh representing a surface of a room, the 3D mesh having z values; find a minimum z value of the 3D mesh; find a maximum z value of the 3D mesh; use the minimum z value and the maximum z value to make a histogram of the z values, with the teachings of FU of having processor being further configured to: use the virtual picture bottom and the virtual picture top, taking a virtual 2D picture of the 3D mesh along the 2D segment; and
Wherein ECKMAN’s system having processor being further configured to: use the virtual picture bottom and the virtual picture top, taking a virtual 2D picture of the 3D mesh along the 2D segment.
The motivation behind the modification would have been to obtain a system that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and FU both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FU provides systems and methods that improves the ability to locate floors and ceilings. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FU et al. (US 20180075285 A1), Abstract and paragraph [0075].
ECKMAN in view of FU and in further view of LI fails to explicitly teach find the hole within the virtual 2D picture.
However, FORD explicitly teaches find the hole within the virtual 2D picture (Fig. 9. Paragraph [0032]-FORD discloses the identification component 104 can identify one or more opening areas (e.g., architectural opening areas, architectural window areas, architectural door areas, architectural skylight areas, etc.) associated with 3D data (e.g., 3D-reconstructed data, captured 3D data, mesh data, etc.) and/or a 3D model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI and in further view of FORD of having a system for locating features within a 3D mesh, the system comprising: a processor in communication with a memory storing a 3D mesh, the processor being configured to: locate, within the memory, a 3D mesh representing a surface of a room, the 3D mesh having z values; find a minimum z value of the 3D mesh; find a maximum z value of the 3D mesh; use the minimum z value and the maximum z value to make a histogram of the z values, with the teachings of FORD of having find the hole within the virtual 2D picture.
Wherein ECKMAN’s system having find the hole within the virtual 2D picture.
The motivation behind the modification would have been to obtain a system that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FORD provides systems and methods that improve 3D models by more accurately finding data associated with an opening area (e.g., windows, doors, skylights, etc.). Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FORD et al. (US 20180144555 A1), Abstract and paragraph [0022-0023].
Regarding claim 16, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teach the system of claim 15, ECKMAN fails to explicitly teach wherein taking a virtual 2D picture of the 3D mesh along the 2D segment comprises taking a virtual picture with a y axis running from the virtual picture bottom to the virtual picture top.
However, FU explicitly teaches wherein taking a virtual 2D picture of the 3D mesh along the 2D segment comprises taking a virtual picture with a y axis running from the virtual picture bottom to the virtual picture top (Fig. 5. Paragraph [0077]-FU discloses after the locations of the floors and ceilings have been detected, this information is then used to segment the whole facility into several level regions and to handle the points level by level. In paragraph [0079]-FU discloses the segmented point cloud data of each level are projected directly to the X-Y plane. In paragraph [0087]-FU discloses the reference grid information is extracted from the 3D voxel structure. The floor plan points are projected on 2D space and the reference grids are extracted along both the X-axis and Y-axis).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI and in further view of FORD of having a system for locating features within a 3D mesh, the system comprising: a processor in communication with a memory storing a 3D mesh, the processor being configured to: locate, within the memory, a 3D mesh representing a surface of a room, the 3D mesh having z values; find a minimum z value of the 3D mesh; find a maximum z value of the 3D mesh; use the minimum z value and the maximum z value to make a histogram of the z values, with the teachings of FU of having wherein taking a virtual 2D picture of the 3D mesh along the 2D segment comprises taking a virtual picture with a y axis running from the virtual picture bottom to the virtual picture top.
Wherein ECKMAN’s system having wherein taking a virtual 2D picture of the 3D mesh along the 2D segment comprises taking a virtual picture with a y axis running from the virtual picture bottom to the virtual picture top.
The motivation behind the modification would have been to obtain a system that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and FU both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FU provides systems and methods that improves the ability to locate floors and ceilings. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FU et al. (US 20180075285 A1), Abstract and paragraph [0075].
Regarding claim 18, ECKMAN in view of FU and in further view of LI explicitly teach the non-transitory machine-readable storage medium of claim 17, ECKMAN in view of FU and in further view of LI fail to explicitly teach further comprising finding a hole.
However, FORD explicitly teaches further comprising finding a hole (Fig. 1. Paragraph [0032]-FORD discloses the identification component 104 can identify one or more opening areas (e.g., architectural opening areas, architectural window areas, architectural door areas, architectural skylight areas, etc.) associated with 3D data (e.g., 3D-reconstructed data, captured 3D data, mesh data, etc.) and/or a 3D model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI of having a non-transitory machine-readable storage medium encoded with instructions for execution by a processor for locating features within a 3D mesh, the non-transitory machine-readable storage medium comprising: instructions for locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; instructions for finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; instructions for making a histogram of the z values, with the teachings of FORD of having further comprising finding a hole.
Wherein ECKMAN’s non-transitory machine-readable storage medium having further comprising finding a hole.
The motivation behind the modification would have been to obtain a non-transitory machine-readable storage medium that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FORD provides systems and methods that improve 3D models by more accurately finding data associated with an opening area (e.g., windows, doors, skylights, etc.). Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FORD et al. (US 20180144555 A1), Abstract and paragraph [0022-0023].
Regarding claim 19, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teach the non-transitory machine-readable storage medium of claim 18, ECKMAN in view of FU and in further view of LI fail to explicitly teach wherein finding a hole further comprises determining a virtual picture bottom and a virtual picture top.
However, FORD explicitly teaches wherein finding a hole (Fig. 1. Paragraph [0032]-FORD discloses the identification component 104 can identify one or more opening areas (e.g., architectural opening areas, architectural window areas, architectural door areas, architectural skylight areas, etc.) associated with 3D data (e.g., 3D-reconstructed data, captured 3D data, mesh data, etc.) and/or a 3D model) further comprises determining a virtual picture bottom and a virtual picture top (Fig. 1. Paragraph [0033]-FORD discloses the identification component 104 can employ predetermined information associated with an architectural opening (e.g., a window, a skylight, a door, etc.) to identify an opening area. A window area generally comprises a height within a certain range (e.g., a height that is less than a door height) and/or generally comprises a lower boundary (e.g., a bottom boundary) that is above (e.g., significantly above) a height of a nearest flat plane associated with a floor. The identification component 104 can identify an architectural opening of a flat surface as a skylight area by employing predetermined information associated with a skylight (e.g., a skylight area is generally located on a flat plane associated with a ceiling, etc.)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI of having a non-transitory machine-readable storage medium encoded with instructions for execution by a processor for locating features within a 3D mesh, the non-transitory machine-readable storage medium comprising: instructions for locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; instructions for finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; instructions for making a histogram of the z values, with the teachings of FORD of having wherein finding a hole further comprises determining a virtual picture bottom and a virtual picture top.
Wherein ECKMAN’s non-transitory machine-readable storage medium having wherein finding a hole further comprises determining a virtual picture bottom and a virtual picture top.
The motivation behind the modification would have been to obtain a non-transitory machine-readable storage medium that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and LI both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FORD provides systems and methods that improve 3D models by more accurately finding data associated with an opening area (e.g., windows, doors, skylights, etc.). Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FORD et al. (US 20180144555 A1), Abstract and paragraph [0022-0023].
Regarding claim 20, ECKMAN in view of FU and in further view of LI and in further view of FORD explicitly teach the non-transitory machine-readable storage medium of claim 18, ECKMAN fails to explicitly teach further comprising: locating a 2D segment correlated with the 3D mesh; and using a virtual picture bottom and a virtual picture top, taking a virtual 2D picture of the 3D mesh along the 2D segment.
However, FU further teaches further comprising: locating a 2D segment correlated with the 3D mesh (Fig. 5. Paragraph [0087]-FU discloses a reference grid information is extracted from the 3D voxel structure. The floor plan points are projected on 2D space and the reference grids are extracted along both the X-axis and Y-axis. In paragraph [0089]-FU discloses a peak in the Y-axis histogram identifies a rough wall location or location marker of the reference grid. In paragraph [0090]-FU discloses peak in the X-axis histogram identifies a rough wall location or location marker of the reference grid); and
using a virtual picture bottom and a virtual picture top, taking a virtual 2D picture of the 3D mesh along the 2D segment (Fig. 5. Paragraph [0077]-FU discloses after the locations of the floors and ceilings have been detected, this information is then used to segment the whole facility into several level regions and to handle the points level by level. In paragraph [0079]-FU discloses the segmented point cloud data of each level are projected directly to the X-Y plane. In paragraph [0087]-FU discloses the reference grid information is extracted from the 3D voxel structure. The floor plan points are projected on 2D space and the reference grids are extracted along both the X-axis and Y-axis).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of ECKMAN in view of FU and in further view of LI and in further view of FORD of having a non-transitory machine-readable storage medium encoded with instructions for execution by a processor for locating features within a 3D mesh, the non-transitory machine-readable storage medium comprising: instructions for locating, within a memory associated with the processor, a 3D mesh representing a surface of a room, the 3D mesh having z values; instructions for finding a minimum z value of the 3D mesh; finding a maximum z value of the 3D mesh; instructions for making a histogram of the z values, with the teachings of FORD of having further comprising: locating a 2D segment correlated with the 3D mesh; and using a virtual picture bottom and a virtual picture top, taking a virtual 2D picture of the 3D mesh along the 2D segment.
Wherein ECKMAN’s non-transitory machine-readable storage medium having further comprising: locating a 2D segment correlated with the 3D mesh; and using a virtual picture bottom and a virtual picture top, taking a virtual 2D picture of the 3D mesh along the 2D segment.
The motivation behind the modification would have been to obtain a non-transitory machine-readable storage medium that improves improve the accuracy, efficiency and reconstruction physical spaces, since both ECKMAN and FU both concern image reconstruction. Wherein ECKMANs systems and methods improve the accuracy of blueprints and maps of physical spaces, while FU provides systems and methods that improves the ability to locate floors and ceilings. Please see ECKMAN et al. (US 20220335688 A1), Abstract and Paragraph [0019-0020] and FU et al. (US 20180075285 A1), Abstract and paragraph [0075].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant`s disclosure.
SELVIAH et al. (US 20200043186 A1)- The present invention lies in the field of 3D surveying, mapping, and imaging. In particular, the present invention relates to the rotational alignment of 3D datasets. Embodiments include an apparatus method and program for rotational and optionally also translational alignment of 3D datasets. The 3D datasets being stored as point clouds, transformed into vector sets, and the vector sets being represented as a unit sphere or Gaussian sphere and compared for best alignment. The found best alignment is used to rotate the two 3D datasets into rotational and translational alignment with one another........................ Please see para. [0325-0326]. Abstract.
BEIR et al. (US 20230325549 A1)- A system and method for automatic floorplan generation is provided. Mesh triangles are gathered for a space having one or more walls and a floor. A request for a floorplan for the space is received. A facing direction of the floor is determined from the mesh triangles. The mesh triangles are rotated until the floor is horizontal. A primary wall facing direction is also determined from the mesh triangles. The mesh triangles are rotated so the primary wall facing direction is parallel to a major axis or other desired direction. The floorplan is generated based on the floor and walls....................... Please see Fig. 1-3 and para. [0027, 0038 and 0043-0044]. Abstract.
BELL et al. (US 20210141965 A1)- Systems and techniques for processing three-dimensional (3D) data are presented. Captured three-dimensional (3D) data associated with a 3D model of an architectural environment is received and at least a portion of the captured 3D data associated with a flat surface is identified. Furthermore, missing data associated with the portion of the captured 3D data is identified and additional 3D data for the missing data is generated based on other data associated with the portion of the captured 3D data....................... Please see Fig. 1-4 and 6-7. Abstract.
PUGH et al. (US 20210142497 A1)- System and method for rendering virtual objects onto an image....................... Please see Fig. 1 and 5 and para. [0051-0053 and 0103-0104]. Abstract.
GAUSEBECK et al. (US 20190035165 A1)- The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises receiving, by a system operatively coupled to a processor, a two-dimensional image, and determining, by the system, auxiliary data for the two-dimensional image, wherein the auxiliary data comprises orientation information regarding a capture orientation of the two-dimensional image. The method further comprises, deriving, by the system, three-dimensional information for the two-dimensional image using one or more neural network models configured to infer the three-dimensional information based on the two-dimensional image and the auxiliary data........................ Please see Fig. 1-7. Abstract.
BELL et al. (US 20210158618 A1)- Systems and methods for generating three-dimensional models with correlated three-dimensional and two dimensional imagery data are provided. In particular, imagery data can be captured in two dimensions and three dimensions. Imagery data can be transformed into models. Two-dimensional data and three-dimensional data can be correlated within models. Two-dimensional data can be selected for display within a three-dimensional model. Modifications can be made to the three-dimensional model and can be displayed within a three-dimensional model or within two-dimensional data. Models can transition between two dimensional imagery data and three dimensional imagery data......................... Please see para. [0076-0077]. Abstract.
XU et al. (US-20230099463-A1)- Various implementations provide a 3D floor plan based on scanning a room and detecting windows, doors, and openings using 2D orthographic projection. Points of a dense set of points (e.g., a dense point cloud) that are close to a plane representing a wall are projected onto the plane and used to identify windows, doors, and opening on the wall. Representations of the detected windows, doors, and openings may then be positioned in a 3D floor plan based on the known position of the wall within the corresponding room, i.e., the location of the wall plane relative to the dense point cloud is known. Other aspects of a 3D floor plan may be detected directly from points of a dense 3D point cloud, windows, doors, and openings may be detected indirectly using projections of the points of the 3D point cloud onto a 2D plane, and the detected aspects may be combined into a single 3D floor plan........................ Please see Fig. 1-7. Abstract.
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to Aaron Bonansinga whose telephone number is (703) 756-5380 The examiner can normally be reached on Monday-Friday, 9:00 a.m. - 6:00 p.m. ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s
supervisor, Chineyere Wills-Burns can be reached by phone at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/AARON TIMOTHY BONANSINGA/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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