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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 23rd, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The amendments to the specification submitted on 03/06/2026 was entered.
Response to Arguments
This is in response to applicant’s amendment/response filed on 03/06/2026 which have been entered and made of record.
Applicant’s arguments regarding claim objections to the drawings have been fully considered and are persuasive. Claim objections for the drawing have been withdrawn.
Applicant’s arguments regarding claim 13 have been fully considered and are persuasive. Claim rejection under 35 U.S.C. 112(b) for claim 13 has been withdrawn as claim 13 has been canceled.
Applicant’s arguments regarding claim rejections under 35 U.S.C. 103 for claims 1-7, 10, and 13 have been fully considered but they are not persuasive.
Applicant argues as referenced by the Office, para. [0056] of Ma describes reducing points in an aggregated point-cloud "according to one or more heuristic rules." In particular, Ma para. [0056] describes "removal of data points including higher intensities relative to other data points." Thus, Ma merely describes removing points from the aggregated point-cloud if they have an intensity that is greater than the intensity of other data points. However, Ma does not describe removing a point from the aggregated point-cloud based on the point being similar to a neighboring point by more than a threshold value. Therefore, Ma does not describe "based on a point from the point cloud being similar to a further point from the point cloud by more than a threshold value, not transmitting the further point, the point and the further point being in the same point cloud and being neighboring points to one another in the point cloud," as recited in amended claim 1. In addition, Agarwal and Mammou do not remedy the deficiencies of Ma.
Furthermore, in its rejection of claim 1, the Office cites to Mammou as allegedly teaching step c) of claim 1.
In paras. [0048]-[0049], Mammou describe encoding "data indicating inclusion or non- inclusion of points at subdivision locations" and "data indicating relocation of any points to be relocated relative to subdivision locations." However, this data only allows a decoder to "include, not include, or relocate points at the subdivision locations" in a decompressed point cloud. Therefore, any point in the captured point cloud that has not been sub-sampled and is not located near a subdivision location is not capable of being included in the decompressed point cloud constructed by the decoder (Mammou paras. [0028]-[0029]). This is because the encoder has not transmitted these points to the decoder, nor has it transmitted any indication data to the decoder for the purpose of including these not-transmitted points in the decompressed point cloud.
As such, Mammou does not describe "generating additional information, the additional information allowing reconstruction of the not-transmitted point as a reconstructed point, wherein steps b) and c) are performed for a plurality of the points of the point cloud resulting in a plurality of not-transmitted points" such that "the additional information allow[s] reconstruction of each of the non-transmitted points as a corresponding reconstructed point in a reconstructed point cloud after the transmission," as recited in amended claim 1. Furthermore, as indicated by the Office (Office Action, p. 6), Ma and Agarwal do not remedy the deficiencies of Mammou.
Examiner respectable disagrees. Ma teaches removing data points from a point cloud based on a heuristic filter/rule (Para. 0006 and 0076). According to broadest reasonable interpretation (BRI) a neighboring point is any nearby/close point to another point, often measure geometrically. Ma teaches several different heuristic filter/rules (Redundant points, Higher Intensities, Below Boundaries, etc…. Para. 0056) used to determine which points to remove (Para. 0076). One of these filters/rules is points having the same or similar reflection intensity at approximately similar z-positions may be ground position points. Points below these z-positions are removed based on relating ground position points. (Para. 0056) Points directly below the z-position points would be neighboring points as they would be nearby/close geometrically. Thus, while Ma doesn’t explicitly use the terminology neighboring points, Ma teaches the point (Ground Level Point) and the further point (Point Below Ground Level) being in the same point cloud and being neighboring points (Nearby/Close Geometrically) to one another in the point cloud.
Mammou teaches capturing a point cloud and sup-sampling the captured point cloud to obtain a compressed point cloud (Para. 0004-0005). The compressed point cloud has fewer points than the captured point cloud (Fig. 1A, 1B, and 1C) The compressed point cloud includes spatial information and additional data about the compressed point cloud such that a decoder can recreate the captured point cloud (Para. 0004-0005). The additional data incorporated can be relocation data for points, subdivision locations, additional points to be added to the decompressed point cloud, or other configuration information(Para. 0033 and 0045). Thus Mammou teaches generating additional information, the additional information allowing reconstruction of the non-transmitted generating additional information (Spatial Information Additional Data), the additional information allowing reconstruction of the not-transmitted point (Points not in compressed point cloud) as a corresponding reconstructed point (Original point in captured point cloud).
Regarding the remaining arguments applicant argues with respect to the amended claim language, which is fully addressed in the prior art rejections set forth below.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-7, 10, 14, 17, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. U.S. Patent Application Publication 20220406014 A1 (hereinafter Ma) in view of Agarwal et al. U.S. Patent Application Publication 20190179022 A1 (hereinafter Agarwal) in further view of Mammou et al. U.S. Patent Application Publication 20200275125 A1 (hereinafter Mammou).
Regarding claim 1, Ma teaches a method for reducing data (Reducing a point-cloud, Para. 0056) with reduced information loss (Para. 0074) in a LiDAR system (LiDAR units, Para. 0026) including a transmitting unit (Communication unit 740, Para. 0083) and a receiving unit (Communication unit 740, Para. 0083), the method comprising the following steps:
a) comparing all points of a point cloud to be compressed for similarity to other points of the point cloud (Para. 0056); To generate the reduced point cloud, points are removed based on a heuristic filter/rule. Points from the point cloud are compared to each other or to a threshold based on the filter/rule to determine if they should be filtered from the point-cloud.
b) based on a point from the point cloud being similar (Redundant Points, Relative Intensities, Below Similar Boundaries, etc…. Para. 0056) to a further point (Different Point in the Point Cloud) from the point cloud by more than a threshold value(Heuristic Filter/Rule, Para. 0056), not transmitting the further point (Removing Points not Satisfied by Heuristic Filter/Rule), the point and the further point being in the same point cloud (Point Cloud Before it is Reduced) and being neighboring points(Nearby/Close Geometrically) to one another in the point cloud; The removed points from the point cloud are not transmitted as they are not a part of the reduce point cloud that is transmitted. As stated above, Ma teaches removing data points from a point cloud based on a heuristic filter/rule (Para. 0006 and 0076). According to broadest reasonable interpretation (BRI) a neighboring point is any nearby/close point to another point, often measure geometrically. Ma teaches several different heuristic filter/rules (Redundant points, Higher Intensities, Below Boundaries, etc…. Para. 0056) used to determine which points to remove (Para. 0076). One of these filters/rules is points having the same or similar reflection intensity at approximately similar z-positions may be ground position points. Points below these z-positions are removed based on relating ground position points. (Para. 0056) Points directly below the z-position points would be neighboring points as they would be nearby/close geometrically. Thus, while Ma doesn’t explicitly use the terminology neighboring points, Ma teaches the point (Ground Level Point) and the further point (Point Below Ground Level) being in the same point cloud and being neighboring points (Nearby/Close Geometrically) to one another in the point cloud.
c) (Removed Points from the Point Cloud), the not-transmitted points being similar(Redundant Points, Relative Intensities, Below Similar Boundaries, etc…. Para. 0056) to points of the point cloud by more than the threshold value(Heuristic Filter/Rule, Para. 0056);
d) based on a reduction of data of the point cloud (After Applying Heuristic Filter/Rule, Para. 0056), transmitting (Reduced Point cloud, Para. 0056) excluding the not-transmitted points via a (Transmitting Reduced Point Cloud),
and e) ending the data reduction after accomplishing d). (After Applying Heuristic Filter/Rule, Para. 0056) Once the point cloud has been reduced by the filter/rule. Generating the reduced point cloud. The reduced point cloud is not furthered reduced.
However, Ma fails to teach
c) for the not-transmitted point from the point cloud, generating additional information, the additional information allowing reconstruction of the not-transmitted point as a corresponding reconstructed point, wherein steps b) and c) are performed for a plurality of the points of the point cloud resulting in a plurality of non-transmitted points, the not-transmitted points being similar to points of the point cloud by more than the threshold value;
d) based on a reduction of data of the point cloud, transmitting the additional information and all points of the point cloud excluding the not-transmitted points via a bandwidth-limited channel, the additional information allowing reconstruction of each of the non-transmitted points as a corresponding reconstructed point in a reconstructed point cloud after the transmission;
Ma and Agarwal are analogous to the claimed invention because both of them are in the same field of LiDAR systems and reducing a point-cloud.
Agarwal teaches:
d) based on a reduction of data of the point cloud(The reduced set of a point cloud is compressed, Para. 0041-0042 and 0048), transmitting (Para. 0041, Agarwal invention is specifically for reducing needed bandwidth, thus it would be obvious that a bandwidth-limited channel can be used to transmit the reduced set of a point cloud.),
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud to incorporate Agarwal’s compression of a reduced point cloud and utilize a bandwidth-limited channel. Since doing so would provide the benefit of reducing the bandwidth and memory needed to compress and store point cloud data. (Agarwal, Para. 0041) As well as utilize a bandwidth limited channel to transmit the reduced point cloud data.
However, Ma and Agarwal fail to teach:
c) for the not-transmitted point from the point cloud, generating additional information, the additional information allowing reconstruction of the not-transmitted point as a corresponding reconstructed point, wherein steps b) and c) are performed for a plurality of the points of the point cloud resulting in a plurality of non-transmitted points, the not-transmitted points being similar to points of the point cloud by more than the threshold value;
d) based on a reduction of data of the point cloud, transmitting the additional information and all points of the point cloud excluding the not-transmitted points via a bandwidth-limited channel, the additional information allowing reconstruction of each of the non-transmitted points as a corresponding reconstructed point in a reconstructed point cloud after the transmission;
Ma, Agarwal, and Mammou are analogous to the claimed invention because all of them are in the same field of reducing point clouds in LiDAR systems.
Mammou teaches:
c) for the not-transmitted point from the point cloud, generating additional information (Spatial Information and Additional Data for Additional Points, (Para. 0004-0005 and 0048-0049), the additional information (Data indicating relocation data for points, subdivision locations, additional points to be added to the decompressed point cloud, inclusion/non-inclusion points, or other configuration information, Para.0033, 0045, and 0049) allowing reconstruction of the not-transmitted point (Not-Included Points or Points not in Sub-Sampled/Subdivided Point Cloud)) as a corresponding reconstructed point (Recreating Original Captured Point Cloud), wherein steps b) and c) are performed for a plurality of the points (Sub-dividing/Sub-Sampling Point Cloud Iteratively, Para. 0029) of the point cloud resulting in a plurality of non-transmitted points (Not-Included Points or Points not in Sub-Sampled/Subdivided Point Cloud), the not-transmitted points being similar (Neighboring Points Evaluated, Para. 0033 and 0052) to points of the point cloud by more than the threshold value (Threshold Distance, Para. 0033); As stated above, Mammou teaches capturing a point cloud and sup-sampling the captured point cloud to obtain a compressed point cloud (Para. 0004-0005). The compressed point cloud has fewer points than the captured point cloud (Fig. 1A, 1B, and 1C) The compressed point cloud includes spatial information and additional data about the compressed point cloud such that a decoder can recreate the captured point cloud (Para. 0004-0005). The additional data incorporated can be relocation data for points, subdivision locations, additional points to be added to the decompressed point cloud, or other configuration information(Para. 0033 and 0045). Thus Mammou teaches generating additional information, the additional information allowing reconstruction of the non-transmitted generating additional information (Spatial Information Additional Data), the additional information allowing reconstruction of the not-transmitted point (Points not in compressed point cloud) as a corresponding reconstructed point (Original point in captured point cloud).
d) based on a reduction of data of the point cloud (Generating Compressed Point Cloud with fewer Points compared to the Original Captured Point Cloud, Para. 0033), transmitting the additional information(Spatial Information and Additional Data for Additional Points, (Para. 0004-0005 and 0048-0049) and all points of the point cloud excluding the not-transmitted points (Compressed Sub-Sampled/Subdivided Point Cloud) via a bandwidth-limited channel (Network, Para. 0098), the additional information(Spatial Information and Additional Data for Additional Points, (Para. 0004-0005 and 0048-0049) allowing reconstruction of each of the non-transmitted points(Recreating Original Captured Point Cloud) as a corresponding reconstructed point in a reconstructed point cloud after the transmission (Decoding Point Cloud to Recreate the Original Captured Point Cloud, Para. 0033); With the additional spatial information the original point cloud can be recreated. The additional spatial information allows for the reconstruction of all points in the point cloud. As the arithmetic encoding process in Mammou can be lossless or lossy.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s compression and Bandwidth Limited Channel to include Mammou’s additional information in the compression. Since doing so would provide the benefit of reconstructing the point cloud as close to the original point cloud as possible, as some points are not transmitted. Thus, reducing the loss of information when compressing the reduced point cloud.
Regarding claim 2, Ma and Agarwal fail to explicitly teach the method according to claim 1, wherein in the method step a), the reduction of the data takes place sequentially such that portions of the point cloud to be compressed are compressed one after the other.
However, Mammou teaches the method according to claim 1, wherein in the method step a), the reduction of the data (Sub-Sampled Point Cloud) takes place sequentially such that portions (Sub-Samples) of the point cloud to be compressed are compressed one after the other. (Para. 0028-0029) The point cloud is sub-sampled at various uniform intervals/distances in the point cloud and each sub-sample is compressed. The sub-sampled point cloud is a point cloud that has been reduced. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s compression to include Mammou’s sub-sampling of a point cloud which are compressed. Since doing so would provide the benefit of compressing the point cloud in portions instead of all at once to reduce the bandwidth needed to transmit the point cloud.
Regarding claim 3, Ma and Agarwal fail to explicitly teach the method according to claim 2, wherein: (i) individual columns of the point cloud, or (ii) segments of columns of the cloud, or (iii) individual rows of the point cloud, or (iv) segments of rows of the point cloud, are compressed one after the other in time. (Para. 0028-0029) The point cloud can be sub-sampled and compressed at various uniform intervals/distances. These intervals can be of any increment or direction as long as its uniform. Thus, the sub-sampled point cloud can be segments of rows/columns or an individual row/column in a point cloud. As points clouds are usually represented in a row/col format. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s compression to include Mammou’s Uniformed Sub-Sampling of a Point Cloud which are Compressed. Since doing so would provide the benefit of compressing the point cloud in uniform sub-samples instead of random sub-samples. As well as increasing the flexibility in which the point cloud is sampled.
Regarding claim 4, Ma and Agarwal fail to explicitly teach the method according to claim 1, wherein, in the method step a) and/or b), a similarity determination takes place by a pairwise comparison of neighboring points.
However, Mammou teaches the method according to claim 1, wherein, in the method step a) and/or b), a similarity determination takes place by a pairwise comparison of neighboring points. (Para. 0029) Each set of neighboring points are evaluated based on distance to determine which subdivision the point should belong to. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s compression to include Mammou’s Comparison of Neighboring Points. Since doing so would provide the benefit of ensuring each point is evaluated by comparing each point to each other.
Regarding claim 5, Ma teaches the method according to claim 4, wherein prioritization within a scanning range (The data points within the scanning range can be represented based on positional coordinates or intensities of light, Para. 0071) of the LiDAR system (LiDAR units, Para. 0026) is achieved by an order of processing (Heuristic Filter/Rule, Para. 0073) . The point cloud is reduced based on the heuristic filter/rule, which is an order of processing.
Regarding claim 6, Ma teaches the method according to claim 4, wherein a metric including a distance dimension (Para. 0026 and 0038-0039, The distance between the LiDAR unit and the point cloud data is included.) is used to transmit the points with maximum information gain (Maximum Granularity, Para. 0061 and 0062) of the point cloud to be compressed. To ensure maximum quality of the point cloud. The granularity level can be set to ensure more accurate or efficient analysis of objects. (Para. 0026 and 0058) As well as most LiDAR systems are fine tuned to capture information at an ideal distance range to ensure the best quality data.
Regarding claim 7, Ma and Agarwal fail to explicitly teach the method according to claim 3, wherein in the method steps a) and/or b), a radial distance of the points is used as a comparison criterion for ascertaining neighboring points.
However, Mammou teaches the method according to claim 3, wherein in the method steps a) and/or b), a radial distance (Minimum/Maximum/Threshold/Euclidian Distance) of the points is used as a comparison criterion for ascertaining neighboring points. (Para. 0033) Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s compression to include Mammou’s Comparison of Neighboring Points utilizing Radial Distance. Since doing so would provide the benefit of determining neighboring points in the point cloud by comparing radial distance between them. Calculating the distance between points to determine its neighboring points is standard when given a point cloud.
Regarding claim 10, Ma teaches the method according to claim 3, wherein in method steps a) and/or b), a background intensity (Reflected Intensity, Para. 0038) of the points is used as a comparison criterion for ascertaining neighboring points. (The heuristic filter/rule is used to remove points and can be based on the reflection intensity of said points, Para. 0056) The reflective intensity provides information about the composition of a material of a scanned point by studying the amount of light that reflects, Para. 0041.
Regarding claim 14, has similar limitations as of claim 7, therefore it is rejected under the same rationale as claim 7.
Regarding claim 17, has similar limitations as of claim 10, therefore it is rejected under the same rationale as claim 10.
Regarding claim 20, Ma and Agarwal fail to teach the method according to claim 1, wherein each reconstructed point corresponding to a respective non-transmitted point has a location in the reconstructed point cloud that is exactly the same as a location of the respective non-transmitted point in the point cloud.
However, Mammou teaches the method according to claim 1, wherein each reconstructed point corresponding to a respective non-transmitted point(Not-Included Points or Points not in Sub-Sampled/Subdivided Point Cloud) has a location in the reconstructed point cloud that is exactly the same as a location of the respective non-transmitted point in the point cloud. (Recreating Original Captured Point Cloud, Para. 0033 and 0049) Mammou teaches a decoder that can recreate the original captured point cloud by utilizing spatial information and additional data to figure out how the compressed point cloud was subdivided. Recreating the original captured point cloud would result in all points in the point cloud being reconstructed in their original locations. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s compression and Bandwidth Limited Channel to include Mammou’s Additional Information during Compression to Recreate the Point Cloud. Since doing so would provide the benefit of reconstructing the point cloud to match the original. Resulting in no loss of information when compressing/decompressing the point cloud (Mammou et al. Para. 0049).
Regarding claim 21, Ma and Agarwal fail to teach the method according to claim 1, wherein the additional information is a respective reconstruction flag for each of the not-transmitted points.
However, Mammou teaches the method according to claim 1, wherein the additional information(Spatial Information and Additional Data for Additional Points, (Para. 0004-0005 and 0048-0049) is a respective reconstruction flag(Metadata/data) for each of the not-transmitted points(Data indicating relocation data for points, subdivision locations, additional points to be added to the decompressed point cloud, inclusion/non-inclusion points, or other configuration information, Para.0033, 0045, and 0049). According to BRI a flag is metadata/data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s compression and Bandwidth Limited Channel to include Mammou’s Additional Information during Compression to Recreate the Point Cloud. Since doing so would provide the benefit of reconstructing the point cloud to match the original. Resulting in no loss of information when compressing/decompressing the point cloud (Mammou et al. Para. 0049).
Claim(s) 8-9, 11-12, 15-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. U.S. Patent Application Publication 20220406014 A1 (hereinafter Ma) in view of Agarwal et al. U.S. Patent Application Publication 20190179022 A1 (hereinafter Agarwal) and Mammou et al. U.S. Patent Application Publication 20200275125 A1 (hereinafter Mammou) in further view of NPL “Improving LiDAR point cloud classification using intensities and multiple echoes” by Christophe Reymann and Simon Lacroix (hereinafter Reymann).
Regarding claim 8, Ma and Agarwal fail to teach the method according to claim 3, wherein in the method steps a) and/or b), a radial distance of the points and an echo intensity of the points are used as comparison criteria for ascertaining neighboring points.
However, Mammou teaches the method according to claim 3, wherein in the method steps a) and/or b), a radial distance(Minimum/Maximum/Threshold/Euclidian Distance) of the points and an echo intensity of the points are used as comparison criteria for ascertaining neighboring points. (Para. 0033) Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s compression to include Mammou’s Comparison of Neighboring Points utilizing Radial Distance. Since doing so would provide the benefit of determining neighboring points in the point cloud by comparing radial distance between them. Calculating the distance between points to determine its neighboring points is standard.
Mammou fails to teach echo intensity of the points are used as comparison criteria for ascertaining neighboring points.
Ma, Agarwal, Mammou, and Reymann are analogous to the claimed invention because all of them are in the same field of reducing/subsampling point cloud data from a LiDAR system.
However, Reymann teaches the method according to claim 3, wherein in the method steps a) and/or b), a radial distance (Section : IV. Point Cloud Classification and Section: A. Geometric Features, Page 5125) of the points and an echo intensity(Section: B. Intensity Features and Section: C. Multi-echo Features, Page 5125) of the points are used as comparison criteria for ascertaining neighboring points. (Section: IV Point Cloud Classification and Section: E. Hierarchical Classification, Page 5125 and 5126) The point cloud is subsampled by comparing points to determine classifications about the environment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s and Mammou to incorporate Reymann’s Comparison of Neighboring Points utilizing Radial Distance and Echo Intensity . Since doing so would provide the benefit of having various modalities to determine the neighboring relationship of points. Specifically using multiple modalities refines the classification of the environment further increasing the accuracy of detected objects/environment. (Reymann, Section E. Hierarchical Classification, Page 5126)
Regarding claim 9, Ma, Agarwal, and Mammou fail to teach the method according to claim 3, wherein in the method step a), an echo intensity of the points is used as a comparison criterion for ascertaining neighboring points.
However, Reymann teaches the method according to claim 3, wherein in the method step a), an echo intensity(Section: B. Intensity Features and Section: C. Multi-echo Features, Page 5125) of the points is used as a comparison criterion for ascertaining neighboring points. (Section: IV Point Cloud Classification and Section: E. Hierarchical Classification, Page 5125 and 5126) Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s and Mammou to incorporate Reymann’s Comparison of Neighboring Points utilizing Echo Intensity . Since doing so would provide the benefit of having various modalities to determine the neighboring relationship of points. Specifically using multiple modalities refines the classification of the environment further increasing the accuracy of detected objects/environment. (Reymann, Section E. Hierarchical Classification, Page 5126)
Regarding claim 11, Ma teaches the method according to claim 3, wherein, in the method steps a) and/or b), a radial distance of the points, an echo intensity of the points, and a background intensity(Reflected Intensity, Para. 0038) of the points are used as comparison criteria for ascertaining neighboring points. (The heuristic filter/rule used to remove points can be based on a reflection intensity, Para. 0056) The reflective intensity provides information about the composition of a material of a scanned point, Para. 0041.
However, Ma and Agarwal fail to teach echo intensity and radial distance of the points are used as comparison criteria for ascertaining neighboring points.
Mammou teaches radial distance(Minimum/Maximum/Threshold/Euclidian Distance) of the points are used as comparison criteria for ascertaining neighboring points. (Para. 0033) Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s compression to include Mammou’s Comparison of Neighboring Points utilizing Radial Distance. Since doing so would provide the benefit of determining neighboring points in the point cloud by comparing radial distance between them. Calculating the distance between points to determine its neighboring points is standard.
However, Mammou fails to teach echo intensity of the points are used as comparison criteria for ascertaining neighboring points.
Reymann teaches the method according to claim 3, wherein, in the method steps a) and/or b), a radial distance of the points(Section : IV. Point Cloud Classification and Section: A. Geometric Features, Page 5125), an echo intensity(Section: B. Intensity Features and Section: C. Multi-echo Features, Page 5125) of the points, and a background intensity(Section: B. Intensity Features, Page 5125) of the points are used as comparison criteria for ascertaining neighboring points. . (Section: IV Point Cloud Classification and Section: E. Hierarchical Classification, Page 5125 and 5126) The point cloud is subsampled by comparing points to determine classifications about the environment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s and Mammou to incorporate Reymann’s Comparison of Neighboring Points utilizing Radial Distance, Echo Intensity, and Background Intensity. Since doing so would provide the benefit of having various modalities to determine the neighboring relationship of points. Specifically using multiple modalities refines the classification of the environment further increasing the accuracy of detected objects/environment. (Reymann, Section E. Hierarchical Classification, Page 5126)
Regarding claim 12, Ma teaches the method according to claim 3, wherein, according to method steps a) and/or b), an echo intensity of the points and a background intensity(Reflected Intensity, Para. 0038) of the points are used as comparison criteria for ascertaining neighboring points. (The heuristic filter/rule used to remove points can be based on a reflection intensity, Para. 0056) The reflective intensity provides information about the composition of a material of a scanned point, Para. 0041.
However, Ma, Agarwal, and Mammou fail to teach echo intensity of the points are used as comparison criteria for ascertaining neighboring points.
Reymann teaches the method according to claim 3, wherein, according to method steps a) and/or b), an echo intensity(Section: B. Intensity Features and Section: C. Multi-echo Features, Page 5125) of the points and a background intensity(Section: B. Intensity Features, Page 5125) of the points are used as comparison criteria for ascertaining neighboring points. (Section: IV Point Cloud Classification and Section: E. Hierarchical Classification, Page 5125 and 5126) The point cloud is subsampled by comparing points to determine classifications about the environment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ma’s Reduced Point-Cloud altered by Agarwal’s and Mammou to incorporate Reymann’s Comparison of Neighboring Points utilizing Radial Distance, Echo Intensity, and Background Intensity. Since doing so would provide the benefit of having various modalities to determine the neighboring relationship of points. Specifically using multiple modalities refines the classification of the environment further increasing the accuracy of detected objects/environments (Reymann, Section E. Hierarchical Classification, Page 5126).
Regarding claim 15, has similar limitations as of claim 8, therefore it is rejected under the same rationale as claim 8.
Regarding claim 16, has similar limitations as of claim 9, therefore it is rejected under the same rationale as claim 9.
Regarding claim 18, has similar limitations as of claim 11, therefore it is rejected under the same rationale as claim 11.
Regarding claim 19, has similar limitations as of claim 12, therefore it is rejected under the same rationale as claim 12.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIANNA R COCHRAN whose telephone number is (571)272-4671. The examiner can normally be reached Mon-Fri. 7:30am - 5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alicia Harrington can be reached at (571) 272-2330. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRIANNA RENAE COCHRAN/Examiner, Art Unit 2615
/ALICIA M HARRINGTON/Supervisory Patent Examiner, Art Unit 2615
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