DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Response to Amendments
Applicant’s arguments see remarks, filed 04/24/2026, with respect to the claim objections, are persuasive due to claim amendments thus have been withdrawn.
Response to Arguments
Applicant’s arguments see remarks, filed 04/24/2026, with respect to claims 1-20, have been fully considered but are not considered persuasive.
Applicant argues on page 9-10 “Respectfully, the cited references do not disclose, suggest, or motivate the claimed mechanism of classifying a point based on a direct comparison between a first point density from a first image and one or more second point densities from second images taken prior to the first image. The rejection is premised on an improper interpretation of Park's teachings.
A careful reading of Park shows that its system distinguishes between static and dynamic objects based purely on the duration (continuity over time) that an object is detected within a spatial unit (voxel). Park determines whether the number of points in a voxel crosses a predefined "minimum detection threshold value" to confirm the mere presence or absence of an object. If an object is present continuously over a certain period, it is classified as static; if it appears and disappears quickly, it is classified as dynamic. Park does not measure, store, or compare the actual density value (number of points) of a first volume with the density value of a second volume over time. In Park, the threshold is static, and the decision relies on temporal continuity, not a comparison of varying point densities between different time frames.”
In response the office does not find this argument persuasive based on the breadth of the claim language the art Park et al. (US 20240192342 A1) explicitly teaches configured to: determine a first point density of a first volume around a first point in a first image (Fig. 6, Paragraph [0061]- Park discloses the processor 120 may determine the installation abnormality of the sensing device 100, by comparing a ratio of an area of a first static point cloud to a static object area with a ratio of an area of a second static point cloud to a static object area. When the time point at which a static object area is determined is set to be a first time point, as illustrated in FIG. 6, while a first static point cloud at a first time point is regularly detected within a static object area, a second static point cloud at a second time point is detected only in a part of the static object area.),
the image comprising image data corresponding to three dimensions (Fig. 2, Paragraph [0024]- Park discloses the sensing device 100 may include a light detection and ranging (LiDAR) sensor as a 3D sensor for sending a three-dimensional space and may obtain volumetric point cloud data.);
determine one or more second point densities (Fig. 2, Paragraph [0062]- Park discloses the processor 120 may determine the installation abnormality of the sensing device 100 in a voxel map by comparing a ratio of the number of voxels forming a first static point cloud at a first time point to the number of all voxels forming the determined static object area with a ratio of the number of voxels forming a second static point cloud to the number of all voxels forming the determined static object area at a second time point.),
each second point density of the one or more second point densities being a point density of a second volume around a second point (Fig. 2, Paragraph [0062]- Park discloses the processor 120 may determine the installation abnormality of the sensing device 100 in a voxel map by comparing a ratio of the number of voxels forming a first static point cloud at a first time point to the number of all voxels forming the determined static object area with a ratio of the number of voxels forming a second static point cloud to the number of all voxels forming the determined static object area at a second time point.);
wherein the one or more second images of the environment are one or more images taken prior to the first image and include image data corresponding to the three dimensions (Fig. 2, Paragraph [0059]- Park discloses the processor 120 may extract a first static point cloud at a first time point and a second static point cloud at a second time point having a certain time difference from first time point, from among the static point clouds over time corresponding to the determined static object area.);
and classify the first point as dynamic or static based on a comparison of the first point density and the one or more second point densities (Fig. 2, Paragraph [0046]- Park discloses the processor 120 may determine a static object area based on a period during which the point clouds in unit areas at corresponding positions between frames of a space information map is continuously detected to be the number of points greater than or equal to a minimum detection threshold value.).
Applicant argues on page 10 “Furthermore, Park's reliance on a fixed "minimum detection threshold" within a static 3D voxel grid makes its system inherently sensitive and vulnerable to the distance between the sensor and the object. As a person of ordinary skill in the art would recognize, the number of point clouds generated by a LiDAR sensor decreases as the distance to the object increases. By relying merely on an absolute point-count threshold, Park's system is susceptible to misclassification based on varying distances. In contrast, the claimed invention solves this technical problem by utilizing a relative comparison of point densities over time.”
In response, the office does not find this argument to be persuasive based on the same reasons set forth above and the rejection below.
The office respectfully brings to applicant’s attention in response to applicant’s argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “sensitive and vulnerable to the distance between the sensor and the object.”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The office respectfully requests the applicant to further amend claims in light of the specification dated 12/01/2023 to overcome current grounds of rejection and prior arts of record.
Applicant argues on page 10 “Because the claimed invention relies on analyzing the change in density rather than checking if an absolute point count crosses a static threshold, the classification mechanism is highly robust and independent of both the object's distance from the robot and its specific geometric shape. This distance-independent and shape-independent density comparison is entirely absent from Park.”
In response, the office does not find this argument to be persuasive based on the same reasons set forth above and the rejection below.
The office respectfully brings to applicant’s attention in response to applicant’s argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “This distance-independent and shape-independent density comparison is entirely absent from Park.”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The office respectfully requests the applicant to further amend claims in light of the specification dated 12/01/2023 to overcome current grounds of rejection and prior arts of record.
Applicant argues on page 10 “With respect to Ishikawa, this reference is directed to a different technical approach entirely. Ishikawa classifies objects using semantic segmentation and clustering, and determines movement by estimating a movement vector via tracking algorithms such as a Kalman filter or a particle filter. Ishikawa provides no teachings regarding determining point densities of volumes, let alone comparing a current point density with previous point densities to classify a point as static or dynamic.”
In response, the office does not find this argument to be persuasive based on the same reasons set forth above and the rejection below.
Applicant argues on page 11 “A person of ordinary skill in the art would not have reasonably expected to modify Park's time-duration based mechanism with Ishikawa's semantic tracking to arrive at the claimed invention. Implementing the claimed density-comparison mechanism would require completely abandoning Park's core tracking algorithm (continuity of threshold-crossing) in favor of analyzing relative differences in point densities across image sets. The cited art fails to teach or suggest this capability, and the proposed combination relies on impermissible hindsight reconstruction using Applicant's own specification.” In response, the office does not find this argument to be persuasive based on the same reasons set forth above and the rejection below.
Applicant argues on page 11 “Because neither Park nor Ishikawa, alone or in combination, teaches or suggests classifying a point as dynamic or static based on a comparison of a first point density and one or more second point densities, the § 103 rejection of independent claim 1 is improper and should be withdrawn.”
In response, the office does not find this argument to be persuasive based on the same reasons set forth above and the rejection below.
Applicant argues on page 11 “Dependent claims 8-11 and 19-20 recite corresponding limitations and are likewise patentable over the cited art”
In response, the office does not find this argument to be persuasive based on the same reasons set forth above and the rejection below.
Applicant argues on page 11 “Because these claims all depend from independent claim 1, they incorporate all the limitations of claim 1, including the classification based on a comparison of the first point density and the second point densities. As demonstrated above, the primary combination of Park and Ishikawa fails to teach or suggest this limitation. None of the additionally cited secondary references cure this fundamental deficiency in the primary combination. Therefore, the rejections of dependent claims 3-4, 6-7, and 12-18 should likewise be withdrawn. “
In response, the office does not find this argument to be persuasive based on the same reasons set forth above and the rejection below.
Applicant argues on page 11-12 “In view of the above, Applicant respectfully submits that independent claim 1 is novel and non-obvious over the cited art. Independent claims 8, 15, and 22 recite corresponding limitations, mutatis mutandis, and are likewise patentable. Dependent claims 2-14 and 16-21, by virtue of their dependency, are also patentable over the references of record.
In response, the office does not find this argument to be persuasive based on the same reasons set forth above and the rejection below.
The office respectfully brings to applicant’s attention in response that claims 8 and 15 do not appear as independent claims but rather depend on claims 1 and 14 respectfully, as well as no claim 22 or 21 appear in the original claim set filed 12/01/2023.
Due to claim amendments that introduce a 112b rejection, see below, the office will be maintaining the rejection dated 04/24/2026 as seen below. The office respectfully encourages the applicant to amend the application to correct the 112b as well as to overcome the prior arts of record to advance prosecution.
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 1-20, and associated dependent claims 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 pre-AIA the applicant regards as the invention.
The renumbering of the claims done in amendments submitted on 04/24/2026 have rendered all claims indefinite as it is unclear how the claim dependencies are supposed to be structured. For example, multiple claims depend on a non-existent claim 0, there are multiple claim 1’s, and claim 3 depends on itself.
The office respectfully requests the Applicant to amend and carefully review all claim dependencies in order to clarify the claimed invention claims and to clearly disclose the metes and bounds in keeping with the invention.
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 of this title, 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.
Claims 1, 8-11, and 19-20 are rejected under 35 U.S.C 103 as being unpatentable over Park et al. (US 20240192342 A1) hereafter referenced as Park in view of Ishikawa et al. (US 20220383749 A1) hereafter referenced as Ishikawa.
Regarding claim 1, Park explicitly teaches a device for detecting a dynamic object (Fig. 2, Paragraph [0036]- Park discloses when the sensing device 100 supports an object classification function by using an object classification model, the processor 120 may identify static objects such as the ground or buildings or dynamic objects such as animals, by applying the point cloud of a three-dimensional space to the object classification model or clustering the point cloud of a three-dimensional space.),
comprising: a processor (Fig. 2, Paragraph [0030]- Park discloses the sensing device 100 according to an embodiment may include a memory 110, a processor 120, a sensor unit 130, and a communication interface 140.),
configured to: determine a first point density of a first volume around a first point in a first image (Fig. 6, Paragraph [0061]- Park discloses the processor 120 may determine the installation abnormality of the sensing device 100, by comparing a ratio of an area of a first static point cloud to a static object area with a ratio of an area of a second static point cloud to a static object area. When the time point at which a static object area is determined is set to be a first time point, as illustrated in FIG. 6, while a first static point cloud at a first time point is regularly detected within a static object area, a second static point cloud at a second time point is detected only in a part of the static object area.),
the image comprising image data corresponding to three dimensions (Fig. 2, Paragraph [0024]- Park discloses the sensing device 100 may include a light detection and ranging (LiDAR) sensor as a 3D sensor for sending a three-dimensional space and may obtain volumetric point cloud data.);
determine one or more second point densities (Fig. 2, Paragraph [0062]- Park discloses the processor 120 may determine the installation abnormality of the sensing device 100 in a voxel map by comparing a ratio of the number of voxels forming a first static point cloud at a first time point to the number of all voxels forming the determined static object area with a ratio of the number of voxels forming a second static point cloud to the number of all voxels forming the determined static object area at a second time point.),
each second point density of the one or more second point densities being a point density of a second volume around a second point (Fig. 2, Paragraph [0062]- Park discloses the processor 120 may determine the installation abnormality of the sensing device 100 in a voxel map by comparing a ratio of the number of voxels forming a first static point cloud at a first time point to the number of all voxels forming the determined static object area with a ratio of the number of voxels forming a second static point cloud to the number of all voxels forming the determined static object area at a second time point.);
wherein the one or more second images of the environment are one or more images taken prior to the first image and include image data corresponding to the three dimensions (Fig. 2, Paragraph [0059]- Park discloses the processor 120 may extract a first static point cloud at a first time point and a second static point cloud at a second time point having a certain time difference from first time point, from among the static point clouds over time corresponding to the determined static object area.);
and classify the first point as dynamic or static based on a comparison of the first point density and the one or more second point densities (Fig. 2, Paragraph [0046]- Park discloses the processor 120 may determine a static object area based on a period during which the point clouds in unit areas at corresponding positions between frames of a space information map is continuously detected to be the number of points greater than or equal to a minimum detection threshold value.).
Park fails to explicitly teach the first image being an image of an environment of a robot.
However, Ishikawa explicitly teaches the first image being an image of an environment of a robot (Fig. 1, paragraph [0071]- Ishikawa discloses the recognition unit 73 performs recognition processing of an environment around the vehicle. Further in Paragraph [0251]- Ishikawa discloses the present technology can also be applied to a case where an occupancy grid map is created in a mobile device other than a vehicle, for example, a drone, a robot, or the like.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Ishikawa the first image being an image of an environment of a robot.
Wherein having Park’s system of abnormality sensing wherein the first image being an image of an environment of a robot.
The motivation behind the modification would have been to a better control and safer control system, since both Park and Ishikawa are both systems use point clouds to create a map of static and dynamic objects. Wherein Park’s system provides a way to increase accuracy, while Ishikawa’s system provides a way to improve safety. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Ishikawa et al. (US 20220383749 A1) Paragraph [0239].
Regarding claim 8, Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 1, Park further teaches wherein the first image of the environment and the one or more second images of the environment are configured as point cloud images (Fig. 2, Paragraph [0045]- Park discloses the processor 120 may determine a static object area based on a period during which a point could is continuously detected in a unit area at corresponding positions between frames of the space information map generated from the point clouds over time with respect to a three-dimensional space obtained by the sensor unit 130.).
Regarding claim 9, Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 8, Park fails to explicitly teach further comprising one or more image sensors configured to generate a plurality of images or depth images; wherein the processor is further configured to generate the point cloud images by resolving the plurality of images or depth images with a position of the one or more image sensors when the plurality of images or depth images are acquired.
However, Ishikawa explicitly teaches further comprising one or more image sensors configured to generate a plurality of images or depth images (Fig. 1, Paragraph [0153]- Ishikawa discloses specifically, the camera 51 captures an image of surroundings of the vehicle 1, and supplies obtained image data to the information processing unit 301.);
wherein the processor is further configured to generate the point cloud images by resolving the plurality of images or depth images with a position of the one or more image sensors when the plurality of images or depth images are acquired (Fig. 1, Paragraph [0245]- Ishikawa discloses a point cloud may be created by a radar, a depth camera (for example, a stereo camera or a ToF camera), or the like.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Ishikawa further comprising one or more image sensors configured to generate a plurality of images or depth images; wherein the processor is further configured to generate the point cloud images by resolving the plurality of images or depth images with a position of the one or more image sensors when the plurality of images or depth images are acquired.
Wherein having Park’s system of abnormality sensing wherein further comprising one or more image sensors configured to generate a plurality of images or depth images; wherein the processor is further configured to generate the point cloud images by resolving the plurality of images or depth images with a position of the one or more image sensors when the plurality of images or depth images are acquired.
The motivation behind the modification would have been to a better control and safer control system, since both Park and Ishikawa are both systems use point clouds to create a map of static and dynamic objects. Wherein Park’s system provides a way to increase accuracy, while Ishikawa’s system provides a way to improve safety. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Ishikawa et al. (US 20220383749 A1) Paragraph [0239].
Regarding claim 10, Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 9, Park fails to explicitly teach wherein the one or more image sensors comprise a stereo camera or a depth camera.
However, Ishikawa explicitly teaches wherein the one or more image sensors comprise a stereo camera or a depth camera (Fig. 1, Paragraph [0245]- Ishikawa discloses a point cloud may be created by a radar, a depth camera (for example, a stereo camera or a ToF camera), or the like.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Ishikawa wherein the one or more image sensors comprise a stereo camera or a depth camera.
Wherein having Park’s system of abnormality sensing wherein the one or more image sensors comprise a stereo camera or a depth camera.
The motivation behind the modification would have been to a better control and safer control system, since both Park and Ishikawa are both systems use point clouds to create a map of static and dynamic objects. Wherein Park’s system provides a way to increase accuracy, while Ishikawa’s system provides a way to improve safety. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Ishikawa et al. (US 20220383749 A1) Paragraph [0239].
Regarding claim 11, Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 9, Park further teaches wherein the one or more image comprise a LiDAR, Light Detection and Ranging, device (Fig. 1, Paragraph [0034]- Park discloses the sensor unit 130 may be a LiDAR sensor, and may include at least one three-dimensional LiDAR sensor to obtain data of a space in a certain range).
Regarding claim 19, Park in view of Ishikawa teaches the device for detecting a dynamic object of claims 1, Park fails to explicitly teach wherein the dynamic object detection device is configured as an autonomous robot.
However, Ishikawa explicitly teaches wherein the dynamic object detection device is configured as an autonomous robot (Fig. 1, Paragraph [0251]- Ishikawa discloses the present technology can also be applied to a case where an occupancy grid map is created in a mobile device other than a vehicle, for example, a drone, a robot, or the like.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Ishikawa wherein the one or more image sensors comprise a stereo camera or a depth camera.
Wherein having Park’s system of abnormality sensing wherein the dynamic object detection device is configured as an autonomous robot.
The motivation behind the modification would have been to a better control and safer control system, since both Park and Ishikawa are both systems use point clouds to create a map of static and dynamic objects. Wherein Park’s system provides a way to increase accuracy, while Ishikawa’s system provides a way to improve safety. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Ishikawa et al. (US 20220383749 A1) Paragraph [0239].
Regarding claim 20, Park teaches a method for detecting a dynamic object (Fig. 3-5, Paragraph [0039]- Park discloses a method of distinguishing a dynamic point cloud corresponding to a dynamic object from a static point cloud corresponding to a static object in space information map, and a method of determining a static object area in a space information map, are described below with reference to FIGS. 3 to 5.),
comprising: determining a first point density of a first volume around a first point in a first image (Fig. 6, Paragraph [0061]- Park discloses the processor 120 may determine the installation abnormality of the sensing device 100, by comparing a ratio of an area of a first static point cloud to a static object area with a ratio of an area of a second static point cloud to a static object area. When the time point at which a static object area is determined is set to be a first time point, as illustrated in FIG. 6, while a first static point cloud at a first time point is regularly detected within a static object area, a second static point cloud at a second time point is detected only in a part of the static object area.),
the image including image data corresponding to three dimensions (Fig. 2, Paragraph [0024]- Park discloses the sensing device 100 may include a light detection and ranging (LiDAR) sensor as a 3D sensor for sending a three-dimensional space and may obtain volumetric point cloud data.);
determining one or more second point densities (Fig. 2, Paragraph [0062]- Park discloses the processor 120 may determine the installation abnormality of the sensing device 100 in a voxel map by comparing a ratio of the number of voxels forming a first static point cloud at a first time point to the number of all voxels forming the determined static object area with a ratio of the number of voxels forming a second static point cloud to the number of all voxels forming the determined static object area at a second time point.),
each second point density of the one or more second point densities being a point density of a second volume about a second point (Fig. 2, Paragraph [0062]- Park discloses the processor 120 may determine the installation abnormality of the sensing device 100 in a voxel map by comparing a ratio of the number of voxels forming a first static point cloud at a first time point to the number of all voxels forming the determined static object area with a ratio of the number of voxels forming a second static point cloud to the number of all voxels forming the determined static object area at a second time point.);
the second point corresponding to the first point in each of one or more second images, the one or more second images of the environment being one or more images taken prior to the first image and including image data corresponding to the three dimensions (Fig. 2, Paragraph [0059]- Park discloses the processor 120 may extract a first static point cloud at a first time point and a second static point cloud at a second time point having a certain time difference from first time point, from among the static point clouds over time corresponding to the determined static object area.);
and classifying the first point as dynamic or static based on a comparison of the first point density and the one or more second point densities (Fig. 2, Paragraph [0046]- Park discloses the processor 120 may determine a static object area based on a period during which the point clouds in unit areas at corresponding positions between frames of a space information map is continuously detected to be the number of points greater than or equal to a minimum detection threshold value.).
Park fails to explicitly teach the first image being an image of an environment of a robot.
However, Ishikawa explicitly teaches the first image being an image of an environment of a robot (Fig. 1, paragraph [0071]- Ishikawa discloses the recognition unit 73 performs recognition processing of an environment around the vehicle. Further in Paragraph [0251]- Ishikawa discloses the present technology can also be applied to a case where an occupancy grid map is created in a mobile device other than a vehicle, for example, a drone, a robot, or the like.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park of a method for detecting a dynamic object, comprising: determining a first point density of a first volume around a first point in a first image, with the teachings of Ishikawa the first image being an image of an environment of a robot.
Wherein having Park’s system of abnormality sensing wherein the first image being an image of an environment of a robot.
The motivation behind the modification would have been to a better control and safer control system, since both Park and Ishikawa are both systems use point clouds to create a map of static and dynamic objects. Wherein Park’s system provides a way to increase accuracy, while Ishikawa’s system provides a way to improve safety. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Ishikawa et al. (US 20220383749 A1) Paragraph [0239].
Claims 3-4 are rejected under 35 U.S.C 103 as being unpatentable over Park et al. (US 20240192342 A1) hereafter referenced as Park in view of Ishikawa et al. (US 20220383749 A1) hereafter referenced as Ishikawa and Li et al. (US 20220366185 A1) hereafter referenced as Li.
Regarding claim 3, Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 1, Park in view of Ishikawa fails to explicitly teach wherein the processor is further configured to change the second point density based on a comparison of the depth information of the first point and the depth information of the second point.
However, Li explicitly teaches wherein the processor is further configured to change the second point density based on a comparison of the depth information of the first point and the depth information of the second point (Fig. 1a, Paragraph [0018]- Li discloses increasing the search radius increases the number of neighboring points and therefore helps to cluster points in lower density areas. Further in Paragraph [0028]- Li discloses as the distance of the point from the LiDAR sensor (range(p.sub.i)) increases, the search radius ε.sub.1st,i increases. Likewise, as the resolution of the LiDAR sensor (resol) increases, the search radius ε.sub.1st,i increases.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Li wherein the processor is further configured to change the second point density based on a comparison of the depth information of the first point and the depth information of the second point.
Wherein having Park’s system of abnormality sensing wherein the processor is further configured to change the second point density based on a comparison of the depth information of the first point and the depth information of the second point.
The motivation behind the modification would have been to have a more accurate system, since both Park and Li are both systems use point clouds. Wherein Park’s system provides a way to increase accuracy, while Li’s system provides a way to further increase accuracy. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Li et al. (US 20220366185 A1) Paragraph [0018].
Regarding claim 4, Park in view of Ishikawa and Li teaches the device for detecting a dynamic object of claim 3, Park in view of Ishikawa fails to explicitly teach wherein the processor is further configured to increase the second point density if the depth information corresponds to a greater distance to the first point than to a second point, or to decrease the second volume if the depth information corresponds to a smaller distance to the first point than to a second point.
However, Li explicitly teaches wherein the processor is further configured to increase the second point density if the depth information corresponds to a greater distance to the first point than to a second point (Fig. 1a, Paragraph [0018]- Li discloses increasing the search radius increases the number of neighboring points and therefore helps to cluster points in lower density areas. Further in Paragraph [0028]- Li discloses as the distance of the point from the LiDAR sensor (range(p.sub.i)) increases, the search radius ε.sub.1st,i increases. Likewise, as the resolution of the LiDAR sensor (resol) increases, the search radius ε.sub.1st,i increases.),
or to decrease the second volume if the depth information corresponds to a smaller distance to the first point than to a second point (Fig. 1a, Paragraph [0018]- Li discloses the minimum point threshold minPt is increased for points located close to the LiDAR sensor and decreased for points related further away from the LiDAR sensor).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Li wherein the processor is further configured to increase the second point density if the depth information corresponds to a greater distance to the first point than to a second point, or to decrease the second volume if the depth information corresponds to a smaller distance to the first point than to a second point.
Wherein having Park’s system of abnormality sensing wherein the processor is further configured to increase the second point density if the depth information corresponds to a greater distance to the first point than to a second point, or to decrease the second volume if the depth information corresponds to a smaller distance to the first point than to a second point.
The motivation behind the modification would have been to have a more accurate system, since both Park and Li are both systems use point clouds. Wherein Park’s system provides a way to increase accuracy, while Li’s system provides a way to further increase accuracy. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Li et al. (US 20220366185 A1) Paragraph [0018].
Claim 6 is rejected under 35 U.S.C 103 as being unpatentable over Park et al. (US 20240192342 A1) hereafter referenced as Park in view of Ishikawa et al. (US 20220383749 A1) hereafter referenced as Ishikawa and in view of Doria et al. (US 20200184234 A1) hereafter referenced as Doria.
Regarding claim 6, Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 1, Park in view of Ishikawa fails to explicitly teach wherein the first volume and the second volume are spherical.
However, Doria explicitly teaches wherein the first volume and the second volume are spherical (Fig. 3, Paragraph [0029]- Doria discloses the neighborhood may be defined by a spatial volume or area. In some examples, the spatial volume is spherical and set by a predetermined radius. Thus, the neighborhood includes the points within the predetermined radius to a starting point.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Doria wherein the first volume and the second volume are spherical.
Wherein having Park’s system of abnormality sensing wherein the first volume and the second volume are spherical.
The motivation behind the modification would have been to have a faster and more efficient machine, since both Park and Doria are both systems use point clouds to create to do object detection. Wherein Park’s system provides a way to increase accuracy, while Doria’s system provides a way to increase speed, efficiency, and accuracy. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Doria et al. (US 20200184234 A1) Paragraph [0020].
Claim 7 is rejected under 35 U.S.C 103 as being unpatentable over Park et al. (US 20240192342 A1) hereafter referenced as Park in view of Ishikawa et al. (US 20220383749 A1) hereafter referenced as Ishikawa and in view of Lee et al. (US 20230243970 A1) hereafter referenced as Lee.
Regarding claim 7, Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 1, Park further teaches wherein the processor is further configured to classify the first point as dynamic (Fig. 3, Paragraph [0042]- Park discloses the vehicles and pedestrians that move correspond to dynamic objects, and the positions of the dynamic point clouds corresponding to dynamic objects are changed in the space information map so that the dynamic point clouds are not continuously detected in the same area for more than a certain period.),
static based on the comparison of the first point density and the one or more second point densities (Fig. 2, Paragraph [0046]- Park discloses the processor 120 may determine a static object area based on a period during which the point clouds in unit areas at corresponding positions between frames of a space information map is continuously detected to be the number of points greater than or equal to a minimum detection threshold value.),
Park in view of Ishikawa fails to explicitly teach or unknown.
However, Lee explicitly teaches or unknown (Fig. 3, Paragraph [0059]- Lee discloses the type of object determined based on the calculated score may be determined to be any one of a static object, a dynamic object, or an unknown object.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Lee or unknown.
Wherein having Park’s system of abnormality sensing wherein or unknown.
The motivation behind the modification would have been to have a more accurate and easier to use system, since both Park and Lee are both systems use point clouds to determine static and dynamic objects Wherein Park’s system provides a way to increase accuracy, while Lee’s system provides a way to increase accuracy and make the system simpler. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Lee et al. (US 20230243970 A1) Paragraph [0120-121].
Claim 12 is rejected under 35 U.S.C 103 as being unpatentable over Park et al. (US 20240192342 A1) hereafter referenced as Park in view of Ishikawa et al. (US 20220383749 A1) hereafter referenced as Ishikawa and in view of Yu et al. (US 20230360406 A1) hereafter referenced as Yu.
Regarding claim 12, Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 9, Park fails to explicitly teach further the comprising one or more image sensors configured to generate a plurality of images.
However, Ishikawa explicitly teaches further the comprising one or more image sensors configured to generate a plurality of images (Fig. 1, Paragraph [0153]- Ishikawa discloses specifically, the camera 51 captures an image of surroundings of the vehicle 1, and supplies obtained image data to the information processing unit 301.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Ishikawa further comprising the one or more image sensors configured to generate a plurality of images.
Wherein having Park’s system of abnormality sensing wherein further the comprising one or more image sensors configured to generate a plurality of images.
The motivation behind the modification would have been to a better control and safer control system, since both Park and Ishikawa are both systems use point clouds to create a map of static and dynamic objects. Wherein Park’s system provides a way to increase accuracy, while Ishikawa’s system provides a way to improve safety. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Ishikawa et al. (US 20220383749 A1) Paragraph [0239].
Park in view of Ishikawa fails to explicitly teach wherein the processor is further configured to generate the point cloud images by resolving the plurality of images using one or more photogrammetry techniques.
However, Yu explicitly teaches wherein the processor is further configured to generate the point cloud images by resolving the plurality of images using one or more photogrammetry techniques (Fig. 1, Paragraph [0042]- Yu discloses photogrammetry methods, which are known in the art, are used to produce the point cloud from the captured image data.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image with the teachings of Yu wherein the processor is further configured to generate the point cloud images by resolving the plurality of images using one or more photogrammetry techniques.
Wherein having Park’s system of abnormality sensing wherein the processor is further configured to generate the point cloud images by resolving the plurality of images using one or more photogrammetry techniques.
The motivation behind the modification would have been to have a more accurate system, since both Park and Yu are both systems use point clouds. Wherein Park’s system provides a way to increase accuracy, while Yu’s system provides a further way to improve accuracy. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Yu et al. (US 20230360406 A1) Paragraph [0049].
Claim 13 is rejected under 35 U.S.C 103 as being unpatentable over Park et al. (US 20240192342 A1) hereafter referenced as Park in view of Ishikawa et al. (US 20220383749 A1) hereafter referenced as Ishikawa and in view of Zhang et al. (US 20150324658 A1) hereafter referenced as Zhang.
Regarding claim 13, Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 1, Although Park further teaches wherein classifying the first point as dynamic or static comprises classifying the first point as dynamic or static based on a comparison of the density of the first point and the density of the one or more second points within a data set (Fig. 2, Paragraph [0046]- Park discloses the processor 120 may determine a static object area based on a period during which the point clouds in unit areas at corresponding positions between frames of a space information map is continuously detected to be the number of points greater than or equal to a minimum detection threshold value.). Park in view of Ishikawa fails to explicitly teach wherein the processor is further configured to generate a modified data set by removing from the first image and the one or more second images data corresponding to a surface traversable by the dynamic object detection device; and wherein classifying the first point as dynamic or static comprises classifying the first point as dynamic or static based on a comparison of the density of the first point and the density of the one or more second points within the modified data set.
However, Zhang explicitly teaches wherein the processor is further configured to generate a modified data set by removing from the first image and the one or more second images data corresponding to a surface traversable by the dynamic object detection device (Fig. 14, Paragraph [0104]- Zhang discloses at 1410, a ground plane in the 3D point cloud information is determined and removed. Modified 3D information may be generated by removing the ground plane from the acquired or obtained 3D information.);
the modified data set (Fig. 13, Paragraph [0096]- Zhang discloses the 3D candidate objects (e.g., blobs) may be identified using the modified 3D information. For example, the 3D module 1314 may cluster (e.g., in an unsupervised manner) proximal points from the modified 3D information into object groups to identify 3D candidate objects.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Zhang wherein the processor is further configured to generate a modified data set by removing from the first image and the one or more second images data corresponding to a surface traversable by the dynamic object detection device and the modified data set.
Wherein having Park’s system of abnormality sensing wherein the processor is further configured to generate a modified data set by removing from the first image and the one or more second images data corresponding to a surface traversable by the dynamic object detection device and the modified data set.
The motivation behind the modification would have been to increase system reliability and reduce false positives, since both Park and Zhang are both systems that use lidar for object detection. Wherein Park’s system provides a way to increase accuracy, while Zhang’s system provides a way to improve reliability. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Zhang et al. (US 20150324658 A1) Paragraph [0108-109].
Claim 14 is rejected under 35 U.S.C 103 as being unpatentable over Park et al. (US 20240192342 A1) hereafter referenced as Park in view of Ishikawa et al. (US 20220383749 A1) hereafter referenced as Ishikawa and in view of Yasuda et al. (US 20240241523 A1) hereafter referenced as Yasuda.
Regarding claim 14, Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 1, Although Park explicitly teaches based on comparison of the first point density and the one or more second point densities. Park in view of Ishikawa fails to explicitly teach wherein the processor is further configured to generate a two-dimensional grid of the environment and to label portions of the two-dimensional grid as dynamic or static based on classification of the first point as dynamic or static based on comparison of the first point density and the one or more second point densities.
However, Yasuda explicitly teaches wherein the processor is further configured to generate a two-dimensional grid of the environment (Fig, 1, Paragraph [0041]- Yasuda discloses the determination unit 12 generates grid data that is obtained by dividing the map data into a plurality of sections (hereinafter also referred to as a plurality of cells), and determines or specifies whether or not there is an obstacle in each cell of the grid, or whether the presence/absence of an obstacle is unknown.)
and to label portions of the two-dimensional grid as dynamic or static based on classification of the first point as dynamic or static (Fig. 5, Paragraph [0132]- Yasuda discloses the label setting means sets a label indicating that there is a stationary obstacle, there is a moving obstacle, or there is no obstacle in each cell of the grid).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Yasuda wherein the processor is further configured to generate a two-dimensional grid of the environment and to label portions of the two-dimensional grid as dynamic or static based on classification of the first point as dynamic or static.
Wherein having Park’s system of abnormality sensing wherein the processor is further configured to generate a two-dimensional grid of the environment and to label portions of the two-dimensional grid as dynamic or static based on classification of the first point as dynamic or static.
The motivation behind the modification would have been to have a more efficient and accurate system, since both Park and Yasuda are both systems determine static and dynamic objects. Wherein Park’s system provides a way to increase accuracy, while Yasuda’s system provides a further way to improve accuracy and efficiency. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Yasuda et al. (US 20240241523 A1) Paragraph [0092].
Claim 15 is rejected under 35 U.S.C 103 as being unpatentable over Park et al. (US 20240192342 A1) hereafter referenced as Park in view of Ishikawa et al. (US 20220383749 A1) hereafter referenced as Ishikawa, in view of Yasuda et al. (US 20240241523 A1) hereafter referenced as Yasuda, in view of Kirstein et al. (US 20210295062 A1) hereafter referenced as Kirstein, in view of Sonoura et al. (US 20080201014 A1) hereafter referenced as Sonoura.
Regarding claim 15, Park in view of Ishikawa and Yasuda teaches the device for detecting a dynamic object of claim 14, Park in view of Ishikawa and Yasuda fails to explicitly teach wherein the processor is further configured to associate height information with at least one cell of the grid.
However, Kirstein explicitly teaches wherein the processor is further configured to associate height information with at least one cell of the grid (Fig, 1, Paragraph [0029]- Kirstein discloses in a step S4, height information is determined by means of the at least one first 2a and/or second environment detection sensor 2b. In step S5, said determined height information is added to each grid cell.),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa and Yasuda of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Kirstein wherein the processor is further configured to associate height information with at least one cell of the grid.
Wherein having Park’s system of abnormality sensing wherein the processor is further configured to associate height information with at least one cell of the grid.
The motivation behind the modification would have been to have an improved environment representation, since both Park and Kirstein are both systems that use lidar to detect in an area. Wherein Park’s system provides a way to increase accuracy, while Kirstein’s system provides a way to improve the representation of the environment. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Kirstein et al. (US 20210295062 A1) Paragraph [0003].
Park in view of Ishikawa, Yasuda, and Kirstein fails to explicitly teach when the height information is within a range, to calculate a first minimum distance to be maintained to an object in the cell, and when the height information is outside the range, to calculate a second minimum distance to be maintained to an object in the cell, wherein the first minimum distance is greater than the second minimum distance.
However, Sonoura explicitly teaches when the height information is within a range, to calculate a first minimum distance to be maintained to an object in the cell (Fig. 8, Paragraph [0047]- Sonoura discloses if the height h is greater than the threshold Dh, then the robot 1 proceeds to step S714 shown in FIG. 8 and judges that the extracted person is not a person of caution level. In this case, the robot 1 further proceeds to step S8 shown in FIG. 2, sets the level of the approach permission distance limitation (degree of caution) of the robot 1 to the person to Lv.),
and when the height information is outside the range, to calculate a second minimum distance to be maintained to an object in the cell (Fig. 12, Paragraph [0059]- Sonoura discloses this traveling velocity is based on a traveling restriction law having two-dimensional matrix condition stipulations in which the distance to the obstacle is increased or the maximum traveling velocity is decreased as the caution level becomes higher as shown in FIG. 12.),
wherein the first minimum distance is greater than the second minimum distance (Fig. 12, Paragraph [0059]- Sonoura discloses the robot 1 in the present embodiment compares a distance which can be ensured between the robot 1 and the obstacle with preset values (=0, L1, L2, L3 and L4) of the approach permission distance, and determines the traveling velocity of the robot 1 on the basis of a result of the comparison and preset levels of the approach permission distance limitation, i.e., levels of the degree of caution (=no limitations, Lv. 1, Lv. 2, Lv. 3 and Lv. 4). This traveling velocity is based on a traveling restriction law having two-dimensional matrix condition stipulations in which the distance to the obstacle is increased or the maximum traveling velocity is decreased as the caution level becomes higher as shown in FIG. 12.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa, Yasuda, and Kirstein of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Sonoura when the height information is within a range, to calculate a first minimum distance to be maintained to an object in the cell, and when the height information is outside the range, to calculate a second minimum distance to be maintained to an object in the cell, wherein the first minimum distance is greater than the second minimum distance.
Wherein having Park’s system of abnormality sensing wherein when the height information is within a range, to calculate a first minimum distance to be maintained to an object in the cell, and when the height information is outside the range, to calculate a second minimum distance to be maintained to an object in the cell, wherein the first minimum distance is greater than the second minimum distance.
The motivation behind the modification would have been to have a safer system to use around people, since both Park and Sonoura are both systems that detect static and dynamic objects. Wherein Park’s system provides a way to increase accuracy, while Sonoura’s system provides a way to improve safety of the system. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Sonoura et al. (US 20080201014 A1) Paragraph [0011 and 0059-61].
Claim 16 is rejected under 35 U.S.C 103 as being unpatentable over Park et al. (US 20240192342 A1) hereafter referenced as Park in view of Ishikawa et al. (US 20220383749 A1) hereafter referenced as Ishikawa and in view of Schafer et al. (US 20230222928 A1) hereafter referenced as Schafer.
Regarding claim 16, Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 1, Park in view of Ishikawa fails to explicitly teach wherein the processor is configured to operate in a first mode of operation when a distance between the robot and an object corresponding to a static point is within a predetermined range, and to operate in a second mode of operation when the distance between the robot and the object corresponding to a dynamic point is within the predetermined range.
However, Schafer explicitly teaches wherein the processor is configured to operate in a first mode of operation when a distance between the robot and an object corresponding to a static point is within a predetermined range (Fig. 9, Paragraph [0089]- Schafer discloses the example process of FIG. 9 also handles static and dynamic objects differently in that dynamic objects are handled with higher priority and may be continuously tracked to estimate their motion trajectory… In case the received ID corresponds to a non-recorded DID or SID, it gets recorded and respective proximity checks are performed.),
and to operate in a second mode of operation when the distance between the robot and the object corresponding to a dynamic point is within the predetermined range (Fig. 9, Paragraph [0089]- Schafer discloses the example process of FIG. 9 also handles static and dynamic objects differently in that dynamic objects are handled with higher priority and may be continuously tracked to estimate their motion trajectory…In case the received ID corresponds to a non-recorded DID or SID, it gets recorded and respective proximity checks are performed.). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Schafer wherein the processor is configured to operate in a first mode of operation when a distance between the robot and an object corresponding to a static point is within a predetermined range, and to operate in a second mode of operation when the distance between the robot and the object corresponding to a dynamic point is within the predetermined range.
Wherein having Park’s system of abnormality sensing wherein the processor is configured to operate in a first mode of operation when a distance between the robot and an object corresponding to a static point is within a predetermined range, and to operate in a second mode of operation when the distance between the robot and the object corresponding to a dynamic point is within the predetermined range.
The motivation behind the modification would have been to minimize risk of collisions while efficiently using the available space, since both Park and Schafer are both systems that detect static and dynamic objects. Wherein Park’s system provides a way to increase accuracy, while Schafer’s system provides a way to improve collision avoidance. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Schafer et al. (US 20230222928 A1) Paragraph [0002-6].
Claim 17 is rejected under 35 U.S.C 103 as being unpatentable over Park et al. (US 20240192342 A1) hereafter referenced as Park in view of Ishikawa et al. (US 20220383749 A1) hereafter referenced as Ishikawa, in view of Schafer et al. (US 20230222928 A1) hereafter referenced as Schafer, and in view of Abramson et al. (US 20200201328 A1) hereafter referenced as Abramson.
Regarding claim 17, Park in view of Ishikawa and Schafer teaches the device for detecting a dynamic object of claim 16, Park in view of Ishikawa and Schafer fails to explicitly teach wherein the first mode of operation comprises the processor not sending a command to decelerate or stop or perform an avoidance maneuver of the robot; and wherein the second mode of operation comprises the processor sending a command to decelerate or stop or perform an avoidance maneuver of the robot.
However, Abramson explicitly teaches wherein the first mode of operation comprises the processor not sending a command to decelerate or stop or perform an avoidance maneuver of the robot (Fig. 2, Paragraph [0151]- Abramson discloses the machine 20 is configured to recognize the animate beings 110 and 112 as animate beings and to conduct a behavior in response to the recognition. (wherein this shows it only sends the command to decelerate/stop based on animate beings similar to dynamic objects));
and wherein the second mode of operation comprises the processor sending a command to decelerate or stop or perform an avoidance maneuver of the robot (Fig. 2, Paragraph [0153]- Abramson discloses a human detection or animate being detection of more than 50% within the section 264B within the immediate pathway of the machine 20 can trigger the control system 60 to control the machine 20 to stop and wait, stop the working mechanism 42, and alert the user via a wirelessly transmitted message.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa and Schafer of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Abramson wherein the first mode of operation comprises the processor not sending a command to decelerate or stop or perform an avoidance maneuver of the robot; and wherein the second mode of operation comprises the processor sending a command to decelerate or stop or perform an avoidance maneuver of the robot.
Wherein having Park’s system of abnormality sensing wherein the first mode of operation comprises the processor not sending a command to decelerate or stop or perform an avoidance maneuver of the robot; and wherein the second mode of operation comprises the processor sending a command to decelerate or stop or perform an avoidance maneuver of the robot.
The motivation behind the modification would have been to improve navigation obstacle avoidance and efficiency, since both Park and Abramson are both systems that use lidar to detect objects. Wherein Park’s system provides a way to increase accuracy, while Schafer’s system provides improve navigation obstacle avoidance and efficiency. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Abramson et al. (US 20200201328 A1) Paragraph [0002].
Claim 18 is rejected under 35 U.S.C 103 as being unpatentable over Park et al. (US 20240192342 A1) hereafter referenced as Park in view of Ishikawa et al. (US 20220383749 A1) hereafter referenced as Ishikawa and in view of Wang et al. (US 20240095928 A1) hereafter referenced as Wang.
Regarding claim 18 Park in view of Ishikawa teaches the device for detecting a dynamic object of claim 1, Park in view of Ishikawa fail to explicitly teach wherein the processor is configured to compare the depth information for a point to an estimated depth information for the point and label the point as being occluded if a difference between the depth information and the estimated depth information is outside a range.
However, Wang explicitly teaches wherein the processor is configured to compare the depth information for a point to an estimated depth information for the point and label the point as being occluded if a difference between the depth information and the estimated depth information is outside a range (Fig. 2, Paragraph [0062]- Wang discloses By individually comparing the magnitude relationship between the difference in depth values of each pixel and multiple pixels directly adjacent to it in its surrounding neighborhood and the predetermined threshold, the occlusion relationship for each pair of adjacent pixels can be determined. If the difference in depth values of any pair of adjacent pixels is greater than the predetermined threshold, an occlusion relationship between that pair of adjacent pixels is determined; otherwise, there is no occlusion relationship.)
and to label the point as being visible if a difference between the depth information and the estimated depth information is within a range (Fig. 2, Paragraph [0073]- Wang discloses for any pixel 100 in the training image, its occlusion relationship with adjacent pixels, such as 101, can fall into three cases: pixel 100 occludes pixel 101 (represented as 1), pixel 100 is occluded by pixel 101 (represented as −1), and there is no occlusion between pixel 100 and pixel 101 (represented as 0).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Park in view of Ishikawa of a device for detecting a dynamic object, comprising: a processor, configured to: determine a first point density of a first volume around a first point in a first image, with the teachings of Wang wherein the processor is configured to compare the depth information for a point to an estimated depth information for the point and label the point as being occluded if a difference between the depth information and the estimated depth information is outside a range.
Wherein having Park’s system of abnormality sensing wherein the processor is configured to compare the depth information for a point to an estimated depth information for the point and label the point as being occluded if a difference between the depth information and the estimated depth information is outside a range.
The motivation behind the modification would have been to improve the accuracy and reliability of the system, since both Park and Wang are both systems that use lidar. Wherein Park’s system provides a way to increase accuracy, while Wang’s system provides enhanced accuracy reliability and robustness of the system. Please see Park et al. (US 20240192342 A1) Paragraph [0027] and Wang et al. (US 20240095928 A1) Paragraph [0005].
Allowable Subject Matter
Claims 2 and 5 along with their dependent claims respectively, are therefrom objected to as being dependent upon rejected base claim, claims 1, respectively but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims and to overcome the 112b rejection.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 2, the prior arts fail to explicitly teach, determine a modified image set as an image set having one or more second images in which the first depth information corresponds to a greater depth than a depth of the second depth information of the corresponding second image, as claimed in claim 2.
Regarding claim 5, the prior arts fail to explicitly teach, wherein varying the point density surrounding the second point based on a depth information of the first point compared to a depth information of the second point comprises increasing the second point density when the first depth information corresponds to a depth less than a depth of the second depth information, and decreasing the second point density when the first depth information corresponds to a depth greater than a depth of the second depth information, as claimed in claim 5.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered
pertinent to applicant`s disclosure.
WATANABE et al. (US 20220207883 A1)- An information processing apparatus according to an embodiment of the present technology includes a classification unit and a generation unit. The classification unit classifies an object detected in a space on a basis of a predetermined criterion. The generation unit sets a priority for the object on a basis of a classification result by the classification unit, and generates position-related information regarding a position in the space on a basis of the set priority. Use of the position-related information makes it possible to improve the accuracy of autonomous movement control. This makes it possible to improve the accuracy of autonomous movement control....................Please see Fig. 1. Abstract.
SHIBATA et al. (US 20240404198 A1)- The present disclosure provides a recognition technology to be executed by a processor. The processor, by executing a program stored in a computer-readable non-transitory storage, is configured to recognize, in a scan space, a target moving object that is movable in the scan space by scanning the target moving object using a scanning device mounted on a host moving object; acquire three-dimensional scan data representing a scan point group generated by scanning of the scan space; read, from a storage medium, a three-dimensional dynamic map representing a mapping point group generated by mapping of an object present in the scan space; and cluster the scan point group based on identification information for identifying a state of the scan space in each of multiple three-dimensional voxels into which the scan space is divided in the three-dimensional dynamic map and generate recognition data by recognizing the target moving object.....................Please see Fig. 1. Abstract.
Izzat et al. (US 20180203124 A1)- An object detection system includes a lidar-unit and a controller. The controller defines an occupancy-grid that segregates the field-of-view into columns, determine a first-occupancy-status of a column based on first-cloud-points detected by the lidar-unit in the column by a first-scan, determine a second-occupancy-status of the column based second-cloud-points detected in the column by a second-scan, determine a first-number of the first-cloud-points and a second-number of the second-cloud-points, and determine a dynamic-status of the column only if the column is classified as occupied by either the first-occupancy-status or the second-occupancy-status. The dynamic-status of the column is determined to be moving when a count-difference between the first-number and the second-number is greater than a difference-threshold, and the dynamic-status of the column is determined to be static when the count-difference is not greater than the difference-threshold and a registration-factor that aligns the first-cloud-points to the second-cloud-points is less than a registration-threshold......................Please see Fig. 1. Abstract.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUCIUS C.G. ALLEN whose telephone number is (703)756-5987. The examiner can normally be reached Mon - Fri 8-5pm (EST).
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/LUCIUS CAMERON GREEN ALLEN/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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