DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Response to Arguments
Applicant’s arguments see remarks, filed 07/21/2026, with respect to the claims 1-21 have been fully considered but are not persuasive.
The applicant argues on page 7, “Applicant respectfully submits that Doria fails to disclose, explicitly or inherently, each and every feature of the pending claims. For example, Doria fails to disclose, explicitly or inherently, "estimate that the object is present at an end portion in a longitudinal direction of the point cloud missing region," as set forth in the pending claims.”
In response, the office does not find this argument to be persuasive. Based on the breadth of the claim language the prior art by Doria et al. (US 20190138823 A1), explicitly teaches wherein the at least one processor is further configured to execute the instructions to: estimate that the object is present at an end portion in a longitudinal direction of the point cloud missing region (Fig. 4, Paragraph [0032]- Doria discloses the end point 605 is within the area of interest and represents a temporary object (e.g., an occlusion, etc.) or a permanent object (e.g., an object to include in the localization model, etc.) (wherein the end point representing a temporary object is seen to be positioned at a longitudinal end of the grid in Fig. 4).).
The applicant argues on page 7, “Claims 3-4 are rejected as allegedly being unpatentable over Doria in view of Uhlenbrock et al. (US 20190392595 A1) hereafter referenced as Uhlenbrock and Sato et al. (US 20250045875 A1) hereafter referenced as Sato as set forth on pages 12-19 of the office action.
Applicant respectfully disagrees. As explained above, Doria fails to disclose each and every limitation of claim 1, from which claims 3-4 depend. Uhlenbrock and Sato fail to remedy the deficiencies of Doria because Uhlenbrock and Sato are relied upon only for limitations recited in the dependent claims.”
In response, the office does not find this argument to be persuasive base on the same reasons set forth above and the rejection below.
The applicant argues on page 8, “Claims 6 is rejected as allegedly being unpatentable over Doria in view of Uhlenbrock as set forth on pages 19-22 of the office action.
Applicant respectfully disagrees. As explained above, Doria fails to disclose each and every limitation of claim 1, from which claim 6 depends. Uhlenbrock fails to remedy the deficiencies of Doria because Uhlenbrock is relied upon only for limitations recited in claim 6 and does not disclose or suggest estimating that an object is present at an end portion in a longitudinal direction of a point cloud missing region.”
In response, the office does not find this argument to be persuasive base on the same reasons set forth above and the rejection below.
The applicant argues on page 8, “Claims 7 is rejected as allegedly being unpatentable over Doria in view of Uhlenbrock and Agarwal et al. (US 8886387 B1) hereafter referenced as Agarwal as set forth on pages 23-25 of the office action.
Applicant respectfully disagrees. As explained above, Doria fails to disclose each and every limitation of claim 1, from which claim 7 depends. Uhlenbrock and Agarwal fail to remedy the deficiencies of Doria because they are relied upon only for limitations recited in the dependent claim and do not disclose or suggest estimating that an object is present at an end portion in a longitudinal direction of a point cloud missing region.”
In response, the office does not find this argument to be persuasive base on the same reasons set forth above and the rejection below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claims 6-7, recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 6; recites the limitation, “Lidar apparatus for generating……,” [Line 5-6].
Claim 7; recites the limitation, “ranging using the Lidar apparatus……,” [Line 2-3].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claim 6-7:
“Lidar apparatus” (Fig. 5, #17 Paragraph [0071]- “That is, the point cloud missing region extraction unit 11 extracts the first point cloud missing region 21 and the second point cloud missing region 22 in each of the first three-dimensional point cloud and the second three-dimensional point cloud in which the ranging positions of the LiDAR apparatus 17 (ranging apparatus) for generating the three-dimensional point cloud are different from each other.” Fig. 5, shows the Lidar apparatus as a Black box. Wherein the Lidar apparatus has the structure of a LiDAR.)
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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.
Claims 1-2, 5, 8-11, and 16 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Doria et al. (US 20190138823 A1) hereafter referenced as Doria.
Regarding claim 1, Doria teaches an object detection system comprising (Fig. 1, Paragraph [0005]- Doria discloses an apparatus for detecting an occlusion from light detection and ranging (LIDAR) data is provided. Further in Fig. 4, Paragraph [0032]- Doria discloses the end point 605 is within the area of interest and represents a temporary object (e.g., an occlusion, etc.) or a permanent object (e.g., an object to include in the localization model, etc.).):
at least one memory storing computer-executable instructions (Fig. 7, Paragraph [0005]- Doria discloses the apparatus includes at least one LIDAR sensor, at least one processor, and at least one memory including computer program code for one or more programs. The memory and the computer program code is configured to, using the processor, cause the apparatus to receive LIDAR data for a region of interest from the LIDAR sensor and to assemble the LIDAR data as point cloud data in a spatial data structure.);
and at least one processor configured to access the at least one memory and execute the computer-executable instructions to (Fig. 7, Paragraph [0005]- Doria discloses the apparatus includes at least one LIDAR sensor, at least one processor, and at least one memory including computer program code for one or more programs. The memory and the computer program code is configured to, using the processor, cause the apparatus to receive LIDAR data for a region of interest from the LIDAR sensor and to assemble the LIDAR data as point cloud data in a spatial data structure.):
extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud (Fig. 5, Paragraph [0046]- Doria discloses at act 705, an occlusion is identified in the region of interest. The occlusion is identified by the developer system 121 of the server 125 based on the hidden spaces of the grid representation. The location of the occlusion is identified, and according to an embodiment, the severity of the occlusion is also identified. For example, the severity of the occlusion may include the size and completeness of the occlusion.);
estimate that the object is present at an end portion in a longitudinal direction of the point cloud missing region (Fig. 4, Paragraph [0032]- Doria discloses the end point 605 is within the area of interest and represents a temporary object (e.g., an occlusion, etc.) or a permanent object (e.g., an object to include in the localization model, etc.) (wherein the end point representing a temporary object is seen to be positioned at a longitudinal end of the grid in Fig. 4).).
Regarding claim 2, Doria teaches the object detection system according to claim 1, Doria further teaches wherein the point cloud missing region is a region within a surface detected based on the three-dimensional point cloud (Fig. 5, Paragraph [0043]- Doria discloses the occlusions may be detected from point cloud data for mapmaking. In an embodiment, two stages or layers of mapmaking are performed. For example, to map roadways, a lane model is generated to identify and map painted lines on the roadway. A localization model is then generated to identify roadside objects. Occlusions may be detected during both stages of mapmaking. Different regions of interest are defined for each stage (wherein a roadway is a surface).).
Regarding claim 8, Doria teaches an object detection apparatus comprising (Fig. 1, Paragraph [0005]- Doria discloses an apparatus for detecting an occlusion from light detection and ranging (LIDAR) data is provided. Further in Fig. 4, Paragraph [0032]- Doria discloses the end point 605 is within the area of interest and represents a temporary object (e.g., an occlusion, etc.) or a permanent object (e.g., an object to include in the localization model, etc.).):
at least one memory storing computer-executable instructions (Fig. 7, Paragraph [0005]- Doria discloses the apparatus includes at least one LIDAR sensor, at least one processor, and at least one memory including computer program code for one or more programs. The memory and the computer program code is configured to, using the processor, cause the apparatus to receive LIDAR data for a region of interest from the LIDAR sensor and to assemble the LIDAR data as point cloud data in a spatial data structure.);
and at least one processor configured to access the at least one memory and execute the computer-executable instructions to (Fig. 7, Paragraph [0005]- Doria discloses the apparatus includes at least one LIDAR sensor, at least one processor, and at least one memory including computer program code for one or more programs. The memory and the computer program code is configured to, using the processor, cause the apparatus to receive LIDAR data for a region of interest from the LIDAR sensor and to assemble the LIDAR data as point cloud data in a spatial data structure.):
extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud (Fig. 5, Paragraph [0046]- Doria discloses at act 705, an occlusion is identified in the region of interest. The occlusion is identified by the developer system 121 of the server 125 based on the hidden spaces of the grid representation. The location of the occlusion is identified, and according to an embodiment, the severity of the occlusion is also identified. For example, the severity of the occlusion may include the size and completeness of the occlusion.);
and estimate that the object is present at an end portion in a longitudinal direction of the point cloud missing region (Fig. 4, Paragraph [0032]- Doria discloses the end point 605 is within the area of interest and represents a temporary object (e.g., an occlusion, etc.) or a permanent object (e.g., an object to include in the localization model, etc.) (wherein the end point representing a temporary object is seen to be positioned at a longitudinal end of the grid in Fig. 4).).
Regarding claim 9, Doria teaches a computer-implemented object detection method comprising (Fig. 1, Paragraph [0005]- Doria discloses a method for detecting an occlusion from point cloud data is provided. Further in Fig. 4, Paragraph [0032]- Doria discloses the end point 605 is within the area of interest and represents a temporary object (e.g., an occlusion, etc.) or a permanent object (e.g., an object to include in the localization model, etc.).):
extracting a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud (Fig. 5, Paragraph [0046]- Doria discloses at act 705, an occlusion is identified in the region of interest. The occlusion is identified by the developer system 121 of the server 125 based on the hidden spaces of the grid representation. The location of the occlusion is identified, and according to an embodiment, the severity of the occlusion is also identified. For example, the severity of the occlusion may include the size and completeness of the occlusion.);
and estimating that the object is present at an end portion in a longitudinal direction of the point cloud missing region (Fig. 4, Paragraph [0032]- Doria discloses the end point 605 is within the area of interest and represents a temporary object (e.g., an occlusion, etc.) or a permanent object (e.g., an object to include in the localization model, etc.) (wherein the end point representing a temporary object is seen to be positioned at a longitudinal end of the grid in Fig. 4).).
Regarding claim 10, Doria teaches the object detection method according to claim 9, Doria further teaches a non-transitory computer-readable storage medium storing a program for causing a computer to execute the computer-implemented object detection method according to claim 9 (Fig. 1, Paragraph [0006]- Doria discloses a non-transitory computer readable medium is provided including instructions that when executed are operable to receive sensor data for a scene, the sensor data comprising an origin point and an end point, and to generate a grid for the scene from the sensor data.).
Regarding claim 11, Doria explicitly teaches the object detection apparatus according to claim 8, Doria further teaches wherein the point cloud missing region is a region within a surface detected based on the three-dimensional point cloud (Fig. 5, Paragraph [0043]- Doria discloses the occlusions may be detected from point cloud data for mapmaking. In an embodiment, two stages or layers of mapmaking are performed. For example, to map roadways, a lane model is generated to identify and map painted lines on the roadway. A localization model is then generated to identify roadside objects. Occlusions may be detected during both stages of mapmaking. Different regions of interest are defined for each stage (wherein a roadway is a surface).).
Regarding claim 16, Doria explicitly teaches the computer-implemented object detection method according to claim 9, Doria further teaches wherein the point cloud missing region is a region within a surface detected based on the three-dimensional point cloud (Fig. 5, Paragraph [0043]- Doria discloses the occlusions may be detected from point cloud data for mapmaking. In an embodiment, two stages or layers of mapmaking are performed. For example, to map roadways, a lane model is generated to identify and map painted lines on the roadway. A localization model is then generated to identify roadside objects. Occlusions may be detected during both stages of mapmaking. Different regions of interest are defined for each stage (wherein a roadway is a surface).).
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 3-4, 12-13, and 17-18 are rejected under 35 U.S.C 103 as being unpatentable over Doria et al. (US 20190138823 A1) hereafter referenced as Doria in view of Uhlenbrock et al. (US 20190392595 A1) hereafter referenced as Uhlenbrock and Sato et al. (US 20250045875 A1) hereafter referenced as Sato.
Regarding claim 3, Doria teaches the object detection system according to claim 2, Doria further teaches wherein the at least one processor is further configured to execute the instructions to: divide, in a grid shape, the surface into a plurality of cells (Fig. 3, Paragraph [0028]- Doria discloses using the cropped/clipped sensor data, a grid 504 is generated. For example, a cropped Boolean grid is constructed from the data remaining after a region of interest is defined and applied. Grid 504 depicts cells with data points after cropping/clipping the sensor data 502.);
Doria fails to explicitly teach calculate a point density index value and extract, as the point cloud missing region, a set of cells in which the point density index value.
However, Uhlenbrock explicitly teaches calculate a point density index value (Fig. 1, Paragraph [0023]- Uhlenbrock discloses the hole detector 120 is configured to generate a rough estimate of hole locations using a mapping of each of the first surface data 106 to a grid and determining a density of points of each cell in the grid.)
and extract, as the point cloud missing region, a set of cells in which the point density index value (Fig. 4, Paragraph [0037]- Uhlenbrock discloses the points that fall within each grid cell are counted, and cells with a point count less than a threshold are labeled hole cells.).
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 Doria an object detection system comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Uhlenbrock calculate a point density index value and extract, as the point cloud missing region, a set of cells in which the point density index value.
Wherein having Doria’s system for occlusion and static object detection wherein calculate a point density index value and extract, as the point cloud missing region, a set of cells in which the point density index value.
The motivation behind the modification would have been to allow for more accurate locations of holes to be obtained, since both Doria and Uhlenbrock are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Uhlenbrock’s system wherein improved accuracy of hole locations. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Uhlenbrock et al. (US 20190392595 A1) Paragraph [0018].
Doria in view of Uhelnbrock fails to explicitly teach or a reflection luminance index value of each cell, or the reflection luminance index value satisfies a predetermined condition.
However, Sato explicitly teaches or a reflection luminance index value of each cell (Fig. 11 Paragraph [0106]- Sato discloses as the intensity correction unit 22, generates the reflection intensity map in which the reflection intensity is assigned to each pixel of the image I on the basis of the three-dimensional point cloud P, the image I, the internal parameter K, the projection matrix R, and the translation vector L. In addition, the CPU 11, as the intensity correction unit 22, corrects the generated reflection intensity map on the basis of the image I.),
or the reflection luminance index value satisfies a predetermined condition (Fig. 10, Paragraph [0075]- Sato discloses an edge between adjacent clusters having a large difference in the average value of the color information and a small difference in the average reflection intensity is extracted from an edge of the cluster graph G1 as an edge having a high probability of being a boundary of the shadow region, and an edge set S extracted is obtained.).
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 Doria in view of Uhelnbrock an object detection system comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Sato or a reflection luminance index value of each cell, or the reflection luminance index value satisfies a predetermined condition.
Wherein having Doria’s system for occlusion and static object detection wherein calculate a point density index value and extract, as the point cloud missing region, a set of cells in which the point density index value.
The motivation behind the modification would have been to allow for more accurate region estimation, since both Doria and Sato are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Sato’s system wherein improved accuracy of region estimation. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Sato et al. (US 20250045875 A1) Paragraph [0005].
Regarding claim 4, Doria teaches the object detection system according to claim 2, Doria further teaches wherein the at least one processor is further configured to execute the instructions to: divide, in a grid shape, the surface into a plurality of cells (Fig. 3, Paragraph [0028]- Doria discloses using the cropped/clipped sensor data, a grid 504 is generated. For example, a cropped Boolean grid is constructed from the data remaining after a region of interest is defined and applied. Grid 504 depicts cells with data points after cropping/clipping the sensor data 502.);
Doria fails to explicitly teach calculate a point density index value.
However, Uhlenbrock explicitly teaches calculate a point density index value (Fig. 1, Paragraph [0023]- Uhlenbrock discloses the hole detector 120 is configured to generate a rough estimate of hole locations using a mapping of each of the first surface data 106 to a grid and determining a density of points of each cell in the 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 Doria an object detection system comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Uhlenbrock calculate a point density index value.
Wherein having Doria’s system for occlusion and static object detection wherein calculate a point density index value.
The motivation behind the modification would have been to allow for more accurate locations of holes to be obtained, since both Doria and Uhlenbrock are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Uhlenbrock’s system wherein improved accuracy of hole locations. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Uhlenbrock et al. (US 20190392595 A1) Paragraph [0018].
Doria in view of Uhelnbrock fails to explicitly teach or a reflection luminance index value of each cell and detect an outline of the point cloud missing region, based on a difference in the point density index value or the reflected luminance index value between two adjacent cells.
However, Sato explicitly teaches or a reflection luminance index value of each cell (Fig. 11 Paragraph [0106]- Sato discloses as the intensity correction unit 22, generates the reflection intensity map in which the reflection intensity is assigned to each pixel of the image I on the basis of the three-dimensional point cloud P, the image I, the internal parameter K, the projection matrix R, and the translation vector L. In addition, the CPU 11, as the intensity correction unit 22, corrects the generated reflection intensity map on the basis of the image I.),
and detect an outline of the point cloud missing region, based on a difference in the point density index value or the reflected luminance index value between two adjacent cells (Fig. 10, Paragraph [0075]- Sato discloses an edge between adjacent clusters having a large difference in the average value of the color information and a small difference in the average reflection intensity is extracted from an edge of the cluster graph G1 as an edge having a high probability of being a boundary of the shadow region, and an edge set S extracted is obtained.).
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 Doria in view of Uhelnbrock an object detection system comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Sato or a reflection luminance index value of each cell and detect an outline of the point cloud missing region, based on a difference in the point density index value or the reflected luminance index value between two adjacent cells.
Wherein having Doria’s system for occlusion and static object detection wherein or a reflection luminance index value of each cell and detect an outline of the point cloud missing region, based on a difference in the point density index value or the reflected luminance index value between two adjacent cells.
The motivation behind the modification would have been to allow for more accurate region estimation, since both Doria and Sato are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Sato’s system wherein improved accuracy of region estimation. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Sato et al. (US 20250045875 A1) Paragraph [0005].
Regarding claim 12, Doria teaches the object detection apparatus according to claim 11, Doria further teaches wherein the at least one processor is further configured to execute the computer-executable instructions to: divide, in a grid shape, the surface into a plurality of cells (Fig. 3, Paragraph [0028]- Doria discloses using the cropped/clipped sensor data, a grid 504 is generated. For example, a cropped Boolean grid is constructed from the data remaining after a region of interest is defined and applied. Grid 504 depicts cells with data points after cropping/clipping the sensor data 502.);
Doria fails to explicitly teach calculate a point density index value and extract, as the point cloud missing region, a set of cells in which the point density index value.
However, Uhlenbrock explicitly teaches calculate a point density index value (Fig. 1, Paragraph [0023]- Uhlenbrock discloses the hole detector 120 is configured to generate a rough estimate of hole locations using a mapping of each of the first surface data 106 to a grid and determining a density of points of each cell in the grid.);
and extract, as the point cloud missing region, a set of cells in which the point density index value (Fig. 4, Paragraph [0037]- Uhlenbrock discloses the points that fall within each grid cell are counted, and cells with a point count less than a threshold are labeled hole cells.).
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 Doria an object detection apparatus comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Uhlenbrock calculate a point density index value and extract, as the point cloud missing region, a set of cells in which the point density index value.
Wherein having Doria’s system for occlusion and static object detection wherein calculate a point density index value and extract, as the point cloud missing region, a set of cells in which the point density index value.
The motivation behind the modification would have been to allow for more accurate locations of holes to be obtained, since both Doria and Uhlenbrock are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Uhlenbrock’s system wherein improved accuracy of hole locations. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Uhlenbrock et al. (US 20190392595 A1) Paragraph [0018].
Doria in view of Uhlenbrock fails to explicitly teach or a reflection luminance index value of each cell, or the reflection luminance index value satisfies a predetermined condition.
However, Sato explicitly teaches or a reflection luminance index value of each cell (Fig. 11 Paragraph [0106]- Sato discloses as the intensity correction unit 22, generates the reflection intensity map in which the reflection intensity is assigned to each pixel of the image I on the basis of the three-dimensional point cloud P, the image I, the internal parameter K, the projection matrix R, and the translation vector L. In addition, the CPU 11, as the intensity correction unit 22, corrects the generated reflection intensity map on the basis of the image I.),
or the reflection luminance index value satisfies a predetermined condition (Fig. 10, Paragraph [0075]- Sato discloses an edge between adjacent clusters having a large difference in the average value of the color information and a small difference in the average reflection intensity is extracted from an edge of the cluster graph G1 as an edge having a high probability of being a boundary of the shadow region, and an edge set S extracted is obtained.).
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 Doria in view of Uhelnbrock an object detection apparatus comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Sato or a reflection luminance index value of each cell, or the reflection luminance index value satisfies a predetermined condition.
Wherein having Doria’s system for occlusion and static object detection wherein or a reflection luminance index value of each cell, or the reflection luminance index value satisfies a predetermined condition.
The motivation behind the modification would have been to allow for more accurate region estimation, since both Doria and Sato are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Sato’s system wherein improved accuracy of region estimation. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Sato et al. (US 20250045875 A1) Paragraph [0005].
Regarding claim 13, Doria teaches the object detection apparatus according to claim 11, Doria further teaches wherein the at least one processor is further configured to execute the computer-executable instructions to: divide, in a grid shape, the surface into a plurality of cells (Fig. 3, Paragraph [0028]- Doria discloses using the cropped/clipped sensor data, a grid 504 is generated. For example, a cropped Boolean grid is constructed from the data remaining after a region of interest is defined and applied. Grid 504 depicts cells with data points after cropping/clipping the sensor data 502.);
Doria fails to explicitly teach calculate a point density index value.
However, Uhlenbrock explicitly teaches calculate a point density index value (Fig. 1, Paragraph [0023]- Uhlenbrock discloses the hole detector 120 is configured to generate a rough estimate of hole locations using a mapping of each of the first surface data 106 to a grid and determining a density of points of each cell in the 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 Doria an object detection apparatus comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Uhlenbrock calculate a point density index value.
Wherein having Doria’s system for occlusion and static object detection wherein calculate a point density index value.
The motivation behind the modification would have been to allow for more accurate locations of holes to be obtained, since both Doria and Uhlenbrock are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Uhlenbrock’s system wherein improved accuracy of hole locations. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Uhlenbrock et al. (US 20190392595 A1) Paragraph [0018].
Doria in view of Uhlenbrock fails to explicitly teach or a reflection luminance index value of each cell, and detect an outline of the point cloud missing region based on a difference in the point density index value or the reflected luminance index value between two adjacent cells.
However, Sato explicitly teaches or a reflected luminance index value of each cell (Fig. 11 Paragraph [0106]- Sato discloses as the intensity correction unit 22, generates the reflection intensity map in which the reflection intensity is assigned to each pixel of the image I on the basis of the three-dimensional point cloud P, the image I, the internal parameter K, the projection matrix R, and the translation vector L. In addition, the CPU 11, as the intensity correction unit 22, corrects the generated reflection intensity map on the basis of the image I.),
and detect an outline of the point cloud missing region based on a difference in the point density index value or the reflected luminance index value between two adjacent cells (Fig. 10, Paragraph [0075]- Sato discloses an edge between adjacent clusters having a large difference in the average value of the color information and a small difference in the average reflection intensity is extracted from an edge of the cluster graph G1 as an edge having a high probability of being a boundary of the shadow region, and an edge set S extracted is obtained.).
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 Doria in view of Uhelnbrock an object detection apparatus comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Sato or a reflection luminance index value of each cell, and detect an outline of the point cloud missing region based on a difference in the point density index value or the reflected luminance index value between two adjacent cells.
Wherein having Doria’s system for occlusion and static object detection wherein or a reflection luminance index value of each cell, and detect an outline of the point cloud missing region based on a difference in the point density index value or the reflected luminance index value between two adjacent cells.
The motivation behind the modification would have been to allow for more accurate region estimation, since both Doria and Sato are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Sato’s system wherein improved accuracy of region estimation. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Sato et al. (US 20250045875 A1) Paragraph [0005].
Regarding claim 17, Doria teaches the computer-implemented object detection method according to claim 16, Doria further teaches further comprising: dividing, in a grid shape, the surface into a plurality of cells (Fig. 3, Paragraph [0028]- Doria discloses using the cropped/clipped sensor data, a grid 504 is generated. For example, a cropped Boolean grid is constructed from the data remaining after a region of interest is defined and applied. Grid 504 depicts cells with data points after cropping/clipping the sensor data 502.);
Doria fails to explicitly teach calculating a point density index value and extracting, as the point cloud missing region, a set of cells in which the point density index value.
However, Uhlenbrock explicitly teaches calculating a point density index value (Fig. 1, Paragraph [0023]- Uhlenbrock discloses the hole detector 120 is configured to generate a rough estimate of hole locations using a mapping of each of the first surface data 106 to a grid and determining a density of points of each cell in the grid.)
and extracting, as the point cloud missing region, a set of cells in which the point density index value (Fig. 4, Paragraph [0037]- Uhlenbrock discloses the points that fall within each grid cell are counted, and cells with a point count less than a threshold are labeled hole cells.).
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 Doria a computer-implemented object detection method comprising: extracting a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Uhlenbrock calculating a point density index value and extracting, as the point cloud missing region, a set of cells in which the point density index value.
Wherein having Doria’s system for occlusion and static object detection wherein calculating a point density index value and extracting, as the point cloud missing region, a set of cells in which the point density index value.
The motivation behind the modification would have been to allow for more accurate locations of holes to be obtained, since both Doria and Uhlenbrock are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Uhlenbrock’s system wherein improved accuracy of hole locations. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Uhlenbrock et al. (US 20190392595 A1) Paragraph [0018].
Doria in view of Uhlenbrock fails to explicitly teach or a reflection luminance index value of each cell or the reflection luminance index value satisfies a predetermined condition.
However, Sato explicitly teaches or a reflection luminance index value of each cell (Fig. 11 Paragraph [0106]- Sato discloses as the intensity correction unit 22, generates the reflection intensity map in which the reflection intensity is assigned to each pixel of the image I on the basis of the three-dimensional point cloud P, the image I, the internal parameter K, the projection matrix R, and the translation vector L. In addition, the CPU 11, as the intensity correction unit 22, corrects the generated reflection intensity map on the basis of the image I.);
or the reflection luminance index value satisfies a predetermined condition (Fig. 10, Paragraph [0075]- Sato discloses an edge between adjacent clusters having a large difference in the average value of the color information and a small difference in the average reflection intensity is extracted from an edge of the cluster graph G1 as an edge having a high probability of being a boundary of the shadow region, and an edge set S extracted is obtained.).
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 Doria in view of Uhelnbrock a computer-implemented object detection method comprising: extracting a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Sato or a reflection luminance index value of each cell or the reflection luminance index value satisfies a predetermined condition.
Wherein having Doria’s system for occlusion and static object detection wherein or a reflection luminance index value of each cell or the reflection luminance index value satisfies a predetermined condition.
The motivation behind the modification would have been to allow for more accurate region estimation, since both Doria and Sato are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Sato’s system wherein improved accuracy of region estimation. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Sato et al. (US 20250045875 A1) Paragraph [0005].
Regarding claim 18, Doria teaches the computer-implemented object detection method according to claim 16, Doria further teaches further comprising: dividing, in a grid shape, the surface into a plurality of cells (Fig. 3, Paragraph [0028]- Doria discloses using the cropped/clipped sensor data, a grid 504 is generated. For example, a cropped Boolean grid is constructed from the data remaining after a region of interest is defined and applied. Grid 504 depicts cells with data points after cropping/clipping the sensor data 502.);
Doria fails to explicitly teach calculating a point density index value.
However, Uhlenbrock explicitly teaches calculating a point density index value (Fig. 1, Paragraph [0023]- Uhlenbrock discloses the hole detector 120 is configured to generate a rough estimate of hole locations using a mapping of each of the first surface data 106 to a grid and determining a density of points of each cell in the 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 Doria a computer-implemented object detection method comprising: extracting a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Uhlenbrock calculating a point density index value.
Wherein having Doria’s system for occlusion and static object detection wherein calculating a point density index value.
The motivation behind the modification would have been to allow for more accurate locations of holes to be obtained, since both Doria and Uhlenbrock are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Uhlenbrock’s system wherein improved accuracy of hole locations. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Uhlenbrock et al. (US 20190392595 A1) Paragraph [0018].
Doria in view of Uhlenbrock fails to explicitly teach or a reflected luminance index value of each cell; and detecting an outline of the point cloud missing region based on a difference in the point density index value or the reflected luminance index value between two adjacent cells.
However, Sato explicitly teaches or a reflected luminance index value of each cell (Fig. 11 Paragraph [0106]- Sato discloses as the intensity correction unit 22, generates the reflection intensity map in which the reflection intensity is assigned to each pixel of the image I on the basis of the three-dimensional point cloud P, the image I, the internal parameter K, the projection matrix R, and the translation vector L. In addition, the CPU 11, as the intensity correction unit 22, corrects the generated reflection intensity map on the basis of the image I.);
and detecting an outline of the point cloud missing region based on a difference in the point density index value or the reflected luminance index value between two adjacent cells (Fig. 10, Paragraph [0075]- Sato discloses an edge between adjacent clusters having a large difference in the average value of the color information and a small difference in the average reflection intensity is extracted from an edge of the cluster graph G1 as an edge having a high probability of being a boundary of the shadow region, and an edge set S extracted is obtained.).
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 Doria in view of Uhelnbrock a computer-implemented object detection method comprising: extracting a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Sato or a reflection luminance index value of each cell; or the reflection luminance index value satisfies a predetermined condition and detect an outline of the point cloud missing region based on a difference in the point density index value or the reflected luminance index value between two adjacent cells.
Wherein having Doria’s system for occlusion and static object detection wherein or a reflected luminance index value of each cell; and detecting an outline of the point cloud missing region based on a difference in the point density index value or the reflected luminance index value between two adjacent cells.
The motivation behind the modification would have been to allow for more accurate region estimation, since both Doria and Sato are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Sato’s system wherein improved accuracy of region estimation. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Sato et al. (US 20250045875 A1) Paragraph [0005].
Claims 6, 14, and 19 are rejected under 35 U.S.C 103 as being unpatentable over Doria et al. (US 20190138823 A1) hereafter referenced as Doria in view of Uhlenbrock et al. (US 20190392595 A1) hereafter referenced as Uhlenbrock.
Regarding claim 6, Doria teaches the object detection system according to claim 1, Although Doria teaches extract a first point cloud missing region in each of a first three-dimensional point cloud (Fig. 5, Paragraph [0046]- Doria discloses at act 705, an occlusion is identified in the region of interest. The occlusion is identified by the developer system 121 of the server 125 based on the hidden spaces of the grid representation. The location of the occlusion is identified, and according to an embodiment, the severity of the occlusion is also identified. For example, the severity of the occlusion may include the size and completeness of the occlusion.);
Doria fails to explicitly teach wherein the at least one processor is further configured to execute the instructions to: extract a first point cloud missing region and a second point cloud missing region in each of a first three-dimensional point cloud and a second three-dimensional point cloud in which ranging positions of a Lidar apparatus for generating the three-dimensional point cloud are different from each other; and, estimate, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region.
However, Uhlenbrock explicitly teaches wherein the at least one processor is further configured to execute the instructions to: extract a first point cloud missing region and a second point cloud missing region in each of a first three-dimensional point cloud and a second three-dimensional point cloud in which ranging positions of a Lidar apparatus for generating the three-dimensional point cloud are different from each other (Fig. 1, Paragraph [0024]- Uhlenbrock the hole detector 120 receives a 2D image of the first portion 162 from the first 3D sensor 102 and a 2D image of the second portion 164 from the second 3D sensor 104, such as when the first 3D sensor 102 and the second 3D sensor 104 include camera-type 3D sensors (e.g., structured light cameras) that are also configured to capture 2D images.);
and, estimate, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region (Fig. 1, Paragraph [0025]- Uhlenbrock discloses the first hole feature cloud 123 indicates the first position 172 of the particular hole 168, relative to the first reference 110, within a first section 170 that corresponds to the overlap portion 166. The second hole feature cloud 125 indicates the second position 176 of the particular hole 168, relative to the second reference 112, within in a second section 174 that corresponds to the overlap portion 166. The hole aligner 130 is configured to determine at least one of the rotation 132 and the translation 134 to align one or more first hole positions in the first hole data 122 with one or more second hole positions in the second hole data 124 so that the hole locations in the first section 170 are substantially aligned with the hole locations of the second section 174. In an example, the hole aligner 130 is configured to rotate, to translate, or both, the second hole data 124 (e.g., rotate and translate an at least one of an orientation or a position of the second reference 112 with reference to the first reference 110) so that the center of the first position 172 corresponds to the center of the second position 176. (wherein the outline is rotated and translated to obtain alignment thus would not match prior to this process)).
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 Doria an object detection system comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Uhlenbrock wherein the at least one processor is further configured to execute the instructions to: extract a first point cloud missing region and a second point cloud missing region in each of a first three-dimensional point cloud and a second three-dimensional point cloud in which ranging positions of a Lidar apparatus for generating the three-dimensional point cloud are different from each other and, estimate, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region.
Wherein having Doria’s system for occlusion and static object detection wherein the at least one processor is further configured to execute the instructions to: extract a first point cloud missing region and a second point cloud missing region in each of a first three-dimensional point cloud and a second three-dimensional point cloud in which ranging positions of a Lidar apparatus for generating the three-dimensional point cloud are different from each other; and, estimate, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region.
The motivation behind the modification would have been to allow for more accurate locations of holes to be obtained, since both Doria and Uhlenbrock are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Uhlenbrock’s system wherein improved accuracy of hole locations. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Uhlenbrock et al. (US 20190392595 A1) Paragraph [0018].
Regarding claim 14, Doria teaches the object detection apparatus according to claim 8, Although Doria teaches extract a first point cloud missing region in each of a first three-dimensional point cloud (Fig. 5, Paragraph [0046]- Doria discloses at act 705, an occlusion is identified in the region of interest. The occlusion is identified by the developer system 121 of the server 125 based on the hidden spaces of the grid representation. The location of the occlusion is identified, and according to an embodiment, the severity of the occlusion is also identified. For example, the severity of the occlusion may include the size and completeness of the occlusion.);
Doria fails to explicitly teach wherein the at least one processor is further configured to execute the computer-executable instructions to: extract a first point cloud missing region and a second point cloud missing region in a first three-dimensional point cloud and a second three-dimensional point cloud, respectively, ranging positions of a LiDAR apparatus for generating the first three-dimensional point cloud and the second three-dimensional point cloud being different from each other; and estimate, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region.
However, Uhlenbrock explicitly teaches wherein the at least one processor is further configured to execute the computer-executable instructions to: extract a first point cloud missing region and a second point cloud missing region in a first three-dimensional point cloud and a second three-dimensional point cloud, respectively, ranging positions of a LiDAR apparatus for generating the first three-dimensional point cloud and the second three-dimensional point cloud being different from each other (Fig. 1, Paragraph [0024]- Uhlenbrock the hole detector 120 receives a 2D image of the first portion 162 from the first 3D sensor 102 and a 2D image of the second portion 164 from the second 3D sensor 104, such as when the first 3D sensor 102 and the second 3D sensor 104 include camera-type 3D sensors (e.g., structured light cameras) that are also configured to capture 2D images.);
and estimate, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region (Fig. 1, Paragraph [0025]- Uhlenbrock discloses the first hole feature cloud 123 indicates the first position 172 of the particular hole 168, relative to the first reference 110, within a first section 170 that corresponds to the overlap portion 166. The second hole feature cloud 125 indicates the second position 176 of the particular hole 168, relative to the second reference 112, within in a second section 174 that corresponds to the overlap portion 166. The hole aligner 130 is configured to determine at least one of the rotation 132 and the translation 134 to align one or more first hole positions in the first hole data 122 with one or more second hole positions in the second hole data 124 so that the hole locations in the first section 170 are substantially aligned with the hole locations of the second section 174. In an example, the hole aligner 130 is configured to rotate, to translate, or both, the second hole data 124 (e.g., rotate and translate an at least one of an orientation or a position of the second reference 112 with reference to the first reference 110) so that the center of the first position 172 corresponds to the center of the second position 176. (wherein the outline is rotated and translated to obtain alignment thus would not match prior to this process)).
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 Doria an object detection apparatus comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Uhlenbrock wherein the at least one processor is further configured to execute the computer-executable instructions to: extract a first point cloud missing region and a second point cloud missing region in a first three-dimensional point cloud and a second three-dimensional point cloud, respectively, ranging positions of a LiDAR apparatus for generating the first three-dimensional point cloud and the second three-dimensional point cloud being different from each other; and estimate, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region.
Wherein having Doria’s system for occlusion and static object detection wherein the at least one processor is further configured to execute the computer-executable instructions to: extract a first point cloud missing region and a second point cloud missing region in a first three-dimensional point cloud and a second three-dimensional point cloud, respectively, ranging positions of a LiDAR apparatus for generating the first three-dimensional point cloud and the second three-dimensional point cloud being different from each other; and estimate, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region.
The motivation behind the modification would have been to allow for more accurate locations of holes to be obtained, since both Doria and Uhlenbrock are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Uhlenbrock’s system wherein improved accuracy of hole locations. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Uhlenbrock et al. (US 20190392595 A1) Paragraph [0018].
Regarding claim 19, Doria teaches the computer-implemented object detection method according to claim 9, Although Doria teaches extracting a first point cloud missing region in each of a first three-dimensional point cloud (Fig. 5, Paragraph [0046]- Doria discloses at act 705, an occlusion is identified in the region of interest. The occlusion is identified by the developer system 121 of the server 125 based on the hidden spaces of the grid representation. The location of the occlusion is identified, and according to an embodiment, the severity of the occlusion is also identified. For example, the severity of the occlusion may include the size and completeness of the occlusion.);
Doria fails to explicitly teach further comprising: extracting a first point cloud missing region and a second point cloud missing region in a first three-dimensional point cloud and a second three-dimensional point cloud, respectively, ranging positions of a LiDAR apparatus for generating the first three-dimensional point cloud and the second three-dimensional point cloud being different from each other; and estimating, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region.
However, Uhlenbrock explicitly teaches further comprising: extracting a first point cloud missing region and a second point cloud missing region in a first three-dimensional point cloud and a second three-dimensional point cloud, respectively, ranging positions of a LiDAR apparatus for generating the first three-dimensional point cloud and the second three-dimensional point cloud being different from each other (Fig. 1, Paragraph [0024]- Uhlenbrock the hole detector 120 receives a 2D image of the first portion 162 from the first 3D sensor 102 and a 2D image of the second portion 164 from the second 3D sensor 104, such as when the first 3D sensor 102 and the second 3D sensor 104 include camera-type 3D sensors (e.g., structured light cameras) that are also configured to capture 2D images.);
and estimating, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region (Fig. 1, Paragraph [0025]- Uhlenbrock discloses the first hole feature cloud 123 indicates the first position 172 of the particular hole 168, relative to the first reference 110, within a first section 170 that corresponds to the overlap portion 166. The second hole feature cloud 125 indicates the second position 176 of the particular hole 168, relative to the second reference 112, within in a second section 174 that corresponds to the overlap portion 166. The hole aligner 130 is configured to determine at least one of the rotation 132 and the translation 134 to align one or more first hole positions in the first hole data 122 with one or more second hole positions in the second hole data 124 so that the hole locations in the first section 170 are substantially aligned with the hole locations of the second section 174. In an example, the hole aligner 130 is configured to rotate, to translate, or both, the second hole data 124 (e.g., rotate and translate an at least one of an orientation or a position of the second reference 112 with reference to the first reference 110) so that the center of the first position 172 corresponds to the center of the second position 176. (wherein the outline is rotated and translated to obtain alignment thus would not match prior to this process)).
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 Doria a computer-implemented object detection method comprising: extracting a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Uhlenbrock further comprising: extracting a first point cloud missing region and a second point cloud missing region in a first three-dimensional point cloud and a second three-dimensional point cloud, respectively, ranging positions of a LiDAR apparatus for generating the first three-dimensional point cloud and the second three-dimensional point cloud being different from each other; and estimating, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region.
Wherein having Doria’s system for occlusion and static object detection wherein further comprising: extracting a first point cloud missing region and a second point cloud missing region in a first three-dimensional point cloud and a second three-dimensional point cloud, respectively, ranging positions of a LiDAR apparatus for generating the first three-dimensional point cloud and the second three-dimensional point cloud being different from each other; and estimating, when the first point cloud missing region and the second point cloud missing region overlap with each other and an outline of the first point cloud missing region and an outline of the second point cloud missing region do not match each other, that the object is present in an overlap region of the first point cloud missing region and the second point cloud missing region.
The motivation behind the modification would have been to allow for more accurate locations of holes to be obtained, since both Doria and Uhlenbrock are systems that determine regions of information from point cloud data. Wherein Doria’s system wherein improved a map making process, while Uhlenbrock’s system wherein improved accuracy of hole locations. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Uhlenbrock et al. (US 20190392595 A1) Paragraph [0018].
Claims 7, 15, and 20 are rejected under 35 U.S.C 103 as being unpatentable over Doria et al. (US 20190138823 A1) hereafter referenced as Doria in view of Uhlenbrock et al. (US 20190392595 A1) hereafter referenced as Uhlenbrock and Agarwal et al. (US 8886387 B1) hereafter referenced as Agarwal.
Regarding claim 7, Doria in view of Uhlenbrock teaches the object detection system according to claim 6, Doria in view of Uhlenbrock is silent to explicitly teach wherein, in a case where the Lidar apparatus is mounted on a moving body and ranging using the Lidar apparatus is performed while the moving body moves: the first three-dimensional point cloud is a point cloud acquired by ranging in a first time period;
Doria in view of Uhlenbrock fails to explicitly teach and the second three-dimensional point cloud is a point cloud acquired in a second time period which is after the first time period.
However, Agarwal explicitly teaches wherein, in a case where the Lidar apparatus is mounted on a moving body and ranging using the Lidar apparatus is performed while the moving body moves: the first three-dimensional point cloud is a point cloud acquired by ranging in a first time period (Fig. 1, Column 13 Lines [0042-49]- Agarwal discloses the image 400 may represent a 3D point cloud that has been captured by a LIDAR device, for example. The image 400 depicts several objects in the environment of the vehicle, such as other vehicles, road signs, road marks, etc. As an example, the first 3D point cloud may depict the vehicle 402 in front of the vehicle (not shown) controlled by the computing device. The vehicle 402 may be any type of vehicles (e.g., cars, trucks, motorcycles, etc.).);
and the second three-dimensional point cloud is a point cloud acquired in a second time period which is after the first time period (Fig. 4B, Column 14 Lines [0020-24]- Agarwal discloses a second 3D point cloud may be captured after the first 3D point cloud, and may depict the rear as well as left side of the vehicle 402 as shown in FIG. 4B. The second 3D point cloud may be captured a given period of time after the first 3D point cloud is captured.).
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 Doria in view of Uhlenbrock an object detection system comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Agarwal wherein, in a case where the Lidar apparatus is mounted on a moving body and ranging using the Lidar apparatus is performed while the moving body moves: the first three-dimensional point cloud is a point cloud acquired by ranging in a first time period; and the second three-dimensional point cloud is a point cloud acquired in a second time period which is after the first time period.
Wherein having Doria’s system for occlusion and static object detection wherein, in a case where the Lidar apparatus is mounted on a moving body and ranging using the Lidar apparatus is performed while the moving body moves: the first three-dimensional point cloud is a point cloud acquired by ranging in a first time period; and the second three-dimensional point cloud is a point cloud acquired in a second time period which is after the first time period.
The motivation behind the modification would have been to allow for more accurate object detection and information gathering, since both Doria and Agarwal are systems that capture point cloud information of an area for further processing. Wherein Doria’s system wherein improved a map making process, while Agarwal’s system wherein improved accuracy of object detection and estimation. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Agarwal et al. (US 8886387 B1) Column 17 Lines [0001-13].
Regarding claim 15, Doria in view of Uhlenbrock teaches the object detection apparatus according to claim 14, Doria in view of Uhlenbrock is silent to explicitly teach wherein, in a case where the LiDAR apparatus is mounted on a moving body and ranging using the LiDAR apparatus is performed while the moving body moves: the first three-dimensional point cloud is a point cloud acquired by ranging in a first time period;
Doria in view of Uhlenbrock fails to explicitly teach and the second three-dimensional point cloud is a point cloud acquired by ranging in a second time period after the first time period.
However, Agarwal explicitly teaches wherein, in a case where the LiDAR apparatus is mounted on a moving body and ranging using the LiDAR apparatus is performed while the moving body moves: the first three-dimensional point cloud is a point cloud acquired by ranging in a first time period(Fig. 1, Column 13 Lines [0042-49]- Agarwal discloses the image 400 may represent a 3D point cloud that has been captured by a LIDAR device, for example. The image 400 depicts several objects in the environment of the vehicle, such as other vehicles, road signs, road marks, etc. As an example, the first 3D point cloud may depict the vehicle 402 in front of the vehicle (not shown) controlled by the computing device. The vehicle 402 may be any type of vehicles (e.g., cars, trucks, motorcycles, etc.).);
and the second three-dimensional point cloud is a point cloud acquired by ranging in a second time period after the first time period (Fig. 4B, Column 14 Lines [0020-24]- Agarwal discloses a second 3D point cloud may be captured after the first 3D point cloud, and may depict the rear as well as left side of the vehicle 402 as shown in FIG. 4B. The second 3D point cloud may be captured a given period of time after the first 3D point cloud is captured.).
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 Doria in view of Uhlenbrock an object detection apparatus comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: extract a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Agarwal wherein, in a case where the LiDAR apparatus is mounted on a moving body and ranging using the LiDAR apparatus is performed while the moving body moves: the first three-dimensional point cloud is a point cloud acquired by ranging in a first time period and the second three-dimensional point cloud is a point cloud acquired by ranging in a second time period after the first time period.
Wherein having Doria’s system for occlusion and static object detection wherein, in a case where the LiDAR apparatus is mounted on a moving body and ranging using the LiDAR apparatus is performed while the moving body moves: the first three-dimensional point cloud is a point cloud acquired by ranging in a first time period and the second three-dimensional point cloud is a point cloud acquired by ranging in a second time period after the first time period.
The motivation behind the modification would have been to allow for more accurate object detection and information gathering, since both Doria and Agarwal are systems that capture point cloud information of an area for further processing. Wherein Doria’s system wherein improved a map making process, while Agarwal’s system wherein improved accuracy of object detection and estimation. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Agarwal et al. (US 8886387 B1) Column 17 Lines [0001-13].
Regarding claim 20, Doria in view of Uhlenbrock teaches the computer-implemented object detection method according to claim 19, Doria in view of Uhlenbrock is silent to explicitly teach wherein, in a case where the LiDAR apparatus is mounted on a moving body and ranging using the LiDAR apparatus is performed while the moving body moves: the first three-dimensional point cloud is acquired by ranging in a first time period;
Doria in view of Uhlenbrock fails to explicitly teach and the second three-dimensional point cloud is acquired by ranging in a second time period after the first time period.
However, Agarwal explicitly teaches wherein, in a case where the LiDAR apparatus is mounted on a moving body and ranging using the LiDAR apparatus is performed while the moving body moves: the first three-dimensional point cloud is acquired by ranging in a first time period (Fig. 1, Column 13 Lines [0042-49]- Agarwal discloses the image 400 may represent a 3D point cloud that has been captured by a LIDAR device, for example. The image 400 depicts several objects in the environment of the vehicle, such as other vehicles, road signs, road marks, etc. As an example, the first 3D point cloud may depict the vehicle 402 in front of the vehicle (not shown) controlled by the computing device. The vehicle 402 may be any type of vehicles (e.g., cars, trucks, motorcycles, etc.).);
and the second three-dimensional point cloud is acquired by ranging in a second time period after the first time period (Fig. 4B, Column 14 Lines [0020-24]- Agarwal discloses a second 3D point cloud may be captured after the first 3D point cloud, and may depict the rear as well as left side of the vehicle 402 as shown in FIG. 4B. The second 3D point cloud may be captured a given period of time after the first 3D point cloud is captured.).
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 Doria in view of Uhlenbrock a computer-implemented object detection method comprising: extracting a point cloud missing region being a region where a point cloud is missing in a three-dimensional point cloud with the teachings of Agarwal wherein, in a case where the LiDAR apparatus is mounted on a moving body and ranging using the LiDAR apparatus is performed while the moving body moves: the first three-dimensional point cloud is acquired by ranging in a first time period; and the second three-dimensional point cloud is acquired by ranging in a second time period after the first time period.
Wherein having Doria’s system for occlusion and static object detection wherein, in a case where the LiDAR apparatus is mounted on a moving body and ranging using the LiDAR apparatus is performed while the moving body moves: the first three-dimensional point cloud is acquired by ranging in a first time period; and the second three-dimensional point cloud is acquired by ranging in a second time period after the first time period.
The motivation behind the modification would have been to allow for more accurate object detection and information gathering, since both Doria and Agarwal are systems that capture point cloud information of an area for further processing. Wherein Doria’s system wherein improved a map making process, while Agarwal’s system wherein improved accuracy of object detection and estimation. Please see Doria et al. (US 20190138823 A1), Paragraph [0040-41] and Agarwal et al. (US 8886387 B1) Column 17 Lines [0001-13].
Allowable Subject Matter
Claims 21 along with its dependent claims respectively, are therefrom objected to as being dependent upon rejected base claim, claims 1, respectively but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 21, the prior arts fail to explicitly teach, estimate that the object is present at the end portion in the longitudinal direction of the point cloud missing region when the point cloud missing region is determined to have the elongated shape, as claimed in claim 21.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered
pertinent to applicant`s disclosure.
Zhang et al. (US 20240124019 A1)- A method for planning a behavior of a vehicle with respect to one or more occluded area(s) along a navigation path of the vehicle, wherein the method comprises an occluded area identification step, during which the occluded area(s) is/are identified, and a phantom object generation step, during which at least one phantom object is generated for at least one of the occluded areas, the occluded area(s) is/are defined based on information from a predefined occlusion scenario catalog during the occluded area identification step...........................Please see Fig. 1. Abstract.
Fujimatsu et al. (US 20140341472 A1)- The purpose of the invention is to increase accuracy in detecting a person on the basis of the size of an object detection region in an omni-directional image. A height-and-width switching section for switching between the height and the width of the object detection region on the basis of the position of the object detection region in the omni-directional image is provided. It is determined on the basis of the height and the width of the object detection region for which the height and the width are switched by the height-and-width switching section whether the object detection region is a person detection region. As a result, the person detection region and a shadow detection region can be correctly separated in the omni-directional image...........................Please see Fig. 1. Abstract.
Ray et al. (US 20180074170 A1)- A method for spatially filtering data includes receiving a plurality of signal parameter vectors including spatial-type information derived from a sensor and associated with a signal emitter, determining error magnitudes of a plurality of first and second coordinates, and transmitting the plurality of coordinates to at least two arrays of differing sparsity in an array data structure when the error magnitudes differ by a predetermined amount, where each array is representative of a physical spatial domain from which a plurality of signals are received by the sensor. The method also includes determining a plurality of elliptical error region probability objects representative of probability density functions of the plurality of coordinates, where each object is stored in association with at least one of the at least two arrays, and determining an intersection region between the plurality of objects that is representative of a location of the signal emitter..........................Please see Fig. 1. Abstract.
Garten et al. (US 20090297049 A1)- A method for detection the presence of a man-made object partially occluded in a natural environment. The method includes the steps of providing an image segment from three dimensional ladar data, grouping one or more coplanar portion of the pixels into a cluster of planar sections, each planar section including three or more pixels, classifying the cluster based on one or more criterion selected from a group of criteria...........................Please see Fig. 1. Abstract.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUCIUS C.G. ALLEN whose telephone number is (703)756-5987. The examiner can normally be reached Mon - Fri 8-5pm (EST).
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, Chineyere Wills-Burns can be reached at (571)272-9752. 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.
/LUCIUS CAMERON GREEN ALLEN/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
Prosecution Insights