DETAILED ACTIONS
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/12/2025, 12/27/2024 and 09/03/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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.
Claims 1 - 18 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.
Regarding independent claims 1, 7, and 13: Claims 1,7, and 13 recites the limitation “specifications of a LiDAR sensor” is indefinite. It is not clear from the claim languages what parameters the limitation “specifications of a LiDAR sensor” is referring to. A clarification of the limitation of independent claims are required.
Regarding dependent claims 3, 9, and 15: claims 3, 9 and 15 recites the limitation “wherein a difference between an azimuth angle comprised in the second position information and an azimuth angle comprised in the first position information is smaller than a difference between an azimuth angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the azimuth angle comprised in the first position information” and the limitation
“a difference between a vertical angle comprised in the second position information and a vertical angle comprised in the first position information is smaller than a difference between a vertical angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the vertical angle comprised in the first position information” are indefinite. It is not clear how the difference between azimuthal angle of first position and second position are compared with difference between “any pieces of position information and the azimuth angle”. A clear claim language with a clear limitation is required.
Claims 2-18 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ) as dependent on independent claims 1, 7 and 13.
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 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.
Claims 1-2,7-8, and 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by ZHAO et al. (WO 2021/088313 A1, hereinafter Zhao, IDS reference, ref).
Regarding Claim 1, Zhao teaches,
A method for detecting anomalies of LiDAR point cloud data (Zhao, Figure 1B, Page 36, middle paragraph, detection of the point cloud data of the lidar. The point cloud rationality diagnosis in this embodiment may be a part of the above periodic fault detection or self-check”), comprising:
obtaining a first position information indicating a position of a target data point in the LiDAR point cloud data (Zhao, Figure 30, Step S61, “Receive point cloud data of a lidar and a corresponding operating parameter of the lidar during generation of the point cloud data”. page 36, middle paragraph, Lidar can generally rotate around a vertical axis to collect point cloud data within a 360-degree range in the horizontal plan. During the rotation, each channel of the lidar emits laser beams in turn according to a certain time interval (for example, 1 microsecond) and performs detection, thereby completing a vertical viewing Line scan on the field, and then perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree)in the horizontal field of view, so that multiple detections are performed during the rotation to form a point cloud, which can be sensed The condition of the surrounding environment”. Note: each degree of rotation represents a position on the horizontal field. First step of rotation at the first interval is interpreted as first position);
determining a second position information corresponding to the target data point according specifications of a LiDAR sensor (Zhao, page 36, bottom paragraph, “multiple detections are performed during the rotation to form a point cloud, which can be sensed the condition of the surrounding environment.”. NOTE: each detection step for each degree of rotation represents a position. Multiple detection can be numbered as first, second third etc. It is a measurement choice. Not an inventive step).; and
determining the target data point is an anomaly point or a normal point according to the first position information and the second position information. (Zhao, Figure 30, step S62, figure 31, Point cloud reasonableness diagnostic unit 604, page 37, upper paragraph, “In step S62, the point cloud data and working parameters are input into a neural network, and the neural network is configured to output a judgment result of whether the point cloud data is reasonable or not according to at least the point cloud data and working parameters of the lidar. Therefore, the neural network can also determine whether the lidar is malfunctioning or working abnormally based on the judgment result of whether the point cloud data is
Reasonable”).
Regarding Claim 2, Zhao teaches the method according claim 1,
Zhao further teaches wherein the obtaining the first position information comprises: determining the first position information of the target data point according to a coordinate of the target data point in the Cartesian coordinate system, wherein the first position information comprising an azimuth angle of the target data point and a vertical angle of the target data point. (Zhao, Page 36, middle paragraph, Lidar can generally rotate around a vertical axis to collect point cloud data within a 360-degree range in the horizontal plan. During the rotation, each channel of the lidar emits laser beams in turn according to a certain time interval (for example, 1 microsecond) and performs detection, thereby completing a vertical viewing Line scan on the field, and then perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree)in the horizontal field of view, so that multiple detections are performed during the rotation to form a point cloud, which can be sensed The condition of the surrounding environment”. Note: each degree of rotation represents a position on the horizontal field. First step of rotation at the first interval is interpreted as first position. The position is measured in cartesian coordinate. This is a known practice in the field of LiDAR target detection method. Not an inventive step.).
Regarding Claim 7, Zhao teaches,
An electronic device comprising: at least one processor; and one or more memories coupled to the at least one processor and storing programming instructions, which when executed by the at least one processor, cause the apparatus to (Zhao, Figure 32, page 42, the control unit 730 is implemented as part of a LIDAR module (such as an FPGA). In other embodiments, the control unit 730 is implemented as a separate unit/module. In general, the control unit 730 can be implemented as any control unit that performs control functions, for example, a processor, a microprocessor, a controller, a microcontroller, a logic device (for example, a programmable logic device, such as FPGA), a dedicated Integrated circuit (ASIC), etc.”):
obtain a first position information indicating a position of a target data point in LiDAR point cloud data (Zhao, Figure 30, Step S61, “Receive point cloud data of a lidar and a corresponding operating parameter of the lidar during generation of the point cloud data”. page 36, middle paragraph, Lidar can generally rotate around a vertical axis to collect point cloud data within a 360-degree range in the horizontal plan. During the rotation, each channel of the lidar emits laser beams in turn according to a certain time interval (for example, 1 microsecond) and performs detection, thereby completing a vertical viewing Line scan on the field, and then perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree)in the horizontal field of view, so that multiple detections are performed during the rotation to form a point cloud, which can be sensed The condition of the surrounding environment”. Note: each degree of rotation represents a position on the horizontal field. First step of rotation at the first interval is interpreted as first position);
determining a second position information corresponding to the target data point according specifications of a LiDAR sensor (Zhao, page 36, bottom paragraph, “multiple detections are performed during the rotation to form a point cloud, which can be sensed the condition of the surrounding environment.”. NOTE: each detection step for each degree of rotation represents a position. Multiple detection can be numbered as first, second third etc. It is a measurement choice. Not an inventive step).; and
determining the target data point is an anomaly point or a normal point according to the first position information and the second position information. (Zhao, Figure 30, step S62, figure 31, Point cloud reasonableness diagnostic unit 604, page 37, upper paragraph, “In step S62, the point cloud data and working parameters are input into a neural network, and the neural network is configured to output a judgment result of whether the point cloud data is reasonable or not according to at least the point cloud data and working parameters of the lidar. Therefore, the neural network can also determine whether the lidar is malfunctioning or working abnormally based on the judgment result of whether the point cloud data is
Reasonable”).
Regarding Claim 8, Zhao teaches the electronic device according to claim 7,
Zhao further teaches wherein the programming instructions, when executed by the at least one processor, cause the apparatus to: determine the first position information of the target data point according to a coordinate of the target data point in the Cartesian coordinate system, wherein the first position information comprising an azimuth angle of the target data point and a vertical angle of the target data point (Zhao, Page 36, middle paragraph, Lidar can generally rotate around a vertical axis to collect point cloud data within a 360-degree range in the horizontal plan. During the rotation, each channel of the lidar emits laser beams in turn according to a certain time interval (for example, 1 microsecond) and performs detection, thereby completing a vertical viewing Line scan on the field, and then perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree)in the horizontal field of view, so that multiple detections are performed during the rotation to form a point cloud, which can be sensed The condition of the surrounding environment”. Note: each degree of rotation represents a position on the horizontal field. First step of rotation at the first interval is interpreted as first position. The position is measured in cartesian coordinate. This is a known practice in the field of LiDAR target detection method. Not an inventive step).
Regarding Claim 13, Zhao teaches,
A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to (Zhao, Figure 32, page 42, upper paragraph, “the control unit 730 is implemented as part of a LIDAR module (such as an FPGA). In other embodiments, the control unit 730 is implemented as a separate unit/module. In general, the control unit 730 can be implemented as any control unit that performs control functions, for example, a processor, a microprocessor, a controller, a microcontroller, a logic device (for example, a programmable logic device, such as FPGA), a dedicated Integrated circuit (ASIC), etc. The storage unit 710 may include any non-volatile memory/device (for example, various types of memory and flash memory, etc.) that records/stores information related to abnormal events.”):
obtain a first position information indicating a position of a target data point in the LiDAR point cloud data (Zhao, page 36, bottom paragraph, “multiple detections are performed during the rotation to form a point cloud, which can be sensed the condition of the surrounding environment.”. NOTE: each detection step for each degree of rotation represents a position. Multiple detection can be numbered as first, second third etc. It is a measurement choice. Not an inventive step).; and
determining the target data point is an anomaly point or a normal point according to the first position information and the second position information. (Zhao, Figure 30, step S62, figure 31, Point cloud reasonableness diagnostic unit 604, page 37, upper paragraph, “In step S62, the point cloud data and working parameters are input into a neural network, and the neural network is configured to output a judgment result of whether the point cloud data is reasonable or not according to at least the point cloud data and working parameters of the lidar. Therefore, the neural network can also determine whether the lidar is malfunctioning or working abnormally based on the judgment result of whether the point cloud data is
Reasonable”).
Regarding Claim 14, Zhao teaches the non-transitory machine-readable medium according to claim 13,
Zhao further teaches wherein the instructions, when executed by the processor, cause the processor to: determine the first position information of the target data point according to a coordinate of the target data point in the Cartesian coordinate system, wherein the first position information comprising an azimuth angle of the target data point and a vertical angle of the target data point. (Zhao, Page 36, middle paragraph, Lidar can generally rotate around a vertical axis to collect point cloud data within a 360-degree range in the horizontal plan. During the rotation, each channel of the lidar emits laser beams in turn according to a certain time interval (for example, 1 microsecond) and performs detection, thereby completing a vertical viewing Line scan on the field, and then perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree)in the horizontal field of view, so that multiple detections are performed during the rotation to form a point cloud, which can be sensed The condition of the surrounding environment”. Note: each degree of rotation represents a position on the horizontal field. First step of rotation at the first interval is interpreted as first position. The position is measured in cartesian coordinate. This is a known practice in the field of LiDAR target detection method. Not an inventive step).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-4, 6, 9-10, 12,15,16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO et al. (WO 2021/088313 A1/ US 2022/0268904 A1, hereinafter Zhao, IDS reference, ref) and in view of Liu et al. (CN 111751852 A, hereinafter Liu).
Regarding Claim 3, Zhao teaches the method according to claim 2,
Zhao further teaches wherein the specifications of the LiDAR sensor (Zhao, Figure 1C, Lidar 1) comprise:
a horizontal angular resolution ((Zhao, Page 36, middle paragraph, “perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree) in the horizontal field of view”),
a firing interval, (Zhao, page 36, middle paragraph, “During the rotation, each channel of the lidar emits laser beams in turn according to a certain time interval (for example, 1 microsecond)”)
a number of channels of the LiDAR sensor, (Zhao, Figure 7, channels)
a vertical angle for each channel; a horizontal angle offset for each channel;
(Zhao, page 36, middle paragraph, “Lidar can generally rotate around a vertical axis to collect point cloud data within a 360-degree range in the horizontal plane. Take 16-line lidar as an example, it can emit a total of 16 lines of laser beams L1, L2,..., L15, L16 in the vertical direction (each line of laser beam corresponds to a channel of the lidar, a total of 16 channels) , Used to detect the surrounding environment. performs detection, thereby completing a vertical viewing Line scan on the field, and then perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree) in the horizontal field of view, so that multiple detections are performed during the rotation to form a point cloud, which can be sensed the condition of the surrounding environment”)., and
the determining the second position information corresponding to the target data point according to the specifications of the LiDAR sensor, comprises (Zhao, Pages 36-37, (Zhao, page 36, bottom paragraph, “multiple detections are performed during the rotation to form a point cloud, which can be sensed the condition of the surrounding environment.”. NOTE: each detection step for each degree of rotation represents a position. Multiple detection can be numbered as first, second third etc. It is a measurement choice. Not an inventive step)
determining a plurality of pieces of position information according to the specifications of the LiDAR sensor (Zhao, Figure 30, step S61, point cloud data of a LIDAR and a corresponding operating parameter of the LIDAR during generation of the point cloud data are received”).
wherein each piece of the position information corresponding to a data point, and the each piece of the position information comprising a azimuth angle of a corresponding data point and a vertical angle of the corresponding data point (Zhao, Page 19, bottom paragraph, the rotation, each channel of the lidar sequentially emits laser beams and performs detection according to a certain time interval (for example, 1 microsecond) to complete a vertical axis. Line scan on the field of
view, and then perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree) in the horizontal field of view, so as to perform multiple detections during the rotation to form a point cloud. Perceive the condition of the surrounding environment.” NOTE: each scan detects location of the target object and detect horizontal and vertical angle of the position of the target object); and
Zhao is silent on determining the second position information from the plurality of pieces of position information according to the first position information,
wherein a difference between an azimuth angle comprised in the second position information and an azimuth angle comprised in the first position information is smaller than a difference between an azimuth angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the azimuth angle comprised in the first position information,
a difference between a vertical angle comprised in the second position information and a vertical angle comprised in the first position information is smaller than a difference between a vertical angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the vertical angle comprised in the first position information. However, Liu teaches determining the second position information from the plurality of pieces of position information according to the first position information (Liu, page 9, lower bottom paragraph, calculating the distance between the two longitude and latitude points and the components of the distance between the two longitude and latitude points on each coordinate axis under the plane rectangular coordinate system, and calculating the azimuth angle between the two longitude and latitude points by utilizing a geodetic subject inverse calculation algorithm. After the longitude and latitude and the azimuth angle of the current point and the nearest navigation point are known, the distance value of the two longitude and latitude points under the rectangular plane coordinate system and the distance component of each coordinate axis can be obtained by utilizing UTM projection” NOTE: current point reads on first position point and nearest navigation point is second position)
wherein a difference between an azimuth angle comprised in the second position information and an azimuth angle comprised in the first position information is smaller than a difference between an azimuth angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the azimuth angle comprised in the first position information (Liu, Page 11, middle paragraph, the azimuth angle, i.e., the included angle with due north, needs to be solved according to the longitude and latitude coordinates of the known two points, as shown in fig. 3, and the solution can be performed according to the inverse calculation problem in the geodetic theme solution. Page 12, Top paragraph, After the two longitude and latitude points and the azimuth angles thereof are known, the two longitude and latitude points are respectively converted by using a UTM (Universal Transverse Mercator) projection algorithm, and then the distance values of the two longitude and latitude points under a plane rectangular coordinate system and the distance components on each coordinate axis can be obtained”, also see equation 1, and figure 2.”NOTE: the azimuth angle difference between the first position point and the nearest point position is smaller compared to any other point further from the first point is known in the art. As the azimuth angles increase and the distance from the zero angle increase the difference between the azimuth angles will be larger. It is known. Not an inventive concept. see page 10, bottom paragraph, “searching a point with the nearest distance in front of the vehicle in the navigation map by taking the current point as a reference, respectively carrying out UTM projection transformation on the points, and calculating the distance values of the two points under a plane rectangular coordinate system;and matching the laser radar Point cloud picture of the current Point with the laser radar Point cloud picture corresponding to the nearest navigation Point by utilizing an ICP (Iterative Closest Point) algorithm, and calculating a distance value between the two groups of Point clouds. The two distance values are subjected to difference and compared with a preset error threshold value parameter, and if the difference between the two distance values is smaller than a set threshold value, the current GNSS positioning signal is reliable; otherwise, it is unreliable”).
a difference between a vertical angle comprised in the second position information and a vertical angle comprised in the first position information is smaller than a difference between a vertical angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the vertical angle comprised in the first position information. (Liu, Figure 2-3, Page 11, upper paragraph, Figure 2 shows the nearest navigation point schematic, where: p0 is the vehicle's current real-time location; p1, p2, p3, etc. are points on the navigation map; α1 is the vehicle's heading angle, meaning the angle between the direction the vehicle is moving and true north; α2 is the angle between the point and the line formed by the vehicle's body and its direction of movement; α3 is the angle between a point on the map and the line formed by the vehicle's body and true north. The value of α3 is calculated assuming that both p0 and p1 are given as latitude and longitude coordinates, based on geodetic principles” NOTE: the vertical angle difference between the first position point and the nearest point position is smaller compared to any other point further from the first point is known in the art.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Zhao’s method to incorporate Liu’s method of calculating difference between the azimuth angle for first and second position and determining the reliability of Lidar cloud point data to obtain an accurate detection of cloud point data abnormality. (Liu, abstract). It would have been obvious to a person of ordinary skill to include the well-known cartesian coordinate measurement to determine LiDAR point cloud data abnormality, in order to yield the predicted results of generating accurate cloud data or target object detect map, yet with higher accuracy (KSR).
Regarding Claim 4, combination of Zhao and Liu teaches the method according to claim 3,
Zhao is silent on wherein the determining the target data point is the anomaly point or the normal point according to the first position information and the second position information comprises: determining an anomaly score according to the first position information and the second position information; and determining the target data point is the anomaly point or the normal point according to the anomaly score.
However, Liu teaches wherein the determining the target data point is the anomaly point or the normal point according to the first position information and the second position information comprises: determining an anomaly score according to the first position information and the second position information; and determining the target data point is the anomaly point or the normal point according to the anomaly score (Liu, Page 13, middle paragraph, between the laser radar point cloud at the current moment and the laser radar point cloud corresponding to the nearest navigation point through the processes, comparing the error between the two distance values with a preset threshold parameter, and if the error between the two distance values is smaller than a set threshold, indicating that the current GNSS positioning signal is reliable; on the contrary, if the error between the two signals is larger than or equal to the set threshold, the current GNSS positioning signal is unreliable, so that a judgment basis is provided for the decision control of the unmanned vehicle”. NOTE: threshold value reads on an anomaly score).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Zhao’s method to incorporate Liu’s method of calculating difference between the azimuth angle for first and second position and determining the reliability of Lidar cloud point data to obtain an accurate detection of cloud point data abnormality. (Liu, abstract). It would have been obvious to a person of ordinary skill to include the well-known cartesian coordinate measurement to determine LiDAR point cloud data abnormality, in order to yield the predicted results of generating accurate cloud data or target object detect map, yet with higher accuracy (KSR).
Regarding Claim 6, combination of Zhao and Liu teaches the method according to claim 4,
Zhao is silent on wherein the determining the target data point is the anomaly point or the normal point according to the anomaly score, comprises: determining the target data point is the anomaly point in response to the anomaly score is greater or equals to a preset threshold; and determining the target data point is the normal point in response to the anomaly score is smaller than the preset threshold.
However, Liu teaches wherein the determining the target data point is the anomaly point or the normal point according to the anomaly score, comprises: determining the target data point is the anomaly point in response to the anomaly score is greater or equals to a preset threshold; and determining the target data point is the normal point in response to the anomaly score is smaller than the preset threshold. (Liu, Page 13, middle paragraph, between the laser radar point cloud at the current moment and the laser radar point cloud corresponding to the nearest navigation point through the processes, comparing the error between the two distance values with a preset threshold parameter, and if the error between the two distance values is smaller than a set threshold, indicating that the current GNSS positioning signal is reliable; on the contrary, if the error between the two signals is larger than or equal to the set threshold, the current GNSS positioning signal is unreliable, so that a judgment basis is provided for the decision control of the unmanned vehicle”. NOTE: threshold value reads on an anomaly score).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Zhao’s method to incorporate Liu’s method of calculating difference between the azimuth angle for first and second position and determining the reliability of Lidar cloud point data to obtain an accurate detection of cloud point data abnormality. (Liu, abstract). It would have been obvious to a person of ordinary skill to include the well-known cartesian coordinate measurement to determine LiDAR point cloud data abnormality, in order to yield the predicted results of generating accurate cloud data or target object detect map, yet with higher accuracy (KSR).
Regarding Claim 9, Zhao teaches the electronic device according to claim 8,
Zhao further teaches wherein the specifications of the LiDAR sensor (Zhao, Figure 1C, Lidar 1) comprise:
a horizontal angular resolution ((Zhao, Page 36, middle paragraph, “perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree) in the horizontal field of view”),
a firing interval, (Zhao, page 36, middle paragraph, “During the rotation, each channel of the lidar emits laser beams in turn according to a certain time interval (for example, 1 microsecond)”)
a number of channels of the LiDAR sensor, (Zhao, Figure 7, channels)
a vertical angle for each channel; a horizontal angle offset for each channel;
(Zhao, page 36, middle paragraph, “Lidar can generally rotate around a vertical axis to collect point cloud data within a 360-degree range in the horizontal plane. Take 16-line lidar as an example, it can emit a total of 16 lines of laser beams L1, L2,..., L15, L16 in the vertical direction (each line of laser beam corresponds to a channel of the lidar, a total of 16 channels) , Used to detect the surrounding environment. performs detection, thereby completing a vertical viewing Line scan on the field, and then perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree) in the horizontal field of view, so that multiple detections are performed during the rotation to form a point cloud, which can be sensed the condition of the surrounding environment”)., and
the determining the second position information corresponding to the target data point according to the specifications of the LiDAR sensor, comprises (Zhao, Pages 36-37, (Zhao, page 36, bottom paragraph, “multiple detections are performed during the rotation to form a point cloud, which can be sensed the condition of the surrounding environment.”. NOTE: each detection step for each degree of rotation represents a position. Multiple detection can be numbered as first, second third etc. It is a measurement choice. Not an inventive step). determining a plurality of pieces of position information according to the specifications of the LiDAR sensor (Zhao, Figure 30, step S61, point cloud data of a LIDAR and a corresponding operating parameter of the LIDAR during generation of the point cloud data are received”).
wherein each piece of the position information corresponding to a data point, and the each piece of the position information comprising a azimuth angle of a corresponding data point and a vertical angle of the corresponding data point (Zhao, Page 19, bottom paragraph, the rotation, each channel of the lidar sequentially emits laser beams and performs detection according to a certain time interval (for example, 1 microsecond) to complete a vertical axis. Line scan on the field of
view, and then perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree) in the horizontal field of view, so as to perform multiple detections during the rotation to form a point cloud. Perceive the condition of the surrounding environment.” NOTE: each scan detect location of the target object and detect horizontal and vertical angle of the position of the target object); and
Zhao is silent on determining the second position information from the plurality of pieces of position information according to the first position information,
wherein a difference between an azimuth angle comprised in the second position information and an azimuth angle comprised in the first position information is smaller than a difference between an azimuth angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the azimuth angle comprised in the first position information,
a difference between a vertical angle comprised in the second position information and a vertical angle comprised in the first position information is smaller than a difference between a vertical angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the vertical angle comprised in the first position information.
However, Liu teaches determining the second position information from the plurality of pieces of position information according to the first position information (Liu, page 9, lower bottom paragraph, calculating the distance between the two longitude and latitude points and the components of the distance between the two longitude and latitude points on each coordinate axis under the plane rectangular coordinate system, and calculating the azimuth angle between the two longitude and latitude points by utilizing a geodetic subject inverse calculation algorithm. After the longitude and latitude and the azimuth angle of the current point and the nearest navigation point are known, the distance value of the two longitude and latitude points under the rectangular plane coordinate system and the distance component of each coordinate axis can be obtained by utilizing UTM projection” NOTE: current point reads on first position point and nearest navigation point is second position)
wherein a difference between an azimuth angle comprised in the second position information and an azimuth angle comprised in the first position information is smaller than a difference between an azimuth angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the azimuth angle comprised in the first position information (Liu, Page 11, middle paragraph, the azimuth angle, i.e., the included angle with due north, needs to be solved according to the longitude and latitude coordinates of the known two points, as shown in fig. 3, and the solution can be performed according to the inverse calculation problem in the geodetic theme solution. Page 12, Top paragraph, After the two longitude and latitude points and the azimuth angles thereof are known, the two longitude and latitude points are respectively converted by using a UTM (Universal Transverse Mercator) projection algorithm, and then the distance values of the two longitude and latitude points under a plane rectangular coordinate system and the distance components on each coordinate axis can be obtained”, also see equation 1, and figure 2.”NOTE: the azimuth angle difference between the first position point and the nearest point position is smaller compared to any other point further from the first point is known in the art. As the azimuth angles increase and the distance from the zero angle increase the difference between the azimuth angles will be larger. It is known. Not an inventive concept. (see page 10, bottom paragraph, “searching a point with the nearest distance in front of the vehicle in the navigation map by taking the current point as a reference, respectively carrying out UTM projection transformation on the points, and calculating the distance values of the two points under a plane rectangular coordinate system; and matching the laser radar Point cloud picture of the current Point with the laser radar Point cloud picture corresponding to the nearest navigation Point by utilizing an ICP (Iterative Closest Point) algorithm, and calculating a distance value between the two groups of Point clouds. The two distance values are subjected to difference and compared with a preset error threshold value parameter, and if the difference between the two distance values is smaller than a set threshold value, the current GNSS positioning signal is reliable; otherwise, it is unreliable”).
a difference between a vertical angle comprised in the second position information and a vertical angle comprised in the first position information is smaller than a difference between a vertical angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the vertical angle comprised in the first position information. (Liu, Figure 2-3, Page 11, upper paragraph, Figure 2 shows the nearest navigation point schematic, where: p0 is the vehicle's current real-time location; p1, p2, p3, etc. are points on the navigation map; α1 is the vehicle's heading angle, meaning the angle between the direction the vehicle is moving and true north; α2 is the angle between the point and the line formed by the vehicle's body and its direction of movement; α3 is the angle between a point on the map and the line formed by the vehicle's body and true north. The value of α3 is calculated assuming that both p0 and p1 are given as latitude and longitude coordinates, based on geodetic principles” NOTE: the vertical angle difference between the first position point and the nearest point position is smaller compared to any other point further from the first point is known in the art.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Zhao’s method to incorporate Liu’s method of calculating difference between the azimuth angle for first and second position and determining the reliability of Lidar cloud point data to obtain an accurate detection of cloud point data abnormality. (Liu, abstract). It would have been obvious to a person of ordinary skill to include the well-known cartesian coordinate measurement to determine LiDAR point cloud data abnormality, in order to yield the predicted results of generating accurate cloud data or target object detect map, yet with higher accuracy (KSR).
Regarding Claim 10, combination of Zhao and Liu teaches the electronic device according to claim 9,
Zhao teaches wherein the programming instructions, when executed by the at least one processor, Zhao, Figure 32, page 42, the control unit 730 is implemented as part of a LIDAR module (such as an FPGA). In other embodiments, the control unit 730 is implemented as a separate unit/module. In general, the control unit 730 can be implemented as any control unit that performs control functions, for example, a processor, a microprocessor, a controller, a microcontroller, a logic device (for example, a programmable logic device, such as FPGA), a dedicated Integrated circuit (ASIC), etc.”): cause the apparatus to:
Zhao is silent on determine an anomaly score according to the first position information and the second position information; and determine the target data point is the anomaly point or the normal point according to the anomaly score.
However, Liu teaches determine an anomaly score according to the first position information and the second position information; and determine the target data point is the anomaly point or the normal point according to the anomaly score. Liu, Page 13, middle paragraph, between the laser radar point cloud at the current moment and the laser radar point cloud corresponding to the nearest navigation point through the processes, comparing the error between the two distance values with a preset threshold parameter, and if the error between the two distance values is smaller than a set threshold, indicating that the current GNSS positioning signal is reliable; on the contrary, if the error between the two signals is larger than or equal to the set threshold, the current GNSS positioning signal is unreliable, so that a judgment basis is provided for the decision control of the unmanned vehicle”. NOTE: threshold value reads on an anomaly score).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Zhao’s method to incorporate Liu’s method of calculating difference between the azimuth angle for first and second position and determining the reliability of Lidar cloud point data to obtain an accurate detection of cloud point data abnormality. (Liu, abstract). It would have been obvious to a person of ordinary skill to include the well-known cartesian coordinate measurement to determine LiDAR point cloud data abnormality, in order to yield the predicted results of generating accurate cloud data or target object detect map, yet with higher accuracy (KSR).
Regarding Claim 12, combination of Zhao and Liu teaches the electronic device according to claim 10,
Zhao is silent on wherein the programming instructions, when executed by the at least one processor, cause the apparatus to: determine the target data point is the anomaly point in response to the anomaly score is greater or equals to a preset threshold; and determine the target data point is the normal point in response to the anomaly score is smaller than the preset threshold.
However, Liu teaches wherein the programming instructions, when executed by the at least one processor, cause the apparatus to: determine the target data point is the anomaly point in response to the anomaly score is greater or equals to a preset threshold; and determine the target data point is the normal point in response to the anomaly score is smaller than the preset threshold(Liu, Page 13, middle paragraph, between the laser radar point cloud at the current moment and the laser radar point cloud corresponding to the nearest navigation point through the processes, comparing the error between the two distance values with a preset threshold parameter, and if the error between the two distance values is smaller than a set threshold, indicating that the current GNSS positioning signal is reliable; on the contrary, if the error between the two signals is larger than or equal to the set threshold, the current GNSS positioning signal is unreliable, so that a judgment basis is provided for the decision control of the unmanned vehicle”. NOTE: threshold value reads on an anomaly score).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Zhao’s method to incorporate Liu’s method of calculating difference between the azimuth angle for first and second position and determining the reliability of Lidar cloud point data to obtain an accurate detection of cloud point data abnormality. (Liu, abstract). It would have been obvious to a person of ordinary skill to include the well-known cartesian coordinate measurement to determine LiDAR point cloud data abnormality, in order to yield the predicted results of generating accurate cloud data or target object detect map, yet with higher accuracy (KSR).
Regarding Claim 15, Zhao teaches the non-transitory machine-readable medium according to claim 14, Zhao further teaches wherein the specifications of the LiDAR sensor (Zhao, Figure 1C, Lidar 1) comprise:
a horizontal angular resolution ((Zhao, Page 36, middle paragraph, “perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree) in the horizontal field of view”),
a firing interval, (Zhao, page 36, middle paragraph, “During the rotation, each channel of the lidar emits laser beams in turn according to a certain time interval (for example, 1 microsecond)”)
a number of channels of the LiDAR sensor, (Zhao, Figure 7, channels)
a vertical angle for each channel; a horizontal angle offset for each channel;
(Zhao, page 36, middle paragraph, “Lidar can generally rotate around a vertical axis to collect point cloud data within a 360-degree range in the horizontal plane. Take 16-line lidar as an example, it can emit a total of 16 lines of laser beams L1, L2,..., L15, L16 in the vertical direction (each line of laser beam corresponds to a channel of the lidar, a total of 16 channels) , Used to detect the surrounding environment. performs detection, thereby completing a vertical viewing Line scan on the field, and then perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree) in the horizontal field of view, so that multiple detections are performed during the rotation to form a point cloud, which can be sensed the condition of the surrounding environment”)., and
the determining the second position information corresponding to the target data point according to the specifications of the LiDAR sensor, comprises (Zhao, Pages 36-37, (Zhao, page 36, bottom paragraph, “multiple detections are performed during the rotation to form a point cloud, which can be sensed the condition of the surrounding environment.”. NOTE: each detection step for each degree of rotation represents a position. Multiple detection can be numbered as first, second third etc. It is a measurement choice. Not an inventive step)
determining a plurality of pieces of position information according to the specifications of the LiDAR sensor (Zhao, Figure 30, step S61, point cloud data of a LIDAR and a corresponding operating parameter of the LIDAR during generation of the point cloud data are received”).
wherein each piece of the position information corresponding to a data point, and the each piece of the position information comprising a azimuth angle of a corresponding data point and a vertical angle of the corresponding data point (Zhao, Page 19, bottom paragraph, the rotation, each channel of the lidar sequentially emits laser beams and performs detection according to a certain time interval (for example, 1 microsecond) to complete a vertical axis. Line scan on the field of
view, and then perform the next line scan of the vertical field of view at a certain angle (for example, 0.1 degree or 0.2 degree) in the horizontal field of view, so as to perform multiple detections during the rotation to form a point cloud. Perceive the condition of the surrounding environment.” NOTE: each scan detect location of the target object and detect horizontal and vertical angle of the position of the target object); and
Zhao is silent on determining the second position information from the plurality of pieces of position information according to the first position information,
wherein a difference between an azimuth angle comprised in the second position information and an azimuth angle comprised in the first position information is smaller than a difference between an azimuth angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the azimuth angle comprised in the first position information,
a difference between a vertical angle comprised in the second position information and a vertical angle comprised in the first position information is smaller than a difference between a vertical angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the vertical angle comprised in the first position information. However, Liu teaches determining the second position information from the plurality of pieces of position information according to the first position information (Liu, page 9, lower bottom paragraph, calculating the distance between the two longitude and latitude points and the components of the distance between the two longitude and latitude points on each coordinate axis under the plane rectangular coordinate system, and calculating the azimuth angle between the two longitude and latitude points by utilizing a geodetic subject inverse calculation algorithm. After the longitude and latitude and the azimuth angle of the current point and the nearest navigation point are known, the distance value of the two longitude and latitude points under the rectangular plane coordinate system and the distance component of each coordinate axis can be obtained by utilizing UTM projection” NOTE: current point reads on first position point and nearest navigation point is second position)
wherein a difference between an azimuth angle comprised in the second position information and an azimuth angle comprised in the first position information is smaller than a difference between an azimuth angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the azimuth angle comprised in the first position information (Liu, Page 11, middle paragraph, the azimuth angle, i.e., the included angle with due north, needs to be solved according to the longitude and latitude coordinates of the known two points, as shown in fig. 3, and the solution can be performed according to the inverse calculation problem in the geodetic theme solution. Page 12, Top paragraph, After the two longitude and latitude points and the azimuth angles thereof are known, the two longitude and latitude points are respectively converted by using a UTM (Universal Transverse Mercator) projection algorithm, and then the distance values of the two longitude and latitude points under a plane rectangular coordinate system and the distance components on each coordinate axis can be obtained”, also see equation 1, and figure 2.”NOTE: the azimuth angle difference between the first position point and the nearest point position is smaller compared to any other point further from the first point is known in the art. As the azimuth angles increase and the distance from the zero angle increase the difference between the azimuth angles will be larger. It is known. Not an inventive concept. see page 10, bottom paragraph, “searching a point with the nearest distance in front of the vehicle in the navigation map by taking the current point as a reference, respectively carrying out UTM projection transformation on the points, and calculating the distance values of the two points under a plane rectangular coordinate system;and matching the laser radar Point cloud picture of the current Point with the laser radar Point cloud picture corresponding to the nearest navigation Point by utilizing an ICP (Iterative Closest Point) algorithm, and calculating a distance value between the two groups of Point clouds. The two distance values are subjected to difference and compared with a preset error threshold value parameter, and if the difference between the two distance values is smaller than a set threshold value, the current GNSS positioning signal is reliable; otherwise, it is unreliable”).
a difference between a vertical angle comprised in the second position information and a vertical angle comprised in the first position information is smaller than a difference between a vertical angle comprised in any piece of position information other than the second position information among the plurality of pieces of position information and the vertical angle comprised in the first position information. (Liu, Figure 2-3, Page 11, upper paragraph, Figure 2 shows the nearest navigation point schematic, where: p0 is the vehicle's current real-time location; p1, p2, p3, etc. are points on the navigation map; α1 is the vehicle's heading angle, meaning the angle between the direction the vehicle is moving and true north; α2 is the angle between the point and the line formed by the vehicle's body and its direction of movement; α3 is the angle between a point on the map and the line formed by the vehicle's body and true north. The value of α3 is calculated assuming that both p0 and p1 are given as latitude and longitude coordinates, based on geodetic principles” NOTE: the vertical angle difference between the first position point and the nearest point position is smaller compared to any other point further from the first point is known in the art.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Zhao’s method to incorporate Liu’s method of calculating difference between the azimuth angle for first and second position and determining the reliability of Lidar cloud point data to obtain an accurate detection of cloud point data abnormality. (Liu, abstract). It would have been obvious to a person of ordinary skill to include the well-known cartesian coordinate measurement to determine LiDAR point cloud data abnormality, in order to yield the predicted results of generating accurate cloud data or target object detect map, yet with higher accuracy (KSR).
Regarding Claim 16, combination of Zhao and Liu teaches the non-transitory machine-readable medium according to claim 15,
Zhao is silent on when executed by the processor, cause the processor to:
determine an anomaly score according to the first position information and the second position information; and determine the target data point is the anomaly point or the normal point according to the anomaly score.
However, Liu teaches when executed by the processor, cause the processor to: determine an anomaly score according to the first position information and the second position information; and determine the target data point is the anomaly point or the normal point according to the anomaly score. (Liu, Page 13, middle paragraph, between the laser radar point cloud at the current moment and the laser radar point cloud corresponding to the nearest navigation point through the processes, comparing the error between the two distance values with a preset threshold parameter, and if the error between the two distance values is smaller than a set threshold, indicating that the current GNSS positioning signal is reliable; on the contrary, if the error between the two signals is larger than or equal to the set threshold, the current GNSS positioning signal is unreliable, so that a judgment basis is provided for the decision control of the unmanned vehicle”. NOTE: threshold value reads on an anomaly score).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Zhao’s method to incorporate Liu’s method of calculating difference between the azimuth angle for first and second position and determining the reliability of Lidar cloud point data to obtain an accurate detection of cloud point data abnormality. (Liu, abstract). It would have been obvious to a person of ordinary skill to include the well-known cartesian coordinate measurement to determine LiDAR point cloud data abnormality, in order to yield the predicted results of generating accurate cloud data or target object detect map, yet with higher accuracy (KSR).
Regarding Claim 18, combination of Zhao and Liu teaches The non-transitory machine-readable medium according to claim 16,
Zhao is silent on wherein the instructions, when executed by the processor, cause the processor to: determine the target data point is the anomaly point in response to the anomaly score is greater or equals to a preset threshold; and determine the target data point is the normal point in response to the anomaly score is smaller than the preset threshold.
However, Liu teaches wherein the instructions, when executed by the processor, cause the processor to: determine the target data point is the anomaly point in response to the anomaly score is greater or equals to a preset threshold; and determine the target data point is the normal point in response to the anomaly score is smaller than the preset threshold.
(Liu, Page 13, middle paragraph, between the laser radar point cloud at the current moment and the laser radar point cloud corresponding to the nearest navigation point through the processes, comparing the error between the two distance values with a preset threshold parameter, and if the error between the two distance values is smaller than a set threshold, indicating that the current GNSS positioning signal is reliable; on the contrary, if the error between the two signals is larger than or equal to the set threshold, the current GNSS positioning signal is unreliable, so that a judgment basis is provided for the decision control of the unmanned vehicle”. NOTE: threshold value reads on an anomaly score).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Zhao’s method to incorporate Liu’s method of calculating difference between the azimuth angle for first and second position and determining the reliability of Lidar cloud point data to obtain an accurate detection of cloud point data abnormality. (Liu, abstract). It would have been obvious to a person of ordinary skill to include the well-known cartesian coordinate measurement to determine LiDAR point cloud data abnormality, in order to yield the predicted results of generating accurate cloud data or target object detect map, yet with higher accuracy (KSR).
Allowable Subject Matter
Claim(s) 5, 11, and 17 would be allowable if rewritten to overcome rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is an examiner's statement of reasons for the objection:
Regarding claim 5, the prior art of record, alone or in combination, does not disclose or suggest the below underlined limitations incorporated together with the other claimed limitations not mentioned herein:
wherein the determining the anomaly score according to the first position information and the second position information comprises: determining the anomaly score according to a following formula:
S
a
n
o
m
=
α
-
α
n
e
a
r
+
ω
-
ω
n
e
a
r
Sanom is the anomaly score, α is the azimuth angle comprised in the first position information, αnear is the azimuth angle comprised in the second position information, ω is the vertical angle comprised in the first position information, and ωnear is the vertical angle comprised in the second
position information.
.
Regarding claim 11, the prior art of record, alone or in combination, does not disclose or suggest the below underlined limitations incorporated together with the other claimed limitations not mentioned herein:
wherein the programming instructions, when executed by the at least one processor, cause the apparatus to determine the anomaly score according to a following formula:
S
a
n
o
m
=
α
-
α
n
e
a
r
+
ω
-
ω
n
e
a
r
Sanom is the anomaly score, α is the azimuth angle comprised in the first position information, αnear is the azimuth angle comprised in the second position information, ω is the vertical angle comprised in the first position information, and ωnear is the vertical angle comprised in the second
position information.
Regarding claim 17, the prior art of record, alone or in combination, does not disclose or suggest the below underlined limitations incorporated together with the other claimed limitations not mentioned herein:
wherein the instructions, when executed by the processor, cause the processor to determine the anomaly score according to a following formula:
S
a
n
o
m
=
α
-
α
n
e
a
r
+
ω
-
ω
n
e
a
r
Sanom is the anomaly score, α is the azimuth angle comprised in the first position information, αnear is the azimuth angle comprised in the second position information, ω is the vertical angle comprised in the first position information, and ωnear is the vertical angle comprised in the second position information
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhao et al. (CN 112946606 A) describes “The invention provides a laser radar calibration method, a laser radar calibration device, laser radar calibration equipment, a laser radar calibration system and a laser radar calibration storage medium. The method is applied to electronic equipment, and the electronic equipment is respectively in communication
connection with a GNSS reference station, a laser radar and a GNSS mobile station; the laser radar and the GNSS reference station are coaxially arranged, the GNSS mobile station and the reflecting plate are coaxially arranged, and the reflecting plate is completely positioned in the field of view of the laser radar; the method comprises the following steps: acquiring reference station positioning information positioned by a GNSS reference station and rover station positioning information positioned by a GNSS rover station; calculating an azimuth angle of the center of the reflecting plate relative to the current position of the laser radar according to the positioning information of the reference station and the positioning information of the mobile station; acquiring total point cloud data which is scanned and received by a laser radar on a reflecting plate; calculating the angle of the center of the reflecting plate relative to the zero point position of the laser radar according to the total point cloud data; and calculating the azimuth angle of the zero point of the laser radar according to the azimuth angle of the center of the reflecting plate relative to the current position of the laser radar and the angle of the center of the reflecting plate relative to the zero point position of the laser radar” (Abstract).
ZHUANG ZHUANGWEI. (CN 110007315 A) recites” This application involves a kind of laser radar detection device, detection method and control systems, the laser radar detection device includes: sensor module, including laser radar and the processing unit being connect with the laser radar, the laser radar is for emitting optical signal to preset field of view, it receives the optical signal and detects the echo-signal that object reflects to form in the preset field of view, the processing unit is used to obtain point cloud data according to the echo-signal, and the point cloud data includes at least the location information of detection object and the strength information of echo signal; Detection module is connect with the sensor module, and for receiving the point cloud data, the abnormal state information of the equipment is obtained according to the point cloud data.By handling the echo-signal that laser radar returns, to obtain the abnormal state information of equipment, it can be interfered in conjunction with caused by external environment and equipment state is detected, to improve the safety of control system
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/DILARA SULTANA/ Examiner, Art Unit 2858
July 9th, 2026
/EMAN A ALKAFAWI/ Supervisory Patent Examiner, Art Unit 2858 7/15/2026
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