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 the Claims
This Final action is in response to the applicant’s amendment/response of July 8, 2025.
Claims 1-11 are pending and have been considered as follows.
Response to Arguments
Applicant’s arguments/amendments with respect to the objection to the claims have been fully considered and are persuasive. Therefore, the objection to the claims as presented in the Office Action of April 9, 2025 has been withdrawn. However, new objection to the claims is presented below based on the amendments to the claims presented in the Amendment of July 8, 2025.
Applicant’s arguments/amendments with respect to the rejection of claims under 35 USC §112(b) have been fully considered and are partially persuasive. With respect to the rejection of claims under 35 USC §112(b), limitations within the claims have been amended and amendments overcome the rejection under 35 USC §112(b). However, the amendments to claims do not overcome the rejection under 35 USC §112(b) going to the specific limitations of “multipass” in claim 11 and “strong reflection” in claim 11. Therefore, the rejection of such claims under 35 USC §112(b) is maintained herein, and new rejection under 35 USC §112(b) is presented below based on the amendments to the claims presented in the Amendment of July 8, 2025.
Applicant’s arguments/amendments with respect to the rejection of claims under 35 USC § 103 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claims 1 and 10 are objected to because of the following informalities:
Claim 1, line 22, “a result” should read “the detection result”.
Claim 1, line 26, “an image” should read “the image”.
Claim 10, line 29, “pdetection result” appears to be a typographical error and should read “the detection result”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“three-dimensional map generation unit” in claims 1, 7, 8, and 10.
“weight estimation unit” in claims 1-5 and 9-11.
“adjustment unit” in claims 1 and 10.
“single attribute estimation unit” in claims 1, 5, and 10.
“sensor fusion unit” in claim 6.
“map prediction unit” in claims 7 and 8.
“composite map creation unit” in claim 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 11, the limitation “… when the combination of the three-dimensional shape for each region of the three-dimensional map and the attribute of the object is a predetermined combination in which multipass or strong reflection is likely to occur” at line 7 is unclear. Specifically, the limitation is grammatically confusing (the Examiner is unsure what is meant by this limitation). Moreover, it is unclear what “multipass” is referring to and what is being claimed in light of Applicant’s original disclosure as the specification does not provide any explanation as to what is meant by “multipass” For purposes of examination, the claim is being treated as the interpretation of this claim limitation based on what is described in the specification, particularly the disclosure at paragraphs 29-33 of the specification.
Further, the limitation “… when the combination of the three-dimensional shape for each region of the three-dimensional map and the attribute of the object is a predetermined combination in which multipass or strong reflection is likely to occur” at line 7 is unclear. Specifically, the term “strong” is a relative term which renders the claim indefinite. The term “strong” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claim(s) 1-3, 6, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over HIRATSUKA et al., JP 2014006588 A, hereinafter referred to as HIRATSUKA, in view of KITAHAMA et al., JP 2007102488 A, hereinafter referred to as KITAHAMA, in view of FUJII, JP 2006252348 A, hereinafter referred to as FUJII, in view of SHIRAISHI, JP 2009019914 A, hereinafter referred to as SHIRAISHI, and further in view of HSU et al., US 2018/0172825 A1, hereinafter referred to as HSU, respectively.
As to claim 1, HIRATSUKA teaches a control system comprising:
an image-capturing device that is mounted on a vehicle (see at least paragraph 29 regarding an imaging device 14, HIRATSUKA); and
a computing device (see at least paragraph 29, HIRATSUKA), wherein the computing device includes
a three-dimensional map generation unit that generates a three-dimensional map of an object around the vehicle from an image captured by the image-capturing device (see at least paragraphs 34-38 regarding a three-dimensional object boundary estimation unit 22 that estimates three-dimensional object boundaries based on the three-dimensional object areas, an edge line segment extraction unit 24 that extracts edge line segments from the captured image captured by the imaging device 14. The three-dimensional object region extraction unit 20 acquires observation data observed by the laser radar 12 and extracts a three-dimensional object region indicating a three-dimensional object from the observation data. The observation data of the laser radar 12 is projected onto a two-dimensional XZ-plane grid map in which the road surface plane is divided into a grid, and the observation data is projected onto cells corresponding to the XZ-plane positions of the observation data. Then, cells in which the variance in the height direction of the observation data projected into each cell is equal to or greater than a predetermined threshold are extracted as solid object regions, and a solid object map 50 such as that shown in FIG. 4 is generated, HIRATSUKA); and
a weight estimation unit that estimates, from a three-dimensional shape of each region of the three-dimensional map, a weight of a detection result by the millimeter wave radar for each region of the three-dimensional map (see at least FIGS. 4-7 and paragraphs 92-105 regarding the weighting coefficient may be a value that increases as the cell of interest and the cell representing the solid object region become closer, and may be determined based on, for example, a Gaussian distribution. The line segment selection unit 426 also calculates an evaluation value Eval<sub>ｉ</sub> by multiplying the edge line segment and the vector direction of the vehicle motion by the similarity between the edge line segment and the vector direction of the vehicle motion as a weight. The line segment selection unit 426 may add a weighting coefficient according to the distance from the edge line segment or a weight that takes into account the similarity with the vehicle motion vector to the evaluation value for boundary line segments estimated by a road model such as a clothoid curve).
HIRATSUKA teaches a case has been described in which a laser radar is used to observe information identifying the positions of multiple reflection points around the vehicle, but this is not limited to this, and electromagnetic waves such as millimeter waves may also be used (see at least paragraph 111, HIRATSUKA), however, HIRATSUKA does not explicitly teach a millimeter wave radar that is mounted on the vehicle and that emits electromagnetic waves; a single attribute estimation unit that estimates both an attribute and a material of the object around the vehicle, the attribute corresponding to an identification of whether the object is a pedestrian, a vehicle, a wall, a tree, a curbstone, a guardrail, or a pole; wherein the weight detection result is based on both an output of the millimeter wave radar and the attribute of the object; or a given detection result is synthesized based on an image captured by the image-capturing device and based on the detection result made by the millimeter wave radar.
However, KITAHAMA teaches a millimeter wave radar that is mounted on the vehicle and that emits electromagnetic waves (see at least paragraph 55 regarding in addition to the camera 10 and communication device 12 described above, sensors such as laser radar and millimeter wave radar may be used as means for recognizing the master and its surrounding environment); a single attribute estimation unit that estimates both an attribute and a material of the object around the vehicle, the attribute corresponding to an identification of whether the object is a pedestrian, a vehicle, a wall, a tree, a curbstone, a guardrail, or a pole (see at least paragraphs 22-25 regarding an image processing unit that processes the captured images to recognize the master and surrounding obstacles, road signs, etc. (surrounding environment). More specifically, the surrounding environment is recognized, such as the owner's position, direction of movement, and speed of movement, the position, shape, direction of movement, speed of movement, and operating status of other pedestrians, bicycles, automobiles, etc., the position and shape of obstacles such as stopped vehicles, utility poles, and fallen objects, and traffic conditions such as the status of traffic lights and road signs. In addition, attributes such as the type, weight, and material of the object are also recognized. Here, attributes relating to the type of object include classifications such as large vehicles, small vehicles, two wheeled vehicles, and bicycles. An attribute relating to weight is whether it is heavy or light, and an attribute relating to material is whether the surface is soft or hard. That is, the camera 10 and the communication device 12 function as a surrounding environment recognition means as defined in the claims); wherein the weight detection result is based on both an output of the millimeter wave radar and the attribute of the object (see at least paragraphs 22-28, 36-38, and 55 regarding the ECU 20 includes a danger level determination unit 22 that determines the degree of danger to the master based on the surrounding environment recognized by the camera 10 and the communication device 12. In addition to the camera 10 and communication device 12 described above, sensors such as laser radar and millimeter wave radar may be used as means for recognizing the master and its surrounding environment); and a given detection result is synthesized based on an image captured by the image-capturing device and based on the detection result made by the millimeter wave radar (see at least paragraphs 22-25 and 55 regarding in addition to the camera 10 and communication device 12 described above, sensors such as laser radar and millimeter wave radar may be used as means for recognizing the master and its surrounding environment).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of KITAHAMA which teaches a millimeter wave radar that is mounted on the vehicle and that emits electromagnetic waves; a single attribute estimation unit that estimates both an attribute and a material of the object around the vehicle, the attribute corresponding to an identification of whether the object is a pedestrian, a vehicle, a wall, a tree, a curbstone, a guardrail, or a pole; wherein the weight detection result is based on both an output of the millimeter wave radar and the attribute of the object; and a given detection result is synthesized based on an image captured by the image-capturing device and based on the detection result made by the millimeter wave radar with the system of HIRATSUKA as both systems are directed to a system and method for determining the presence of an object based on the detected information, and one of ordinary skill in the art would have recognized the established utility of having a millimeter wave radar that is mounted on the vehicle and that emits electromagnetic waves; a single attribute estimation unit that estimates both an attribute and a material of the object around the vehicle, the attribute corresponding to an identification of whether the object is a pedestrian, a vehicle, a wall, a tree, a curbstone, a guardrail, or a pole; wherein the weight detection result is based on both an output of the millimeter wave radar and the attribute of the object; and a given detection result is synthesized based on an image captured by the image-capturing device and based on the detection result made by the millimeter wave radar and would have predictably applied it to improve the system of HIRATSUKA.
HIRATSUKA, as modified by KITAHAMA, does not explicitly teach an adjustment unit that is configured to adjust a result by the millimeter wave radar based on the weight; wherein braking and acceleration of the vehicle are controlled based on distance to the object and relative speed; the adjustment unit adjusts the given detection result based on the weight; or the computing device generates a signal configured to control the vehicle based on the adjusted given detection result.
However, FUJII teaches an adjustment unit that is configured to adjust a result by the millimeter wave radar based on the weight (see at least paragraphs 9-13 regarding the voting value calculation unit calculates the voting value using weighting according to a decreasing function that reduces the weight as the distance from the measured object detected by the detection unit increases. The voting values are weighted according to a decreasing function that decreases the weight as the distance to the object increases, so even if the detected position shifts as the robot moves, By lowering the weight on distant measurement points where the influence of angular deviation is greater, it is possible to reduce the influence of detection position deviation and prevent false detection. See also at least paragraphs 34-35 regarding the obstacle detection unit 11 may be configured with an infrared type sensor or a millimeter wave radar type sensor. See also at least paragraphs 49-55 regarding the voting value calculation unit 97 calculates the voting value by applying the following weighting in addition to the weighting according to the distance described above. That is, the voting value calculation unit 97 performs a process of weighting according to a decreasing function that reduces weighting as the distance between adjacent measurement points increases); wherein braking and acceleration of the vehicle are controlled based on distance to the object and relative speed (see at least paragraphs 45 regarding when an abnormality is determined, an abnormality signal is output from the communication unit 17, and the movement control unit 5 performs predetermined processing such as stopping or decelerating the movement, avoiding obstacles, or tracking. See at least paragraphs 68-70 regarding a signal from the section 11 is input, and the movement control section 5 controls the moving means 3 to start moving along the white line tape 101 which is a guide means. Travel control may be the same as when generating an existing object. The process (S21, S23, S25) up to projecting the measurement point on the environmental map based on the input from the obstacle detection unit 11 is the same as (S1, S3, S5) in the existing object information generation procedure, so it will be explained here. omitted. Next, detection and determination of an intruding object that has newly appeared within the monitoring area is performed (S27). Details of the intruder determination process will be described later. In the intruder determination process, if there is an intruder, it is determined that there is an abnormality. If there is an intruder (S29, Yes), the mobile robot 1 stops traveling (S35)); the adjustment unit adjusts the given detection result based on the weight (see at least paragraphs 9-13 regarding the voting value calculation unit calculates the voting value using weighting according to a decreasing function that reduces the weight as the distance from the measured object detected by the detection unit increases. The voting values are weighted according to a decreasing function that decreases the weight as the distance to the object increases, so even if the detected position shifts as the robot moves, By lowering the weight on distant measurement points where the influence of angular deviation is greater, it is possible to reduce the influence of detection position deviation and prevent false detection. See also at least paragraphs 34-35 regarding the obstacle detection unit 11 may be configured with an infrared type sensor or a millimeter wave radar type sensor. See also at least paragraphs 49-55 regarding the voting value calculation unit 97 calculates the voting value by applying the following weighting in addition to the weighting according to the distance described above. That is, the voting value calculation unit 97 performs a process of weighting according to a decreasing function that reduces weighting as the distance between adjacent measurement points increases); and the computing device generates a signal configured to control the vehicle based on the adjusted given detection result (see at least paragraph 45 regarding when an abnormality is determined, an abnormality signal is output from the communication unit 17, and the movement control unit 5 performs predetermined processing such as stopping or decelerating the movement, avoiding obstacles, or tracking. See also at least paragraphs 72-76 regarding In FIG. 11, first, the corresponding target grid is determined from the coordinates of the measurement point on the environmental map, and the voting value is cumulatively added to the corresponding grid in the voting table (S51), as in the case of generating the existing object information. At this time, weighting is performed in the same way as when generating existing property information, that is, weighted voting values are calculated and the voting values are added. As described above, a weight is given in inverse proportion to the distance from the mobile robot 1 to the measurement point, and a weight is given in inverse proportion to the distance between adjacent measurement points. Note that although two weighting processes are performed here, only one of the weighting processes may be performed. Next, referring to the existing object information, if there is an existing object in the target grid to which the voting value has been added, the voting value of the target grid on the voting table is set to 0 (S53). This prevents existing objects from being detected when detecting objects from voting values. Therefore, by providing this step, a process is realized in which it is determined that there is an abnormality only when the object determining section determines that an object exists in a grid different from the existing object grid).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of FUJII which teaches an adjustment unit that is configured to adjust a result by the millimeter wave radar based on the weight; wherein braking and acceleration of the vehicle are controlled based on distance to the object and relative speed; the adjustment unit adjusts the given detection result based on the weight; and the computing device generates a signal configured to control the vehicle based on the adjusted given detection result with the system of HIRATSUKA, as modified by KITAHAMA, as both systems are directed to a system and method for determining the presence of an object based on the detected information on the environment map, and one of ordinary skill in the art would have recognized the established utility of having an adjustment unit that is configured to adjust a result by the millimeter wave radar based on the weight; wherein braking and acceleration of the vehicle are controlled based on distance to the object and relative speed; the adjustment unit adjusts the given detection result based on the weight; and the computing device generates a signal configured to control the vehicle based on the adjusted given detection result and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA.
HIRATSUKA, as modified by KITAHAMA and FUJII, does not explicitly teach the three-dimensional map including distance information in a depth direction, a height direction, and a lateral direction of the object.
However, such matter is taught by SHIRAISHI (see at least paragraphs 14-16 regarding the stereo image target is object information recognized based on the stereo image data captured by the stereo camera 3, and includes size information of the vehicle ahead. That is, for the stereo image target, the distance to the front end face of the object, the lateral position of the center of the front end face of the object, the relative speed with respect to the object, the lateral width of the object (the length in the left-right direction with respect to the vehicle), the depth of the object (the length in the direction away from the vehicle), the height of the object, and the height position of the object are set, which can be obtained from the stereo image data. See also at least paragraph 23 regarding the collision mitigation ECU 8 uses the difference in how the object appears in the left and right stereo images to triangulate and identify the object in front in three dimensions, and calculates the position from the stereo camera 3 to the object (the distance to the front end face of the object, the lateral position of the center of the object's front end face, and the height position of the object), as well as the three-dimensional size of the object (width, depth, height). In this way, the collision-reduction ECU 8 obtains size information (width, depth, height) of the object based on the stereo image data from the stereo camera 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of SHIRAISHI which teaches the three-dimensional map including distance information in a depth direction, a height direction, and a lateral direction of the object with the system of HIRATSUKA, as modified by KITAHAMA and FUJII, as both systems are directed to a system and method for determining the presence of an object based on the detected information, and one of ordinary skill in the art would have recognized the established utility of the three-dimensional map including distance information in a depth direction, a height direction, and a lateral direction of the object and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA and FUJII.
HIRATSUKA, as modified by KITAHAMA, FUJII, and SHIRAISHI, does not explicitly teach estimating both an attribute and a material of the object around the vehicle, the material corresponding to an identification of whether the material is metal, stone, wood, or plastic.
However, such matter is taught by HSU (see at least paragraphs 18-27 regarding recognizing types of obstructions 32 outside a vehicle. The environment recognition system 20 using vehicular millimeter wave radar is installed in various vehicles. The obstructions 32 comprise four various obstructions 32a, 32b, 32c and 32d. The obstructions 32a, 32b and 32c are respectively a street tree, a pedestrian and a traffic light, and the obstruction 32d comprises a bicycle and a driver. Due to different material of the obstructions 32a, 32b, 32c and 32d, such as a human body, a traffic light made of metal and a trunk of a street tree, they have energy intensity information in different energy ranges. Using parameter training of neural network, the energy intensity ranges of the different obstructions 32a, 32b, 32c and 32d are classified to recognize the different obstructions 32a, 32b, 32c and 32d. The energy intensity information and the signal-to-noise ratios of the obstructions 32a, 32b, 32c and 32d in front of the car 34 are detected, so as to determine whether the obstructions 32a, 32b, 32c and 32d are human bodies, metal or plants and use the width information to determine the obstructions 32a, 32b, 32c and 32d).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of HSU which teaches estimating both an attribute and a material of the object around the vehicle, the material corresponding to an identification of whether the material is metal, stone, wood, or plastic with the system of HIRATSUKA, as modified by KITAHAMA, FUJII, and SHIRAISHI, as both systems are directed to a system and method for determining the presence of an object based on the detected information, and one of ordinary skill in the art would have recognized the established utility of estimating both an attribute and a material of the object around the vehicle, the material corresponding to an identification of whether the material is metal, stone, wood, or plastic and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA, FUJII, and SHIRAISHI.
As to claim 2, HIRATSUKA, as modified by KITAHAMA, does not explicitly teach wherein in a case where the object around the vehicle has a predetermined size or larger in the three-dimensional map, the weight estimation unit lowers a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map around the object around the vehicle.
However, such matter is taught by FUJII (see at least paragraphs 65-66 regarding by performing a process of removing isolated existing object grids, the influence of disturbance can be further reduced. An existing facility grid is a grid with an existing facility whose voting value is equal to or higher than a threshold value, and an isolated existing facility grid is an existing facility grid that is not adjacent to other existing facility grids. Normally, objects are measured with a size larger than a certain value, and this processing eliminates the influence of minute objects such as grass and tree branches, as well as environmental noise such as snow and rain, which are measured only on a single grid. can be removed. In this embodiment, in the object determination process described above, isolated existing object grids are determined by determining whether the number of existing object grids within a predetermined range is less than or equal to a predetermined number (one or more). The corresponding existing object grid that is determined to be isolated is deleted from the existing object information).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of FUJII which teaches wherein in a case where the object around the vehicle has a predetermined size or larger in the three-dimensional map, the weight estimation unit lowers a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map around the object around the vehicle with the system of HIRATSUKA, as modified by KITAHAMA, as both systems are directed to a system and method for determining the presence of an object based on the detected information on the environment map, and one of ordinary skill in the art would have recognized the established utility of lowering a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map around the object around the vehicle wherein in a case where the object around the vehicle has a predetermined size or larger in the three-dimensional map and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA.
As to claim 3, HIRATSUKA, as modified by KITAHAMA, does not explicitly teach wherein in a case where there is a second object close to the object around the vehicle in the three-dimensional map, the weight estimation unit lowers a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map around the object around the vehicle and the second object.
However, such matter is taught by FUJII (see at least paragraphs 12-13 regarding the voting value calculation unit may further calculate the voting value by weighting according to a decreasing function that decreases the weight as the distance between the relative positions of adjacent objects detected by the detection unit increases. More specifically, the voting value calculation unit decreases the weight as the distance between the relative positions of the objects to be measured increases when the detection unit scans at regular time intervals. The voting value may be calculated by weighting according to a function. See also at least paragraphs 65-66).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of FUJII which teaches wherein in a case where there is a second object close to the object around the vehicle in the three-dimensional map, the weight estimation unit lowers a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map around the object around the vehicle and the second object with the system of HIRATSUKA, as modified by KITAHAMA, as both systems are directed to a system and method for determining the presence of an object based on the detected information on the environment map, and one of ordinary skill in the art would have recognized the established utility of lowering a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map around the object around the vehicle and the second object wherein in a case where there is a second object close to the object around the vehicle in the three-dimensional map and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA.
As to claim 6, HIRATSUKA teaches a sensor fusion unit that synthesizes the given detection result (see at least paragraphs 29-34 regarding the imaging device 14 is installed at the front of the vehicle and captures images of the surrounding environment in an observation area common to the laser radar 12, with a field of view centered on the fore-and-aft direction of the vehicle body, and outputs image data representing the captured image to the computer 16. Furthermore, since the installation positions and angles of the laser radar 12 and the imaging device 14 are known, it is possible to associate the position of an object observed by the laser radar 12 with the position of the object in the image captured by the imaging device 14, HIRATSUKA).
As to claim 10, Examiner notes claim 10 recites similar limitations to claim 1 and is rejected under the same rational.
Claim(s) 4 is rejected under 35 U.S.C. 103 as being unpatentable over HIRATSUKA et al., JP 2014006588 A, hereinafter referred to as HIRATSUKA, in view of KITAHAMA et al., JP 2007102488 A, hereinafter referred to as KITAHAMA, in view of FUJII, JP 2006252348 A, hereinafter referred to as FUJII, in view of SHIRAISHI, JP 2009019914 A, hereinafter referred to as SHIRAISHI, in view of HSU et al., US 2018/0172825 A1, hereinafter referred to as HSU, and further in view of SUGIURA et al., US 2020/0292704 A1, hereinafter referred to as SUGIURA, respectively.
As to claim 4, HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, does not explicitly teach wherein the weight estimation unit estimates a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map from a combination of the three-dimensional shape for each region of the three-dimensional map and the attribute of the object around the vehicle.
However, such matter is taught by SUGIURA (see at least paragraphs 81-87 regarding the observation unit 130 observes the environment around the moving object 200 and acquires observation data D3. It is preferable that the observation unit 130 can observe the shapes of obstacles 410, 420, and 430 represented by the map data D1. For example, in the present embodiment, a distance sensor is used to observe the distances from the self position of the moving object 200 to the obstacles 410, 420, and 430 in the respective directions, and as shown in FIG. 10, observation data D3 is acquired such that the shapes of the obstacles 410, 420, and 430 around the moving object 200 are processed as a set of observation points 530. The self position 510 of the moving object 200 includes position coordinates 512 of the moving object 200 and the moving direction 514 of the moving object 200. As the acquisition method of observation data D3, a method of estimating an attribute of an observable object using a camera image and acquiring observation data using a typical shape model of the attribute is known, and a method of estimating a distance from the moving object using geometric constraints is also known. That is, the acquisition method of observation data D3 is not limited to what has been described above. Where a weight is calculated for each of three-dimensional points, the correspondence based on Iterative Closest Point (ICP) can be used, and where a weight is calculated for each of the cells of a grid, the correspondence based on Normal Distributions Transform (NDT) can be used.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of SUGIURA which teaches wherein the weight estimation unit estimates a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map from a combination of the three-dimensional shape for each region of the three-dimensional map and the attribute of the object around the vehicle with the system of HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, as both systems are directed to a system and method for determining the presence of an object based on the detected information on the environment map, and one of ordinary skill in the art would have recognized the established utility of estimating a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map from a combination of the three-dimensional shape for each region of the three-dimensional map and the attribute of the object around the vehicle and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU.
Claim(s) 5 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over HIRATSUKA et al., JP 2014006588 A, hereinafter referred to as HIRATSUKA, in view of KITAHAMA et al., JP 2007102488 A, hereinafter referred to as KITAHAMA, in view of FUJII, JP 2006252348 A, hereinafter referred to as FUJII, in view of SHIRAISHI, JP 2009019914 A, hereinafter referred to as SHIRAISHI, in view of HSU et al., US 2018/0172825 A1, hereinafter referred to as HSU, and further in view of Wang et al., US 2016/0065903 A1, hereinafter referred to as Wang, respectively.
As to claim 5, HIRATSUKA does not explicitly teach wherein the single attribute estimation unit estimates the material of the object around the vehicle detected from the image captured by the image-capturing device.
However, such matter is taught by KITAHAMA (see at least paragraphs 22-25 regarding an image processing unit that processes the captured images to recognize the master and surrounding obstacles, road signs, etc. (surrounding environment). More specifically, the surrounding environment is recognized, such as the owner's position, direction of movement, and speed of movement, the position, shape, direction of movement, speed of movement, and operating status of other pedestrians, bicycles, automobiles, etc., the position and shape of obstacles such as stopped vehicles, utility poles, and fallen objects, and traffic conditions such as the status of traffic lights and road signs. In addition, attributes such as the type, weight, and material of the object are also recognized. Here, attributes relating to the type of object include classifications such as large vehicles, small vehicles, two wheeled vehicles, and bicycles. An attribute relating to weight is whether it is heavy or light, and an attribute relating to material is whether the surface is soft or hard. That is, the camera 10 and the communication device 12 function as a surrounding environment recognition means as defined in the claims).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of KITAHAMA which teaches wherein the single attribute estimation unit estimates the material of the object around the vehicle detected from the image captured by the image-capturing device with the system of HIRATSUKA as both systems are directed to a system and method for determining the presence of an object based on the detected information, and one of ordinary skill in the art would have recognized the established utility of having wherein the single attribute estimation unit estimates the material of the object around the vehicle detected from the image captured by the image-capturing device and would have predictably applied it to improve the system of HIRATSUKA.
HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, does not explicitly teach wherein the weight estimation unit estimates a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map from a combination of the three-dimensional shape for each region of the three-dimensional map and the material of the object around the vehicle.
However, such matter is taught by Wang (see at least paragraphs 254-261 regarding assigning weights to the image features detected in the at least one image according to the at least one gaze image location. The weights may be determined according to the result of the clustering of a plurality of gaze image locations. For example, one or more groups of the gaze image locations may be determined according to the clustering, and thus one more image regions may be determined based on the determined groups of the gaze image locations. One image region may be determined based on one group of the gaze image locations. A weight assigned to an image feature that is within or overlapped with an image region may be determined according to the number of the gaze image locations used to determine the image region. The larger number of the gaze image locations, the higher value may be determined for the weight).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of Wang which teaches wherein the weight estimation unit estimates a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map from a combination of the three-dimensional shape for each region of the three-dimensional map and the material of the object around the vehicle with the system of HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, as both systems are directed to a system and method for determining the presence of an object based on the detected information on the environment map, and one of ordinary skill in the art would have recognized the established utility of estimating a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map from a combination of the three-dimensional shape for each region of the three-dimensional map and the material of the object around the vehicle and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU.
As to claim 9, HIRATSUKA, as modified by KITAHAMA, does not explicitly teach the weight estimation unit lowers a weight of the detection result by the millimeter wave radar in a region of the three-dimensional map corresponding to the three-dimensional shape.
However, such matter is taught by FUJII (see at least paragraphs 9-13 regarding the voting value calculation unit calculates the voting value using weighting according to a decreasing function that reduces the weight as the distance from the measured object detected by the detection unit increases. The voting values are weighted according to a decreasing function that decreases the weight as the distance to the object increases, so even if the detected position shifts as the robot moves, By lowering the weight on distant measurement points where the influence of angular deviation is greater, it is possible to reduce the influence of detection position deviation and prevent false detection. See also at least paragraphs 34-35. See also at least paragraphs 49-55 regarding the voting value calculation unit 97 calculates the voting value by applying the following weighting in addition to the weighting according to the distance described above. That is, the voting value calculation unit 97 performs a process of weighting according to a decreasing function that reduces weighting as the distance between adjacent measurement points increases).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of FUJII which teaches the weight estimation unit lowers a weight of the detection result by the millimeter wave radar in a region of the three-dimensional map corresponding to the three-dimensional shape with the system of HIRATSUKA, as modified by KITAHAMA, as both systems are directed to a system and method for determining the presence of an object based on the detected information on the environment map, and one of ordinary skill in the art would have recognized the established utility of lowering a weight of the detection result by the millimeter wave radar in a region of the three-dimensional map corresponding to the three-dimensional shape and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA.
HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, does not explicitly teach the three-dimensional shape is a corner shape or a circle shape.
However, such matter is taught by Wang (see at least paragraphs 147-149 regarding the image region of interest may also be determined as surrounding regions (e.g. represented by various 2D geometrical shapes) around the at least one gaze image location. For example, a circle or a rectangle or a square could be determined based on one or more gaze image locations, as a center point, or as corner points, or as points on boarders to restrict the 2D geometrical shape).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of Wang which teaches the three-dimensional shape is a corner shape or a circle shape with the system of HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, as both systems are directed to a system and method for determining the presence of an object based on the detected information on the environment map, and one of ordinary skill in the art would have recognized the established utility of having the three-dimensional shape is a corner shape or a circle shape and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU.
Claim(s) 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over HIRATSUKA et al., JP 2014006588 A, hereinafter referred to as HIRATSUKA, in view of KITAHAMA et al., JP 2007102488 A, hereinafter referred to as KITAHAMA, in view of FUJII, JP 2006252348 A, hereinafter referred to as FUJII, in view of SHIRAISHI, JP 2009019914 A, hereinafter referred to as SHIRAISHI, in view of HSU et al., US 2018/0172825 A1, hereinafter referred to as HSU, and further in view of NEHMADI et al., US 2016/0292905 A1, hereinafter referred to as NEHMADI, respectively.
As to claim 7, HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, does not explicitly teach a map prediction unit that predicts a three-dimensional map in a next frame from the three-dimensional map, wherein the three-dimensional map generation unit generates a three-dimensional map around the vehicle from the three-dimensional map of the next frame predicted by the map prediction unit and the image captured by the image-capturing device.
However, NEHMADI teaches a map prediction unit that predicts a three-dimensional map in a next frame from the three-dimensional map (see at least paragraphs 34-37 regarding a prediction frame may be generated based on 3D or 2D mapping information as well as a location and orientation of a sensor. The prediction frame may be a 3D image illustrating predicted positions of stationary (i.e., non-moving) objects included in the 3D-mapping information), wherein the three-dimensional map generation unit generates a three-dimensional map around the vehicle from the three-dimensional map of the next frame predicted by the map prediction unit and the image captured by the image-capturing device (see at least paragraphs 34-37 regarding determining whether objects in images are stationary may be based on predicted locations of objects in an image. A prediction image may be generated based on a frame of a currently acquired image or generated 3D-map. The prediction image may be further based on a movement of the sensor acquiring the current image or providing information used to generate the current 3D-map. Based on the current frame and/or any new position or orientation of the sensor, a prediction frame illustrating predicted positions of the objects at a subsequent time (assuming the objects do not move) is generated. The prediction frame may be compared to a subsequent frame based on sensor readings at the subsequent time to determine if there are any differences between locations of the objects. A prediction frame may be generated based on 3D or 2D mapping information as well as a location and orientation of a sensor. The prediction frame may be a 3D image illustrating predicted positions of stationary (i.e., non-moving) objects included in the 3D-mapping information).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of NEHMADI which teaches a map prediction unit that predicts a three-dimensional map in a next frame from the three-dimensional map, wherein the three-dimensional map generation unit generates a three-dimensional map around the vehicle from the three-dimensional map of the next frame predicted by the map prediction unit and the image captured by the image-capturing device with the system of HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, as both systems are directed to a system and method for determining the presence of an object based on the detected information on the environment map, and one of ordinary skill in the art would have recognized the established utility of predicting a three-dimensional map in a next frame from the three-dimensional map; and generating a three-dimensional map around the vehicle from the three-dimensional map of the next frame predicted by the map prediction unit and the image captured by the image-capturing device and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU.
As to claim 8, HIRATSUKA teaches a composite map creation unit that creates a three-dimensional map from the given detection result synthesized by the sensor fusion unit (see at least paragraphs 29-34 regarding the imaging device 14 is installed in front of the vehicle, and images the surrounding environment in a common observation area with the laser radar 12 with a viewing angle centered on the longitudinal direction of the vehicle body, and displays an image showing the captured image obtained by imaging. The position of the object observed by the laser radar 12 and the position of the object in the captured image captured by the imaging device 14 correspond. See also at least paragraph 38 regarding the three-dimensional object region extraction unit 20 acquires observation data observed by the laser radar 12, and extracts a three-dimensional object region indicating a three-dimensional object from the observation data. Cells in which the dispersion in the height direction of the observation data group projected into each cell is equal to or greater than a predetermined threshold are extracted as solid object regions, and a solid object map 50 as shown in FIG. 4 is generated, HIRATSUKA).
HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, does not explicitly teach a map prediction unit that predicts a three-dimensional map in a next frame from the three-dimensional map created by the composite map creation unit, wherein the three-dimensional map generation unit generates a three-dimensional map around the vehicle from the three-dimensional map of the next frame having been predicted and the image captured by the image-capturing device.
However, NEHMADI teaches a map prediction unit that predicts the three-dimensional map in a next frame from the three-dimensional map created by the composite map creation unit (see at least paragraphs 34-37 regarding a prediction frame may be generated based on 3D or 2D mapping information as well as a location and orientation of a sensor. The prediction frame may be a 3D image illustrating predicted positions of stationary (i.e., non-moving) objects included in the 3D-mapping information), wherein the three-dimensional map generation unit generates a three-dimensional map around the vehicle from the three-dimensional map of the next frame having been predicted and the image captured by the image-capturing device (see at least paragraphs 34-37 regarding determining whether objects in images are stationary may be based on predicted locations of objects in an image. A prediction image may be generated based on a frame of a currently acquired image or generated 3D-map. The prediction image may be further based on a movement of the sensor acquiring the current image or providing information used to generate the current 3D-map. Based on the current frame and/or any new position or orientation of the sensor, a prediction frame illustrating predicted positions of the objects at a subsequent time (assuming the objects do not move) is generated. The prediction frame may be compared to a subsequent frame based on sensor readings at the subsequent time to determine if there are any differences between locations of the objects. A prediction frame may be generated based on 3D or 2D mapping information as well as a location and orientation of a sensor. The prediction frame may be a 3D image illustrating predicted positions of stationary (i.e., non-moving) objects included in the 3D-mapping information).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of NEHMADI which teaches a map prediction unit that predicts a three-dimensional map in a next frame from the three-dimensional map created by the composite map creation unit, wherein the three-dimensional map generation unit generates a three-dimensional map around the vehicle from the three-dimensional map of the next frame having been predicted and the image captured by the image-capturing device with the system of HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, as both systems are directed to a system and method for determining the presence of an object based on the detected information on the environment map, and one of ordinary skill in the art would have recognized the established utility of predicting a three-dimensional map in a next frame from the three-dimensional map created by the composite map creation unit; and generating a three-dimensional map around the vehicle from the three-dimensional map of the next frame having been predicted and the image captured by the image-capturing device and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU.
Claim(s) 11 (as best understood by the Examiner in light of the rejections under 35 USC 112 above) is rejected under 35 U.S.C. 103 as being unpatentable over HIRATSUKA et al., JP 2014006588 A, hereinafter referred to as HIRATSUKA, in view of KITAHAMA et al., JP 2007102488 A, hereinafter referred to as KITAHAMA, in view of FUJII, JP 2006252348 A, hereinafter referred to as FUJII, in view of SHIRAISHI, JP 2009019914 A, hereinafter referred to as SHIRAISHI, in view of HSU et al., US 2018/0172825 A1, hereinafter referred to as HSU, and further in view of AKIYAMA et al., DE 112013004950 T5, hereinafter referred to as AKIYAMA, respectively.
As to claim 11, HIRATSUKA, as modified by KITAHAMA, does not explicitly teach wherein the weight estimation unit lowers a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map.
However, such matter is taught by FUJII (see at least paragraphs 12-13 regarding the voting value calculation unit may further calculate the voting value by weighting according to a decreasing function that decreases the weight as the distance between the relative positions of adjacent objects detected by the detection unit increases. More specifically, the voting value calculation unit decreases the weight as the distance between the relative positions of the objects to be measured increases when the detection unit scans at regular time intervals. The voting value may be calculated by weighting according to a function. See also at least paragraphs 65-66).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of FUJII which teaches wherein the weight estimation unit lowers a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map with the system of HIRATSUKA, as modified by KITAHAMA, as both systems are directed to a system and method for determining the presence of an object based on the detected information on the environment map, and one of ordinary skill in the art would have recognized the established utility of lowering a weight of the detection result by the millimeter wave radar for each region of the three-dimensional map and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA.
HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, does not explicitly teach lowering a weight of the detection result by the millimeter wave radar when the combination of the three-dimensional shape for each region of the three-dimensional map and the attribute of the object around the vehicle is a predetermined combination in which multipass or strong reflection is likely to occur.
However, such matter is taught by AKIYAMA (see at least paragraphs 46-48 regarding the estimation of reflection points based on the principle of triangulation is based on the assumption that probing waves from distance measuring sensors are reflected at the same point of an object between adjacent sensor positions. As shown in Fig. 8, since a real reflection point 3a at time t1 when a surface angle is gentle is close to a real reflection point 3b at time t2, the foregoing assumption is substantially satisfied. For this reason, a reflection point 4a estimated from the sensor positions 2a, 2b and detection distances La, Lb to t1 and t2 substantially coincides with the actual reflection points 3a and 3b. The reflection point 4a is an intersection point between an arc 5a having the sensor position 2a as a center and the detection distance La as a radius and an arc 5b having the sensor position 2b as a center and the detection distance Lb as a radius. In contrast, at times t2 and t3 when the surface angle of the corner 7a becomes steep, a variation (Lc-Lb) of the detection distance increases with respect to the amount of movement d1 of the distance measuring sensor 2. For this reason, the previous assumptions would be violated and the reflection point 4b estimated from the sensor positions 2b, 2c and the detection distances Lb, Lc at times t2 and t3 would be mostly far away from the real reflection point 3c. The reflection point 4b is an intersection point between the arc 5d having the sensor position 2b as a center and the detection distance Lb as a radius and an arc 5c having the sensor position 2c as a center and the detection distance Lc as a radius. In this way, when the surface angle of the object having a round shape, such as the neighborhood of the corner of the parked vehicle, is steep, the principle of triangulation is not established, and the accuracy of the estimated reflection points is reduced. If the accuracy of the reflection points is reduced, the detection accuracy of the corner is reduced).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of AKIYAMA which teaches lowering a weight of the detection result by the millimeter wave radar when the combination of the three-dimensional shape for each region of the three-dimensional map and the attribute of the object around the vehicle is a predetermined combination in which multipass or strong reflection is likely to occur with the system of HIRATSUKA, as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU, as both systems are directed to a system and method for determining the presence of an object based on the detected information on the environment map, and one of ordinary skill in the art would have recognized the established utility of lowering a weight of the detection result by the millimeter wave radar when the combination of the three-dimensional shape for each region of the three-dimensional map and the attribute of the object around the vehicle is a predetermined combination in which multipass or strong reflection is likely to occur and would have predictably applied it to improve the system of HIRATSUKA as modified by KITAHAMA, FUJII, SHIRAISHI, and HSU.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Cattle et al. (US 20200158861 A1) regarding a system for identifying material characteristics of an object within the field of view.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.S.P./Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
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