DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 submission filed on 3/31/2026 has been entered. In response to amendment filed on 3/13/2026, claims 1, 9, 15, 23, 29, and 30 are amended and claims 1- 30 are pending for examinations.
Response to Arguments
Applicant’s arguments with respect to claim(s) in the remarks on 3/13/2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant has amended independent claim; hence examiner believes that the scope has been changed, therefore examiner has considered new reference Park et al. (KR 20230036651 A), please see attached translated copy. Park states wherein the spherical projection converts three-dimensional (3D) point cloud data into two-dimensional (2D) image data; see in abstract regarding method and system for detecting data based on three-dimensional point cloud data received from a LiDAR, the present invention provides a method and system for detecting an object that convert three-dimensional point cloud data into spherical coordinates two-dimensional image data and BEV two-dimensional image data; enable each image feature to be detected from the image data converted into two types of coordinate systems and enable thereof to be fused; enable the three-dimensional point cloud data, while converting to the two-dimensional image data, to compensate for the lost information by detecting the object based on the fused image feature; and improve an accuracy of object detection; further see page 4 last seven lines…The receiving unit 100 receives 3D point cloud data collected from LIDAR. The first conversion data extractor 200 converts the received point cloud data into spherical coordinate system 2D image data…..; further see Fig. 5 step 800.. 3D point cloud data output from lidar is received. In step 810, the received 3D point cloud data is converted into spherical coordinate 2D image data, and in step 820, the received 3D point cloud data is converted into BEV 2D image data.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claim(s) 1- 8, 11- 12, 14- 22, 25- 30 are rejected under 35 U.S.C. 103 as being unpatentable over Ohira et al. (US Pub. No. 2018/0024229 A1) in view of Zou (US Pub. No. 2019/0187718 A1) and in further view of Park et al. (KR 20230036651 A), please see attached translated copy.
Regarding claim 1, Ohira teaches an apparatus for wireless communication at a user equipment (UE), comprising: at least one memory; and at least one processor coupled to the at least one memory and, based at least in part on information stored in the at least one memory, the at least one processor (see Fig. 1 and [0007, 0116] wherein #1 as an apparatus; further see [0056- 0060] and Fig. 2), is configured to:
convert a set of point clouds associated with an environment to a set of range images based on a spherical projection (see [0059] and Fig. 1 about .. distance measurement sensor 11 is an optical sensor using light detection and ranging (LIDAR), widely emits laser light, which is a detection wave, to an object and receives reflection light (reflection wave) from the object. Accordingly, the distance measurement sensor 11 detects a position of the object to be detected 50 or a distance to the object to be detected 50. Also, a detected result of the distance measurement sensor 11 will be described in detail with reference to FIG. 3A to FIG. 3C to be described later.);
apply at least one of Fast Fourier Transform (FFT) or Discrete Wavelet Transform (DWT) to the set of range images to obtain a set of FFT coefficients or a set of DWT coefficients; and identify a level of a condition for the environment based on a sparsity of the set of FFT coefficients or the set of DWT coefficients (see [0077- 0084] and Fig. 19 regarding rainfall is detected based on a presence of isolated points, which are discretely positioned, among the objects to be detected in the detection image; and its commonplace for Lidar sensor to collect data by FFT/DWT). Though it’s a commonplace technique regarding data collection by FFT/DWT, but Ohira is silent to teach about applying at least one of Fast Fourier Transform (FFT) or Discrete Wavelet Transform (DWT) to the set of range images to obtain a set of FFT coefficients or a set of DWT coefficients; however examiner has incorporated other reference Zou which states about sensor system see Fig. 1 #28 as a part of an apparatus (# 10); see [0045].. sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle 10. The sensing devices 40a-40n can include, but are not limited to, radars, lidars, global positioning systems, optical cameras 140a-140n, thermal cameras, ultrasonic sensors, and/or other sensors…; now in context with [0024, 0043, 0066- 0068] pls refer to [0069] regarding .. a discrete wavelet transform process, as applied by the wavelet transformer 204, the input image data 208 is received by the first filter bank 220 and, after down sampling by the respective down samplers 221, is split into low frequency components or coefficients 228 and high frequency components or coefficients 230. The low frequency coefficients 228 extract coarse or approximate information from the input image data 208 and the high the frequency coefficients 230 extract detailed information from the input image data 208. The low frequency coefficients 228 are input to the second filter bank 224 and the high frequency coefficients 230 are input to the third filter bank 226. The first filter bank 220 performs a one dimensional discrete wavelet transform on rows of the input image data 208 to produce low frequency and high frequency coefficients 228, 230. The second filter bank 224 performs a column wise one dimensional discrete wavelet transform on the low frequency coefficients 228 and the third filter bank 226 performs a column wise one dimensional discrete wavelet transform on the high frequency coefficients 230. In this way, a first stage 232 of the wavelet transformer 204 performs a two-dimensional discrete wavelet transform to produce first level discrete wavelet transform coefficients LL.sub.1, LH.sub.1, HL.sub.1, HH.sub.1; further see [0070]. It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Zou with the teachings of Ohira to make system more standardized.
But Ohira is silent about wherein the spherical projection converts three-dimensional (3D) point cloud data into two-dimensional (2D) image data; however Park teaches in abstract regarding method and system for detecting data based on three-dimensional point cloud data received from a LiDAR, the present invention provides a method and system for detecting an object that convert three-dimensional point cloud data into spherical coordinates two-dimensional image data and BEV two-dimensional image data; enable each image feature to be detected from the image data converted into two types of coordinate systems and enable thereof to be fused; enable the three-dimensional point cloud data, while converting to the two-dimensional image data, to compensate for the lost information by detecting the object based on the fused image feature; and improve an accuracy of object detection; further see page 4 last seven lines…The receiving unit 100 receives 3D point cloud data collected from LIDAR. The first conversion data extractor 200 converts the received point cloud data into spherical coordinate system 2D image data…..; further see Fig. 5 step 800.. 3D point cloud data output from lidar is received. In step 810, the received 3D point cloud data is converted into spherical coordinate 2D image data, and in step 820, the received 3D point cloud data is converted into BEV 2D image data. It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Park with the teachings of Ohira in view of Zou to make system more standardized. Having a mechanism wherein the spherical projection converts three-dimensional (3D) point cloud data into two-dimensional (2D) image data; greater way standardized approach can be carried out in the communication system.
Regarding claim 2, Ohira in view of Zou and Park teaches as per claim 1, wherein the at least one processor, individually or in any combination, is further configured to:
output an indication of the identified level of the condition for the environment based on the sparsity of the set of FFT coefficients or the set of DWT coefficients; Ohira see [0080], Fig. 14 and [0148]; further already described above pls refer to Zou [0067-0070].
Regarding claim 3, Ohira in view of Zou and Park teaches as per claim 2, wherein to output the indication of the identified level of the condition for the environment, the at least one processor, individually or in any combination, is configured to: transmit the indication of the identified level of the condition for the environment; or store, in a memory or a cache, the indication of the identified level of the condition for the environment; Zou see [0066].
Regarding claim 4, Ohira in view of Zou and Park, teaches as per claim 1, wherein the at least one processor, individually or in any combination, is further configured to:
detect the sparsity of the set of FFT coefficients or the set of DWT coefficients prior to the identification of the level of the condition for the environment, wherein the identification of the level of the condition for the environment is based on the detected sparsity of the set of FFT coefficients or the set of DWT coefficients; Zou see [0066]... the image feature detection system 200 includes a wavelet transformer 204 that receives input image data 208 from one or more image capture devices (e.g. optical camera) 40a to 40n, or from converters that convert other data modalities captured from various other sensors into a grid-like image format and decomposes the input image data 208 into decomposed image data 212 that includes frequency sub-bands. An optional filter 206 is configured to remove or set to zero data in the decomposed image data 212 having coefficients that are substantially zero or coefficients that are below a threshold, representing pixels with little information content with respect to the purpose of detecting image features. An artificial neural network 202 is configured to process one or more of the frequency sub-bands included in the decomposed image data 212 or the filtered and decomposed image data 214 and to provide detected image features data 210. The detected image features data 210 are used by a driving system such as a driving assist system or the autonomous driving system 200 described with respect to FIG. 3 in some embodiments or are used by another application using automated feature perception as an input.
Regarding claim 5, Ohira in view of Zou and Park, teaches as per claim 1, wherein the condition is an adverse weather condition or a clear weather condition, and wherein to identify the level of the condition for the environment, the at least one processor, individually or in any combination, is configured to identify the level of the adverse weather condition or the clear weather condition; Ohira see Fig. 14 and [0077, 0148] rainfall state.
Regarding claim 6, Ohira in view of Zou and Park, teaches as per claim 1, wherein the sparsity of the set of FFT coefficients or the set of DWT coefficients is based on an L1 norm; already stated above Zou see [0066]... the image feature detection system 200 includes a wavelet transformer 204 that receives input image data 208 from one or more image capture devices (e.g. optical camera) 40a to 40n, or from converters that convert other data modalities captured from various other sensors into a grid-like image format and decomposes the input image data 208 into decomposed image data 212 that includes frequency sub-bands. An optional filter 206 is configured to remove or set to zero data in the decomposed image data 212 having coefficients that are substantially zero or coefficients that are below a threshold, representing pixels with little information content with respect to the purpose of detecting image features. An artificial neural network 202 is configured to process one or more of the frequency sub-bands included in the decomposed image data 212 or the filtered and decomposed image data 214 and to provide detected image features data 210. The detected image features data 210 are used by a driving system such as a driving assist system or the autonomous driving system 200 described with respect to FIG. 3 in some embodiments or are used by another application using automated feature perception as an input..
Regarding claim 7, Ohira in view of Zou and Park, teaches as per claim 1, wherein the at least one processor, individually or in any combination, is further configured to:
obtain, from at least one sensor, the set of point clouds associated with the environment prior to the conversion of the set of point clouds, wherein the conversion of the set of point clouds is based on the obtained set of point clouds; Zou see [0066]…. wavelet transformer 204 that receives input image data 208 from one or more image capture devices (e.g. optical camera) 40a to 40n, or from converters that convert other data modalities captured from various other sensors into a grid-like image format and decomposes the input image data 208 into decomposed image data 212 that includes frequency sub-bands. An optional filter 206 is configured to remove or set to zero data in the decomposed image data 212 having coefficients that are substantially zero or coefficients that are below a threshold, representing pixels with little information content with respect to the purpose of detecting image features. An artificial neural network 202 is configured to process one or more of the frequency sub-bands included in the decomposed image data 212 or the filtered and decomposed image data 214 and to provide detected image features data 210. The detected image features data 210 are used by a driving system such as a driving assist system or the autonomous driving system 200 described with respect to FIG. 3 in some embodiments or are used by another application using automated feature perception as an input.
Regarding claim 8, Ohira in view of Zou and Park, teaches as per claim 1, wherein the at least one sensor includes at least one light detection and ranging (Lidar) sensor; Ohira see [0059]…
Regarding claim 11, Ohira in view of Zou and Park, teaches as per claim 1, wherein the at least one processor, individually or in any combination, is further configured to: capture an image for the environment using at least one camera; and pair the captured image with at least one other image based on the sparsity of the set of FFT coefficients or the set of DWT coefficients; Zou see [0066].. the image feature detection system 200 includes a wavelet transformer 204 that receives input image data 208 from one or more image capture devices (e.g. optical camera) 40a to 40n, or from converters that convert other data modalities captured from various other sensors into a grid-like image format…. ; further see [0069]… In a discrete wavelet transform process, as applied by the wavelet transformer 204, the input image data 208 is received by the first filter bank 220 and, after down sampling by the respective down samplers 221, is split into low frequency components or coefficients 228 and high frequency components or coefficients 230. The low frequency coefficients 228 extract coarse or approximate information from the input image data 208 and the high the frequency coefficients 230 extract detailed information from the input image data 208. The low frequency coefficients 228 are input to the second filter bank 224 and the high frequency coefficients 230 are input to the third filter bank 226. The first filter bank 220 performs a one dimensional discrete wavelet transform on rows of the input image data 208 to produce low frequency and high frequency coefficients 228, 230. The second filter bank 224 performs a column wise one dimensional discrete wavelet transform on the low frequency coefficients 228 and the third filter bank 226 performs a column wise one dimensional discrete wavelet transform on the high frequency coefficients 230. In this way, a first stage 232 of the wavelet transformer 204 performs a two-dimensional discrete wavelet transform to produce first level discrete wavelet transform coefficients LL.sub.1, LH.sub.1, HL.sub.1, HH.sub.1.
Regarding claim 12, Ohira in view of Zou and Park, teaches as per claim 11, wherein the at least one processor, individually or in any combination, is further configured to:
train an artificial intelligence (AI)/machine learning (ML) (AI/ML) model to identify a set of features for the environment based on the pairing of the captured image with the at least one other image; Zou see [0074- 0075] Artificial neural network.
Regarding claim 14, Ohira in view of Zou and Park, teaches as per claim 1, wherein the at least one processor, individually or in any combination, is further configured to: modify at least one control parameter of a vehicle based on the identification of the level of the condition for the environment; Zou see [065, 0066, 0078] control part.
Regarding claim 15, Ohira teaches a method of wireless communication at a user equipment (UE), comprising (see Fig. 1 and [0007, 0116] wherein #1 as an apparatus; further see [0056- 0060] and Fig. 2):
converting a set of point clouds associated with an environment to a set of range images based on a spherical projection (see [0059] and Fig. 1 about .. distance measurement sensor 11 is an optical sensor using light detection and ranging (LIDAR), widely emits laser light, which is a detection wave, to an object and receives reflection light (reflection wave) from the object. Accordingly, the distance measurement sensor 11 detects a position of the object to be detected 50 or a distance to the object to be detected 50. Also, a detected result of the distance measurement sensor 11 will be described in detail with reference to FIG. 3A to FIG. 3C to be described later.);
applying at least one of Fast Fourier Transform (FFT) or Discrete Wavelet Transform (DWT) to the set of range images to obtain a set of FFT coefficients or a set of DWT coefficients; and identifying a level of a condition for the environment based on a sparsity of the set of FFT coefficients or the set of DWT coefficients (see [0077- 0084] and Fig. 19 regarding rainfall is detected based on a presence of isolated points, which are discretely positioned, among the objects to be detected in the detection image; and its commonplace for Lidar sensor to collect data by FFT/DWT). Though it’s a commonplace technique regarding data collection by FFT/DWT, but Ohira is silent to teach about applying at least one of Fast Fourier Transform (FFT) or Discrete Wavelet Transform (DWT) to the set of range images to obtain a set of FFT coefficients or a set of DWT coefficients; however examiner has incorporated other reference Zou which states about sensor system see Fig. 1 #28 as a part of an apparatus (# 10); see [0045].. sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle 10. The sensing devices 40a-40n can include, but are not limited to, radars, lidars, global positioning systems, optical cameras 140a-140n, thermal cameras, ultrasonic sensors, and/or other sensors…; now in context with [0024, 0043, 0066- 0068] pls refer to [0069] regarding .. a discrete wavelet transform process, as applied by the wavelet transformer 204, the input image data 208 is received by the first filter bank 220 and, after down sampling by the respective down samplers 221, is split into low frequency components or coefficients 228 and high frequency components or coefficients 230. The low frequency coefficients 228 extract coarse or approximate information from the input image data 208 and the high the frequency coefficients 230 extract detailed information from the input image data 208. The low frequency coefficients 228 are input to the second filter bank 224 and the high frequency coefficients 230 are input to the third filter bank 226. The first filter bank 220 performs a one dimensional discrete wavelet transform on rows of the input image data 208 to produce low frequency and high frequency coefficients 228, 230. The second filter bank 224 performs a column wise one dimensional discrete wavelet transform on the low frequency coefficients 228 and the third filter bank 226 performs a column wise one dimensional discrete wavelet transform on the high frequency coefficients 230. In this way, a first stage 232 of the wavelet transformer 204 performs a two-dimensional discrete wavelet transform to produce first level discrete wavelet transform coefficients LL.sub.1, LH.sub.1, HL.sub.1, HH.sub.1; further see [0070]. It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Zou with the teachings of Ohira to make system more standardized. But Ohira is silent about wherein the spherical projection converts three-dimensional (3D) point cloud data into two-dimensional (2D) image data; however Park teaches in abstract regarding method and system for detecting data based on three-dimensional point cloud data received from a LiDAR, the present invention provides a method and system for detecting an object that convert three-dimensional point cloud data into spherical coordinates two-dimensional image data and BEV two-dimensional image data; enable each image feature to be detected from the image data converted into two types of coordinate systems and enable thereof to be fused; enable the three-dimensional point cloud data, while converting to the two-dimensional image data, to compensate for the lost information by detecting the object based on the fused image feature; and improve an accuracy of object detection; further see page 4 last seven lines…The receiving unit 100 receives 3D point cloud data collected from LIDAR. The first conversion data extractor 200 converts the received point cloud data into spherical coordinate system 2D image data…..; further see Fig. 5 step 800.. 3D point cloud data output from lidar is received. In step 810, the received 3D point cloud data is converted into spherical coordinate 2D image data, and in step 820, the received 3D point cloud data is converted into BEV 2D image data. It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Park with the teachings of Ohira in view of Zou to make system more standardized. Having a mechanism wherein the spherical projection converts three-dimensional (3D) point cloud data into two-dimensional (2D) image data; greater way standardized approach can be carried out in the communication system.
Regarding claim 16, Ohira in view of Zou and Park teaches as per claim 15, further comprising: outputting an indication of the identified level of the condition for the environment based on the sparsity of the set of FFT coefficients or the set of DWT coefficients; Ohira see [0080], Fig. 14 and [0148]; further already described above pls refer to Zou [0067-0070].
Regarding claim 17, Ohira in view of Zou and Park teaches as per claim 16, wherein outputting the indication of the identified level of the condition for the environment comprises: transmitting the indication of the identified level of the condition for the environment; or storing, in a memory or a cache, the indication of the identified level of the condition for the environment; Zou see [0066].
Regarding claim 18, Ohira in view of Zou and Park, teaches as per claim 15, further comprising: detecting the sparsity of the set of FFT coefficients or the set of DWT coefficients prior to the identification of the level of the condition for the environment, wherein the identification of the level of the condition for the environment is based on the detected sparsity of the set of FFT coefficients or the set of DWT coefficients; Zou see [0066]... the image feature detection system 200 includes a wavelet transformer 204 that receives input image data 208 from one or more image capture devices (e.g. optical camera) 40a to 40n, or from converters that convert other data modalities captured from various other sensors into a grid-like image format and decomposes the input image data 208 into decomposed image data 212 that includes frequency sub-bands. An optional filter 206 is configured to remove or set to zero data in the decomposed image data 212 having coefficients that are substantially zero or coefficients that are below a threshold, representing pixels with little information content with respect to the purpose of detecting image features. An artificial neural network 202 is configured to process one or more of the frequency sub-bands included in the decomposed image data 212 or the filtered and decomposed image data 214 and to provide detected image features data 210. The detected image features data 210 are used by a driving system such as a driving assist system or the autonomous driving system 200 described with respect to FIG. 3 in some embodiments or are used by another application using automated feature perception as an input.
Regarding claim 19, Ohira in view of Zou and Park, teaches as per claim 15, wherein the condition is an adverse weather condition or a clear weather condition, and wherein identifying the level of the condition for the environment comprises identifying the level of the adverse weather condition or the clear weather condition; Ohira see Fig. 14 and [0077, 0148] rainfall state.
Regarding claim 20, Ohira in view of Zou and Park, teaches as per claim 15, wherein the sparsity of the set of FFT coefficients or the set of DWT coefficients is based on an L1 norm; already stated above Zou see [0066]... the image feature detection system 200 includes a wavelet transformer 204 that receives input image data 208 from one or more image capture devices (e.g. optical camera) 40a to 40n, or from converters that convert other data modalities captured from various other sensors into a grid-like image format and decomposes the input image data 208 into decomposed image data 212 that includes frequency sub-bands. An optional filter 206 is configured to remove or set to zero data in the decomposed image data 212 having coefficients that are substantially zero or coefficients that are below a threshold, representing pixels with little information content with respect to the purpose of detecting image features. An artificial neural network 202 is configured to process one or more of the frequency sub-bands included in the decomposed image data 212 or the filtered and decomposed image data 214 and to provide detected image features data 210. The detected image features data 210 are used by a driving system such as a driving assist system or the autonomous driving system 200 described with respect to FIG. 3 in some embodiments or are used by another application using automated feature perception as an input..
Regarding claim 21, Ohira in view of Zou and Park, teaches as per claim 15, further comprising: obtaining, from at least one sensor, the set of point clouds associated with the environment prior to the conversion of the set of point clouds, wherein the conversion of the set of point clouds is based on the obtained set of point clouds; Zou see [0066]…. wavelet transformer 204 that receives input image data 208 from one or more image capture devices (e.g. optical camera) 40a to 40n, or from converters that convert other data modalities captured from various other sensors into a grid-like image format and decomposes the input image data 208 into decomposed image data 212 that includes frequency sub-bands. An optional filter 206 is configured to remove or set to zero data in the decomposed image data 212 having coefficients that are substantially zero or coefficients that are below a threshold, representing pixels with little information content with respect to the purpose of detecting image features. An artificial neural network 202 is configured to process one or more of the frequency sub-bands included in the decomposed image data 212 or the filtered and decomposed image data 214 and to provide detected image features data 210. The detected image features data 210 are used by a driving system such as a driving assist system or the autonomous driving system 200 described with respect to FIG. 3 in some embodiments or are used by another application using automated feature perception as an input.
Regarding claim 22, Ohira in view of Zou and Park, teaches as per claim 21, wherein the at least one sensor includes at least one light detection and ranging (Lidar) sensor; Ohira see [0059]…
Regarding claim 25, Ohira in view of Zou and Park, teaches as per claim 15, further comprising: capturing an image for the environment using at least one camera; and pairing the captured image with at least one other image based on the sparsity of the set of FFT coefficients or the set of DWT coefficients; Zou see [0066].. the image feature detection system 200 includes a wavelet transformer 204 that receives input image data 208 from one or more image capture devices (e.g. optical camera) 40a to 40n, or from converters that convert other data modalities captured from various other sensors into a grid-like image format…. ; further see [0069]… In a discrete wavelet transform process, as applied by the wavelet transformer 204, the input image data 208 is received by the first filter bank 220 and, after down sampling by the respective down samplers 221, is split into low frequency components or coefficients 228 and high frequency components or coefficients 230. The low frequency coefficients 228 extract coarse or approximate information from the input image data 208 and the high the frequency coefficients 230 extract detailed information from the input image data 208. The low frequency coefficients 228 are input to the second filter bank 224 and the high frequency coefficients 230 are input to the third filter bank 226. The first filter bank 220 performs a one dimensional discrete wavelet transform on rows of the input image data 208 to produce low frequency and high frequency coefficients 228, 230. The second filter bank 224 performs a column wise one dimensional discrete wavelet transform on the low frequency coefficients 228 and the third filter bank 226 performs a column wise one dimensional discrete wavelet transform on the high frequency coefficients 230. In this way, a first stage 232 of the wavelet transformer 204 performs a two-dimensional discrete wavelet transform to produce first level discrete wavelet transform coefficients LL.sub.1, LH.sub.1, HL.sub.1, HH.sub.1.
Regarding claim 26, Ohira in view of Zou and Park, teaches as per claim 25, further comprising: training an artificial intelligence (AI)/machine learning (ML) (AI/ML) model to identify a set of features for the environment based on the pairing of the captured image with the at least one other image; Zou see [0074- 0075] Artificial neural network.
Regarding claim 27, Ohira in view of Zou and Park, teaches as per claim 15, wherein the at least one processor, individually or in any combination, is further configured to: modify at least one control parameter of a vehicle based on the identification of the level of the condition for the environment; Zou see [065, 0066, 0078] control part.
Regarding claim 28, Ohira in view of Zou and Park, teaches as per claim 15, further comprising: modifying at least one control parameter of a vehicle based on the identification of the level of the condition for the environment; Zou see [065, 0066, 0078] control part.
Regarding claim 29, Ohira teaches a apparatus for wireless communication at a user equipment (UE), comprising (see Fig. 1 and [0007, 0116] wherein #1 as an apparatus (UE); further see [0056- 0060] and Fig. 2):
means for converting a set of point clouds associated with an environment to a set of range images based on a spherical projection (see [0059] and Fig. 1 about .. distance measurement sensor 11 is an optical sensor using light detection and ranging (LIDAR), widely emits laser light, which is a detection wave, to an object and receives reflection light (reflection wave) from the object. Accordingly, the distance measurement sensor 11 detects a position of the object to be detected 50 or a distance to the object to be detected 50. Also, a detected result of the distance measurement sensor 11 will be described in detail with reference to FIG. 3A to FIG. 3C to be described later.);
means for applying at least one of Fast Fourier Transform (FFT) or Discrete Wavelet Transform (DWT) to the set of range images to obtain a set of FFT coefficients or a set of DWT coefficients; and means for identifying a level of a condition for the environment based on a sparsity of the set of FFT coefficients or the set of DWT coefficients (see [0077- 0084] and Fig. 19 regarding rainfall is detected based on a presence of isolated points, which are discretely positioned, among the objects to be detected in the detection image; and its commonplace for Lidar sensor to collect data by FFT/DWT). Though it’s a commonplace technique regarding data collection by FFT/DWT, but Ohira is silent to teach about applying at least one of Fast Fourier Transform (FFT) or Discrete Wavelet Transform (DWT) to the set of range images to obtain a set of FFT coefficients or a set of DWT coefficients; however examiner has incorporated other reference Zou which states about sensor system see Fig. 1 #28 as a part of an apparatus (# 10); see [0045].. sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle 10. The sensing devices 40a-40n can include, but are not limited to, radars, lidars, global positioning systems, optical cameras 140a-140n, thermal cameras, ultrasonic sensors, and/or other sensors…; now in context with [0024, 0043, 0066- 0068] pls refer to [0069] regarding .. a discrete wavelet transform process, as applied by the wavelet transformer 204, the input image data 208 is received by the first filter bank 220 and, after down sampling by the respective down samplers 221, is split into low frequency components or coefficients 228 and high frequency components or coefficients 230. The low frequency coefficients 228 extract coarse or approximate information from the input image data 208 and the high the frequency coefficients 230 extract detailed information from the input image data 208. The low frequency coefficients 228 are input to the second filter bank 224 and the high frequency coefficients 230 are input to the third filter bank 226. The first filter bank 220 performs a one dimensional discrete wavelet transform on rows of the input image data 208 to produce low frequency and high frequency coefficients 228, 230. The second filter bank 224 performs a column wise one dimensional discrete wavelet transform on the low frequency coefficients 228 and the third filter bank 226 performs a column wise one dimensional discrete wavelet transform on the high frequency coefficients 230. In this way, a first stage 232 of the wavelet transformer 204 performs a two-dimensional discrete wavelet transform to produce first level discrete wavelet transform coefficients LL.sub.1, LH.sub.1, HL.sub.1, HH.sub.1; further see [0070]. It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Zou with the teachings of Ohira to make system more standardized. But Ohira is silent about wherein the spherical projection converts three-dimensional (3D) point cloud data into two-dimensional (2D) image data; however Park teaches in abstract regarding method and system for detecting data based on three-dimensional point cloud data received from a LiDAR, the present invention provides a method and system for detecting an object that convert three-dimensional point cloud data into spherical coordinates two-dimensional image data and BEV two-dimensional image data; enable each image feature to be detected from the image data converted into two types of coordinate systems and enable thereof to be fused; enable the three-dimensional point cloud data, while converting to the two-dimensional image data, to compensate for the lost information by detecting the object based on the fused image feature; and improve an accuracy of object detection; further see page 4 last seven lines…The receiving unit 100 receives 3D point cloud data collected from LIDAR. The first conversion data extractor 200 converts the received point cloud data into spherical coordinate system 2D image data…..; further see Fig. 5 step 800.. 3D point cloud data output from lidar is received. In step 810, the received 3D point cloud data is converted into spherical coordinate 2D image data, and in step 820, the received 3D point cloud data is converted into BEV 2D image data. It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Park with the teachings of Ohira in view of Zou to make system more standardized. Having a mechanism wherein the spherical projection converts three-dimensional (3D) point cloud data into two-dimensional (2D) image data; greater way standardized approach can be carried out in the communication system.
Regarding claim 30, Ohira teaches a non-transitory computer-readable medium storing computer executable code at a user equipment (UE), the code when executed by at least one processor causes the at least one processor to (see Fig. 1 and [0007, 0116] wherein #1 as an apparatus; further see [0056- 0060] and Fig. 2):
convert a set of point clouds associated with an environment to a set of range images based on a spherical projection (see [0059] and Fig. 1 about .. distance measurement sensor 11 is an optical sensor using light detection and ranging (LIDAR), widely emits laser light, which is a detection wave, to an object and receives reflection light (reflection wave) from the object. Accordingly, the distance measurement sensor 11 detects a position of the object to be detected 50 or a distance to the object to be detected 50. Also, a detected result of the distance measurement sensor 11 will be described in detail with reference to FIG. 3A to FIG. 3C to be described later.);
apply at least one of Fast Fourier Transform (FFT) or Discrete Wavelet Transform (DWT) to the set of range images to obtain a set of FFT coefficients or a set of DWT coefficients; and identify a level of a condition for the environment based on a sparsity of the set of FFT coefficients or the set of DWT coefficients (see [0077- 0084] and Fig. 19 regarding rainfall is detected based on a presence of isolated points, which are discretely positioned, among the objects to be detected in the detection image; and its commonplace for Lidar sensor to collect data by FFT/DWT). Though it’s a commonplace technique regarding data collection by FFT/DWT, but Ohira is silent to teach about applying at least one of Fast Fourier Transform (FFT) or Discrete Wavelet Transform (DWT) to the set of range images to obtain a set of FFT coefficients or a set of DWT coefficients; however examiner has incorporated other reference Zou which states about sensor system see Fig. 1 #28 as a part of an apparatus (# 10); see [0045].. sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle 10. The sensing devices 40a-40n can include, but are not limited to, radars, lidars, global positioning systems, optical cameras 140a-140n, thermal cameras, ultrasonic sensors, and/or other sensors…; now in context with [0024, 0043, 0066- 0068] pls refer to [0069] regarding .. a discrete wavelet transform process, as applied by the wavelet transformer 204, the input image data 208 is received by the first filter bank 220 and, after down sampling by the respective down samplers 221, is split into low frequency components or coefficients 228 and high frequency components or coefficients 230. The low frequency coefficients 228 extract coarse or approximate information from the input image data 208 and the high the frequency coefficients 230 extract detailed information from the input image data 208. The low frequency coefficients 228 are input to the second filter bank 224 and the high frequency coefficients 230 are input to the third filter bank 226. The first filter bank 220 performs a one dimensional discrete wavelet transform on rows of the input image data 208 to produce low frequency and high frequency coefficients 228, 230. The second filter bank 224 performs a column wise one dimensional discrete wavelet transform on the low frequency coefficients 228 and the third filter bank 226 performs a column wise one dimensional discrete wavelet transform on the high frequency coefficients 230. In this way, a first stage 232 of the wavelet transformer 204 performs a two-dimensional discrete wavelet transform to produce first level discrete wavelet transform coefficients LL.sub.1, LH.sub.1, HL.sub.1, HH.sub.1; further see [0070]. It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Zou with the teachings of Ohira to make system more standardized. But Ohira is silent about wherein the spherical projection converts three-dimensional (3D) point cloud data into two-dimensional (2D) image data; however Park teaches in abstract regarding method and system for detecting data based on three-dimensional point cloud data received from a LiDAR, the present invention provides a method and system for detecting an object that convert three-dimensional point cloud data into spherical coordinates two-dimensional image data and BEV two-dimensional image data; enable each image feature to be detected from the image data converted into two types of coordinate systems and enable thereof to be fused; enable the three-dimensional point cloud data, while converting to the two-dimensional image data, to compensate for the lost information by detecting the object based on the fused image feature; and improve an accuracy of object detection; further see page 4 last seven lines…The receiving unit 100 receives 3D point cloud data collected from LIDAR. The first conversion data extractor 200 converts the received point cloud data into spherical coordinate system 2D image data…..; further see Fig. 5 step 800.. 3D point cloud data output from lidar is received. In step 810, the received 3D point cloud data is converted into spherical coordinate 2D image data, and in step 820, the received 3D point cloud data is converted into BEV 2D image data. It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Park with the teachings of Ohira in view of Zou to make system more standardized. Having a mechanism wherein the spherical projection converts three-dimensional (3D) point cloud data into two-dimensional (2D) image data; greater way standardized approach can be carried out in the communication system.
Claim(s) 9, 13, 23 are rejected under 35 U.S.C. 103 as being unpatentable over Ohira et al. (US Pub. No. 2018/0024229 A1) in view of Zou (US Pub. No. 2019/0187718 A1) and in further view of Park et al. (KR 20230036651 A), please see attached translated copy and in further view of Lunn et al. (US Pub. No. 2024/0385315 A1).
Regarding claim 9, Ohira in view of Zou and Park, teaches as per claim 8, but Ohira is silent about wherein the at least one processor, individually or in any combination, is further configured to: obtain the set of range images via multiple timestamps of one or more Lidar sensors, and wherein to apply at least one of the FFT or the DWT to the set of range images, the at least one processor, individually or in any combination, is configured to: apply at least one of a three-dimensional (3D) FFT or a 3D DWT to the set of range images; however Lunn states in Fig. 6 and [0092- 0096] regarding .. most radars have many receive ports (including real and virtual ones in a MIMO radar), the processor may be configured to calculate instantaneous range-velocity measurements as described above for each of the receive ports. This allows the processor to form a 3D matrix. The processor can be configured to perform a further FFT (which is described herein as a 3.sup.rd ‘angle’ FFT or 3D FFT)…It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Lunn with the teachings of Ohira in view of Zou and Park to make system more standardized.
Regarding claim 13, Ohira in view of Zou and Park, teaches as per claim 8, but Ohira is silent about wherein the set of point clouds corresponds to a 3D visualization of the environment that comprises a plurality of georeferenced points; however Lunn states in Fig. 6 and [0092- 0096] regarding .. most radars have many receive ports (including real and virtual ones in a MIMO radar), the processor may be configured to calculate instantaneous range-velocity measurements as described above for each of the receive ports. This allows the processor to form a 3D matrix. The processor can be configured to perform a further FFT (which is described herein as a 3.sup.rd ‘angle’ FFT or 3D FFT)…It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Lunn with the teachings of Ohira in view of Zou and Park to make system more standardized.
Regarding claim 23, Ohira in view of Zou and Park, teaches as per claim 22, but Ohira is silent about further comprising: obtaining the set of range images via multiple timestamps of one or more Lidar sensors, and wherein applying at least one of the FFT or the DWT to the set of range images comprises: applying at least one of a 3D FFT or a 3D DWT to the set of range images; however Lunn states in Fig. 6 and [0092- 0096] regarding .. most radars have many receive ports (including real and virtual ones in a MIMO radar), the processor may be configured to calculate instantaneous range-velocity measurements as described above for each of the receive ports. This allows the processor to form a 3D matrix. The processor can be configured to perform a further FFT (which is described herein as a 3.sup.rd ‘angle’ FFT or 3D FFT)…It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Lunn with the teachings of Ohira in view of Zou and Park to make system more standardized.
Claim(s) 10, 24 are rejected under 35 U.S.C. 103 as being unpatentable over Ohira et al. (US Pub. No. 2018/0024229 A1) in view of Zou (US Pub. No. 2019/0187718 A1) and in further view of Park et al. (KR 20230036651 A), please see attached translated copy and in further view of Xing et al. (CN 108648764 B), see machine translated copy.
Regarding claim 10, Ohira in view of Zou and Park, teaches as per claim 8, but Ohira is silent about wherein the condition is an adverse weather condition, and wherein the adverse weather condition is more severe when the set of FFT coefficients or the set of DWT coefficients is denser compared to the set of FFT coefficients or the set of DWT coefficients that is less dense; however Xing states in page 11 regarding .. in the embodiment of the present invention, in the step 4) of the measuring method of the invention, BP neural network input layer node is set as 12, the output layer node is 3, hidden layer node number is 6. the extracted rainstorm, heavy rain, moderate rain; the MFCC coefficient data of respectively rain is stored in mydata1.mat, mydata2.mat, mydata3.mat, mydata4.mat database file; four kinds of rain sound signal respectively marked by 1, 2, 3, 4. According to the identification number, the expected output vector of rainstorm is [1 0 0], the heavy rain is [0 1 0], moderate rain is [0 0 1 0], light rain is [0 0 1]. randomly selecting the rain sound signal characteristic value as the training sample, learning error target is 0.01. the rest is the test sample, detecting the identification rate. …It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Xing with the teachings of Ohira in view of Zou and Park to make system more standardized.
Regarding claim 24, Ohira in view of Zou and Park, teaches as per claim 15, but Ohira is silent about wherein the condition is an adverse weather condition, and wherein the adverse weather condition is more severe when the set of FFT coefficients or the set of DWT coefficients is denser compared to the set of FFT coefficients or the set of DWT coefficients that is less dense; however Xing states in page 11 regarding .. in the embodiment of the present invention, in the step 4) of the measuring method of the invention, BP neural network input layer node is set as 12, the output layer node is 3, hidden layer node number is 6. the extracted rainstorm, heavy rain, moderate rain; the MFCC coefficient data of respectively rain is stored in mydata1.mat, mydata2.mat, mydata3.mat, mydata4.mat database file; four kinds of rain sound signal respectively marked by 1, 2, 3, 4. According to the identification number, the expected output vector of rainstorm is [1 0 0], the heavy rain is [0 1 0], moderate rain is [0 0 1 0], light rain is [0 0 1]. randomly selecting the rain sound signal characteristic value as the training sample, learning error target is 0.01. the rest is the test sample, detecting the identification rate. …It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention was made to consider the teachings of Xing with the teachings of Ohira in view of Zou and Park to make system more standardized.
Conclusion
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PARTH PATEL
Primary Examiner
Art Unit 2479
/PARTH PATEL/ Primary Examiner, Art Unit 2479
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