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 .
This is in response to Applicant’s Amendments and Remarks filed on 8/18/2026. Claims 1-10 have been amended. Claim 11 has been canceled. Claims 1-10 are present for examination.
The 35 USC 112(f) interpretations of the claims have been withdrawn in view of the amendments.
Response to Arguments
Applicant's arguments filed 8/18/2026 with respect to the 35 USC 103 rejections have been fully considered but they are not persuasive.
Applicant submits: Liu does not teach, suggest, or render obvious at least, for example, the newly presented feature of “wherein the frequency data represents a histogram that indicates, for each of a plurality of positions in the depth direction, a number of points of the three-dimensional point cloud data,” as recited in amended independent claim 1. (See Remarks filed on 8/18/2026, p. 8, 1st para.)
The examiner respectfully disagrees. Liu, Translation, para. [0082], discloses dividing the three-dimensional space above the preset two-dimensional region OABC into multiple three-dimensional grids, and the 3D point cloud density in the 3D grid can be determined as the number of 3D points in each 3D grid, and a 3D point cloud density histogram can be created based on the 3D point cloud density in each 3D grid. On the other hand, amended claim 1 recites, wherein the frequency data represents a histogram that indicates, for each of a plurality of positions in the depth direction, a number of points of the three-dimensional cloud data. The three-dimensional space in Liu includes a depth direction. When the three-dimensional space is divided into 3D grids, for any position in a plurality of positions in the depth direction, there is a corresponding 3D grids includes that position. Because each 3D grid has a 3D point cloud density as the number of 3D points in each 3D grid, and a 3D point cloud density histogram can be created based on the 3D point cloud density in each 3D grid, the 3D point cloud density can correspond to the frequency data representing a histogram that indicates a number of points of the three-dimensional cloud data including each of a plurality of positions in the depth direction. Therefore, contrary to Applicant’s assertion, Liu does disclose wherein the frequency data represents a histogram that indicates, for each of a plurality of positions in the depth direction, a number of points of the three-dimensional cloud data.
Therefore, Applicant’s arguments are not persuasive.
Note by the definition of the frequency data in the amended claim 1, a frequency of a point in at least a depth direction comprised in the frequency data can in interpreted as a density and/or a number of points in the corresponding 3D grid containing the point in at least the depth direction. Because the amendments further limits claim 1 with a definition of frequency data taught by Liu, amended claim 1 has been rejected under 35 USC 102(a)(1) instead of 35 USC 103. See the following corresponding sections for details.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 3, and 7-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites, inter alia, wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction of the depth direction, and the frequencies of the point includes the frequency of the point. (Emphasis added.) Because Claim 2 recites “frequencies of the point in two directions” and “the frequency of the point”. It is not clear which of the frequency “the frequency of the point” refers to. Also it is inherent that “frequencies of the point in two directions which are the depth direction and an orthogonal direction of the depth direction” will include the frequency of the point in the depth direction and the frequency of the point in the orthogonal direction of the depth direction. If the frequency of the point refers to either of these two frequencies, the limitation “the frequencies of the point includes the frequency of the point” seems to be redundant. If the frequency of the point refers to another frequency in another direction, then this limitation is indefinite because there is no mentioning of another frequency in another direction in the claim.
Therefore, claim 2 is rejected as indefinite.
Claim 3 recites, inter alia, wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and a lateral direction, and the frequencies of the point includes the frequency of the point. (Emphasis added.) Because Claim 3 recites “frequencies of the point in two directions” and “the frequency of the point”. It is not clear which of the frequency “the frequency of the point” refers to. Also it is inherent that “frequencies of the point in two directions which are the depth direction and a lateral direction” will include the frequency of the point in the depth direction and the frequency of the point in the lateral direction. If the frequency of the point refers to either of these two frequencies, the limitation “the frequencies of the point includes the frequency of the point” seems to be redundant. If the frequency of the point refers to another frequency in another direction, then this limitation is indefinite because there is no mentioning of another frequency in another direction in the claim.
Therefore, claim 3 is rejected as indefinite.
Claim 7 recites, inter alia, “wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction, the frequencies of the point includes the frequency of the point.”
Because Claim 7 recites “frequencies of the point in two directions” and “the frequency of the point”. It is not clear which of the frequency “the frequency of the point” refers to. Also it is inherent that “frequencies of the point in two directions which are the depth direction and an orthogonal direction” will include the frequency of the point in the depth direction and the frequency of the point in the orthogonal direction. If the frequency of the point refers to either of these two frequencies, the limitation “the frequencies of the point includes the frequency of the point” seems to be redundant. If the frequency of the point refers to another frequency in another direction, then this limitation is indefinite because there is no mentioning of another frequency in another direction in the claim.
Therefore, claim 7 is rejected as indefinite.
Claim 8 recites, inter alia, wherein the CPU is configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction of the depth direction, wherein the frequencies of the point includes the frequency of the point.
This is similar to the limitations discussed above with respect to claim 2. Therefore, claim 8 is similarly rejected as indefinite.
Claim 9 recites, inter alia, wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction of the depth direction, wherein the frequencies of the point includes the frequency of the point
This is similar to the limitations discussed above with respect to claim 2. Therefore, claim 9 is similarly rejected as indefinite.
For examination purposes, the frequency of the point has been interpreted as the frequency of the point in the depth direction.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 5, 6, and 10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chinese Patent Publication No. CN 111742242A to Liu et al..
Regarding claim 1, Liu discloses A signal processing device comprising (Liu, Translation, para. [0053], disclosing a point cloud processing method applied to a mobile platform, which is equipped with a detection device):
a central processing unit (CPU) (Liu, Translation, para. [0013], disclosing a point cloud processing system comprising a detection device, a memory, and a processor) configured to:
generate, based on three-dimensional point cloud data acquired through three-dimensional measurement of a target space, frequency data comprises a frequency of a point in at least a depth direction, wherein the frequency data represents a histogram that indicates, for each of a plurality of positions in the depth direction, a number of points of the three-dimensional point cloud data (Liu, Translation, para. [0054], disclosing the detection equipment detects the objects around the vehicle in real time and obtains a three-dimensional point cloud, para. [0061], disclosing detecting three-dimensional point cloud including ground point cloud, ground obstacle point cloud, and aerial obstacle point cloud representing or approximately representing the areas of the ground, ground obstacles, and aerial obstacles in actual space, respectively, para. [0082], disclosing dividing the three-dimensional space above the preset two-dimensional region OABC into multiple three-dimensional grids, and the 3D point cloud density in the 3D grid can be determined as the number of 3D points in each 3D grid, and a 3D point cloud density histogram can be created based on the 3D point cloud density in each 3D grid, indicating the 3D point cloud can correspond to three-dimensional point cloud data acquired through three-dimensional measurement of a target space, the 3D point cloud density histogram and/or the 3D point cloud density can correspond to the frequency data representing a histogram that indicates a number of points of the three-dimensional point cloud data for each of a plurality of positions in the depth direction because for any position in the depth direction, there is a corresponding 3D grid, which is obtained by dividing the three-dimensional space (as the target space), containing that position and having point cloud density indicating the number of 3D points in the 3D grid, and the point cloud density indicates a frequency of a point in at least a depth direction (note by the definition of the frequency data in the claim, a frequency of a point in at least a depth direction comprised in the frequency data can in interpreted as a density and/or a number of points in the corresponding 3D grid containing the point in at least the depth direction)); and
execute, based on the generated frequency data, data reduction processing for the three-dimensional point cloud data (Liu, Translation, para. [0083], disclosing removing the noise information space in the three-dimensional point cloud according to the three-dimensional point cloud density in each of the plurality of three-dimensional grids).
Regarding claim 5, Liu discloses the signal processing device according to claim 1, wherein the CPU is further configured to:
acquire a plurality of pieces of three-dimensional point cloud data through the three-dimensional measurement of the target space from different viewpoints (Liu, Translation, para. [0054], disclosing performing a 360-degree rotation scan, obtaining a large number of laser points and forming the laser point cloud data of the object, which is a three-dimensional point cloud, para. [0058], disclosing the three-dimensional point cloud can be obtained by superimposing multiple frames of three-dimensional point clouds detected by the detection equipment, para. [0082], disclosing dividing the three-dimensional space above the preset two-dimensional region OABC into multiple three-dimensional grids, and the 3D point cloud density in the 3D grid can be determined as the number of 3D points in each 3D grid, and a 3D point cloud density histogram can be created based on the 3D point cloud density in each 3D grid, indicating the 360-degree rotation scan can correspond to acquiring a plurality of pieces of three-dimensional point cloud data through the three-dimensional measurement of the target space from different viewpoints (360-degree viewpoints);
combine the plurality of pieces of three-dimensional point cloud data to obtain the three-dimensional point cloud data (Liu, Translation, para. [0054], disclosing performing a 360-degree rotation scan, obtaining a large number of laser points and forming the laser point cloud data of the object, which is a three-dimensional point cloud, para. [0058], disclosing the three-dimensional point cloud can be obtained by superimposing multiple frames of three-dimensional point clouds detected by the detection equipment, para. [0082], disclosing dividing the three-dimensional space above the preset two-dimensional region OABC into multiple three-dimensional grids, and the 3D point cloud density in the 3D grid can be determined as the number of 3D points in each 3D grid, and a 3D point cloud density histogram can be created based on the 3D point cloud density in each 3D grid, indicating the 360-degree rotation scan and/or superimposing multiple frames of three-dimensional point clouds can correspond to combining the plurality of pieces of three-dimensional point cloud data to obtain the three-dimensional point cloud data); and
generate the frequency data based on the three-dimensional point cloud data obtained after the combination of the plurality of pieces of three-dimensional point cloud data (Liu, Translation, para. [0054], disclosing performing a 360-degree rotation scan, obtaining a large number of laser points and forming the laser point cloud data of the object, which is a three-dimensional point cloud, para. [0058], disclosing the three-dimensional point cloud can be obtained by superimposing multiple frames of three-dimensional point clouds detected by the detection equipment, para. [0082], disclosing dividing the three-dimensional space above the preset two-dimensional region OABC into multiple three-dimensional grids, and the 3D point cloud density in the 3D grid can be determined as the number of 3D points in each 3D grid, and a 3D point cloud density histogram can be created based on the 3D point cloud density in each 3D grid, indicating the 360-degree rotation scan and/or and superimposing multiple frames of three-dimensional point clouds can correspond to combining a plurality of pieces of three-dimensional point cloud data to obtain the three-dimensional point cloud data, and the 3D point cloud density in each 3D grid can correspond to the frequency data indicating a frequency (number of 3D points), which is obtained after the 3D point cloud data is obtained by the 360-degree scan and/or superimposing corresponding to the combination).
Regarding claim 6, Liu discloses the signal processing device according to claim 1, wherein the CPU is further configured to extract and output specific point cloud data included in the three-dimensional point cloud data and the specific point cloud data has a frequency equal to or higher than a threshold value in the frequency data (Liu, para. [0076], disclosing obtaining a denoised 3D point cloud by acquiring the 3D point cloud and removing noise points from it, para. [0090], disclosing whether the three-dimensional point cloud in three-dimensional grids is a noisy point cloud based on the point cloud density and density thresholds (if the density is less than density thresholds, then it is noisy point cloud), para. [0093], disclosing removing the noisy point cloud, indicating obtaining the denoised 3D point cloud can correspond to extracting and outputting specific point cloud data included in the three-dimensional point cloud that having a density (corresponding to frequency) equal to or higher than a threshold value in the frequency data (because the noisy data with density below the threshold is removed)).
Regarding claim 10, it recites similar limitations of claim 1 but in a method form. The rationale of claim 1 rejection is applied to reject claim 10.
Claim(s) 2-4 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Chinese Patent Publication No. CN 112348893 A to Yan et al.
Regarding claim 2, Liu discloses the signal processing device according to claim 1. However, Liu does not expressly disclose wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction of the depth direction, and the frequencies of the point includes the frequency of the point.
On the other hand, Yan discloses wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction of the depth direction, and the frequencies of the point includes the frequency of the point (Yan, Translation, para. [0008], disclosing a preset target plane is located on a horizontal ground and a matching detection block is set in this preset target plane area, para. [0065], disclosing in step 4, determining whether the number of point clouds distributed within the detection block is less than a positioning number threshold, indicating the number of point clouds distributed within the detection block can correspond to the data indicating frequencies of the point in the detection block comprising two directions which are the depth direction and the X direction as an orthogonal direction of the depth direction because the detection block is located in the preset target plane area which is located on a horizontal ground (horizontal plane includes X direction and Z (depth) direction and X is orthogonal to Z. Note in the current application, Specification, para. [0020], discloses the X axis is orthogonal to the Z axis and also a lateral direction. Therefore, the Z direction and X direction of the detection block disclosed in Yan are considered as the depth direction and an orthogonal direction of the depth direction, and the frequencies of the point in these two directions includes the frequency of the point in the depth direction).
Before the invention was effectively filed, it would have been obvious for a person skilled in the art to combine Liu and Yan. The suggestion/motivation would have been to reduce noise interference and improve accuracy of identifying obstacles that are too close, as suggested by Yan (see Yan, Translation, para. [0027]).
Regarding claim 3, Liu discloses the signal processing device according to claim 1. However, Liu does not expressly disclose wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and a lateral direction, and the frequencies of the point includes the frequency of the point.
On the other hand, Yan discloses wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and a lateral direction, and the frequencies of the point includes the frequency of the point (Yan, Translation, para. [0008], disclosing a preset target plane is located on a horizontal ground and a matching detection block is set in this preset target plane area, para. [0065], disclosing in step 4, determining whether the number of point clouds distributed within the detection block is less than a positioning number threshold, indicating the number of point clouds distributed within the detection block can correspond to the data indicating frequencies of the point in the detection block comprising two directions which are the depth direction and the X direction as a lateral direction of the depth direction because the detection block is located in the preset target plane area which is located on a horizontal ground (horizontal plane includes X direction and Z (depth) direction and X is a lateral direction to Z. Note in the current application, Specification, para. [0020], discloses the X axis is orthogonal to the Z axis and also a lateral direction. Therefore, the Z direction and X direction of the detection block disclosed in Yan are considered as the depth direction and lateral direction, and the frequencies of the point in these two directions includes the frequency of the point in the depth direction).
Before the invention was effectively filed, it would have been obvious for a person skilled in the art to combine Liu and Yan. The suggestion/motivation would have been to reduce noise interference and improve accuracy of identifying obstacles that are too close, as suggested by Yan (see Yan, Translation, para. [0027]).
Regarding claim 4, Liu discloses the signal processing device according to claim 1. However, Liu does not expressly disclose wherein the CPU is further configured to execute coordinate transformation for the three-dimensional point cloud data, generate the frequency data based on the coordinate transformation for the three-dimensional point cloud data.
On the other hand, Yan discloses wherein the CPU is further configured to execute coordinate transformation for the three-dimensional point cloud data (Yan, Translation, para. [0058], disclosing in step 2, performing pose transformation to control the feature points of the depth image to become a point cloud in the camera coordinate system, therefore, the feature points collected from various directions are transformed into the same local point cloud coordinate system, para. [0059], disclosing in step 3 setting preset target plane area and matching detection block, para. [0061], disclosing setting the origin of the 3D coordinate system to the position of the 3D-TOF camera, setting the horizontal axis and vertical axis, as well as the detection block, and using histogram statistics to transform the point cloud of the local map into a three-dimensional point map on a specific detection area in the coordinate system of the local map, indicating the feature points have been coordinate transformed to obtain the three-dimensional point cloud data and the 3D coordinate system has been transformed to match the 3D-TOF camera), generate the frequency data based on the coordinate transformation for the three-dimensional point cloud data (Yan, Translation, para. [0059], disclosing in step 3 setting preset target plane area and matching detection block, para. [0065], disclosing in step 4, determining whether the number of point clouds distributed within the detection block is less than a positioning number threshold, indicating determining the number of point clouds distributed within the detection block in step 4 can correspond to generating the frequency data based on the coordinate transformation for the point cloud data performed in steps 2 and 3).
Before the invention was effectively filed, it would have been obvious for a person skilled in the art to combine Liu and Yan. The suggestion/motivation would have been to help to reconstruct a more accurate local point cloud map, facilitating real-time positioning and navigation of mobile robots, as suggested by Yan (see Yan, para. [0058]).
Regarding claim 7, Liu discloses the signal processing device according to claim 6, wherein the specific point cloud data has a frequency equal to or higher than the threshold value in the frequency data (Liu, para. [0076], disclosing obtaining a denoised 3D point cloud by acquiring the 3D point cloud and removing noise points from it, para. [0090], disclosing whether the three-dimensional point cloud in three-dimensional grids is a noisy point cloud based on the point cloud density and density thresholds (if the density is less than density thresholds, then it is noisy point cloud), para. [0093], disclosing removing the noisy point cloud, indicating the denoised 3D point cloud can correspond to the specific point cloud data has a density/frequency equal to or higher than a threshold value in the frequency data (because the noisy data with density below the threshold is removed)).
However, Liu does not expressly disclose wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction, the frequencies of the point includes the frequency of the point.
On the other hand, Yan discloses wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction, the frequencies of the point includes the frequency of the point (Yan, Translation, para. [0008], disclosing a preset target plane is located on a horizontal ground and a matching detection block is set in this preset target plane area, para. [0065], disclosing in step 4, determining whether the number of point clouds distributed within the detection block is less than a positioning number threshold, indicating the number of point clouds distributed within the detection block can correspond to the data indicating frequencies of the point in the detection block comprising two directions which are the depth direction and the X direction as an orthogonal direction of the depth direction because the detection block is located in the preset target plane area which is located on a horizontal ground (horizontal plane includes X direction and Z (depth) direction and X is orthogonal to Z. Note in the current application, Specification, para. [0020], discloses the X axis is orthogonal to the Z axis and also a lateral direction. Therefore, the Z direction and X direction of the detection block disclosed in Yan are considered as the depth direction and an orthogonal direction, and the frequencies of the point in these two directions includes the frequency of the point in the depth direction).
Before the invention was effectively filed, it would have been obvious for a person skilled in the art to combine Liu and Yan. The suggestion/motivation would have been to reduce noise interference and improve accuracy of identifying obstacles that are too close, as suggested by Yan (see Yan, Translation, para. [0027]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Yan and US Patent Publication No. 20130127851 A1 to Richards.
Regarding claim 8, Liu discloses the signal processing device according to claim 1, wherein the CPU is further configured to extract specific point cloud data included in the three-dimensional point cloud data, wherein the specific point cloud data has a frequency equal to or higher than a threshold value in the frequency data (Liu, para. [0076], disclosing obtaining a denoised 3D point cloud by acquiring the 3D point cloud and removing noise points from it, para. [0090], disclosing whether the three-dimensional point cloud in three-dimensional grids is a noisy point cloud based on the point cloud density and density thresholds (if the density is less than density thresholds, then it is noisy point cloud), para. [0093], disclosing removing the noisy point cloud, indicating obtaining the denoised 3D point cloud can correspond to extracting specific point cloud data included in the three-dimensional point cloud that has a density/frequency equal to or higher than a threshold value in the frequency data (because the noisy data with density below the threshold is removed)).
However, Liu does not expressly disclose wherein the CPU is configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction of the depth direction, wherein the frequencies of the point includes the frequency of the point; and output two-dimensional data obtained by projection of the extracted specific point cloud data on a two-dimensional plane, wherein the two dimensional plane is defined by an axis in the depth direction and an axis in the orthogonal direction.
On the other hand, Yan discloses wherein the CPU is configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction of the depth direction, wherein the frequencies of the point includes the frequency of the point (Yan, Translation, para. [0008], disclosing a preset target plane is located on a horizontal ground and a matching detection block is set in this preset target plane area, para. [0065], disclosing in step 4, determining whether the number of point clouds distributed within the detection block is less than a positioning number threshold, indicating the number of point clouds distributed within the detection block can correspond to the data indicating frequencies of the point in the detection block comprising two directions which are the depth direction and the X direction as an orthogonal direction of the depth direction because the detection block is located in the preset target plane area which is located on a horizontal ground (horizontal plane includes X direction and Z (depth) direction and X is orthogonal to Z. Note in the current application, Specification, para. [0020], discloses the X axis is orthogonal to the Z axis and also a lateral direction. Therefore, the Z direction and X direction of the detection block disclosed in Yan are considered as the depth direction and an orthogonal direction of the depth direction, and the frequencies of the point in these two directions includes the frequency of the point in the depth direction).
Before the invention was effectively filed, it would have been obvious for a person skilled in the art to combine Liu and Yan. The suggestion/motivation would have been to reduce noise interference and improve accuracy of identifying obstacles that are too close, as suggested by Yan (see Yan, Translation, para. [0027]).
However, Liu or Yan does not expressly disclose output two-dimensional data obtained by projection of the extracted specific point cloud data on a two-dimensional plane, wherein the two dimensional plane is defined by an axis in the depth direction and an axis in the orthogonal direction.
On the other hand, Richards discloses output two-dimensional data obtained by projection of the extracted specific point cloud data on a two-dimensional plane, wherein the two dimensional plane is defined by an axis in the depth direction and an axis in the orthogonal direction (Richards, para. [0036], discloses projecting the point cloud into the x-z or y-z plane, indicating the projecting point cloud can correspond to outputting two-dimensional data obtained by projection of the point cloud data as the extracted specific point cloud data on a two-dimensional plane defined by an axis in the depth direction (Z) and an axis in the orthogonal direction (X or Y)).
Before the invention was effectively filed, it would have been obvious for a person skilled in the art to combine Liu in view of Yan with Richards. The suggestion/motivation would have been to identify specific buildings, track changes within a landscape, as suggested by Richards (see Richards, para. [0034]).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Yan and US Patent Publication No. 20210019937 A1 to Gallaway et al.
Regarding claim 9, Liu discloses the signal processing device according to claim 1, wherein the CPU is further configured to extract specific point cloud data included in the three-dimensional point cloud data, wherein the specific point cloud data has a frequency equal to or higher than a threshold value in the frequency data (Liu, para. [0076], disclosing obtaining a denoised 3D point cloud by acquiring the 3D point cloud and removing noise points from it, para. [0090], disclosing whether the three-dimensional point cloud in three-dimensional grids is a noisy point cloud based on the point cloud density and density thresholds (if the density is less than density thresholds, then it is noisy point cloud), para. [0093], disclosing removing the noisy point cloud, indicating obtaining the denoised 3D point cloud can correspond to extracting specific point cloud data included in the three-dimensional point cloud that has a density/frequency equal to or higher than a threshold value in the frequency data (because the noisy data with density below the threshold is removed)).
However, Liu does not expressly disclose wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction of the depth direction, wherein the frequencies of the point includes the frequency of the point; and extract and output only coordinate information in the depth direction of the extracted specific point cloud data.
On the other hand, Yan discloses wherein the CPU is further configured to generate, as the frequency data, data indicating frequencies of the point in two directions which are the depth direction and an orthogonal direction of the depth direction, wherein the frequencies of the point includes the frequency of the point (Yan, Translation, para. [0008], disclosing a preset target plane is located on a horizontal ground and a matching detection block is set in this preset target plane area, para. [0065], disclosing in step 4, determining whether the number of point clouds distributed within the detection block is less than a positioning number threshold, indicating the number of point clouds distributed within the detection block can correspond to the data indicating frequencies of the point in the detection block comprising two directions which are the depth direction and the X direction as an orthogonal direction of the depth direction because the detection block is located in the preset target plane area which is located on a horizontal ground (horizontal plane includes X direction and Z (depth) direction and X is orthogonal to Z. Note in the current application, Specification, para. [0020], discloses the X axis is orthogonal to the Z axis and also a lateral direction. Therefore, the Z direction and X direction of the detection block disclosed in Yan are considered as the depth direction and an orthogonal direction of the depth direction, and the frequencies of the point in these two directions includes the frequency of the point in the depth direction).
Before the invention was effectively filed, it would have been obvious for a person skilled in the art to combine Liu and Yan. The suggestion/motivation would have been to reduce noise interference and improve accuracy of identifying obstacles that are too close, as suggested by Yan (see Yan, Translation, para. [0027]).
However, Liu or Yan does not expressly disclose extract and output only coordinate information in the depth direction of the extracted specific point cloud data.
On the other hand, Gallaway discloses extract and output only coordinate information in the depth direction of the extracted specific point cloud data (Gallaway, para. [0092], disclosing determining mid-Z value for the point cloud and output to point cloud repository. Although Gallaway discloses the Z value as height, it would be obvious to extract and output depth value as Z value as design choice because the depth can be a relative term based on the viewpoint).
Before the invention was effectively filed, it would have been obvious for a person skilled in the art to combine Liu in view of Yan with Gallaway. The suggestion/motivation would have been to provide points clouds or models by other processes or directly exploited by a user, as suggested by Gallaway (see Gallaway, para. [0093]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Publication No. 20200364929 A1 to Li et al., which discloses segmenting a story and an inter-story connection region based on an elevation histogram of the point cloud in a vertical direction.
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/HAIXIA DU/Primary Examiner, Art Unit 2611
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