DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Certified copies of papers submitted under 35 U.S.C. 119(a)-(d) have not been placed of record in the file.
Response to Amendment
Applicant’s remarks, filed 06/08/2026, regarding the objections made to the claims and the 101 rejection submitted in the non-final office action dated 03/09/2026 are withdrawn due to the amendments made to the claims.
Response to Arguments
Applicant’s arguments, see remarks filed 06/08/2026, with respect to the claims 1-10 have been fully considered but are moot because they do not apply to the new grounds of rejection being given and the combination of references being relied upon.
Claim Objections
Claims 1, 9, and 10 are objected to because of the following informalities:
In claim 1, line 8-9, the term “when the distribution of the distance values is constant” should be changed to “when the distribution of the distance values are constant” in order to avoid a grammatical issue.
In claim 9, line 6-7, the term “when the distribution of the distance values is constant” should be changed to “when the distribution of the distance values are constant” in order to avoid a grammatical issue.
In claim 10, line 8-9, the term “when the distribution of the distance values is constant” should be changed to “when the distribution of the distance values are constant” in order to avoid a grammatical issue.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 9, 10 and their associated dependent claims are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1, 9, and 10 recite the limitation “indicating that the one or more clusters is a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets.” in Lines 8-13, 6-10, and 7-12, respectively. The office find the term “a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets” rendering the claim indefinite. It is not clear what the applicant refers to as “the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets”, whether or not it is the same noise point cloud or a new/different noise point cloud since the claim previously recited “a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device.”
For purpose of examination the examiner is interpreting the limitation as “indicating that the one or more clusters is a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud generated from the blind spot region results from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets”.
The office respectfully requests the Applicant to amend claims 1, 9, and 10 in order to clarify the claimed invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5, 7, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over MARUYAMA et al. (US 20200057905 A1), hereinafter referenced as MARUYAMA, in view of OZKUCUR et al. (US 20200202107 A1), hereinafter referenced as OZKUCUR, and further in view of TSUBOI et al. (US 20220260719 A1), hereinafter referenced as TSUBOI.
Regarding claim 1, MARUYAMA explicitly teaches an information processing apparatus comprising (Fig. 1. #10 called a point group data processing device. Paragraph [0030].):
at least one memory (Fig. 1, #10 includes a memory. Paragraph [0030]-MARUYAMA discloses the point group data processing device 10 is assumed to include a central processing unit (CPU), a memory, and a storage such as a hard disk drive which may normally be included in a general computer.) storing processing instructions (Fig. 1. Paragraph [0030]-MARUYAMA discloses various pieces of processing are executed by a program in order to cause these general computers to function as the point group data processing device 10 of the example.); and
at least one processor (Fig. 1, #10 includes a CPU. Paragraph [0030]-MARUYAMA discloses the point group data processing device 10 is assumed to include a central processing unit (CPU).) configured to execute the processing instructions to (Fig. 1. Paragraph [0030]-MARUYAMA discloses various pieces of processing are executed by a program in order to cause these general computers to function as the point group data processing device 10 of the example.):
classify three-dimensional point cloud data (Fig. 1. Paragraph [0036]-MARUYAMA discloses subsets p, p′ of the point group data to be projected respectively onto a target area b and an enlargement area b′ are obtained for each of a plurality of target areas in an image to create histograms h, h′ from pieces of depth information of p, p′ (wherein a point group is a point cloud and wherein the subsets and histograms are clusters the data is classified to). Further in paragraph [0034]-MARUYAMA discloses the point group data by the distance measurement device is the three-dimensional coordinate information.) including distance values to a target in a specified region into one or more clusters based on the distance values (Figs. 5 and 6. Paragraph [0043]-MARUYAMA discloses for each of the subsets p, p′, the histograms h, h′ are created from the depth information (step S05). A peak class of the histogram h is set to i (step S06). Since a peak class portion with the most concentrated distribution in the histogram corresponds to the distance of the subject when the subject is properly included in the box b, a portion including the peak class i is assumed to be a range of the target point group.); and
determine the cluster to adopt (Fig. 6, illustrates histogram with a range of selected point group data based on depth of a point group and frequency of a point group (wherein the range of selected point group data is the cluster to adopt). Paragraph [0050].) based on a distribution of the distance values of the three-dimensional point cloud data included by the cluster obtained by the classification (Fig. 6. Paragraph [0050]-MARUYAMA discloses the range of the point group data of the subject can be selected from the distribution of the histogram composed of all pieces of point group data in the box as shown in FIG. 6 by performing the above target point group specifying processing. Therefore, solely the point group data of the subject is specified and thus unneeded point group data can be deleted and the data amount to be handled can be reduced (wherein the selected point group data is the cluster to adopt and wherein the distribution is the histogram). Further in paragraph [0043]-MARUYAMA discloses for each of the subsets p, p′, the histograms h, h′ are created from the depth information (step S05) (wherein depth information is distance values). Further in paragraph [0034]-MARUYAMA discloses the point group data by the distance measurement device is the three-dimensional coordinate information.).
wherein the at least one processor is configured to (Fig. 1, #10 includes a CPU. Paragraph [0030]-MARUYAMA discloses the point group data processing device 10 is assumed to include a central processing unit (CPU).)
MARUYAMA fails to explicitly teach reject the one or more clusters when the distribution of the distance values is constant.
However, OZKUCUR explicitly teaches reject the one or more clusters when the distribution of the distance values is constant (Fig. 3-4, illustrate distributions of distance values. Paragraph [0043]-OZKUCUR discloses the mobile device 122 and/or the server 125 may filter individual grid cells as “noise,” such that image cells with too little points are eliminated. Further in paragraph [0052]-OZKUCUR indicated above, the determination of whether the grid cells 141 are occupied or unoccupied is based on the number of point cloud data points 140 assigned to the corresponding grid cell 141. In the example shown in FIG. 3, the number of point cloud data points 140 assigned to grid cells 144 is below the predetermined threshold (i.e., unoccupied) while the number of point cloud data points 140 assigned to grid cells 143 is above the predetermined threshold (i.e., occupied). As shown in FIG. 3, there are 10 occupied grid cells 143 and 32 unoccupied grid cells 144 (wherein unoccupied regions have a constant distribution of distance values of 0). Further in paragraph [0064]-OZKUCUR discloses the received data may be range data (e.g., LiDAR) or image data (e.g., camera) (wherein range data is distance values).),
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 MARUYAMA of an information processing apparatus comprising: at least one memory storing processing instructions; and at least one processor configured to execute the processing instructions to: classify three-dimensional point cloud data including distance values to a target in a specified region into one or more clusters based on the distance values; and determine the cluster to adopt based on a distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification with the teachings of OZKUCUR of reject the one or more clusters when the distribution of the distance values is constant.
Wherein having MARUYAMA’s point cloud processing device that can reject the one or more clusters when the distribution of the distance values is constant.
The motivation behind the modification would have been to obtain a point cloud processing device that enhances the efficiency and accuracy of analyzing and detecting points/targets. Since both MARUYAMA and OZKUCUR relate to analyzing LiDAR data from a vehicle to detect objects, wherein MARUYAMA unneeded point group data can be deleted and the data amount to be handled can be reduced, while OZKUCUR filtering the data points using the above-mentioned classification method reduces the amount of data points being processed, which increases efficiency and speed of the processing and requires less storage and bandwidth to operate the applications mentioned above. Please see MARUYAMA et al. (US 20200057905 A1), Paragraph [0051], and OZKUCUR et al. (US 20200202107 A1), Paragraph [0030].
MARUYAMA in view of OZKUCUR fail to explicitly teach indicating that the one or more clusters is a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets.
However, TSUBOI explicitly teaches indicating that the one or more clusters is a noise point cloud (Fig. 44. Paragraph [0163]-TSUBOI discloses while moving along a movement path (for example, the road R), the movable body measures the position of an object that exists in surroundings of the movement path, and outputs point group data. Further in paragraph [0189]-TSUBOI disclose the MMS 10 can measure a measurement insensible region and a measurement impossible region, which cannot be measured in conventional cases, by acquiring and analyzing the positional data obtained as described above.) generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device (Fig. 42. Paragraph [0160]-TSUBOI discloses the measurement low-accuracy region a r.sup.‡ is, for example, a region in which the accuracy of measurement by the MMS decreases as the interval of laser beam irradiation increases (the measurement density decreases) because the region is positioned nearly in parallel to a measurement surface.),
the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets (Fig. 42. Paragraph [0160]-TSUBOI discloses the measurement impossible region a r.sup.† is, for example, a region that is a blind spot when viewed in the position of a lidar and in which measurement by the MMS is difficult because no laser beam is incident. The measurement low-accuracy region a r.sup.‡ is, for example, a region in which the accuracy of measurement by the MMS decreases as the interval of laser beam irradiation increases (the measurement density decreases) because the region is positioned nearly in parallel to a measurement surface.).
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 MARUYAMA in view of OZKUCUR of an information processing apparatus comprising: at least one memory storing processing instructions; and at least one processor configured to execute the processing instructions to: classify three-dimensional point cloud data including distance values to a target in a specified region into one or more clusters based on the distance values; and determine the cluster to adopt based on a distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification with the teachings of TSUBOI of indicating that the one or more clusters is a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets.
Wherein having MARUYAMA’s point cloud processing device having indicating that the one or more clusters is a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets.
The motivation behind the modification would have been to obtain a point cloud processing device that enhances the efficiency and accuracy of analyzing and detecting points/targets. Since both MARUYAMA and TSUBOI relate to analyzing LiDAR data from a vehicle to detect objects, wherein MARUYAMA unneeded point group data can be deleted and the data amount to be handled can be reduced, while TSUBOI is intended to solve the above-described problem and provide a measurement device and a measurement method that are capable of reducing a region in which measurement is impossible and a region in which the accuracy of measurement decreases. Please see MARUYAMA et al. (US 20200057905 A1), Paragraph [0051], and TSUBOI et al. (US 20220260719 A1), Paragraph [0009].
Regarding claim 2, MARUYAMA in view of OZKUCUR and further in view of TSUBOI explicitly teach the information processing apparatus according to claim 1,
MARUYAMA further explicitly teaches wherein the at least one processor is configured to execute the processing instructions to (Fig. 1. Paragraph [0030]-MARUYAMA discloses the point group data processing device 10 is assumed to include a central processing unit (CPU), a memory, and a storage such as a hard disk drive which may normally be included in a general computer. The device is assumed to further include a graphics processing unit (GPU) as needed (not shown). It is needless to say that various pieces of processing are executed by a program in order to cause these general computers to function as the point group data processing device 10 of the example.):
determine the cluster to adopt based on a histogram of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification (Figs. 6, illustrates an adopted cluster selected based on a histogram of distance values of 3D points (wherein the selected point group data is the cluster to adopt and wherein a depth of point group is a distance value). Paragraph [0037]-MARUYAMA discloses the target point group specifying unit 16 has a function of comparing the histogram of the target area created with the histogram of the enlargement area by the histogram creation unit 15 to specify a point group in a range where distributions approximately match as the point group data of the subject (wherein group data of the subject is the cluster to adopt). Further in paragraph [0043]-MARUYAMA discloses for each of the subsets p, p′, the histograms h, h′ are created from the depth information (step S05) (wherein the histogram contains data that has already been classified into subsets.).).
Regarding claim 5, MARUYAMA in view of OZKUCUR and further in view of TSUBOI explicitly teach the information processing apparatus according to claim 1,
MARUYAMA further explicitly teaches wherein the at least one processor is configured to execute the processing instructions to (Fig. 1. #10 includes a CPU. Paragraph [0030]-MARUYAMA discloses the point group data processing device 10 is assumed to include a central processing unit (CPU). Further in paragraph [0030]-MARUYAMA discloses various pieces of processing are executed by a program in order to cause these general computers to function as the point group data processing device 10 of the example.):
determine the cluster to adopt based on a frequency of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification (Fig. 6. Paragraph [0043]-MARUYAMA discloses peak class of the histogram h is set to i (step S06). Since a peak class portion with the most concentrated distribution in the histogram corresponds to the distance of the subject when the subject is properly included in the box b, a portion including the peak class i is assumed to be a range of the target point group (wherein the peak class is the cluster to adopt and wherein the most concentrated distribution is based on frequency of the distance values). Further in paragraph [0045]-MARUYAMA discloses FIG. 6 is an image diagram representing an example of the distribution of the histogram. In FIG. 6, the horizontal axis is the depth (distance) from the distance measurement device to a target point, and the vertical axis is a frequency of the point group.).
Regarding claim 7, MARUYAMA in view of OZKUCUR and further in view of TSUBOI explicitly teach the information processing apparatus according to claim 1,
MARUYAMA further explicitly teaches wherein the at least one processor is configured to execute the processing instructions to (Fig. 1. #10 includes a CPU. Paragraph [0030]-MARUYAMA discloses the point group data processing device 10 is assumed to include a central processing unit (CPU). Further in paragraph [0030]-MARUYAMA discloses various pieces of processing are executed by a program in order to cause these general computers to function as the point group data processing device 10 of the example.):
calculate a representative value of the distance values of the three-dimensional point cloud data included by the cluster to adopt, based on the distance values (Fig. 5 and 6. Paragraph [0044]-MARUYAMA discloses determination is made whether h′[i.sub.r] which is a histogram value of the class i.sub.r in the histogram h′ of the enlargement area b′ is not zero (h′[i.sub.r]≠0) (step S08). When h′[i.sub.r]≠0, next, determination is made whether ((h′[i.sub.r]−h[i.sub.r])/h[i.sub.r])>ε (step S09). This is an equation for determining whether a difference between the histogram values h[i.sub.r], h′[i.sub.r] in the boxes b, b′ in the class i.sub.r is equal to or larger than a predetermined value (wherein histogram values are values representative of distance values and wherein the cluster to adopt histogram class i, and the histogram is based on 3D point cloud data, as previously stated in prior claim rejections.). Further in paragraph [0045]-MARUYAMA discloses FIG. 6 is an image diagram representing an example of the distribution of the histogram. In FIG. 6, the horizontal axis is the depth (distance) from the distance measurement device to a target point, and the vertical axis is a frequency of the point group.).
Regarding claim 9, MARUYAMA explicitly teaches an information processing method comprising (Fig. 1. #10 called a point group data processing device. Paragraph [0030].):
classifying three-dimensional point cloud data (Fig. 1. Paragraph [0036]-MARUYAMA discloses subsets p, p′ of the point group data to be projected respectively onto a target area b and an enlargement area b′ are obtained for each of a plurality of target areas in an image to create histograms h, h′ from pieces of depth information of p, p′ (wherein a point group is a point cloud and wherein the subsets and histograms are clusters the data is classified to). Further in paragraph [0034]-MARUYAMA discloses the point group data by the distance measurement device is the three-dimensional coordinate information.) including distance values to a target in a specified region into one or more clusters based on the distance values (Figs. 5 and 6. Paragraph [0043]-MARUYAMA discloses for each of the subsets p, p′, the histograms h, h′ are created from the depth information (step S05). A peak class of the histogram h is set to i (step S06). Since a peak class portion with the most concentrated distribution in the histogram corresponds to the distance of the subject when the subject is properly included in the box b, a portion including the peak class i is assumed to be a range of the target point group.); and
determining the cluster to adopt (Fig. 6, illustrates histogram with a range of selected point group data based on depth of a point group and frequency of a point group (wherein the range of selected point group data is the cluster to adopt). Paragraph [0050].) based on a distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification (Fig. 6. Paragraph [0050]-MARUYAMA discloses the range of the point group data of the subject can be selected from the distribution of the histogram composed of all pieces of point group data in the box as shown in FIG. 6 by performing the above target point group specifying processing. Therefore, solely the point group data of the subject is specified and thus unneeded point group data can be deleted and the data amount to be handled can be reduced (wherein the selected point group data is the cluster to adopt and wherein the distribution is the histogram). Further in paragraph [0043]-MARUYAMA discloses for each of the subsets p, p′, the histograms h, h′ are created from the depth information (step S05) (wherein depth information is distance values). Further in paragraph [0034]-MARUYAMA discloses the point group data by the distance measurement device is the three-dimensional coordinate information.),
MARUYAMA fails to explicitly teach wherein the determining includes rejecting the one or more clusters when the distribution of the distance values is constant.
However, OZKUCUR explicitly teaches wherein the determining includes rejecting the one or more clusters when the distribution of the distance values is constant (Fig. 3-4, illustrate distributions of distance values. Paragraph [0043]-OZKUCUR discloses the mobile device 122 and/or the server 125 may filter individual grid cells as “noise,” such that image cells with too little points are eliminated. Further in paragraph [0052]-OZKUCUR indicated above, the determination of whether the grid cells 141 are occupied or unoccupied is based on the number of point cloud data points 140 assigned to the corresponding grid cell 141. In the example shown in FIG. 3, the number of point cloud data points 140 assigned to grid cells 144 is below the predetermined threshold (i.e., unoccupied) while the number of point cloud data points 140 assigned to grid cells 143 is above the predetermined threshold (i.e., occupied). As shown in FIG. 3, there are 10 occupied grid cells 143 and 32 unoccupied grid cells 144 (wherein unoccupied regions have a constant distribution of distance values of 0). Further in paragraph [0064]-OZKUCUR discloses the received data may be range data (e.g., LiDAR) or image data (e.g., camera) (wherein range data is distance values).),
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 MARUYAMA of an information processing method comprising: classifying three-dimensional point cloud data including distance values to a target in a specified region into one or more clusters based on the distance values; and determining the cluster to adopt based on a distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification with the teachings of OZKUCUR of wherein the determining includes rejecting the one or more clusters when the distribution of the distance values is constant.
Wherein having MARUYAMA’s point cloud processing method wherein the determining includes rejecting the one or more clusters when the distribution of the distance values is constant.
The motivation behind the modification would have been to obtain a point cloud processing method that enhances the efficiency and accuracy of analyzing and detecting points/targets. Since both MARUYAMA and OZKUCUR relate to analyzing LiDAR data from a vehicle to detect objects, wherein MARUYAMA unneeded point group data can be deleted and the data amount to be handled can be reduced, while OZKUCUR filtering the data points using the above-mentioned classification method reduces the amount of data points being processed, which increases efficiency and speed of the processing and requires less storage and bandwidth to operate the applications mentioned above. Please see MARUYAMA et al. (US 20200057905 A1), Paragraph [0051], and OZKUCUR et al. (US 20200202107 A1), Paragraph [0030].
MARUYAMA in view of OZKUCUR fail to explicitly teach indicating that the one or more clusters is a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets.
However, TSUBOI explicitly teaches indicating that the one or more clusters is a noise point cloud (Fig. 44. Paragraph [0163]-TSUBOI discloses while moving along a movement path (for example, the road R), the movable body measures the position of an object that exists in surroundings of the movement path, and outputs point group data. Further in paragraph [0189]-TSUBOI disclose the MMS 10 can measure a measurement insensible region and a measurement impossible region, which cannot be measured in conventional cases, by acquiring and analyzing the positional data obtained as described above.) generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device (Fig. 42. Paragraph [0160]-TSUBOI discloses the measurement low-accuracy region a r.sup.‡ is, for example, a region in which the accuracy of measurement by the MMS decreases as the interval of laser beam irradiation increases (the measurement density decreases) because the region is positioned nearly in parallel to a measurement surface.),
the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets (Fig. 42. Paragraph [0160]-TSUBOI discloses the measurement impossible region a r.sup.† is, for example, a region that is a blind spot when viewed in the position of a lidar and in which measurement by the MMS is difficult because no laser beam is incident. The measurement low-accuracy region a r.sup.‡ is, for example, a region in which the accuracy of measurement by the MMS decreases as the interval of laser beam irradiation increases (the measurement density decreases) because the region is positioned nearly in parallel to a measurement surface.).
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 MARUYAMA in view of OZKUCUR of an information processing method comprising: classifying three-dimensional point cloud data including distance values to a target in a specified region into one or more clusters based on the distance values; and determining the cluster to adopt based on a distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification with the teachings of TSUBOI of indicating that the one or more clusters is a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets.
Wherein having MARUYAMA’s point cloud processing method having indicating that the one or more clusters is a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets.
The motivation behind the modification would have been to obtain a point cloud processing method that enhances the efficiency and accuracy of analyzing and detecting points/targets. Since both MARUYAMA and TSUBOI relate to analyzing LiDAR data from a vehicle to detect objects, wherein MARUYAMA unneeded point group data can be deleted and the data amount to be handled can be reduced, while TSUBOI is intended to solve the above-described problem and provide a measurement device and a measurement method that are capable of reducing a region in which measurement is impossible and a region in which the accuracy of measurement decreases. Please see MARUYAMA et al. (US 20200057905 A1), Paragraph [0051], and TSUBOI et al. (US 20220260719 A1), Paragraph [0009].
Regarding claim 10, MARUYAMA explicitly teaches a non-transitory computer-readable storage medium (Fig. 1. #10 includes a memory. Paragraph [0030]-MARUYAMA discloses the point group data processing device 10 is assumed to include a central processing unit (CPU), a memory, and a storage such as a hard disk drive which may normally be included in a general computer.) storing a program comprising instructions for causing a computer to execute processes to (Fig. 1. Paragraph [0030]-MARUYAMA discloses various pieces of processing are executed by a program in order to cause these general computers to function as the point group data processing device 10 of the example.):
classify three-dimensional point cloud data (Fig. 1. Paragraph [0036]-MARUYAMA discloses subsets p, p′ of the point group data to be projected respectively onto a target area b and an enlargement area b′ are obtained for each of a plurality of target areas in an image to create histograms h, h′ from pieces of depth information of p, p′ (wherein a point group is a point cloud and wherein the subsets and histograms are clusters the data is classified to). Further in paragraph [0034]-MARUYAMA discloses the point group data by the distance measurement device is the three-dimensional coordinate information.) including distance values to a target in a specified region into one or more clusters based on the distance values (Figs. 5 and 6. Paragraph [0043]-MARUYAMA discloses for each of the subsets p, p′, the histograms h, h′ are created from the depth information (step S05). A peak class of the histogram h is set to i (step S06). Since a peak class portion with the most concentrated distribution in the histogram corresponds to the distance of the subject when the subject is properly included in the box b, a portion including the peak class i is assumed to be a range of the target point group.); and
determine the cluster to adopt (Fig. 6, illustrates histogram with a range of selected point group data based on depth of a point group and frequency of a point group (wherein the range of selected point group data is the cluster to adopt). Paragraph [0050].) based on a distribution of the distance values of the three-dimensional point cloud data included by the cluster obtained by the classification (Fig. 6. Paragraph [0050]-MARUYAMA discloses the range of the point group data of the subject can be selected from the distribution of the histogram composed of all pieces of point group data in the box as shown in FIG. 6 by performing the above target point group specifying processing. Therefore, solely the point group data of the subject is specified and thus unneeded point group data can be deleted and the data amount to be handled can be reduced (wherein the selected point group data is the cluster to adopt and wherein the distribution is the histogram). Further in paragraph [0043]-MARUYAMA discloses for each of the subsets p, p′, the histograms h, h′ are created from the depth information (step S05) (wherein depth information is distance values). Further in paragraph [0034]-MARUYAMA discloses the point group data by the distance measurement device is the three-dimensional coordinate information.),
wherein the instructions for causing the computer to execute the process to determine (Fig. 6, illustrates histogram with a range of selected point group data based on depth of a point group and frequency of a point group (wherein the range of selected point group data is the cluster to adopt). Paragraph [0030]-MARUYAMA discloses various pieces of processing are executed by a program in order to cause these general computers to function as the point group data processing device 10 of the example.)
MARUYAMA fails to explicitly teach include instructions for rejecting the one or more clusters when the distribution of the distance values is constant.
However, OZKUCUR explicitly teaches include instructions for rejecting the one or more clusters when the distribution of the distance values is constant (Fig. 3-4, illustrate distributions of distance values. Paragraph [0043]-OZKUCUR discloses the mobile device 122 and/or the server 125 may filter individual grid cells as “noise,” such that image cells with too little points are eliminated. Further in paragraph [0052]-OZKUCUR indicated above, the determination of whether the grid cells 141 are occupied or unoccupied is based on the number of point cloud data points 140 assigned to the corresponding grid cell 141. In the example shown in FIG. 3, the number of point cloud data points 140 assigned to grid cells 144 is below the predetermined threshold (i.e., unoccupied) while the number of point cloud data points 140 assigned to grid cells 143 is above the predetermined threshold (i.e., occupied). As shown in FIG. 3, there are 10 occupied grid cells 143 and 32 unoccupied grid cells 144 (wherein unoccupied regions have a constant distribution of distance values of 0). Further in paragraph [0064]-OZKUCUR discloses the received data may be range data (e.g., LiDAR) or image data (e.g., camera) (wherein range data is distance values).),
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 MARUYAMA of a non-transitory computer-readable storage medium storing a program comprising instructions for causing a computer to execute processes to: classify three-dimensional point cloud data including distance values to a target in a specified region into one or more clusters based on the distance values; and determine the cluster to adopt based on a distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification with the teachings of OZKUCUR of include instructions for rejecting the one or more clusters when the distribution of the distance values is constant.
Wherein having MARUYAMA’s point cloud processing method that can include instructions for rejecting the one or more clusters when the distribution of the distance values is constant.
The motivation behind the modification would have been to obtain a point cloud processing method that enhances the efficiency and accuracy of analyzing and detecting points/targets. Since both MARUYAMA and OZKUCUR relate to analyzing LiDAR data from a vehicle to detect objects, wherein MARUYAMA unneeded point group data can be deleted and the data amount to be handled can be reduced, while OZKUCUR filtering the data points using the above-mentioned classification method reduces the amount of data points being processed, which increases efficiency and speed of the processing and requires less storage and bandwidth to operate the applications mentioned above. Please see MARUYAMA et al. (US 20200057905 A1), Paragraph [0051], and OZKUCUR et al. (US 20200202107 A1), Paragraph [0030].
MARUYAMA in view of OZKUCUR fail to explicitly teach indicating that the one or more clusters is a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets.
However, TSUBOI explicitly teaches indicating that the one or more clusters is a noise point cloud (Fig. 44. Paragraph [0163]-TSUBOI discloses while moving along a movement path (for example, the road R), the movable body measures the position of an object that exists in surroundings of the movement path, and outputs point group data. Further in paragraph [0189]-TSUBOI disclose the MMS 10 can measure a measurement insensible region and a measurement impossible region, which cannot be measured in conventional cases, by acquiring and analyzing the positional data obtained as described above.) generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device (Fig. 42. Paragraph [0160]-TSUBOI discloses the measurement low-accuracy region a r.sup.‡ is, for example, a region in which the accuracy of measurement by the MMS decreases as the interval of laser beam irradiation increases (the measurement density decreases) because the region is positioned nearly in parallel to a measurement surface.),
the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets (Fig. 42. Paragraph [0160]-TSUBOI discloses the measurement impossible region a r.sup.† is, for example, a region that is a blind spot when viewed in the position of a lidar and in which measurement by the MMS is difficult because no laser beam is incident. The measurement low-accuracy region a r.sup.‡ is, for example, a region in which the accuracy of measurement by the MMS decreases as the interval of laser beam irradiation increases (the measurement density decreases) because the region is positioned nearly in parallel to a measurement surface.).
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 MARUYAMA in view of OZKUCUR a non-transitory computer-readable storage medium storing a program comprising instructions for causing a computer to execute processes to: classify three-dimensional point cloud data including distance values to a target in a specified region into one or more clusters based on the distance values; and determine the cluster to adopt based on a distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification with the teachings of TSUBOI of indicating that the one or more clusters is a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets.
Wherein having MARUYAMA’s point cloud processing method having indicating that the one or more clusters is a noise point cloud generated from a blind spot region that is substantially parallel to a line of sight of a LiDAR device, the noise point cloud resulting from decreased received light intensity and decreased ranging accuracy caused by independently entering reflected pulse lights from front and back targets.
The motivation behind the modification would have been to obtain a point cloud processing method that enhances the efficiency and accuracy of analyzing and detecting points/targets. Since both MARUYAMA and TSUBOI relate to analyzing LiDAR data from a vehicle to detect objects, wherein MARUYAMA unneeded point group data can be deleted and the data amount to be handled can be reduced, while TSUBOI is intended to solve the above-described problem and provide a measurement device and a measurement method that are capable of reducing a region in which measurement is impossible and a region in which the accuracy of measurement decreases. Please see MARUYAMA et al. (US 20200057905 A1), Paragraph [0051], and TSUBOI et al. (US 20220260719 A1), Paragraph [0009].
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over MARUYAMA et al. (US 20200057905 A1), hereinafter referenced as MARUYAMA, in view of OZKUCUR et al. (US 20200202107 A1), hereinafter referenced as OZKUCUR, and further in view of TSUBOI et al. (US 20220260719 A1), hereinafter referenced as TSUBOI, and further in view of JENSEN et al. (US 6697497 B1), hereinafter referenced as JENSEN.
Regarding claim 3, MARUYAMA in view of OZKUCUR and further in view of TSUBOI explicitly teach the information processing apparatus according to Claim 2,
MARUYAMA further explicitly teaches wherein the at least one processor is configured to execute the processing instructions to (Fig. 1. Paragraph [0030]-MARUYAMA discloses the point group data processing device 10 is assumed to include a central processing unit (CPU), a memory, and a storage such as a hard disk drive which may normally be included in a general computer. The device is assumed to further include a graphics processing unit (GPU) as needed (not shown). It is needless to say that various pieces of processing are executed by a program in order to cause these general computers to function as the point group data processing device 10 of the example.):
MARUYAMA in view of OZKUCUR and further in view of TSUBOI fail to explicitly teach determine the cluster to adopt based on a shape of the histogram.
However, JENSEN explicitly teaches determine the cluster to adopt based on a shape of the histogram (Figs. 6-8, illustrates chosen points and their identified clusters (Fig. 6.), the chosen points mapped to interval values (Fig. 7.), which are then used to create pixel histograms (Fig. 8.). Col. 9, Lines [6-19]- JENSEN discloses after suitable points have been chosen, during a step 904 clusters of sample points are chosen. The sample points are distributed about the two chosen points in some specified manner. Clusters can be defined in part by a radius, as shown by the example clusters 606, 608, but other known or inventive methods may also be used. For instance, the sample points may be chosen to fit a Gaussian distribution about the given point. Other cluster distributions may also be used to form clusters of sample points during step 904, including without limitation the following familiar distribution functions: binomial, Cauchy, chi, exponential, non-central, alpha, beta, gamma, geometric, log, Pareto, power, Poisson, semi-circular, triangular, and their variations, alone and in combination.).
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 MARUYAMA in view of OZKUCUR and further in view of TSUBOI of an information processing apparatus comprising: at least one memory storing processing instructions; and at least one processor configured to execute the processing instructions to: classify three-dimensional point cloud data including distance values to a target in a specified region into one or more clusters based on the distance values; and determine the cluster to adopt based on a distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification with the teachings of JENSEN of determine the cluster to adopt based on a shape of the histogram.
Wherein having MARUYAMA’s point cloud processing device that can determine the cluster to adopt based on a shape of the histogram.
The motivation behind the modification would have been to obtain a point cloud processing device that enhances the efficiency and accuracy of analyzing and detecting points/targets. Since both MARUYAMA and JENSEN relate to analyzing three dimensional data sets through the use of histograms, wherein MARUYAMA unneeded point group data can be deleted and the data amount to be handled can be reduced, while JENSEN provides improved tools and techniques for detecting and/or characterizing boundaries in digital data. Please see MARUYAMA et al. (US 20200057905 A1), Paragraph [0051], and JENSEN et al. (US 6697497 B1), Col. 2. Lines [12-17].
Regarding claim 4, MARUYAMA in view of OZKUCUR and further in view of TSUBOI and further in view of JENSEN explicitly teach the information processing apparatus according to Claim 3,
MARUYAMA further explicitly teaches wherein the at least one processor is configured to execute the processing instructions to (Fig. 1. Paragraph [0030]-MARUYAMA discloses the point group data processing device 10 is assumed to include a central processing unit (CPU), a memory, and a storage such as a hard disk drive which may normally be included in a general computer. The device is assumed to further include a graphics processing unit (GPU) as needed (not shown). It is needless to say that various pieces of processing are executed by a program in order to cause these general computers to function as the point group data processing device 10 of the example.):
MARUYAMA in view of OZKUCUR and further in view of TSUBOI fail to explicitly determine, as the cluster to adopt, the cluster that the shape of the histogram is a Gaussian distribution shape.
However, JENSEN explicitly teaches determine, as the cluster to adopt, the cluster that the shape of the histogram is a Gaussian distribution shape (Figs. 6-8, illustrates chosen points and their identified clusters (Fig. 6.), the chosen points mapped to interval values (Fig. 7.), which are then used to create pixel histograms (Fig. 8.). Col. 9, Lines [6-19]- JENSEN discloses after suitable points have been chosen, during a step 904 clusters of sample points are chosen. The sample points are distributed about the two chosen points in some specified manner. Clusters can be defined in part by a radius, as shown by the example clusters 606, 608, but other known or inventive methods may also be used. For instance, the sample points may be chosen to fit a Gaussian distribution about the given point. Other cluster distributions may also be used to form clusters of sample points during step 904, including without limitation the following familiar distribution functions: binomial, Cauchy, chi, exponential, non-central, alpha, beta, gamma, geometric, log, Pareto, power, Poisson, semi-circular, triangular, and their variations, alone and in combination.).
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 MARUYAMA in view of OZKUCUR and further in view of TSUBOI of an information processing apparatus comprising: at least one memory storing processing instructions; and at least one processor configured to execute the processing instructions to: classify three-dimensional point cloud data including distance values to a target in a specified region into one or more clusters based on the distance values; and determine the cluster to adopt based on a distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification with the teachings of JENSEN of determine, as the cluster to adopt, the cluster that the shape of the histogram is a Gaussian distribution shape.
Wherein having MARUYAMA’s point cloud processing device that can determine, as the cluster to adopt, the cluster that the shape of the histogram is a Gaussian distribution shape.
The motivation behind the modification would have been to obtain a point cloud processing device that enhances the efficiency and accuracy of analyzing and detecting points/targets. Since both MARUYAMA and JENSEN relate to analyzing three dimensional data sets through the use of histograms, wherein MARUYAMA unneeded point group data can be deleted and the data amount to be handled can be reduced, while JENSEN provides improved tools and techniques for detecting and/or characterizing boundaries in digital data. Please see MARUYAMA et al. (US 20200057905 A1), Paragraph [0051], and JENSEN et al. (US 6697497 B1), Col. 2. Lines [12-17].
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over MARUYAMA et al. (US 20200057905 A1), hereinafter referenced as MARUYAMA, in view of OZKUCUR et al. (US 20200202107 A1), hereinafter referenced as OZKUCUR, and further in view of TSUBOI et al. (US 20220260719 A1), hereinafter referenced as TSUBOI, and further in view of LIM et al. (US 20170146648 A1), hereinafter referenced as LIM.
Regarding claim 6, MARUYAMA in view of OZKUCUR and further in view of TSUBOI explicitly teach the information processing apparatus according to Claim 5,
MARUYAMA further explicitly teaches wherein the at least one processor is configured to execute the processing instructions to (Fig. 1. #10 includes a CPU. Paragraph [0030]-MARUYAMA discloses the point group data processing device 10 is assumed to include a central processing unit (CPU). Further in paragraph [0030]-MARUYAMA discloses various pieces of processing are executed by a program in order to cause these general computers to function as the point group data processing device 10 of the example.):
MARUYAMA in view of OZKUCUR and further in view of TSUBOI fail to explicitly teach determine, as the cluster to adopt, the cluster that the frequency of the distance values of the three-dimensional point cloud data included by the cluster obtained by the classification is equal to or greater than a preset threshold value.
However, LIM explicitly teaches determine, as the cluster to adopt, the cluster that the frequency of the distance values of the three-dimensional point cloud data included by the cluster obtained by the classification is equal to or greater than a preset threshold value (Fig. 6. Paragraph [0046]-LIM discloses in a condition where a sum of frequency variation according to the distance of the target and frequency variation according to the velocity of the target is greater than zero (alternatively, a condition where the sum is equal to or greater than zero), in which the frequency variations are calculated through a pair of the up-chirp and down-chirp signals, that is, in a general driving environment, the signal processing unit 50 determines, as an actual target, a target satisfying a pairing condition for finding an intersection point at which a pair of the up-chirp and down-chirp signals and the added down-chirp signal meet (wherein up-chirp, down-chirp, added down-chirp signals are types of classifications). Further in paragraph [0070]-LIM discloses when the sum of frequency variation according to the distance of the target and frequency variation according to the velocity of the target is greater than zero (alternatively, a case where the sum is equal to or greater than zero), the signal processing unit 50 determines S20 an actual technical feature under a pairing condition for finding an intersection point (wherein the intersection point is the cluster to adopt, a preset threshold value is zero, and frequency variation according to distance is frequency of the distance values.).).
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 MARUYAMA in view of OZKUCUR and further in view of TSUBOI of an information processing apparatus comprising: at least one memory storing processing instructions; and at least one processor configured to execute the processing instructions to: classify three-dimensional point cloud data including distance values to a target in a specified region into one or more clusters based on the distance values; and determine the cluster to adopt based on a distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification with the teachings of LIM of determine, as the cluster to adopt, the cluster that the frequency of the distance values of the three-dimensional point cloud data included by the cluster obtained by the classification is equal to or greater than a preset threshold value.
Wherein having MARUYAMA’s point cloud processing device that can determine, as the cluster to adopt, the cluster that the frequency of the distance values of the three-dimensional point cloud data included by the cluster obtained by the classification is equal to or greater than a preset threshold value.
The motivation behind the modification would have been to obtain a point cloud processing device that enhances the efficiency and accuracy of analyzing and detecting points/targets. Since both MARUYAMA and LIM relate to determining position of a target from a set of data, which includes distance values, wherein MARUYAMA unneeded point group data can be deleted and the data amount to be handled can be reduced, while LIM provides a radar device for a vehicle, enabling the determination of a target. Please see MARUYAMA et al. (US 20200057905 A1), Paragraph [0051], and LIM et al. (US 20170146648 A1), Paragraph [0012].
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over MARUYAMA et al. (US 20200057905 A1), hereinafter referenced as MARUYAMA, in view of OZKUCUR et al. (US 20200202107 A1), hereinafter referenced as OZKUCUR, and further in view of TSUBOI et al. (US 20220260719 A1), hereinafter referenced as TSUBOI, and further in view of BELL et al. (US 20160055268 A1), hereinafter referenced as BELL.
Regarding claim 8, MARUYAMA in view of OZKUCUR and further in view of TSUBOI explicitly teach the information processing apparatus according to claim 1,
MARUYAMA further explicitly teaches wherein the at least one processor is configured to execute the processing instructions to (Fig. 1. #10 includes a CPU. Paragraph [0030]-MARUYAMA discloses the point group data processing device 10 is assumed to include a central processing unit (CPU). Further in paragraph [0030]-MARUYAMA discloses various pieces of processing are executed by a program in order to cause these general computers to function as the point group data processing device 10 of the example.):
MARUYAMA further explicitly teaches the three-dimensional point cloud data included by the cluster obtained by the classification into a plurality of clusters based on the distribution of the distance values of the three-dimensional point cloud data included by the cluster (Fig. 6, illustrates a plurality of clusters (wherein each bar on the histogram is a class). Paragraph [0043]-MARUYAMA discloses peak class of the histogram h is set to i (step S06). Since a peak class portion with the most concentrated distribution in the histogram corresponds to the distance of the subject when the subject is properly included in the box b, a portion including the peak class i is assumed to be a range of the target point group (wherein the peak class is the included cluster and wherein the most concentrated distribution is a distribution of distance values). Further in paragraph [0045]-MARUYAMA discloses the horizontal axis is the depth (distance) from the distance measurement device to a target point, and the vertical axis is a frequency of the point group.).
Although MARUYAMA explicitly teaches the three-dimensional point cloud data included by the cluster obtained by the classification into a plurality of clusters based on the distribution of the distance values of the three-dimensional point cloud data included by the cluster, MARUYAMA in view of OZKUCUR and further in view of TSUBOI fail to explicitly teach reclassify the three-dimensional point cloud data; and determine the cluster to adopt based on the distribution of the distance values of the three-dimensional point cloud data included by the cluster obtained by the reclassification.
However, BELL explicitly teaches reclassify the three-dimensional point cloud data (Fig. 14. Paragraph [0095]-BELL discloses the portion of the captured 3D data and the other portion of the captured 3D data are reclassified as a corresponding portion of the captured 3D data (e.g., using an identification component 104) based on distance data and/or orientation data associated with the portion of the captured 3D data and the other portion of the captured 3D data.); and
determine the cluster to adopt based on the distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the reclassification (Fig. 14. Paragraph [0051]-BELL discloses the first identification component 202 can reclassify (e.g., merge, combine, etc.) portions of the captured 3D data that are identified as flat surfaces based on distance criteria and/or orientation criteria. Distance criteria can include, but are not limited to, a determination that portions of the captured 3D data that are identified as flat surfaces overlap, that portions of the captured 3D data that are identified as flat surfaces are contiguous (e.g., connected, touching, etc.), that distance between an edge of a particular portion of the captured 3D data and an edge of another portion of the captured 3D data is below a threshold level, etc. (wherein distribution of distance values is captured 3D data distributed over a surface.). Further in paragraph [0095]-BELL discloses the portion of the captured 3D data and the other portion of the captured 3D data can be reclassified as the same flat surface (e.g., the portion of the captured 3D data and the other portion of the captured 3D data can correspond to a single flat surface and/or be reclassified as corresponding data) (wherein the corresponding data is the cluster to adopt.).).
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 MARUYAMA in view of OZKUCUR and further in view of TSUBOI of an information processing apparatus comprising: at least one memory storing processing instructions; and at least one processor configured to execute the processing instructions to: classify three-dimensional point cloud data including distance values to a target in a specified region into one or more clusters based on the distance values; and determine the cluster to adopt based on a distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the classification with the teachings of BELL reclassify the three-dimensional point cloud data; and determine the cluster to adopt based on the distribution of the distance values of the three-dimensional point cloud data included by the cluster obtained by the reclassification.
Wherein having MARUYAMA’s point cloud processing device that can reclassify the three-dimensional point cloud data included by the cluster obtained by the classification into a plurality of clusters based on the distribution of the distance values of the three-dimensional point cloud data included by the cluster; and determine the cluster to adopt based on the distribution of the distance values of the three- dimensional point cloud data included by the cluster obtained by the reclassification.
The motivation behind the modification would have been to obtain a point cloud processing device that enhances the efficiency, accuracy, and quality of analysis when attempting to detect 3D points/targets. Since both MARUYAMA and BELL relate to analyzing and evaluating data associated with a 3D environment, wherein MARUYAMA shows unneeded point group data can be deleted and the data amount to be handled can be reduced, while BELL is to accurately generate, interpret and/or modify a 3D model. Please see MARUYAMA et al. (US 20200057905 A1), Paragraph [0051], and BELL et al. (US 20160055268 A1), Paragraph [0002].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
ISHIKAWA et al. (US 20200160526 A1) – An apparatus that can obtain a subtraction image efficiently is provided. An image processing apparatus obtains a target image constituted by a set of voxels arranged in a discretized manner; sets a search area in the target image; and obtains, in a partial area included in the search area, on the basis of at least one of a voxel value included in the partial area and an interpolated value obtained by interpolation of a voxel of the target image, at least one of a maximum and a minimum of a voxel value and an interpolated value within the search area…Abstract, Fig. 11A-11C.
AGATA et al. (US 20180205867 A1) - In a method for determining upper and lower limit values for a target brightness when image contrast is extended, an upper and lower limit value search processing unit establishes two adjacent areas in accordance with brightness of a grayscale histogram, and, while scanning the positions of those areas, compares the frequency of those areas to a threshold, and if one frequency value is greater than or equal to the threshold value and the other frequency value is lower than the threshold, performs upper and lower limit value search processing wherein a brightness value at the boundary of the two areas is determined as an upper or lower limit value. Thresholds for upper and lower limit value search start position and frequency are established based on the shape of the grayscale histogram of an image to be processed. The shape of the grayscale histogram is identified according to preset classifications…Abstract, Fig. 6.
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 ETHAN N WOLFSON whose telephone number is (571)272-1898. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, 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.
/ETHAN N WOLFSON/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
Prosecution Insights