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 .
Notice to Applicant
The following is a Non-Final, first Office Action responsive to Applicant’s communication of 10/18/23, in which applicant filed the application. Claims 1-9 are pending in the instant application and have been rejected below.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: Information Processing Device for a Ship using LiDAR.
Other Titles may also be acceptable; just cannot be generally a computer without any context.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/14/23 and 1/2/25 are being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to a device which is a statutory category.
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
An information processing … comprising:
acquire point cloud data generated by a measurement …;
extract data obtained by measuring a position which is a predetermined distance away from a shore and which is within a predetermined distance from a measurement position of the measurement device, as a water-surface reflection data measured by reflection at a water surface; and
calculate a water-surface height based on the water-surface reflection data.
As drafted, this is, under its broadest reasonable interpretation, directed to the Abstract idea groupings of “mathematical relationships” as here we have acquire point cloud data from a measurement, measuring a position that is a distance away from a shore that is within a predetermined distance from a “measurement position” as a water-surface reflection data measured by reflection at a water surface. At this time, the claim is viewed as a series of mathematical relationships.
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements that are:
information processing device comprising:
a memory configured to store instructions; and
a processor configured to execute the instructions to:
acquire … data… by a measurement device provided on a ship.
The claim has a device comprising a processor executing stored instructions from a memory. This is viewed as mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)) and individually or in combination is “field of use” (MPEP 2106.05h) for the measurement device being “on a ship”. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The computer just “receives/acquires” the measurement data from a measurement device on a ship.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer system, memory, executing instructions, where the measurement is “on a ship”, are MPEP 2106.05(f) (Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and MPEP 2106.05h (field of use). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. The claim is not patent eligible. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent claim 8 is directed to a computer at step 1, which is a statutory category. Claim 8 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2 and step 2b.
Independent claim 9 is directed to an article of manufacture at step 1, which is a statutory category. Claim 9 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2 and step 2b.
Claim 2 further narrows the abstract idea by calculating an average.
Claim 3 further narrows the abstract idea by calculating an average and variance, and using the average when the variance is small.
Claim 4 narrows the abstract idea by setting a search range to detect an obstacle and ship-wave, which is considered math range of distance for where another ship is located.
Claim 5 narrows the abstract idea by performing math relationships by determining whether the data points form a linear shape representing a wave from a ship.
Claim 6 narrows the abstract idea by performing math relationships by determining whether the data points form a cluster representing an obstacle.
Claim 7 narrows the abstract idea by performing math relationships by changing a height range based on a variance.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4, and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Qingqing, et al., “Detecting Water Reflection Symmetries in Point Clouds for
Camera Position Calibration in Unmanned Surface Vehicles,” 2019 19th International Symposium on Communications and Information Technologies, pages 507-512, in view of Hiromatsu (US 2024/0085553).
Concerning claim 1, Qingqing discloses:
An information processing device (Qingqing – see page 509, Col. 2, Section A - The first step is to pre-process data and apply some filters to the point cloud in order to reduce the number of points and reduce the computational team necessary to run our proposed algorithm. This is essential to enable real-time operation. Taking into account that a considerable part of the point cloud
does not appear in the reflection, we use GNSS or lidar data to estimate the distance to the lakeside or river banks; See FIG. 2 – paper focuses on information from Lidar in “distance to coastline,” “water surface plane estimation”, and “stereo camera orientation”) comprising:
a memory configured to store … (Qingqing – see page 511, Col. 1, 2nd paragraph – hardware installed is Intel IoT edge gateway with CPU, and 4GB RAM); and
a processor configured to execute…(Qingqing – see page 511, Col. 1, 2nd paragraph – hardware installed is Intel IoT edge gateway with CPU, and 4GB RAM).
To any extent Qingqing is unclear on processor executing stored instructions from memory, Hiromatsu discloses:
a memory configured to store “instructions”; a processor configured to “execute the instructions” (Hiromatsu – see par 68-69, FIG. 4 – memory is storage device; main memory used as a work area of the processor; processes stored in memory 6 developed on processor 5; par 71 - The auxiliary storage device 21 stores the obstacle detection program, programs run by the processor 5, data to be used when running the programs, and so on. A specific example of the auxiliary storage device 21 is a Hard Disk Drive (HDD) or a Solid-State Drive (SSD). The auxiliary storage device 21 may be a portable recording medium; claim 11 - . A non-transitory computer-readable recording medium recorded with an obstacle detection program that causes a computer).
Qingqing and Hiromatsu disclose:
acquire point cloud data generated by a measurement device provided on a ship (Qingqing – see abstract - We propose a method to estimate the water plane based on the detection of local symmetry planes that naturally occur when objects are reflected in the water. By using a point cloud generated with a
stereo camera, we are able to accurately estimate the water level and, at the same time, calibrate the camera position and improve the localization of obstacles; see page 510, FIG. 2 – sensors installed on vessel (See FIG. 3 – ship) – includes Intel Realsense camera sensor; see page 510, col. 1, 1st paragraph - We only take into account points above the water surface and try to find their reflection in the point cloud. A KD-tree search algorithm is used to find the nearest point in the point cloud to the expected reflection; see page 511, Col. 1 – Lidar, Intel RealSense stereo camera installed on-board ship; use on-board computer and lidars… and Realsense’s point cloud
see also Hiromatsu – see par 55-58 – data D(k) – water surface separation unit 3d takes as input, point cloud data);
extract data obtained by measuring a position which is a predetermined distance away from a shore and which is within a predetermined distance from a measurement position of the measurement device, as a water-surface reflection data measured by reflection at a water surface (Qingqing – see page 509, col. 2, section A - Taking into account that a considerable part of the point cloud does not appear in the reflection, we use GNSS or lidar data to estimate the distance to the lakeside or river banks. Then, this distance is used to filter points so that we only take into
accounts objects near the coastline, which is where reflections occur.); and
a water-surface height calculation means configured to calculate a water-surface height based on the water-surface reflection data (Qingqing – see page 509, Col. 2, “Camera Height estimation”, 1st paragraph ; page 509, Col. 2, last paragraph - Then, given a pre-defined interval (hl; hh) where the camera is assumed to be, we choose a step _h and calculate, for each height h = hmin+k_h, the reflection of all points such that xiy > h and xiy h < hth. The constant hth 2 R+ is a threshold defined as a function of the distance to the coastline, and
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is an integer that used to sample the interval (hl; hh). We estimate the water level height hw to be defined by:
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424
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See page 511, col. 2, last paragraph - We have applied the water height estimation in (1) to the point cloud data included in Figures 4 and 5. The computation
of the error according to (1) is shown in Figures 6 and 7).
Qingqing and Hiromatsu are analogous art as they are directed to analyzing point cloud data and Lidar on water (see Qingqing Abstract; Hiromatsu Abstract, FIG. 2). Qingqing discloses having a computer to execute operations (See page 511). Hiromatsu improves upon Qingqing by disclosing having a computer and memory/storage for storing instructions for executing processes. One of ordinary skill in the art would be motivated to further include specific computer implementation including storage for instructions to efficiently improve upon the computer and RAM in Qingqing.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the camera height estimation with respect to water surface (See page 509, Col. 2, section B) in Qingqing to further use storage/memory for computer instructions as disclosed in Hiromatsu, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning independent claim 8, Qingqing and Hiromatsu disclose:
A control method executed by a computer (Qingqing – see page 511, Col. 1, 2nd paragraph – hardware installed is Intel IoT edge gateway with CPU, and 4GB RAM
see also Hiromatsu – see par 68-69, FIG. 4 – memory is storage device; main memory used as a work area of the processor; processes stored in memory 6 developed on processor 5; par 71 - The auxiliary storage device 21 stores the obstacle detection program, programs run by the processor 5, data to be used when running the programs, and so on. A specific example of the auxiliary storage device 21 is a Hard Disk Drive (HDD) or a Solid-State Drive (SSD). The auxiliary storage device 21 may be a portable recording medium; claim 11 - . A non-transitory computer-readable recording medium recorded with an obstacle detection program that causes a computer).
The remaining limitations are similar to claim 1 above and are rejected for the same reasons.
It would have been obvious to combine Qingqing and Hiromatsu for the same reasons as claim 1.
Concerning claim 9, Qingqing and Hiromatsu disclose:
A non-transitory computer-readable program causing a computer to execute processing of (Qingqing – see page 511, Col. 1, 2nd paragraph – hardware installed is Intel IoT edge gateway with CPU, and 4GB RAM;
see also Hiromatsu – see par 68-69, FIG. 4 – memory is storage device; main memory used as a work area of the processor; processes stored in memory 6 developed on processor 5; par 71 - The auxiliary storage device 21 stores the obstacle detection program, programs run by the processor 5, data to be used when running the programs, and so on. A specific example of the auxiliary storage device 21 is a Hard Disk Drive (HDD) or a Solid-State Drive (SSD). The auxiliary storage device 21 may be a portable recording medium; claim 11 - . A non-transitory computer-readable recording medium recorded with an obstacle detection program that causes a computer).
The remaining limitations are similar to claim 1 above and are rejected for the same reasons.
It would have been obvious to combine Qingqing and Hiromatsu for the same reasons as claim 1.
Concerning claim 2, Qingqing discloses that filters to point cloud used to reduce number of points and reduce computational needs and points can be filtered based on both absolute distance to the boat and vertical distance (See page 509, col. 2, section A) and that there can be a pre-defined interval of the height of the camera (See page 509, col. 2, section B, 3rd paragraph).
Hiromatsu discloses:
The information processing device according to claim 1, wherein the processor calculates an average of the values of the water-surface reflection data in a height direction as the water-surface height (Hiromatsu – see par 121 - The height of the point cloud data is, in a specific example, an average of heights of all points belonging to the point cloud data corresponding to the plane, or a height of the tallest point or the lowest point, among the points belonging to the point cloud data; see par 173 - , the boundary detection unit 35 [0174] finds an average value of the reflection intensities of all points included in P.sub.b,r(m.sub.r), as an average reflection intensity).
It would have been obvious to combine Qingqing and Hiromatsu for the same reasons as claim 1. Qinqqing discloses that filters to point cloud used to reduce number of points and reduce computational needs and points can be filtered based on both absolute distance to the boat and vertical distance (See page 509, col. 2, section A) and that there can be a pre-defined interval of the height of the camera (See page 509, col. 2, section B, 3rd paragraph). Hiromatsu improves upon Qingqing by calculating an average for height. One of ordinary would be motivated to further include average values for height to efficiently improve upon the height camera estimation in Qingqing.
Concerning claim 4, Qingqing and Hiromatsu disclose:
The information processing device according to claim 1, wherein the processor is further configured to set a search range based on the measurement position and the water-surface height, and detect an obstacle … based on the point cloud data belonging to the search range (Qingqing – see page 510, FIG. 2 – Object Detection and Collision Avoidance; see page 510, col. 1, 1st paragraph - The value hth is chosen so that only reflections of objects near the water surface are taken into account when the ship is near the coastline, and bigger objects are accounted for otherwise. A KD-tree search algorithm is used to find the nearest point in the point cloud to the expected reflection. see page 512, col. 1, 2nd paragraph - In the situation where the ship is close to the river bank, we
have used a height threshold hth = 0:5 m as reflections are more clear for objects close to the water level. In the case of the ship bow pointing towards the center of the river, we have used hth=3 m to account for taller trees and their reflections.
Analogously, we have used absolute threshold distances of 5 and 8m, respectively, taking into account the distance to the river bank estimated from the lidar data. These constants have an impact on the number of points that are used to calculate the error).
Qingqing discloses that waves from water surface can have impact on performance of different methods, based on reflective properties of still waters (See page 2, col. 1, 2nd paragraph).
Hiromatsu disclose:
The information processing device according to claim 1, wherein the processor is further configured to set a search range based on the measurement position and the water-surface height, and detect an obstacle and “a ship- wave” based on the point cloud data belonging to the search range (Hiromatsu – see par 93-101 - The plane detection unit 31 selects a maximum of K pieces of points belonging to a vicinity of a certain plane from the point cloud data P, and generates point cloud data P.sub.v(1) (1≤1≤I.sub.MAX where I.sub.MAX is an upper-limit number of planes to be detected by the plane detection unit 31; definition of distance to plane model; see par 151 - The wave peak detection unit 34 takes into account the fact that a point cloud group at a high position is highly unlikely to represent reflection points 14 of the water surface 10, and extracts a wave peak having a height estimated to represent a wave. see par 205 - the pseudo water surface determination process S2 and the wave peak detection unit 34 detect the reflection point 14 on the water surface 10 or on the wave peak 16 from the point cloud data acquired from the three-dimensional LiDAR 2, by fitting the reflection point 14 to the wave peak model 19, and [0207] the pseudo water surface determination process S2 selects point cloud data located at the lowest position in the height direction; see par 185-188 - An obstacle detection device 3 [0186] receives information on observation point cloud data formed of a plurality of reflection points observed within a measurement range including a water surface 10, the information including position information of each reflection point, [0187] and is provided with [0188] a plane detection unit 31 to detect planes extending near a plurality of reflection points included in the observation point cloud data).
It would have been obvious to combine Qingqing and Hiromatsu for the same reasons as claim 1 and claim 2. Qinqqing discloses that waves from water surface can have impact on performance of different methods, based on reflective properties of still waters (See page 2, col. 1, 2nd paragraph) and discloses a system that includes “object detection and collision avoidance” (See page 510, FIG. 2). Hiromatsu improves upon Qingqing by detecting “waves” and obstacles using various points and data within a vicinity/range (See par 93-101, 185-188). One of ordinary would be motivated to further include detecting waves and obstacles using various points and data within a vicinity/range to efficiently improve upon the “object detection and collision avoidance” in Qingqing.
Claims 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Qingqing, et al., “Detecting Water Reflection Symmetries in Point Clouds for Camera Position Calibration in Unmanned Surface Vehicles,” 2019 19th International Symposium on Communications and Information Technologies, pages 507-512, in view of Hiromatsu (US 2024/0085553), as applied to claims 1-2, 4, and 8-9 above, and further in view of Chao (CN 109917414).
Concerning claim 3, Qingqing discloses that filters to point cloud used to reduce number of points and reduce computational needs and points can be filtered based on both absolute distance to the boat and vertical distance (See page 509, col. 2, section A) and that there can be a pre-defined interval of the height of the camera (See page 509, col. 2, section B, 3rd paragraph).
Hiromatsu discloses:
The information processing device according to claim 1, wherein the processor calculates an average and a variance of the values in the height direction of the water-surface reflection data (Hiromatsu – see par 121 - The height of the point cloud data is, in a specific example, an average of heights of all points belonging to the point cloud data corresponding to the plane, or a height of the tallest point or the lowest point, among the points belonging to the point cloud data), and calculates the average as the water-surface height when the variance … (Hiromatsu see par 144 - FIG. 8 is a diagram illustrating examples of the wave peak 16 to be detected by the wave peak detection unit 34, in which white circles represent points in a point cloud group. Note that (a) of FIG. 8 illustrates an example of a feature of a wave peak 16a which forms a linearly long wave, (b) of FIG. 8 illustrates an example of a feature of a wave peak 16b which forms a pyramidal wave generated by synthesis of waves coming from a plurality of directions, and (c) of FIG. 8 illustrates an example of a wave peak model 19 expressed by statistic such as an average and a variance. The wave peak model 19 may be formed by another method.)
It would have been obvious to combine Qingqing and Hiromatsu for the same reasons as claim 1 and claim 2.
Chao discloses:
The information processing device according to claim 1, wherein the processor calculates an average and a variance of the values in the height direction of the water-surface reflection data, and calculates the average as the water-surface height when the variance “is smaller than a predetermined value.” (Chao – see page 6, 1st paragraph - If the variance is greater than the threshold value, it is proved that the corresponding area there is ship, area of other non-ship through which is surface area. If the two differ by less than the threshold of variance, which demonstrates that there is no ship, then it selects the portion of relatively small variance calculating water surface height. See page 8, 4th paragraph - calculating the average value of the x coordinate and the z coordinate of each line namely obtaining the mean of the mean line and the horizontal direction vertical direction lines. in the vertical direction line for example, any one of the line as the reference line, searching in turn line satisfying the following condition is composed of vertical plane: (1) the line does not belong to any surface; (2) the x coordinate and the z coordinate of the line with the average value of the obtained the difference is within a certain range).
Qingqing, Hiromatsu, and Chao are analogous art as they are directed to analyzing point cloud data and Lidar on water (see Qingqing Abstract; See Hiromatsu Abstract; Chao Abstract, page 8, 1st paragraph). Qinqqing discloses that filters to point cloud used to reduce number of points and reduce computational needs and points can be filtered based on both absolute distance to the boat and vertical distance (See page 509, col. 2, section A) and that there can be a pre-defined interval of the height of the camera (See page 509, col. 2, section B, 3rd paragraph). Hiromatsu discloses calculating an average for height and a variance (par 121, 144). Chao improves upon Qingqing and Hiromatsu by using an average value of a z-coordinate (i.e. height) when differences in data gathered is within a certain range or a small variance (See page 6, or page 8). One of ordinary skill in the art would be motivated to further include having considering smaller differences in a range of height values for then just using an “average” value to efficiently improve upon the height camera estimation in Qingqing and the average and variance for height calculated in Hiromatsu.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the camera height estimation with respect to water surface (See page 509, Col. 2, section B) in Qingqing, the average and variance for height in Hiromatsu, and to further use average value for height when variance/range is small as disclosed in Chao, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claim 7, Qingqing, Hiromatsu, and Chao disclose:
The information processing device according to claim 4 wherein the processor changes a height range of the search range based on a variance of a value in a height direction of the water-surface reflection data.
(Qingqing – see page 510, FIG. 2 – Object Detection and Collision Avoidance; see page 510, col. 1, 1st paragraph - The value hth is chosen so that only reflections of objects near the water surface are taken into account when the ship is near the coastline, and bigger objects are accounted for otherwise. A KD-tree search algorithm is used to find the nearest point in the point cloud to the expected reflection. see page 512, col. 1, 2nd paragraph - In the situation where the ship is close to the river bank, we have used a height threshold hth = 0:5 m as reflections are more clear for objects close to the water level.
Hiromatsu see par 144- FIG. 8 is a diagram illustrating examples of the wave peak 16 to be detected by the wave peak detection unit 34, in which white circles represent points in a point cloud group. Note that (a) of FIG. 8 illustrates an example of a feature of a wave peak 16a which forms a linearly long wave, (b) of FIG. 8 illustrates an example of a feature of a wave peak 16b which forms a pyramidal wave generated by synthesis of waves coming from a plurality of directions, and (c) of FIG. 8 illustrates an example of a wave peak model 19 expressed by statistic such as an average and a variance. The wave peak model 19 may be formed by another method..
Chao – see page 6, 1st paragraph - If the variance is greater than the threshold value, it is proved that the corresponding area there is ship, area of other non-ship through which is surface area. If the two differ by less than the threshold of variance, which demonstrates that there is no ship, then it selects the portion of relatively small variance calculating water surface height. See page 8, 4th paragraph - calculating the average value of the x coordinate and the z coordinate of each line namely obtaining the mean of the mean line and the horizontal direction vertical direction lines. in the vertical direction line for example, any one of the line as the reference line, searching in turn line satisfying the following condition is composed of vertical plane: (1) the line does not belong to any surface; (2) the x coordinate and the z coordinate of the line with the average value of the obtained the difference is within a certain range.
It would have been obvious to combine Qingqing, Hiromatsu, and Chao for the same reasons as claim 1 and claim 3.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Qingqing, et al., “Detecting Water Reflection Symmetries in Point Clouds for Camera Position Calibration in Unmanned Surface Vehicles,” 2019 19th International Symposium on Communications and Information Technologies, pages 507-512, in view of Hiromatsu (US 2024/0085553), as applied to claims 1-2, 4, and 8-9 above, and further in view of Muhovic, et al., “Obstacle tracking for unmanned surface vessels using 3-D point cloud,” 2019, IEEE Journal of Oceanic Engineering, Vol. 45, No. 3, pages 786-798.
Concerning claim 5, Qingqing discloses that filters to point cloud used to reduce number of points and reduce computational needs and points can be filtered based on both absolute distance to the boat and vertical distance (See page 509, col. 2, section A) and that there can be a pre-defined interval of the height of the camera (See page 509, col. 2, section B, 3rd paragraph).
Hiromatsu discloses:
The information processing device according to claim 4, wherein the processor determines whether or not the point cloud data belonging to the search range forms a linear shape, and detects a linear point cloud data as a ship-wave … (Hiromatsu – see par 144 - FIG. 8 is a diagram illustrating examples of the wave peak 16 to be detected by the wave peak detection unit 34, in which white circles represent points in a point cloud group. Note that (a) of FIG. 8 illustrates an example of a feature of a wave peak 16a which forms a linearly long wave).
Muhovic discloses:
The information processing device according to claim 4, wherein the processor determines whether or not the point cloud data belonging to the search range forms a linear shape, and detects a linear point cloud data as a ship-wave “when the linear point cloud data exists for a predetermined time” (Muhovic – see page 792, col. 1, 1st paragraph - On many occasions, false stereo matches occur due to repeated vertical structures such as multiple boat masts in the marina, as shown in Fig. 11; To filter out these inconsistencies, temporal consistency verification must be included, to track only objects that are consistently appearing in multiple sequential frames. 2nd paragraph- To track the detections between sequential frames, we need to connect ones representing the same object as well as detect nonmatched objects and newly seen ones).
Qingqing, Hiromatsu, and Muhovic are analogous art as they are directed to analyzing point cloud data on water (see Qingqing Abstract; See Hiromatsu Abstract; Muhovic Abstract). Qingqing discloses that filters to point cloud used to reduce number of points and reduce computational needs and points can be filtered based on both absolute distance to the boat and vertical distance (See page 509, col. 2, section A) and that there can be a pre-defined interval of the height of the camera (See page 509, col. 2, section B, 3rd paragraph). Hiromatsu improves upon Qingqing by calculating a wave that is “linearly” long. Muhovic improves upon Qingqing and Hiromatsu by having object detection that looks for temporal consistency in sequential frames, to filter out inconsistencies. One of ordinary skill in the art would be motivated to further include further check for temporal consistency when analyzing the data to efficiently improve upon the filtering of data in Qingqing and the detection for linear shapes in Hiromatsu.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the camera height estimation with respect to water surface (See page 509, Col. 2, section B) in Qingqing, the average and variance for height and detection of waves with linear shapes in Hiromatsu, and to further check for temporal consistency when analyzing the data as disclosed in Muhovic, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claim 6, Qingqing discloses that it can realize point cloud acquisition, filtering and “segmentation” (See page 511, Col. 2).
Hiromatsu and Muhovic disclose:
The information processing device according to claim 4, wherein the processor determines whether or not the point cloud data belonging to the search range forms a cluster, and detects a point cloud data forming the cluster as an obstacle (Hiromatsu – see par 130- The grouping unit 33 executes a pre-process for extracting a wave peak. see par 131 - The grouping unit 33 generates point cloud data P.sub.b(m) (1≤m) formed of points included in P.sub.r,2, by connecting points that are at short distances from each other. see par 137 - The grouping unit 33 may divide the plane using one or more types of polygons, instead of dividing the plane using square sections 15).
Muhovic discloses:
The information processing device according to claim 4, wherein the processor determines whether or not the point cloud data belonging to the search range “forms a cluster,” and detects a point cloud data forming the cluster as an obstacle “when the point cloud data forming the cluster exists for a predetermined time” (Muhovic – see page 792, col. 1, 1st paragraph - On many occasions, false stereo matches occur due to repeated vertical structures such as multiple boat masts in the marina, as shown in Fig. 11; To filter out these inconsistencies, temporal consistency verification must be included, to track only objects that
are consistently appearing in multiple sequential frames. 2nd paragraph- To
track the detections between sequential frames, we need to connect
ones representing the same object as well as detect nonmatched
objects and newly seen ones. see page 793, Col. 1, Section D - The obstacle detection method sometimes fragments large obstacles (e.g., large ships, sailboats, and piers) into many 3-D obstacles. This can happen due to local sparsity of the point cloud caused by the lighting conditions on the water surface, or by repeatable structures in the images. When projected back into the 2-D image, these detections can overlap (because of the projection to 2-D image plane) and cause errors. We used unsupervised clustering to address such problems, specifically the affinity propagation algorithm [26]. The algorithm clusters a set of data points based on a distance metric. It detects representative elements called exemplars and clusters the data points based on the distance to the nearest exemplar. The number of exemplars is determined automatically. In our case, data points are detection bounding boxes and the distance metric is the negative squared distance between bounding box centers. After the exemplars are detected, the bounding box with the largest area in each cluster is kept).
It would have been obvious to combine Qingqing, Hiromatsu, and Muhovic for the same reasons as claim 1. In addition, Hiromatsu discloses “grouping” points together. Muhovic improves upon Qingqing and Hiromatsu by further clustering points together based on affinity for tracking objects/obstacles.
Conclusion
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619