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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submissions, filed on July 28th, 2026, have been entered.
Information Disclosure Statement(s)
The Information Disclosure Statement filed on July 16th, 2026 has been reviewed and acknowledged by the examiner.
Status of Claims
Claims 1-5, 7-8 and 10-11 are pending, claims 1-2, 4-5 and 7-8 have been amended, claims 6 and 9 have been canceled, claims 10-11 have been added. Claims 1-5, 7-8 and 10-11 remains rejected.
Response to Argument(s)
112(f) interpretation and 112(a) and 112(b) rejection:
The amendment filed has removed all the 112(f) evocation terms hence, 112(f) interpretation no longer holds, the 112(a) and 112(b) rejections have been overcome with the amendment.
Prior Art Rejection:
In view of the Amendments to independent claims 1 and 7-8, the previously applied prior art rejections are withdrawn. Applicants’ arguments are rendered moot in view of the new grounds of rejection set forth below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5, 7-8 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Wenzhong Shi et. al. (“US 10,692,280 B2” hereinafter as “Shi”) in view of Sreevatsan Bhaskaran et. al. (“US 2020/0309957 A1” hereinafter as “Bhaskaran”).
Regarding claim 1, Shi teaches an information processing device comprising: a processor coupled to a memory storing instructions that, when executed by the processor, cause the processor to (Figure 2 illustrates a system with a processor, memory used to execute the instructions of the invention): acquire point cloud data which is a set of data for each point measured by a measurement device (Column 4, lines 65-67, discloses pre-processing of point cloud data which indicates an acquisition of the point cloud data, obtained from a laser scanning device according to Column 4, lines 44-51 [analogous to the recited measurement device]); calculate an evaluation value for each data of the point cloud data (Column 11, lines 64-67, discloses assigning tags to objects or non-surface objects [outliers according to column 14, lines 11-16], the tagging is based on an energy function according to equation 9 and column 10, lines 59-67 to column 11, lines 1-11, which is analogous to the recited evaluation value calculation, since the energy function is used to smooth the data point [for each data of the point cloud data], and the result of equation 9 is analogous to the evaluation value as claimed, wherein each point cloud data has the result of the energy calculated), based on an evaluation function for evaluating whether the each data is an object point (The tagging of the object as object surface or non-surface object as disclosed in column 11, lines 64-67, which is analogous to using the evaluation function to evaluate whether the data of the point cloud data is an object data/point), which is a measured point of an object, or a noise point, which is generated by noise (“or” indicates a selection, only one option is the instant scope of the claim, the examiner selects “which is a measured point of an object” which is disclosed in column 11, which is a 3D data point of the object according to column 11, lines 57-67); set a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function (The energy function as discussed previously, is further based on setting of a threshold value according to column 11, lines 57-67, and col. 10, lines 34-67, which is based on a probability density function of the energy function with the normal distribution [analogous to “statistic”]); determine the noise point included in the point cloud data, based on the evaluation value and the threshold value (The non-surface object data point being tagged based on the energy function and the thresholding results as disclosed in col. 11, lines 34-67); calculate the statistic based on the point cloud information (Column 14, lines 1-9, discloses statistical outlier removal indicating a calculation of statistic is being performed on the point cloud information generated) corresponding to a measurement in which no object is present (Column 13, lines 60-67 to column 14, lines 1-9, disclose the statistical outlier removal is based on the returned laser pulse [the range of the measurement device] when there is outlier needs to be removed, as that within the retuned laser pulse the outliers are being removed in calculation of a statistical outlier removal, indicating no object [when there is outliers/false object] in the measurement range [within the retuned laser pulse]; which is further supported in column 8, lines 1-19, in its processing, wherein the processing of the removal of outliers is based on region growth which is to determine if the plane being processed is treated as a false plane (there is no object in the measurement range) to remove the corresponding outliers).
However, Shi does not explicitly teach the measurement being a measurement direction.
Bhaskaran teaches the measurement being a measurement direction (Par. [0014] discloses “identifying false return and/or removing…a false detection generated based at least in part on a light sensor’s output signals. As used herein a false detection is a false positive indication that a surface exists in an environment surveyed by a light sensor” indicating a false detection of a surface removal/outlier removal which is analogous to Shi’s outlier removal of detecting false plane in the returned laser range; moreover, Bhaskaran’s Par. [0020] discloses “detecting that the output signal includes a false return may additionally or alternatively include….measure associated with a second LIDAR sensor (e.g., a first channel of the first LIDAR sensor took the first measurement in a direction that…)” indicating the measurement of data of the light signal sensor include measurement in a direction which is analogous to the recited measurement direction; hence, there is outlier or false surface/plane in the measurement direction indicating no object [false detection indicating no actual object] in the measurement direction which is analogous to the recited limitation).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Shi of having an information processing device comprising: a processor coupled to a memory storing instructions that, when executed by the processor, cause the processor to: acquire point cloud data which is a set of data for each point measured by a measurement device; calculate an evaluation value for each data of the point cloud data, based on an evaluation function for evaluating whether the each data is an object point, which is a measured point of an object, or a noise point, which is generated by noise; set a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function; determine the noise point included in the point cloud data, based on the evaluation value and the threshold value; calculate the statistic based on the point cloud information corresponding to a measurement information in which no object is present, with the teachings of Bhaskaran of having wherein the measurement being a measurement direction.
Wherein having Shi’s information processing device comprising wherein having wherein the measurement being a measurement direction.
The motivation behind the modification would have been to strengthen recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, and further to improve accuracy of LIDAR detections by reducing number of false positives generated. Since both Shi and Bhaskaran share the same endeavor of systems that perform 3D lidar data generation and recognition. Wherein Shi’s system improve 3D recognition and reconstruction by strengthening recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, see Shi’s Abstract and Bhaskaran’s system improves accuracy of LIDAR detections by reducing number of false positives generated, see Bhaskaran’s Par. [0029].
Regarding claim 2, Shi in view of Bhaskaran teaches the information processing device according to claim 1, Shi teaches wherein the instructions cause the processor to set the threshold value based on an average and variance of the probability density function (Col. 10, lines 43-67, discloses the threshold is based on the normal distribution of the probability density function which is a mean value [an average] and further based on a covariance according to column 6, lines 1-4).
Regarding claim 5, Shi in view of Bhaskaran teaches the information processing device according to claim 1, Shi teaches further comprising a memory configured to store the statistic (Col. 4, lines 1-4, discloses a storage medium storing the information being processed, hence it can be understood that the discussed statistic is being stored in addition), wherein the instructions cause the processor to set the threshold value based on the statistic stored in the memory (The processing analogous to the threshold setting unit as discussed, as disclosed in column 11, lines 64-67., and col. 10, lines 34-67, to be analogous to setting the threshold value which is based on the normal distribution the statistic as claimed).
Regarding claim 7, Shi teaches a control method executed by an information processing device, the control method comprising (Col. 13, lines 9-22, discloses the processing is part of a computer system [processing device, control method]): acquiring point cloud data which is a set of data for each point measured by a measurement device (Column 4, lines 65-67, discloses pre-processing of point cloud data which indicates an acquisition of the point cloud data, the processor programed [such as illustrated in Fig. 2] to perform this step is analogous to the recited acquisition unit, obtained from a laser scanning device according to column 4, lines 44-51, [analogous to the recited measurement device]); calculating an evaluation value for each data of the point cloud data (Column 11, lines 64-67, discloses assigning tags to objects or non-surface objects [outliers according to column 14, lines 11-16], the tagging is based on an energy function according to equation 9 and column 10, lines 59-67 and column 11, lines 1-11, which is analogous to the recited evaluation value calculation as claimed, since the energy function is used to smooth the data point [for each data of the point cloud data], and the result of equation 9 is analogous to the evaluation value as claimed, wherein each point cloud data has the result of the energy calculated), based on an evaluation function for evaluating whether the each data is an object point (The tagging of the object as object surface or non-surface object as disclosed in col. 11, lines 64-67, which is analogous to using the evaluation function to evaluate whether the data of the point cloud data is an object data/point), which is a measured point of an object, or a noise point, which is generated by noise (“or” indicates a selection, only one option is the instant scope of the claim, the examiner selects “which is a measured point of an object” which is disclosed in column 11, which is a 3D data point of the object according to column 11, lines 57-67); setting a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function (The energy function as discussed previously, is further based on setting of a threshold value according to column 11, lines 57-67, and col. 10, lines 34-67, which is based on a probability density function of the energy function with the normal distribution [analogous to “statistic”]); determining the noise point included in the point cloud data, based on the evaluation value and the threshold value (As discussed, the non-surface object data point being tagged based on the energy function and the thresholding results as disclosed in col. 11, lines 49-67); and calculating the statistic based on the point cloud information (Column 14, lines 1-9, discloses statistical outlier removal indicating a calculation of statistic is being performed on the point cloud information generated) corresponding to a measurement in which no object is present (Column 13, lines 60-67 to column 14, lines 1-9, discloses the statistical outlier removal is based on the returned laser pulse [the range of the measurement device] when there is outlier to be removed, therefore, is within the retuned laser pulse the outliers are being removed and calculated a statistical outlier removal, when there is no object [when there is outliers/false objects] in the measurement range [within the retuned laser pulse]; the processing would determine the statistical outliers according to the returned laser pulse and remove them, which is further supported in column 8, lines 1-19, in its processing, wherein the processing of the removal of outliers is based on region growth which is to determine if the plane being processed is treated as a false plane [there is no object in the measurement range] to remove the corresponding outliers).
However, Shi does not explicitly teach the measurement being a measurement direction.
Bhaskaran teaches the measurement being a measurement direction (Par. [0014] discloses “identifying false return and/or removing…a false detection generated based at least in part on a light sensor’s output signals. As used herein a false detection is a false positive indication that a surface exists in an environment surveyed by a light sensor” indicating a false detection of a surface removal/outlier removal which is analogous to Shi’s outlier removal of detecting false plane in the returned laser range; moreover, Bhaskaran’s Par. [0020] discloses “detecting that the output signal includes a false return may additionally or alternatively include….measure associated with a second LIDAR sensor (e.g., a first channel of the first LIDAR sensor took the first measurement in a direction that…)” indicating the measurement of data of the light signal sensor include measurement in a direction which is analogous to the recited measurement direction; hence, there is outlier or false surface/plane in the measurement direction indicating no object [false detection indicating no actual object] in the measurement direction which is analogous to the recited limitation).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Shi of having a method of acquiring point cloud data which is a set of data for each point measured by a measurement device; calculating an evaluation value for each data of the point cloud data, based on an evaluation function for evaluating whether the each data is an object point, which is a measured point of an object, or a noise point, which is generated by noise; setting a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function; determining the noise point included in the point cloud data, based on the evaluation value and the threshold value; calculating the statistic based on the point cloud information corresponding to a measurement information in which no object is present, with the teachings of Bhaskaran of having wherein the measurement being a measurement direction.
Wherein having Shi’s method of acquiring point cloud data wherein having wherein the measurement being a measurement direction.
The motivation behind the modification would have been to strengthen recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, and further to improve accuracy of LIDAR detections by reducing number of false positives generated. Since both Shi and Bhaskaran share the same endeavor of systems that perform 3D lidar data generation and recognition. Wherein Shi’s system improve 3D recognition and reconstruction by strengthening recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, see Shi’s Abstract and Bhaskaran’s system improves accuracy of LIDAR detections by reducing number of false positives generated, see Bhaskaran’s Par. [0029].
Regarding claim 8, Shi teaches a non-transitory computer readable medium storing a program causing a computer to (Col. 13, lines 9-22, discloses the processing is part of a computer system [processing device, control method] which indicates the use of a RAM or ROM [non-transitory computer readable medium]): acquire point cloud data which is a set of data for each point measured by a measurement device (Column 4, lines 65-67, discloses pre-processing of point cloud data which indicates an acquisition of the point cloud data, the processor programed [such as illustrated in Fig. 2] to perform this step is analogous to the recited acquisition unit, obtained from a laser scanning device according to column 4, lines 44-52, [analogous to the recited measurement device]); calculate an evaluation value for each data of the point cloud data (Column 11, lines 64-67, discloses assigning tags to objects or non-surface objects [outliers according to column 14, lines 11-16], the tagging is based on an energy function according to equation 9 and column 10, lines 59-67 and column 11, lines 1-11, which is analogous to the recited evaluation value calculation as claimed, since the energy function is used to smooth the data point [for each data of the point cloud data], and the result of equation 9 is analogous to the evaluation value as claimed, wherein each point cloud data has the result of the energy calculated), based on an evaluation function for evaluating whether the each point is an object point (The tagging of the object as object surface or non-surface object as disclosed in col. 11, lines 64-67, which is analogous to using the evaluation function to evaluate whether the data of the point cloud data is an object data/point), which is a measured point of an object, or a noise point, which is generated by noise (“or” indicates a selection, only one option is the instant scope of the claim, the examiner selects “which is a measured point of an object” which is disclosed in column 11, which is a 3D data point of the object according to column 11, lines 34-67); set a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function (The energy function as discussed previously, is further based on setting of a threshold value according to col. 10, lines 34-67 to column 11, lines 34-67, which is based on a probability density function of the energy function with the normal distribution [analogous to statistic]); determine the noise point included in the point cloud data, based on the evaluation value and the threshold value (The non-surface object data point being tagged based on the energy function and the thresholding results as disclosed in column 11, lines 34-67); and calculate the statistic based on the point cloud information (Column 14, lines 1-9, discloses statistical outlier removal indicating a calculation of statistic is being perfumed on the point cloud information generated) corresponding to a measurement in which no object is present (Column 13, lines 60-67 to column 14, lines 1-9, discloses the statistical outlier removal is based on the returned laser pulse [the range of the measurement device] when there is outlier needs to be removed, therefore, it can be understood as that within the retuned laser pulse the outliers are being removed and calculated a statistical outlier removal, when there is no object [when there is outliers/false objects] in the measurement range [within the retuned laser pulse]; which is further supported in column 8, lines 1-19, in its processing, wherein the processing of the removal of outliers is based on region growth which is to determine if the plane being processed is treated as a false plane [there is no object in the measurement range] to remove the corresponding outliers).
However, Shi does not explicitly teach the measurement being a measurement direction.
Bhaskaran teaches the measurement being a measurement direction (Par. [0014] discloses “identifying false return and/or removing…a false detection generated based at least in part on a light sensor’s output signals. As used herein a false detection is a false positive indication that a surface exists in an environment surveyed by a light sensor” indicating a false detection of a surface removal/outlier removal which is analogous to Shi’s outlier removal of detecting false plane in the returned laser range; moreover, Bhaskaran’s Par. [0020] discloses “detecting that the output signal includes a false return may additionally or alternatively include….measure associated with a second LIDAR sensor (e.g., a first channel of the first LIDAR sensor took the first measurement in a direction that…)” indicating the measurement of data of the light signal sensor include measurement in a direction which is analogous to the recited measurement direction; hence, there is outlier or false surface/plane in the measurement direction indicating no object [false detection indicating no actual object] in the measurement direction which is analogous to the recited limitation).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Shi of having a system to: acquire point cloud data which is a set of data for each point measured by a measurement device; calculate an evaluation value for each data of the point cloud data, based on an evaluation function for evaluating whether the each data is an object point, which is a measured point of an object, or a noise point, which is generated by noise; set a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function; determine the noise point included in the point cloud data, based on the evaluation value and the threshold value; calculate the statistic based on the point cloud information corresponding to a measurement information in which no object is present, with the teachings of Bhaskaran of having wherein the measurement being a measurement direction.
Wherein having Shi’s system wherein having wherein the measurement being a measurement direction.
The motivation behind the modification would have been to strengthen recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, and further to improve accuracy of LIDAR detections by reducing number of false positives generated. Since both Shi and Bhaskaran share the same endeavor of systems that perform 3D lidar data generation and recognition. Wherein Shi’s system improve 3D recognition and reconstruction by strengthening recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, see Shi’s Abstract and Bhaskaran’s system improves accuracy of LIDAR detections by reducing number of false positives generated, see Bhaskaran’s Par. [0029].
Regarding claim 10, Shi in view of Bhaskaran teaches the information processing device according to claim 1, Shi teaches wherein the point cloud information corresponding to the measurement in which no object is present (Column 14, lines 1-9, discloses statistical outlier removal indicating a calculation of statistic is being perfumed on the point cloud information generated; Column 13, lines 60-67 to column 14, lines 1-9, discloses the statistical outlier removal is based on the returned laser pulse [the range of the measurement device] when there is outlier needs to be removed, is within the retuned laser pulse the outliers are being removed and calculated a statistical outlier removal, when there is no object [when there is outliers/false objects] in the measurement range [within the retuned laser pulse]) comprises point cloud information generated in a condition where no object is present (which is further supported in column 8, lines 1-19, in its processing, wherein the processing of the removal of outliers is based on region growth which is to determine if the plane being processed is treated as a false plane [there is no object in the measurement range] to remove the corresponding outliers).
However, Shi does not explicitly teach the measurement being a measurement direction, where no object is present in a field of view of the measurement device.
Bhaskaran teaches the measurement being a measurement direction (Par. [0014] discloses “identifying false return and/or removing…a false detection generated based at least in part on a light sensor’s output signals. As used herein a false detection is a false positive indication that a surface exists in an environment surveyed by a light sensor” indicating a false detection of a surface removal/outlier removal which is analogous to Shi’s outlier removal of detecting false plane in the returned laser range; moreover, Bhaskaran’s Par. [0020] discloses “detecting that the output signal includes a false return may additionally or alternatively include….measure associated with a second LIDAR sensor (e.g., a first channel of the first LIDAR sensor took the first measurement in a direction that…)” indicating the measurement of data of the light signal sensor include measurement in a direction which is analogous to the recited measurement direction; hence, there is outlier or false surface/plane in the measurement direction indicating no object [false detection indicating no actual object] in the measurement direction which is analogous to the recited limitation), where no object is present in a field of view of the measurement device (Bhaskaran’s Par. [0020] discloses “detecting that the output signal includes a false return may additionally or alternatively include….measure associated with a second LIDAR sensor (e.g., a first channel of the first LIDAR sensor took the first measurement in a direction that…)” indicating the measurement of data of the light signal sensor include measurement in a direction which is analogous to the recited measurement direction or in the field of view of the measurement device [light sensor]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Shi of having an information processing device comprising: a processor coupled to a memory storing instructions that, when executed by the processor, cause the processor to: acquire point cloud data which is a set of data for each point measured by a measurement device; calculate an evaluation value for each data of the point cloud data, based on an evaluation function for evaluating whether the each data is an object point, which is a measured point of an object, or a noise point, which is generated by noise; set a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function; determine the noise point included in the point cloud data, based on the evaluation value and the threshold value; calculate the statistic based on the point cloud information corresponding to a measurement information in which no object is present, with the teachings of Bhaskaran of having wherein the measurement being a measurement direction, where no object is present in a field of view of the measurement device.
Wherein having Shi’s information processing device comprising wherein having wherein the measurement being a measurement direction, where no object is present in a field of view of the measurement device.
The motivation behind the modification would have been to strengthen recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, and further to improve accuracy of LIDAR detections by reducing number of false positives generated. Since both Shi and Bhaskaran share the same endeavor of systems that perform 3D lidar data generation and recognition. Wherein Shi’s system improve 3D recognition and reconstruction by strengthening recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, see Shi’s Abstract and Bhaskaran’s system improves accuracy of LIDAR detections by reducing number of false positives generated, see Bhaskaran’s Par. [0029].
Regarding claim 11, Shi in view of Bhaskaran teaches the information processing device according to claim 1, Shi teaches wherein the point cloud information corresponding to the measurement in which no object is present (Column 14, lines 1-9, discloses statistical outlier removal indicating a calculation of statistic is being perfumed on the point cloud information generated; Column 13, lines 60-67 to column 14, lines 1-9, discloses the statistical outlier removal is based on the returned laser pulse [the range of the measurement device] when there is outlier to be removed, is within the retuned laser pulse the outliers are being removed and calculated a statistical outlier removal, when there is no object [when there is outliers/false objects] in the measurement range [within the retuned laser pulse]; which is further supported in column 8, lines 1-19, in its processing, wherein the processing of the removal of outliers is based on region growth which is to determine if the plane being processed is treated as a false plane [there is no object in the measurement range] to remove the corresponding outliers).
However, Shi does not explicitly teach the measurement being a measurement direction in which no object is present comprises point cloud information of a scanning point identified as having no object point based on a determination result in a past frame.
Bhaskaran teaches the measurement being a measurement direction (Par. [0014] discloses “identifying false return and/or removing…a false detection generated based at least in part on a light sensor’s output signals. As used herein a false detection is a false positive indication that a surface exists in an environment surveyed by a light sensor” indicating a false detection of a surface removal/outlier removal which is analogous to Shi’s outlier removal of detecting false plane in the returned laser range; moreover, Bhaskaran’s Par. [0020] discloses “detecting that the output signal includes a false return may additionally or alternatively include….measure associated with a second LIDAR sensor (e.g., a first channel of the first LIDAR sensor took the first measurement in a direction that…)” indicating the measurement of data of the light signal sensor include measurement in a direction which is analogous to the recited measurement direction; hence, there is outlier or false surface/plane in the measurement direction indicating no object [false detection indicating no actual object] in the measurement direction which is analogous to the recited limitation), in which no object is present comprises point cloud information of a scanning point identified as having no object point based on a determination result in a past frame (Par. [0059] discloses “the detector may identify the return as a false return. In some examples, the detector may identify, as a false return, a portion of the output signal that comprises a local maximum that is less than a previous (in time) local maximum” indicating the determination of the false return or the outlier to be removed, include point cloud information of the scanning point having no object [false return of the object detection is analogous to having no object] is further based on a determination result in a past frame [previous in time local maximum]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Shi of having an information processing device comprising: a processor coupled to a memory storing instructions that, when executed by the processor, cause the processor to: acquire point cloud data which is a set of data for each point measured by a measurement device; calculate an evaluation value for each data of the point cloud data, based on an evaluation function for evaluating whether the each data is an object point, which is a measured point of an object, or a noise point, which is generated by noise; set a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function; determine the noise point included in the point cloud data, based on the evaluation value and the threshold value; calculate the statistic based on the point cloud information corresponding to a measurement information in which no object is present, with the teachings of Bhaskaran of having the measurement being a measurement direction in which no object is present comprises point cloud information of a scanning point identified as having no object point based on a determination result in a past frame.
Wherein having Shi’s information processing device comprising wherein having the measurement being a measurement direction in which no object is present comprises point cloud information of a scanning point identified as having no object point based on a determination result in a past frame.
The motivation behind the modification would have been to strengthen recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, and further to improve accuracy of LIDAR detections by reducing number of false positives generated. Since both Shi and Bhaskaran share the same endeavor of systems that perform 3D lidar data generation and recognition. Wherein Shi’s system improve 3D recognition and reconstruction by strengthening recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, see Shi’s Abstract and Bhaskaran’s system improves accuracy of LIDAR detections by reducing number of false positives generated, see Bhaskaran’s Par. [0029].
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Wenzhong Shi et. al. (“US 10,692,280 B2” hereinafter as “Shi”) in view of Sreevatsan Bhaskaran et. al. (“US 2020/0309957 A1” hereinafter as “Bhaskaran”) further in view of Hisanaga Satoshi (Foreign Patent Document “JP 2010-54315 A” hereinafter as “Satoshi”) and Evangelos Alexiou et. al. (“On the Performance of Metrics to Predict Quality in Point Cloud Representations, 2017, Applications of Digital Image Processing XL, Proc. Of SPIE, Vol. 10396” hereinafter as “Alexiou”).
Regarding claim 3, Shi in view of Bhaskaran teaches the information processing device according to claim 1, Shi teaches wherein the point cloud data is a set of data representing a measurement distance for the each point (Col. 4, lines 5-24, discloses the processed point cloud data include measurement distance for each point for the object).
However, Shi in view of Bhaskaran does not explicitly teach wherein the evaluation function is a function which outputs the evaluation value for calculating the evaluation value and a point of the point cloud data other than the each point.
Satoshi teaches wherein the evaluation function is a function which outputs the evaluation value (Par. [0057] discloses the evaluation value being calculated based on an evaluation function) for calculating the evaluation value and a point of the point cloud data other than the each point (Which is to calculate the evaluation value for the point of the cloud data for each point which include all the points including the other than the each point as claimed, according to paragraphs [0056-0058]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Shi in view of Bhaskaran of having an information processing device comprising: a processor coupled to a memory storing instructions that, when executed by the processor, cause the processor to: acquire point cloud data which is a set of data for each point measured by a measurement device; calculate an evaluation value for each data of the point cloud data, based on an evaluation function for evaluating whether the each data is an object point, which is a measured point of an object, or a noise point, which is generated by noise; set a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function; determine the noise point included in the point cloud data, based on the evaluation value and the threshold value; calculate the statistic based on the point cloud information corresponding to a measurement information in which no object is present, wherein the measurement information being measurement direction, with the teachings of Satoshi of having wherein the evaluation function is a function which outputs the evaluation value for calculating the evaluation value and a point of the point cloud data other than the each point.
Wherein having Shi’s information processing device comprising wherein having the evaluation function is a function which outputs the evaluation value for calculating the evaluation value and a point of the point cloud data other than the each point.
The motivation behind the modification would have been to strengthen recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, and further to measure geometric shape of 3D object effectively. Since both Shi and Satoshi share the same endeavor of systems that perform 3D lidar data generation and recognition. Wherein Shi’s system improve 3D recognition and reconstruction by strengthening recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, see Shi’s Abstract and Satoshi’s system improves measuring geometric shape of 3D object effectively, see Satoshi’s page 1, 1st 3 Pars.
However, Shi in view of Bhaskaran and Satoshi does not explicitly teach the evaluation function is based on a difference in the measurement distance, a difference in a measurement direction, and a difference in a measurement time between the each point of the point cloud data.
In the same field of evaluating point cloud data (Title and abstract, Alexiou) Alexiou discloses the evaluation function is based on a difference in the measurement distance, a difference in a measurement direction (Section 6 discloses the evaluation function is based on relative differences such as geometrical distances which includes difference in angles [direction] according to section 3.4, 3rd par.), and a difference in a measurement time between the each point of the point cloud data (The evaluation function is also based on the difference in measurement time according to section 3.2.1, since the processing is performed in real-time for scene of the same scene taken from different viewpoints hence, indicates difference in measurement time for the frame).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Shi in view of Bhaskaran and Satoshi of having an information processing device comprising: a processor coupled to a memory storing instructions that, when executed by the processor, cause the processor to: acquire point cloud data which is a set of data for each point measured by a measurement device; calculate an evaluation value for each data of the point cloud data, based on an evaluation function for evaluating whether the each data is an object point, which is a measured point of an object, or a noise point, which is generated by noise; set a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function; determine the noise point included in the point cloud data, based on the evaluation value and the threshold value; calculate the statistic based on the point cloud information corresponding to a measurement information in which no object is present, wherein the measurement information being measurement direction, wherein the evaluation function is a function which outputs the evaluation value for calculating the evaluation value and a point of the point cloud data other than the each point, with the teachings of Alexiou of having basing on a difference in the measurement distance, a difference in a measurement direction, and a difference in a measurement time between the each point of the point cloud data.
Wherein having Shi’s an information processing device comprising wherein having basing on a difference in the measurement distance, a difference in a measurement direction, and a difference in a measurement time between the each point of the point cloud data.
The motivation behind the modification would have been to strengthen recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, and further to perform quality assessment of point cloud data representation more effectively. Since both Shi and Alexiou share the same endeavor of systems that perform 3D lidar data generation and recognition. Wherein Shi’s system improve 3D recognition and reconstruction by strengthening recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, see Shi’s Abstract and Alexiou’s system improves performing of quality assessment of point cloud data representation more effectively, see Alexiou’s Abstract.
Regarding claim 4, Shi in view of Bhaskaran teaches the information processing device according to claim 1.
However, Shi in view of Bhaskaran does not explicitly teach wherein the instructions cause the processor to calculate the evaluation value based on a current frame which is the point cloud information acquired by the acquisition unit at a current processing time, and the evaluation function.
Satoshi discloses wherein the instructions cause the processor to calculate the evaluation value based on a current frame which is the point cloud information acquired by the acquisition unit at a current processing time (Par. [0057] discloses the evaluation value being calculated based on an evaluation function which is performed on the current frame acquired at the current time of the processing) and the evaluation function (As based on the evaluation function as discussed and disclosed in paragraphs [0056]-[0058]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Shi in view of Bhaskaran of having an information processing device comprising: a processor coupled to a memory storing instructions that, when executed by the processor, cause the processor to: acquire point cloud data which is a set of data for each point measured by a measurement device; calculate an evaluation value for each data of the point cloud data, based on an evaluation function for evaluating whether the each data is an object point, which is a measured point of an object, or a noise point, which is generated by noise; set a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function; determine the noise point included in the point cloud data, based on the evaluation value and the threshold value; calculate the statistic based on the point cloud information corresponding to a measurement information in which no object is present, wherein the measurement information being measurement direction, with the teachings of Satoshi of having the instructions cause the processor to calculate the evaluation value based on a current frame which is the point cloud information acquired by the acquisition unit at a current processing time and the evaluation function.
Wherein having Shi’s information processing device comprising wherein having the instructions cause the processor to calculate the evaluation value based on a current frame which is the point cloud information acquired by the acquisition unit at a current processing time and the evaluation function.
The motivation behind the modification would have been to strengthen recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, and further to measure geometric shape of 3D object effectively. Since both Shi and Satoshi share the same endeavor of systems that perform 3D lidar data generation and recognition. Wherein Shi’s system improve 3D recognition and reconstruction by strengthening recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, see Shi’s Abstract and Satoshi’s system improves measuring geometric shape of 3D object effectively, see Satoshi’s page 1, 1st 3 Pars.
However, Shi in view of Bhaskaran and Satoshi does not explicitly teach the evaluation value based on a past frame which is the point cloud information acquired by the acquisition unit at a time before the current processing time.
Alexiou discloses the evaluation value based on a past frame which is the point cloud information acquired by the acquisition unit at a time before the current processing time (the evaluation function is also based on the difference in measurement time according to section 3.2.1, since the processing is performed in real-time for scene of the same scene taken from different viewpoints hence, indicates difference in measurement time for the frame including the past frame prior to the current frame taken in time-series).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaches of Shi in view of Bhaskaran and Satoshi of having an information processing device comprising: a processor coupled to a memory storing instructions that, when executed by the processor, cause the processor to: acquire point cloud data which is a set of data for each point measured by a measurement device; calculate an evaluation value for each data of the point cloud data, based on an evaluation function for evaluating whether the each data is an object point, which is a measured point of an object, or a noise point, which is generated by noise; set a threshold value for the evaluation value, based on a statistic of a probability density function of the evaluation function; determine the noise point included in the point cloud data, based on the evaluation value and the threshold value; calculate the statistic based on the point cloud information corresponding to a measurement information in which no object is present, wherein the measurement information being measurement direction, wherein the evaluation function is a function which outputs the evaluation value for calculating the evaluation value and a point of the point cloud data other than the each point, with the teachings of Alexiou of having the evaluation value based on a past frame which is the point cloud information acquired by the acquisition unit at a time before the current processing time.
Wherein having Shi’s information processing device comprising wherein having the evaluation value based on a past frame which is the point cloud information acquired by the acquisition unit at a time before the current processing time.
The motivation behind the modification would have been to strengthen recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, and further to perform quality assessment of point cloud data representation more effectively. Since both Shi and Alexiou share the same endeavor of systems that perform 3D lidar data generation and recognition. Wherein Shi’s system improve 3D recognition and reconstruction by strengthening recognition and reconstruction of specific form of surface in 3D modeling of indoor scene with high complexity, see Shi’s Abstract and Alexiou’s system improves performing of quality assessment of point cloud data representation more effectively, see Alexiou’s Abstract.
Pertinent Prior Art(s)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Armstrong-Crews, Nicholas et. al., “US 2023/0244242 A1”, teaches addressing shortcomings of the existing technology by enabling lidar-assisted segmentation and identification of particulate matter in autonomous vehicle (AV) applications, by: obtaining, by a sensing system of the AV, a plurality of return points, each return point having one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system, identifying, in view of the one or more velocity values of each of a first set of the return points of the plurality of return points, that the first set of the return points is associated with a particulate matter in an environment of the AV, and causing a driving path of the AV to be determined in view of the particulate matter.
Ebrahimi Afrouzi, Ali et. al., “US 2021/0089040 A1”, teaches a method for operating a robot, including capturing images of a workspace, comparing at least one object from the captured images to objects in an object dictionary, identifying a class to which the at least one object belongs using an object classification unit, instructing the robot to execute at least one action based on the object class identified, capturing movement data of the robot, and generating a planar representation of the workspace based on the captured images and the movement data, wherein the captured images indicate a position of the robot relative to objects within the workspace and the movement data indicates movement of the robot.
KOROBKIN, Mikhail Vladimirovich et. al., “US 2021/0221398 A1”, teaches method and device for processing LIDAR sensor data are disclosed. The method includes (i) receiving from the LIDAR sensor a first dataset having a plurality of first data points representative of respective coordinates and associated with respective normal vectors, (ii) determining an uncertainty parameter for a given first data point based on a normal covariance of the normal vector of the given first data point where the normal covariance takes into account a measurement error of the LIDAR sensor when determining the respective coordinates of the given first data point, (iii) in response to the uncertainty parameter being above a pre-determined threshold, excluding the given first data point from the plurality of first data points, (iv) using the filtered plurality of first data points, instead of the plurality of first data points, for merging the first dataset of the LIDAR sensor with a second dataset of the LIDAR sensor.
Verma, Vivek et. al., “US 2006/0061566 A1”, teaches a method and apparatus for automatically generating a three-dimensional computer model from a “point cloud” of a scene produced by a laser radar (LIDAR) system. Given a point cloud of an indoor or outdoor scene, the method extracts certain structures from the imaged scene, i.e., ceiling, floor, furniture, rooftops, ground, and the like, and models these structures with planes and/or prismatic structures to achieve a three-dimensional computer model of the scene. The method may then add photographic and/or synthetic texturing to the model to achieve a realistic model.
Conclusion
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/PHUONG HAU CAI/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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