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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/25/2024 is being considered by the examiner.
Claim Objections
Claims 2-8 are objected to because of the following informalities:
In claims 2-8, line 2, the term “the processing instructions to” should be changed to “the processing instructions to:” in order to avoid typographical issue.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101
Regarding Independent Claim 1 and its dependent claims 2-8,
Step 1 Analysis: Claim 1 is directed to an apparatus/device, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part:
“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”
The limitations as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes/Mathematical Concept” grouping of abstract ideas. The limitations of:
“classify three-dimensional…..based on the distance values” is a step that the human mind can perform based on observation and evaluation such as, the human mind can observe 3D data, which includes some given values corresponding to target and clusters based on the distance values (this is merely recitation of already given/processed data/information, the step of classifying is performed on);
“determine the cluster…obtained by the classification” is a series of steps including a mix of mental process and mathematical concepts such as, the human mind can observe a distribution of values (a mathematical relationship) and some 3D data by the classification (as discussed above to be a result of a mental process step, under BRI) and make a determination.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. Particularly, the claim recites the following additional element(s) –
“An information processing apparatus comprising:
at least one memory storing processing instructions; and
at least one processor configured to execute the processing instructions to”
The additional elements “an information processing apparatus comprising: at least one memory storing processing instructions; and at least one processor configured to execute the processing instructions to” - recited at a high level of generality (i.e. as a processor performing executing instructions stored, a memory storing instruction program, a computer to have computer components executing the instructions of the invention, a non-transitory computer readable medium performing storing instructions, generic devices [interface, screen, camera, sensor, etc.], etc.) such that they amount to no more than mere instructions to apply the exception.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Please see MPEP §2106.04.(d).III.C.
Step 2B Analysis: there are no additional elements, such as for these additional elements as indicated above, that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea.
For all of the foregoing reasons, claim 1 does not comply with the requirements of 35 USC 101.
Accordingly, the dependent claims 2-8 do not provide elements that overcome the deficiencies of the independent claim 1. Moreover, claim 2 recites, in part, “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” is a series of steps including a mix of mental process and mathematical concepts such as, the human mind can observe a histogram values (a mathematical relationship) and some 3D data by the classification (as discussed above to be a result of a mental process step, under BRI) and make a determination. Claim 3 recites, in part, “determine the cluster to adopt based on a shape of the histogram” is a mental process step, under BRI, such as the human mind can observe some histogram and its shape to determine a cluster. Claim 4 recites, in part, “determine, as the cluster to adopt, the cluster that the shape of the histogram is a Gaussian distribution shape” is a series of mixed steps of mental process and mathematical concepts such as the human mind can observe some shape information and determine if it is a certain shape of a mathematical relationships here being a Gaussian distribution shape. Claim 5 recites, in part, “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” is a series of mixed steps of mental process and mathematical concepts, such as the human mind can observe some given mathematical relationships (here being a frequency of distance values) and some results of classification (such as discussed above in claim 1 to be a result of a mental process) to make a determination (evaluation mental process) of the cluster to adopt. Claim 6 recites, in part, “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” is a series of mixed steps of mental process and mathematical concepts, such as the human mind can observe some given mathematical relationships (here being a frequency of distance values) and some results of classification (such as discussed above in claim 1 to be a result of a mental process) to meet a certain condition/criteria (such as recited to be equal to greater than a certain threshold) to make a determination (evaluation mental process) of the cluster to adopt. Claim 7 recites, in part, “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” is an explicit mathematical calculation step. Claim 8 recites, in part, “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” is a series of steps of mental process and mathematical concepts, such as the human mind can observe some given 3D data and a result of a classification based on a distribution of values (mathematical relationship) to make an evaluation to reclassify the data/information; “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” is a series of mixed steps of mental process and mathematical concepts, such as the human mind can observe some given mathematical relationships (here being a distribution of distance values) and some results of classification (such as discussed above in claim 1 to be a result of a mental process) to make a determination (evaluation mental process) of the cluster to adopt.
Accordingly, the dependent claims 2-8 are not patent eligible under 101.
Regarding the independent claims 9 and 10:
Claim 9 (a method/process claim) and claim 10 (medium/device claim) recite analogous limitations to the independent claim 1 hence, under the same analysis approach, these limitations are ineligible under 101 requirements. Moreover, claim 10 recites further additional elements of “a non-transitory computer-readable storage medium storing a program comprising instructions for causing a computer to execute processes to” recited at a high level of generality (i.e. as a processor performing executing instructions stored, a non-transitory computer-readable storage medium [such as a RAM or ROM] storing instruction program, etc.) such that they amount to no more than mere instructions to apply the exception.
Claim Rejections - 35 USC § 102
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 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.
Claims 1-2, 5, 7, 9-10 are rejected under 35 U.S.C. 102(a)(1)/((a)(2) as being anticipated by MARUYAMA et al. (US 20200057905 A1), hereinafter referenced as MARUYAMA.
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.).
Regarding claim 2, MARUYAMA explicitly teaches 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 explicitly teaches 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 explicitly teaches 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.).
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.).
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 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 JENSEN et al. (US 6697497 B1), hereinafter referenced as JENSEN.
Regarding claim 3, MARUYAMA explicitly teaches 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 fails 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 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 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 fails to explicitly teach 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 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 and LIM et al. (US 20170146648 A1), hereinafter referenced as LIM.
Regarding claim 6, MARUYAMA explicitly teaches 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 fails 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 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 and BELL et al. (US 20160055268 A1), hereinafter referenced as BELL.
Regarding claim 8, MARUYAMA explicitly teaches 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 fails 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 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.
JANG et al. (US 20220406037 A1) - A method of classifying an object according to an embodiment includes extracting a first feature by transforming rectangular coordinates of points included in the box of the object, obtained from a point cloud acquired using a LiDAR sensor, into complex coordinates and performing Fast Fourier Transform (FFT) on the complex coordinates, obtaining an average and a standard deviation as a second feature, the average and the standard deviation being parameters of a Gaussian model for the points included in the box of the object, and classifying the type of object based on at least one of the first feature or the second feature.
SCHWIESOW (US 20220365186 A1) - Systems and methods include obtaining point cloud data representing a point cloud; selecting a subset of the point cloud data based at least in part on a contour metric; grouping sets of points of the subset of the point cloud into one or more clusters based at least in part on one or more distance metrics; for a cluster that satisfies one or more cluster size criteria based on dimensions of a calibration standard, determining whether a distribution of signal intensities of points of the cluster satisfies a distribution criterion; based on a determination that the distribution of signal intensities of points satisfies the distribution criterion, determining boundaries of a region that represents the calibration standard; and storing data identifying a set of points of the point cloud that correspond to the calibration standard.
Heinonen (US 20210264245 A1) - A method for classifying a spatial data carried out by a data computing environment. The method includes: receiving a spatial data from a data source; generating a first feature from the spatial data; dividing the first feature into a first sub-feature and a second sub-feature; analysing the first sub-feature to derive a first sub-feature data; analysing the second sub-feature to derive a second sub-feature data; using the first sub-feature data and the second sub-feature data as a first input data for analysing the first feature; and analysing at least one of the first sub-feature data, the second sub-feature data, and the first feature to classify the spatial data into a plurality of object classes.
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/ETHAN N WOLFSON/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673