DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to Applicant’s RCE and amendments and remarks filed on 10/16/2025. The Applicant has amended claims 1 – 2, 4, 6, 11 – 17, and 19 – 20. No claims have been cancelled or added and no new matter has been introduced. Claims 1 – 20 are currently pending in the application and are addressed below.
Response to Amendment
The amendment filed on 10/16/2025 has been entered. Claims 1 – 20 remain pending in the application. Applicant’s amendments have fully overcome the claim objections and 35 U.S.C. 112(b) and 101 rejections previously set forth in the Final Office Action mailed on 07/16/2025.
Reply to Applicant’s Remarks
Applicant’s remarks filed 10/16/2025 have been fully considered and are addressed as follows:
Claim Rejections Under 35 U.S.C. 112:
Applicant’s amendments to the claims filed on 10/16/2025 have overcome the 35 U.S.C. 112(b) rejections previously set forth in the Final Office Action mailed 07/16/2025, therefore, the Examiner withdraws the 35 U.S.C. 112(b) rejections.
Claim Rejections Under 35 U.S.C. 101:
Applicant’s amendments to the claims filed on 10/16/2025 have overcome the 35 U.S.C. 101 rejections previously set forth in the Final Office Action mailed 07/16/2025, therefore, the Examiner withdraws the 35 U.S.C. 101 rejections.
Claim Rejections Under 35 U.S.C. 103:
Applicant’s arguments (see Arguments/Remarks, filed 10/16/2025) with respect to claim rejections under 35 U.S.C. 103 have been fully considered but, respectfully, are not persuasive.
Regarding the Applicant’s arguments that “Kim, Horigome, Dorian, Nguyen, Bell, Ohkado, Kim '374, taken individually or combined, fail to teach or suggest inventive features of the presently claimed invention including i) calculating an overlap between the matching data and a variable grid created based on the stationary obstacle candidate and calculating a final evaluation value by applying a weight factor to the overlap, ii) determining that the stationary obstacle candidate is the stationary obstacle based on the final evaluation value, and iii) wherein the weight factor is determined to be negative or positive depending on whether the matching data is the moving object data or stationary object data, as is called for by claims 1 and 11.”, “Kim fails to disclose calculating an overlap between the matching data and a variable grid created based on the stationary obstacle candidate and calculating a final evaluation value by applying a weight factor to the overlap, as is presently claimed”, “in the presently claimed invention, the variable grid is created based on a stationary obstacle candidate that is recognized as a stationary obstacle in accordance with feature”, “Kim fails to disclose determining that the stationary obstacle candidate is the stationary obstacle based on the final evaluation value, as is presently claimed.”, “Kim fails to disclose the weight factor determined to be negative or positive depending on whether the matching data is the moving object data or stationary object data, as is presently claimed.”, and, “Bell, Horigome, Dorian, Nguyen, Ohkado, and Kim'374, fail to account for these deficiencies of Kim because the Examiner only cited Kim to support such features”, the Applicant's arguments are moot in view of the art being applied for the amended claim limitations. . See 35 U.S.C. 103 section below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170248693 Kim, in view of US 20210403037 HORIGOME et al. (HORIGOME hereafter).
Regarding Claim 1, Kim discloses A method for detecting stationary obstacles for a vehicle by sensor fusion, performed by a first processor (see at least Kim [¶0003, 0118, Clm. 15, Fig. 11], “the vehicle may judge location of obstacles or perform various controls using combined data instead of separately judging Radar dot data and Lidar dot data.”), the method comprising:
receiving Light Detection and Ranging (LiDAR) data on a surrounding area of the vehicle from a LiDAR sensor (see at least Kim [¶0056, Fig. 3], “[0056] The controller 130 receives sensing values of the Radar sensor 110 and the Lidar sensor 120”);
extracting a stationary obstacle candidate recognized as a stationary obstacle from the LiDAR data (see at least Kim [¶0020], “the controller estimates an outline of the obstacle by connecting the plurality of Lidar dots.”);
extracting matching data from the non-LiDAR data (see at least Kim [¶0069], “when a plurality of Radar dots Da5 and Da5-1 are found in the obstacle area A5, the controller 130 may match both of the Radar dots Da5 and Da5-1 to the obstacle area”) wherein the matching data comprises at least one of moving object data and stationary object data (see at least Kim [¶0088] “when the process of clustering Lidar dot data and Radar dot data is completed, the controller 130 may estimate an outline, a velocity, and the like of each cluster including Lidar dot data and Radar dot data matching thereto.”, i.e., the clusters represent the obstacles and the cluster velocity indicates whether it’s a moving or stationary obstacle.”);
calculating an overlap between the matching data and a variable grid created based on the stationary obstacle candidate (see at least Kim [¶0015, 0020, 0027, 0062 and Fig 5, 0071, 0110], “The controller estimates at least one of an outline and a velocity of the obstacle based on the clustered Radar dot data and Lidar dot data.”, ”When the Radar dot is found in the obstacle area, the clustering of the one or more Radar dots includes matching the Radar dot to a cluster corresponding to the obstacle area.”);
calculating a final evaluation value by applying a weight factor to the overlap (see at least Kim [¶0079, 0081], “the controller 130 may select a cluster of Lidar dots to be judged on whether Radar dot Da matches the cluster by applying weights”).
determining that the stationary obstacle candidate is the stationary obstacle based on the final evaluation value (see at least Kim [¶0088] “when the process of clustering Lidar dot data and Radar dot data is completed, the controller 130 may estimate an outline, a velocity, and the like of each cluster including Lidar dot data and Radar dot data matching thereto.”, i.e., the clusters represent the obstacles and the cluster velocity indicates whether it’s a moving or stationary obstacle.”) and
controlling the vehicle based on the final evaluation value, (see at least Kim [¶0118], “the vehicle may judge location of obstacles or perform various controls using combined data instead of separately judging Radar dot data and Lidar dot data.”)
wherein the weight factor is determined to be negative or positive depending on whether the matching data is the moving object data or stationary object data (see at least Kim [¶0079 – 0081, 0088], “the controller 130 may select a cluster of Lidar dots to be judged on whether Radar dot Da matches the cluster by applying weights”, “when the process of clustering Lidar dot data and Radar dot data is completed, the controller 130 may estimate an outline, a velocity, and the like of each cluster including Lidar dot data and Radar dot data matching thereto.”, i.e., the clusters represent the object data, the velocity indicates whether it’s a moving or stationary object, and the controller can determine weight factors as positive or negative values, based on the cluster velocity.”).
Kim does not explicitly disclose receiving non-LiDAR data comprising at least one of camera data and radar data on the surrounding area of the vehicle from anon-LiDAR sensor comprising at least one of a camera and a Radar.
However, HORIGOME is directed towards an arithmetic operation system for vehicles and discloses receiving non-LiDAR data comprising at least one of camera data and radar data on the surrounding area of the vehicle from anon-LiDAR sensor comprising at least one of a camera and a Radar (see at least HORIGOME [¶0196] “The result of recognition by the object recognition unit 701 is transmitted to a map generation unit 702. The map generation unit 702 divides an area around the subject vehicle into a plurality of areas (e.g., front, right, left, and rear areas), and performs map generation processing for each area. In the map generation processing, the object information recognized by the cameras 50 and the object information recognized by the radars 51 are integrated together for each area and reflected on the map.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of HORIGOME to modify Kim, with a reasonable expectation of success, to use the technique of receiving non-LiDAR data comprising at least one of camera data and radar data to improve the functional safety level of an automotive obstacle detection system so that a safety function that ensures safety can be achieved, and by extension, a functional safety level can be improved, as taught by HORIGOME.
Regarding Claim 2, Kim and HORIGOME in combination disclose The method of claim 1, Kim further discloses wherein the calculating and determining includes:
calculating the final evaluation value by applying the weight factor to the evaluation value (see at least Kim [¶0079 – 0081, 0088], “the controller 130 may select a cluster of Lidar dots to be judged on whether Radar dot Da matches the cluster by applying weights”, “when the process of clustering Lidar dot data and Radar dot data is completed, the controller 130 may estimate an outline, a velocity, and the like of each cluster including Lidar dot data and Radar dot data matching thereto.”, i.e., the clusters represent the obstacles and the cluster velocity indicates whether it’s a moving or stationary obstacle.”); and
determining the stationary obstacle when the final evaluation value is greater than or equal to a reference value (see at least Kim [¶0060, 0088] “the controller 130 [] may judge Lidar dots Di having a distance therebetween less than a predetermined reference interval as one cluster of an obstacle, and estimate an outline S1 of the obstacle by connecting the Lidar dots Di belonging to the cluster.”, “when the process of clustering Lidar dot data and Radar dot data is completed, the controller 130 may estimate an outline, a velocity, and the like of each cluster including Lidar dot data and Radar dot data matching thereto.”, i.e., the clusters represent the obstacles and the cluster velocity indicates whether it’s a moving or stationary obstacle.).
Kim does not explicitly disclose generating the variable grid corresponding to the stationary obstacle candidate;
setting a matching data area corresponding to the matching data on the variable grid;
determining the overlap as an evaluation value;
However, HORIGOME discloses generating the variable grid corresponding to the stationary obstacle candidate (see at least HORIGOME [¶0084 – 0087, Fig. 3], “[0084] First, as shown in FIG. 3, the first route calculation unit 113 executes grid point set processing based on roadway information”, “[0087] The grid interval may be a variable value according to the vehicle speed or the like. Since the roadway 5 shown in FIG. 3 is a straight section, the grid area RW and the grid sections are respectively set in a rectangular shape. When the roadway includes a curved section, the grid area and the grid sections may or may not be set in a rectangular shape.”);
setting a matching data area corresponding to the matching data on the variable grid (see at least HORIGOME [¶0084 – 0087, Fig. 3], “[0084] First, as shown in FIG. 3, the first route calculation unit 113 executes grid point set processing based on roadway information”, “[0087] The grid interval may be a variable value according to the vehicle speed or the like. Since the roadway 5 shown in FIG. 3 is a straight section, the grid area RW and the grid sections are respectively set in a rectangular shape. When the roadway includes a curved section, the grid area and the grid sections may or may not be set in a rectangular shape.”);
determining the overlap as an evaluation value (see at least HORIGOME [¶0196], “the object information recognized by the cameras 50 and the object information recognized by the radars 51 are integrated together for each area and reflected on the map.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of HORIGOME to modify Kim, with a reasonable expectation of success, to use the technique of determining an evaluation value according to an overlap between a variable grid area and the matching data area to improve the functional safety level of an automotive obstacle detection system, so that a safety function that ensures safety can be achieved, and by extension, a functional safety level can be improved, as taught by HORIGOME.
Regarding Claim 7, Kim and HORIGOME in combination disclose all limitations of claim 2 as discussed above, HORIGOME further discloses wherein the matching data includes at least one of camera data on the moving object, radar data on the moving object, and radar data on a stationary object (see at least HORIGOME [¶0169], “Examples of the vehicle external information acquisition device M1 include (1) a plurality of cameras 50, (2) a plurality of radars 51”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of HORIGOME to modify Kim, with a reasonable expectation of success, to use matching data that includes at least one of camera data on a moving object, radar data on the moving object, and radar data on a stationary object, with the purpose of estimating the position of the subject vehicle with respect to the moving object and the stationary object using highly accurate map information, as taught by HORIGOME.
Claims 16, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170248693 Kim, in view of US 20210403037 HORIGOME et al. and further in view of US 20200401825 OHKADO et al. (OHKADO hereafter).
Regarding Claim 16, Kim discloses A stationary obstacle detection system for a vehicle using sensor fusion technology (see at least Kim [¶0003, 0118, Clm. 15, Fig. 11], “[0118] the vehicle may judge location of obstacles or perform various controls using combined data instead of separately judging Radar dot data and Lidar dot data.”), the stationary obstacle detection system comprising:
a Light Detection and Ranging (LiDAR) sensor configured to obtain LiDAR data on a surrounding area of the vehicle (see at least Kim [¶0047], “The Lidar sensor 120 refers to a sensing device to monitor a distance from an object, a direction, an altitude, and a velocity of the object, and the like, by transmitting laser beams having shorter wavelengths than electromagnetic waves (e.g., infrared light or visible light) to the object and receiving light that reflect from the object.”);
a first processor receiving the LiDAR data and the non-LiDAR data from the LiDar sensor and the non-LiDAR sensor (see at least Kim [¶0056] “The controller 130 receives sensing values of the Radar sensor 110 and the Lidar sensor 120 and point clusters the Radar dot data and Lidar dot data.”),
wherein the first processor is configured to:
extract a stationary obstacle candidate recognized as a stationary obstacle from the LiDAR data (see at least Kim [¶0020], “the controller estimates an outline of the obstacle by connecting the plurality of Lidar dots.”);
extract matching data from the non-LiDAR data (see at least Kim [¶0069], “when a plurality of Radar dots Da5 and Da5-1 are found in the obstacle area A5, the controller 130 may match both of the Radar dots Da5 and Da5-1 to the obstacle area”), wherein the matching data comprises at least one of moving object data and stationary object data (see at least Kim [¶0088] “when the process of clustering Lidar dot data and Radar dot data is completed, the controller 130 may estimate an outline, a velocity, and the like of each cluster including Lidar dot data and Radar dot data matching thereto.”, i.e., the clusters represent the obstacles and the cluster velocity indicates whether it’s a moving or stationary obstacle.”);
calculate an overlap between the matching data and a variable grid, created based on the stationary obstacle candidate (see at least Kim [¶0015, 0020, 0027, 0062 and Fig 5, 0071, 0110], “The controller estimates at least one of an outline and a velocity of the obstacle based on the clustered Radar dot data and Lidar dot data.”, ”When the Radar dot is found in the obstacle area, the clustering of the one or more Radar dots includes matching the Radar dot to a cluster corresponding to the obstacle area.”); and
calculate a final evaluation value by applying a weight factor to the overlap (see at least Kim [¶0079, 0081], “the controller 130 may select a cluster of Lidar dots to be judged on whether Radar dot Da matches the cluster by applying weights”); and
determine that the stationary obstacle candidate is the stationary obstacle based on the final evaluation value (see at least Kim [¶0088] “when the process of clustering Lidar dot data and Radar dot data is completed, the controller 130 may estimate an outline, a velocity, and the like of each cluster including Lidar dot data and Radar dot data matching thereto.”, i.e., the clusters represent the obstacles and the cluster velocity indicates whether it’s a moving or stationary obstacle.”), and
wherein the weight factor is determined to be negative or positive depending on whether the matching data is the moving object data or stationary object data (see at least Kim [¶0079 – 0081, 0088], “the controller 130 may select a cluster of Lidar dots to be judged on whether Radar dot Da matches the cluster by applying weights”, “when the process of clustering Lidar dot data and Radar dot data is completed, the controller 130 may estimate an outline, a velocity, and the like of each cluster including Lidar dot data and Radar dot data matching thereto.”, i.e., the clusters represent the object data, the velocity indicates whether it’s a moving or stationary object, and the controller can determine weight factors as positive or negative values, based on the cluster velocity.”),
Kim does not explicitly disclose a non-LiDAR sensor comprising at least one of a camera and a Radar and configured to collect non-LiDAR data comprising at least one of camera data and radar data on the surrounding area of the vehicle.
However, HORIGOME is directed towards an arithmetic operation system for vehicles and discloses a non-LiDAR sensor comprising at least one of a camera and a Radar and configured to collect non-LiDAR data comprising at least one of camera data and radar data on the surrounding area of the vehicle (see at least HORIGOME [¶0196] “The result of recognition by the object recognition unit 701 is transmitted to a map generation unit 702. The map generation unit 702 divides an area around the subject vehicle into a plurality of areas (e.g., front, right, left, and rear areas), and performs map generation processing for each area. In the map generation processing, the object information recognized by the cameras 50 and the object information recognized by the radars 51 are integrated together for each area and reflected on the map.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of HORIGOME to modify Kim, with a reasonable expectation of success, to use the technique of a non-LiDAR sensor comprising at least one of a camera and a Radar and configured to collect non-LiDAR data comprising at least one of camera data and radar data to improve the functional safety level of an automotive obstacle detection system so that a safety function that ensures safety can be achieved, and by extension, a functional safety level can be improved, as taught by HORIGOME.
Kim and HORIGOME in combination do not explicitly disclose wherein the first processor is further configured to output a control signal based on the final evaluation value, and a second processor is configured to control the vehicle according to the control signal from the first processor.
However, OHKADO is directed towards an object detection device and method and discloses wherein the first processor is further configured to output a control signal based on the final evaluation value, and a second processor is configured to control the vehicle according to the control signal from the first processor (see at least OHKADO [¶0041], ”in a case where an object is detected in the traveling direction of the vehicle, a traveling path for avoiding the object is calculated so that the traveling of the vehicle can be controlled, or in a case where an object is detected at close range from the vehicle, the vehicle can be stopped.”, “the drive control device controls the driving of the steering drive device, the accelerator drive device, the brake drive device, and the like to control operation of the vehicle.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of OHKADO to modify Kim, with a reasonable expectation of success, to use a second processor configured to output a control signal for a brake and/or a steering apparatus according to the control signal from the first processor, to ensure that the vehicle is safely driven on the basis of the result of these measurements, as taught by OHKADO.
Regarding Claim 17, Kim, HORIGOME, and OHKADO in combination disclose all limitations of claim 16 as discussed above, Kim further discloses calculate the final evaluation value by applying a weight factor to the evaluation value (see at least Kim [¶0079 – 0081, 0088], “the controller 130 may select a cluster of Lidar dots to be judged on whether Radar dot Da matches the cluster by applying weights”, “when the process of clustering Lidar dot data and Radar dot data is completed, the controller 130 may estimate an outline, a velocity, and the like of each cluster including Lidar dot data and Radar dot data matching thereto.”, i.e., the clusters represent the obstacles and the cluster velocity indicates whether it’s a moving or stationary obstacle.”); and
determine the stationary obstacle when the final evaluation value is greater than or equal to a reference value (see at least Kim [¶0060, 0088] “the controller 130 [] may judge Lidar dots Di having a distance therebetween less than a predetermined reference interval as one cluster of an obstacle, and estimate an outline S1 of the obstacle by connecting the Lidar dots Di belonging to the cluster.”, “when the process of clustering Lidar dot data and Radar dot data is completed, the controller 130 may estimate an outline, a velocity, and the like of each cluster including Lidar dot data and Radar dot data matching thereto.”, i.e., the clusters represent the obstacles and the cluster velocity indicates whether it’s a moving or stationary obstacle.).
HORIGOME further discloses generate the variable grid corresponding to the stationary obstacle candidate data (see at least HORIGOME [¶0084 – 0087, Fig. 3], “[0084] First, as shown in FIG. 3, the first route calculation unit 113 executes grid point set processing based on roadway information”, “[0087] The grid interval may be a variable value according to the vehicle speed or the like. Since the roadway 5 shown in FIG. 3 is a straight section, the grid area RW and the grid sections are respectively set in a rectangular shape. When the roadway includes a curved section, the grid area and the grid sections may or may not be set in a rectangular shape.”);
determine the overlap as an evaluation value (see at least HORIGOME [¶0196], “the object information recognized by the cameras 50 and the object information recognized by the radars 51 are integrated together for each area and reflected on the map.”);
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of HORIGOME to modify Kim, with a reasonable expectation of success, to use the technique of generating the variable grid corresponding to the stationary obstacle candidate data and determining the overlap as an evaluation value, for the purpose of estimating the position of the subject vehicle with respect to the moving object and the stationary object using highly accurate map information, as taught by HORIGOME.
Regarding Claim 20, Kim, HORIGOME, and OHKADO in combination disclose all limitations of claim 16 as discussed above, OHKADO further discloses the second processor configured to output the control signal for a brake and/or a steering apparatus according to the control signal from the first processor (see at least OHKADO [¶0041], ”in a case where an object is detected in the traveling direction of the vehicle, a traveling path for avoiding the object is calculated so that the traveling of the vehicle can be controlled, or in a case where an object is detected at close range from the vehicle, the vehicle can be stopped.”, “the drive control device controls the driving of the steering drive device, the accelerator drive device, the brake drive device, and the like to control operation of the vehicle.”).
Claims 3, 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170248693 Kim, in view of US 20210403037 HORIGOME et al. and further in view of US 20190138823 Dorian et al. (Dorian hereafter).
Regarding Claim 3, Kim and HORIGOME in combination disclose The method of claim 2 as discussed above, but do not explicitly disclose wherein the overlap is determined by a ratio of a number of cells overlapping the matching data area to a total number of cells in a variable grid area.
However, Dorian is directed towards automatically detecting the location and severity of occluded regions within input data, and discloses wherein the overlap is determined by a ratio of a number of cells overlapping the matching data area to a total number of cells in a variable grid area (see at least Dorian [¶0020 – 0030, Figs. 2 – 3], “[0020] using a light detection and ranging (LIDAR) data set, a grid representation of the environment is generated by ray tracing (e.g., traversal) the LIDAR data points to track and characterize spaces of the grid representation as free, occupied, and hidden/occluded. The grid is then bounded, such as using a lane model for a roadway, keeping only a portion of the grid representation”, “A connected component analysis is then performed on the hidden space inside the lane model to identifying connected regions of hidden space, and thresholding the resulting connected regions of hidden space by size to indicate where LIDAR occlusions exist.”, “[0028] Using the cropped/clipped sensor data, a grid 504 is generated. For example, a cropped Boolean grid is constructed from the data remaining after a region of interest is defined and applied.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Dorian to modify Kim, with a reasonable expectation of success, to use the technique of defining the overlap as determined by a ratio of a number of cells overlapping the matching data area to a total number of cells in the variable grid area. The detected occlusions may be used to indicate where additional data may be collected to supplement the input data set, or to report that a gap exists within the input data set. By detecting occlusions, the resulting 3D map/model based on the input data can be made more accurate and comprehensive, as taught by Dorian.
Regarding Claim 4, Kim, HORIGOME, and Dorian in combination disclose all limitations of claim 3 as discussed above, HORIGOME further discloses wherein the variable grid includes a constant number of cells regardless of sizes of the stationary obstacle candidate and the matching data (see at least HORIGOME [¶0085, Fig. 3], “The grid area RW ranges from the periphery of the subject vehicle 1 to a predetermined distance ahead of the subject vehicle 1 along the roadway 5.”, “the distance L may be a predetermined fixed distance (e.g., 100 m)”, “The width W of the grid area RW is set to be the width of the roadway 5.”). That means that a having a fixed value for L and W will contain a constant number of cells in the grid.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of HORIGOME to modify Kim, with a reasonable expectation of success, to use the technique of defining the variable grid to include a constant number of cells regardless of sizes of the stationary obstacle candidate and the matching data, to improve the functional safety level of an automotive arithmetic system having the function of using deep learning, so that a safety function that ensures safety can be achieved, and by extension, a functional safety level can be improved, as taught by HORIGOME.
Regarding Claim 5, Kim, HORIGOME, and Dorian in combination disclose all limitations of claim 3 as discussed above, HORIGOME further discloses wherein the variable grid has a rectangular shape (see at least HORIGOME [¶0086, 0087], “[0086] The grid area RW is divided into a large number of rectangular grid sections”, “[0087] the grid area RW and the grid sections are respectively set in a rectangular shape.”),
wherein the cells in the variable grid area are divided into m cells in a horizontal direction and n cells in a longitudinal direction in the variable grid area, wherein the m and the n are integers greater than zero (see at least HORIGOME [¶0085, 0086, Fig. 3], “The grid area RW is divided into a large number of rectangular grid sections by a plurality of grid lines extending along the extending direction X and width direction (lateral direction) Y of the roadway 5. Points of intersection of the grid lines in the X and Y directions are grid points Gn. Intervals in the X and Y directions between the grid points Gn are respectively set to fixed values.”). That is, the number of intervals in the Y and X directions between the grid points Gn along the width W and distance L of the grid, respectively, correspond to m and n.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of HORIGOME to modify Kim, with a reasonable expectation of success, to use the technique of defining the variable grid shape and number of cells to improve the functional safety level of an automotive arithmetic system having the function of using deep learning, so that a safety function that ensures safety can be achieved, and by extension, a functional safety level can be improved, as taught by HORIGOME.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over US 20170248693 Kim, in view of US 20210403037 HORIGOME et al. and further in view of US 20160180177 NGUYEN et al. (NGUYEN hereafter).
Regarding Claim 6, Kim and HORIGOME disclose all limitations of claim 2 as discussed above, Kim and HORIGOME do not explicitly disclose wherein the variable grid is determined by maximum and minimum values in a horizontal direction and maximum and minimum values in a longitudinal direction in the stationary obstacle candidate.
However, NGUYEN is directed towards a fused raised pavement marker detection system for autonomous vehicles using LiDAR and camera, NGUYEN discloses wherein the variable grid is determined by maximum and minimum values in a horizontal direction and maximum and minimum values in a longitudinal direction in the stationary obstacle candidate (see at least NGUYEN [¶0054], “Lidar processing module 60 may be pre-programmed with minimum and maximum size thresholds, such that any clusters having size less than the maximum size threshold and greater than the minimum size threshold are identified as candidate raised pavement markers 195. The above-described thresholds may be determined by machine learning methodologies, such as support vector machine, or may be determined empirically.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of NGUYEN to modify Kim, with a reasonable expectation of success, to use the technique of defining the variable grid as determined by maximum and minimum values in a horizontal direction and maximum and minimum values in a longitudinal direction in the LiDAR data on the extracted stationary obstacle candidate. The use of camera and image information in combination with a LiDAR data and lidar processing enhances performance of the detection system, as taught by NGUYEN.
Claims 8 thru 10, and 13 thru 15 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170248693 Kim, in view of US 20210403037 HORIGOME et al., and further in view of US 20130004060 Bell et al. (Bell hereafter).
Regarding Claim 8, Kim and HORIGOME in combination disclose all limitations of claim 7 as discussed above, HORIGOME further discloses a fourth evaluation value for the LiDAR data on the extracted stationary obstacle candidate itself (see at least HORIGOME [¶0169], “Examples of the vehicle external information acquisition device M1 include (1) a plurality of cameras 50, (2) a plurality of radars 51”).
Kim and HORIGOME do not explicitly disclose wherein the final evaluation value is determined according to at least one of a first evaluation value determined from a first overlap between an area corresponding to the camera data on the moving object and the variable grid area, a second evaluation value determined from a second overlap between an area corresponding to the radar data on the moving object and the variable grid area, a third evaluation value determined from a third overlap between an area corresponding to the radar data on the stationary object and the variable grid area.
However, Bell is directed towards capture and alignment of multiple 3D scenes, Bell discloses wherein the final evaluation value is determined according to at least one of a first evaluation value determined from a first overlap between an area corresponding to the camera data on the moving object and the variable grid area, a second evaluation value determined from a second overlap between an area corresponding to the radar data on the moving object and the variable grid area, a third evaluation value determined from a third overlap between an area corresponding to the radar data on the stationary object and the variable grid area (see at least Bell [¶0026, 0030], “[0026] Potential matching points between two scenes (step 114) may be found by picking a point or region from the feature tree of one 3D scene and then looking for neighbors based on the features of this point or region in the feature tree of the other scene. The selection of three or more points or regions from the first 3D scene plus their corresponding neighbors in the other 3D scene produces a candidate transformation from one 3D scene to the other. The candidate transformation may then be evaluated by a scoring function”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Bell to modify Kim, with a reasonable expectation of success, to use evaluation values from data that includes camera data on a moving object, radar data on the moving object, and radar data on a stationary object. This data produces scene data, including information about the position and appearance of objects in physical space and information regarding the position and/or orientation of the capture device and can then be used as inputs to an algorithm that determines potential alignments between different 3D scenes, as taught by Bell, for the purpose of determining objects in the environment.
Regarding Claim 9, Kim, HORIGOME, and Bell in combination disclose all limitations of claim 8 as discussed above, Bell further discloses wherein the final evaluation value is determined by adding a weight factor to each of the first evaluation value, the second evaluation value, the third evaluation value, and the fourth evaluation value (see at least Bell [¶0030 - 32], “[0030] the degree and nature of the overlap between regions of known empty space from the first 3D scene and the second 3D scene--may also influence the score.”, “Examples of such information include extractions of planes, cylindrical areas, spherical areas, or other geometric primitives, object recognition algorithms”, “[0031] components of these scores may be weighted, adjusted, or scaled (e.g., by adding a forgiveness factor) based on various aspects of the data or feature data.”, “[0032] The overall score for an alignment may be a sum of point and/or region scores, a weighted sum of point and/or region scores, or another method.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Bell to modify Kim, with a reasonable expectation of success, to use the technique of determining the evaluation value by adding a weight factor to each of the first evaluation value, the second evaluation value, the third evaluation value, and the fourth evaluation value for the purpose of obtaining more reliable data by assigning different weight factors depending on the type of data, e.g. boundary or edge data, noisy data, and the like, as taught by Bell.
Regarding Claim 10, Kim, HORIGOME, and Bell in combination disclose all limitations of claim 8 as discussed above, Bell further discloses wherein the final evaluation value (Vf) is determined by a following equation: Vf=a*V1+b*V2+c*V3+d*V4 (see at least Bell [¶0032], “The overall score for an alignment may be a sum of point and/or region scores, a weighted sum of point and/or region scores, or another method.”),
wherein Vi denotes the first evaluation value, V2 denotes the second evaluation value, V3 denotes the third evaluation value, and V4 denotes the fourth evaluation value (see at least Bell [¶0026, 0030], “[0026] Potential matching points between two scenes (step 114) may be found by picking a point or region from the feature tree of one 3D scene and then looking for neighbors based on the features of this point or region in the feature tree of the other scene. The selection of three or more points or regions from the first 3D scene plus their corresponding neighbors in the other 3D scene produces a candidate transformation from one 3D scene to the other. The candidate transformation may then be evaluated by a scoring function”), and
wherein a is a first weight factor for the first evaluation value, b is a second weight factor for the second evaluation value, c is a third weight factor for the third evaluation value, and d is a fourth weight factor for the fourth evaluation value (see at least Bell [¶0030 - 32], “[0030] the degree and nature of the overlap between regions of known empty space from the first 3D scene and the second 3D scene--may also influence the score.”, “Examples of such information include extractions of planes, cylindrical areas, spherical areas, or other geometric primitives, object recognition algorithms”, “[0031] components of these scores may be weighted, adjusted, or scaled (e.g., by adding a forgiveness factor) based on various aspects of the data or feature data.”, “[0032] The overall score for an alignment may be a sum of point and/or region scores, a weighted sum of point and/or region scores, or another method.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Bell to modify Kim, with a reasonable expectation of success, to use the technique of determining the evaluation value by adding a weight factor to each of the first evaluation value, the second evaluation value, the third evaluation value, and the fourth evaluation value for the purpose of obtaining more reliable data by assigning different weight factors depending on the type of data, e.g. boundary or edge data, noisy data, and the like, as taught by Bell.
Regarding Claims 13 thru 15, Kim, HORIGOME and Bell in combination disclose all limitations of claim 8 as discussed above.
Regarding Claim 13, Bell further discloses wherein the first weight factor is a negative number (see at least Bell [¶0029], “Scoring of points or regions may be influenced by whether the position of the point is plausible from the point of view of the 3D capture device of the other scene. For example, if the transformed position of a first point or region from a first 3D scene places it in a position in the second 3D scene that is known to be empty space (e.g., because the first point/region is substantially closer to the second 3D capture device than the point/region from the second 3D scene detected along the ray from the second 3D capture device to the first point/region) then this point is likely in the wrong position and the match score may be penalized.”), that means that when the difference in the captured data points increases, i.e., the overlap increases, then the scoring is penalized, i.e., the evaluation is set smaller.
Regarding Claim 14, Bell further discloses wherein the second weight factor is a negative number (see at least Bell [¶0029] above).
Regarding Claim 15, Bell further discloses wherein the third weight factor is a positive number (see at least Bell [¶0029] above).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Bell to modify Kim, with a reasonable expectation of success, to use the technique of setting the evaluation value smaller as the overlap increases for the purpose of obtaining more reliable image data alignment, as taught by Bell.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over US 20170248693 Kim, US 20210403037 HORIGOME et al., US 20130004060 Bell et al., and further in view of US 20190279374 KIM et al. (KIM 2 hereafter).
Regarding Claim 12, (Original), Kim, HORIGOME and Bell in combination disclose all limitations of claim 8 as discussed above, but do not explicitly disclose wherein the area corresponding to the camera data on the moving object is adjusted in size in consideration of a location of a camera.
However, KIM 2 is directed towards an image analysis method, device, system, and program, which use vehicle driving information, KIM 2 discloses wherein the area corresponding to the camera data on the moving object is adjusted in size in consideration of a location of a camera (see at least KIM 2 [¶0106, 0127] ”[0106] the image analysis device 450 may estimate the setting parameters of a camera based on the identified background motion pattern, and may compare the estimate camera setting parameters with actually set camera setting parameters to correct the setting parameters of the camera.”, “the current camera setting information and the current orientation direction of the camera may differ from each other. For accurate image analysis, therefore, it may be important to correct the current camera setting information so as to correspond to the current orientation direction of the camera.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of KIM 2 to modify Kim, with a reasonable expectation of success, to use the technique of adjusting in size the area corresponding to the camera data on the moving object in consideration of a location of a camera, for the purpose of increasing image analysis accuracy, as taught by KIM 2.
Claims 11, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170248693 Kim, US 20210403037 HORIGOME et al., US 20130004060 Bell et al., and further in view of US 20200401825 OHKADO et al. (OHKADO hereafter).
Regarding Claim 11, Kim, HORIGOME and Bell in combination disclose all limitations of claim 10 as discussed above, but do not explicitly disclose further comprising generating a steering control signal when the final evaluation value is greater than or equal to a reference value so that the vehicle changes lanes to avoid a collision or generating a braking control signal to generate a demand braking torque for deceleration or stopping of the vehicle.
However, OHKADO is directed towards an object detection device and method, OHKADO discloses further comprising generating a steering control signal when the final evaluation value is greater than or equal to a reference value so that the vehicle changes lanes to avoid a collision or generating a braking control signal to generate a demand braking torque for deceleration or stopping of the vehicle (see at least OHKADO [¶0041], ”in a case where an object is detected in the traveling direction of the vehicle, a traveling path for avoiding the object is calculated so that the traveling of the vehicle can be controlled, or in a case where an object is detected at close range from the vehicle, the vehicle can be stopped.”, “the drive control device controls the driving of the steering drive device, the accelerator drive device, the brake drive device, and the like to control operation of the vehicle.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of OHKADO to modify Kim, with a reasonable expectation of success, to use the technique of generating a steering control signal so that the vehicle changes lanes to avoid a collision or to generate a demand braking torque for deceleration or stopping of the vehicle when the final evaluation value is greater than or equal to a reference value, such that in a case where an object is detected in the traveling direction of the vehicle, a traveling path for avoiding the object is calculated so that the traveling of the vehicle can be controlled, or in a case where an object is detected at close range from the vehicle, the vehicle can be stopped, increasing safety, as taught by OHKADO .
Regarding Claim 18, Kim, HORIGOME, and OHKADO in combination disclose all limitations of claim 17 as discussed above, HORIGOME further discloses wherein the matching data includes at least one of camera data on the moving object, radar data on the moving object, and radar data on the stationary object (see at least HORIGOME [¶0169], “Examples of the vehicle external information acquisition device M1 include (1) a plurality of cameras 50, (2) a plurality of radars 51”);
a fourth evaluation value for the LiDAR data on the extracted stationary obstacle candidate itself (see at least HORIGOME [¶0169], “Examples of the vehicle external information acquisition device M1 include (1) a plurality of cameras 50, (2) a plurality of radars 51”).
Kim, HORIGOME, and OHKADO in combination do not explicitly disclose wherein the final evaluation value is determined according to at least one of a first evaluation value determined from a first overlap between an area corresponding to the camera data on the moving object and the variable grid area, a second evaluation value determined from a second overlap between an area corresponding to the radar data on the moving object and the variable grid area, a third evaluation value determined from a third overlap between an area corresponding to the radar data on the stationary object and the variable grid area.
However, Bell discloses wherein the final evaluation value is determined according to at least one of a first evaluation value determined from a first overlap between an area corresponding to the camera data on the moving object and the variable grid area, a second evaluation value determined from a second overlap between an area corresponding to the radar data on the moving object and the variable grid area, a third evaluation value determined from a third overlap between an area corresponding to the radar data on the stationary object and the variable grid area (see at least Bell [¶0026, 0030], “[0026] Potential matching points between two scenes (step 114) may be found by picking a point or region from the feature tree of one 3D scene and then looking for neighbors based on the features of this point or region in the feature tree of the other scene. The selection of three or more points or regions from the first 3D scene plus their corresponding neighbors in the other 3D scene produces a candidate transformation from one 3D scene to the other. The candidate transformation may then be evaluated by a scoring function”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Bell to modify Kim, with a reasonable expectation of success, to use evaluation values from data that includes camera data on a moving object, radar data on the moving object, and radar data on a stationary object. This data produces scene data, including information about the position and appearance of objects in physical space and information regarding the position and/or orientation of the capture device and can then be used as inputs to an algorithm that determines potential alignments between different 3D scenes, as taught by Bell, for the purpose of determining objects in the environment.
Regarding Claim 19, Kim, HORIGOME, OHKADO, and Bell in combination disclose all limitations of claim 18 as discussed above, Bell further discloses wherein a final evaluation value (Vf) is determined by a following equation: Vf=a*V1+b*V2+c*V3+d*V4 (see at least Bell [¶0032], “The overall score for an alignment may be a sum of point and/or region scores, a weighted sum of point and/or region scores, or another method.”),
wherein Vi denotes the first evaluation value, V2 denotes the second evaluation value, V3 denotes the third evaluation value, and V4 denotes the fourth evaluation value, and wherein a is a first weight factor for the first evaluation value, b is a second weight factor for the second evaluation value, c is a third weight factor for the third evaluation value, and d is a fourth weight factor for the fourth evaluation value (see at least Bell [¶0030 - 32], “[0030] the degree and nature of the overlap between regions of known empty space from the first 3D scene and the second 3D scene--may also influence the score.”, “Examples of such information include extractions of planes, cylindrical areas, spherical areas, or other geometric primitives, object recognition algorithms”, “[0031] components of these scores may be weighted, adjusted, or scaled (e.g., by adding a forgiveness factor) based on various aspects of the data or feature data.”, “[0032] The overall score for an alignment may be a sum of point and/or region scores, a weighted sum of point and/or region scores, or another method.”).
OHKADO further discloses wherein the first processor is further configured to output the control signal when the final evaluation value is greater than or equal to a reference value (see at least OHKADO [¶0041], ”in a case where an object is detected in the traveling direction of the vehicle, a traveling path for avoiding the object is calculated so that the traveling of the vehicle can be controlled, or in a case where an object is detected at close range from the vehicle, the vehicle can be stopped.”, “the drive control device controls the driving of the steering drive device, the accelerator drive device, the brake drive device, and the like to control operation of the vehicle.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of OHKADO to modify Kim, with a reasonable expectation of success, to use the technique of output the control signal when the final evaluation value is greater than or equal to a reference value to ensure that the vehicle is safely driven on the basis of the result of these measurements, as taught by OHKADO.
Conclusion
Examiner notes that the fundamentals of the rejection are based on the broadest reasonable interpretation of the claim language. Any reference to specific figures, column, line and paragraphs should not be considered limiting in any way, the entire cited reference, as well as any secondary teaching reference(s), are considered to provide relevant disclosure relating to the claimed invention. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Examiner encourages Applicant to fill out and submit form PTO-SB-439 to allow internet communications in accordance with 37 CFR 1.33 (MPEP 502.03). Should the need arise to perfect applicant-proposed or examiner’s amendments, authorization for e-mail correspondence would have already been authorized and would save time.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Neit J. Nieves Flores whose telephone number is (703)756-5864. The examiner can normally be reached M-F 0930-1800 AST.
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/Neit J. Nieves Flores/
Patent Examiner
Art Unit 3664
/RACHID BENDIDI/ Supervisory Patent Examiner, Art Unit 3664
Prosecution Insights