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
Claims 1-4 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/10/2024 has been considered by the examiner and an initialed copy of the IDS is hereby attached.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the object type of the object" in "and the object type of the object to an external device…". There is insufficient antecedent basis for this limitation in the claim as it is unclear which “the object” is being referred to.
Claim 1 recites the limitation "the object" in “the object being determined to have identity...”. There is insufficient antecedent basis for this limitation in the claim as it is unclear which “the object” is being referred to.
Claim 4 recites four instances of the limitation "the object" in “and determine whether the object indicated by the radar detection position is identical to the object indicated by the camera detection position based on whether a position of the object indicated by the radar detection position is identical to a position of the object indicated by the camera detection position;”. There is insufficient antecedent basis for this limitation in the claim as it is unclear which “the object” is being referred to.
All dependent claim are also rejected under 35 U.S.C. 112(b) due to their dependency on a claim rejected under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 3 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sakashita (US 20220036043 A1).
Regarding claim 1, Sakashita discloses
An object detection apparatus (see Fig. 1, vehicle control system 100) comprising:
a radar to emit electromagnetic waves and receive reflected signals from objects including a moving object and a stationary object (see Fig. 2, further see paragraph 0093, “The data acquisition section 102A includes a camera 201 and a millimeter-wave radar 202.”, further see paragraph 0079, “Examples of the recognition-target state of the surroundings of the own automobile include the type and a location of a stationary object around the own automobile; the type, a location, and movement (such as speed, acceleration, and a movement direction) of a moving object around the own automobile; a structure of a road around the own automobile and a condition of the surface of the road; and weather, temperature, humidity, and brightness around the own automobile”);
a camera to acquire image data on the objects by imaging the objects (see Fig. 2, further see paragraph 0093, “The data acquisition section 102A includes a camera 201 and a millimeter-wave radar 202.”, further see paragraph 0173, “Thus, the camera 201 and the millimeter-wave radar 202 (a captured image and millimeter-wave data) are fused to perform processing of recognizing a target object, and this makes it possible to compensate for a drawback caused when only a captured image is used.”);
radar signal processing circuitry to detect radar detection positions and radar detection speeds by performing signal processing on the reflected signals (see Fig. 1, millimeter wave radar 202 coupled with signal processor 222), the radar detection positions being positions of the objects, the radar detection speeds being speeds of the objects (see paragraph 0097, “The signal processor 222 performs specified signal processing on millimeter-wave data to generate a millimeter-wave image that is an image indicating a result of sensing performed by the millimeter-wave radar 202. Note that the signal processor 222 generates two types of millimeter-wave images that are, for example, a signal-intensity image and a speed image. The signal-intensity image is a millimeter-wave image indicating a location of each object situated ahead of the vehicle 10 and the intensity of a signal reflected off the object (a reception signal). The speed image is a millimeter-wave image indicating a location of each object situated ahead of the vehicle 10 and a relative speed of the object with respect to the vehicle 10.”);
camera image processing circuitry to detect camera detection positions and object types based on the image data (see Fig. 2, camera 201 coupled to image processor 221), the camera detection positions being positions of the objects (see paragraph 0096, “The image processor 221 performs specified image processing on a captured image. For example, the image processor 221 performs processing of reduction in number or filtering processing with respect to a pixel in the captured image according to the image size for which the object recognition section 224 can perform processing, and reduces the number of pixels in the captured image (reduces the resolution). The image processor 221 supplies the captured image with a reduced resolution (hereinafter referred to as a low-resolution image) to the object recognition section 224”), the object types being types of some of the objects (see paragraph 0099, “The object recognition section 224 performs processing of recognizing a target object situated ahead of the vehicle 10 on the basis of the low-resolution image, the geometrically transformed signal-intensity image, and the geometrically transformed speed image…The data indicating a result of recognizing a target object includes, for example, the location and the size of a target object in a captured image, and the type of object.”); and
fusion processing circuitry to determine whether a position indicated by the radar detection position is identical to a position indicated by the camera detection position (see paragraph 0098 further see paragraph 0176), and output the radar detection position, the radar detection speed, and the object type of the object to an external device (see paragraph 0176, “On the other hand, according to the present technology, a geometric transformation is performed on a millimeter-wave image (a signal-intensity image and a speed image) to obtain an image (a geometrically transformed signal-intensity image and a geometrically transformed speed image) of which a coordinate system has been matched to the coordinate system of a captured image, and the object recognition model 251 is caused to perform learning using the obtained image. This results in facilitating matching of each pixel of the captured image with a reflection point in the millimeter-wave image, and in improving the accuracy in learning. Further, in actual processing of recognizing a vehicle, the use of a geometrically transformed signal-intensity image and a geometrically transformed speed image results in improving the accuracy in recognizing a target object.”, where the output data is provided to a movement controller to control the movement of the vehicle based on the results), the object being determined to have identity (see paragraph 0079, “Examples of the recognition-target state of the surroundings of the own automobile include the type and a location of a stationary object around the own automobile; the type, a location, and movement (such as speed, acceleration, and a movement direction) of a moving object around the own automobile;”, where an object type is the object determined to have identity), wherein
the camera image processing circuitry detects the camera detection position of the moving object and the object type of the moving object (see Fig. 2, camera coupled to image processor 221 and object recognition section 224 determines the position and type of the moving objects, further see paragraph 0079), and
the radar signal processing circuitry determines the stationary object among the objects based on the radar detection positions, the camera detection position of the moving object, and the object type of the moving object (see Fig. 2, radar coupled to signal processor 222 and object recognition section 224 determines the stationary objects based on the radar detection points, the camera detection positive of the moving object and the type of the moving object, further see paragraph 0079); calculates a speed of an own vehicle as an own vehicle speed based on the radar detection speed of the stationary object, the own vehicle being a vehicle in which an own apparatus is installed (see paragraph 0073, “The vehicle state detector 143 performs a process of detecting a state of the own automobile on the basis of data or a signal from each structural element of the vehicle control system 100. Examples of the detection-target state of the own automobile include speed, acceleration, a steering angle, the presence or absence of anomaly and its details, a driving operation state, a position and an inclination of a power seat, a state of a door lock, and states of other pieces of vehicle-mounted equipment.”); and causes the radar to track the moving object and the stationary object based on the own vehicle speed (see paragraph 0071, “The vehicle-exterior-information detector 141 performs a process of detecting information regarding the outside of the own automobile on the basis of data or a signal from each structural element of the vehicle control system 100. For example, the vehicle-exterior-information detector 141 performs processes of detecting, recognizing, and tracking an object around the own automobile, and a process of detecting a distance to the object. Examples of the detection-target object include a vehicle, a person, an obstacle, a structure, a road, a traffic light, a traffic sign, and a road sign. Further, for example, the vehicle-exterior-information detector 141 performs a process of detecting an environment surrounding the own automobile. Examples of the detection-target surrounding environment include weather, temperature, humidity, brightness, and a road surface condition.”, further see paragraph 0078).
Regarding claim 3, Sakashita further discloses
The object detection apparatus according to claim 1, wherein
the fusion processing circuitry (see Fig. 2, object recognition section 224) includes:
identity determination circuitry to acquire the camera detection position of the moving object and the object type of the moving object from the camera image processing circuitry, acquire the radar detection position of the stationary object and the radar detection speed of the stationary object from the radar signal processing circuitry (see Fig. 2, image processor 221 and signal processor 222 use the data detected from the camera and radar of the stationary and moving objects, further see paragraph 0079), and determine whether the object indicated by the radar detection position is identical to the object indicated by the camera detection position based on whether a position of the object indicated by the radar detection position is identical to a position of the object indicated by the camera detection position (see paragraph 0098, “The geometric transformation section 223 performs a geometric transformation on a millimeter-wave image to transform the millimeter-wave image into an image of which a coordinate system is identical to the coordinate system of a captured image. In other words, the geometric transformation section 223 transforms a millimeter-wave image into an image (hereinafter referred to as a geometrically transformed millimeter-wave image) obtained as viewed from the same viewpoint as a captured image. More specifically, the geometric transformation section 223 transforms the coordinate system of a signal-intensity image and a speed image from the coordinate system of a millimeter-wave image into the coordinate system of a captured image. Note that the signal-intensity image and the speed image on which a geometric transformation has been performed are respectively referred to as a geometrically transformed signal-intensity image and a geometrically transformed speed image. The geometric transformation section 223 supplies the geometrically transformed signal-intensity image and the geometrically transformed speed image to the object recognition section 224.”, where the transformation is used to them match the points from the radar and image data); and
output circuitry to output the radar detection position, the radar detection speed, and the object type of the object to an external device (see paragraph 0176, “On the other hand, according to the present technology, a geometric transformation is performed on a millimeter-wave image (a signal-intensity image and a speed image) to obtain an image (a geometrically transformed signal-intensity image and a geometrically transformed speed image) of which a coordinate system has been matched to the coordinate system of a captured image, and the object recognition model 251 is caused to perform learning using the obtained image. This results in facilitating matching of each pixel of the captured image with a reflection point in the millimeter-wave image, and in improving the accuracy in learning. Further, in actual processing of recognizing a vehicle, the use of a geometrically transformed signal-intensity image and a geometrically transformed speed image results in improving the accuracy in recognizing a target object.”, where the output data is provided to a movement controller to control the movement of the vehicle based on the results), the object being determined to have identity by the identity determination circuitry (see paragraph 0079, “Examples of the recognition-target state of the surroundings of the own automobile include the type and a location of a stationary object around the own automobile; the type, a location, and movement (such as speed, acceleration, and a movement direction) of a moving object around the own automobile;”, where an object type is the object determined to have identity).
Allowable Subject Matter
Claims 2 and 4 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
In reference to dependent claims 2 and 4, the prior arts made of record individually or in any combination, failed to teach, render obvious, or fairly suggest to one of ordinary skill in the art at the time of filing the combination of the claimed features of claims 2 and 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
LEE (US 20210302564 A1) discloses a radar apparatus on a vehicle which fuses radar and camera data for object identification (see paragraph 0044, “The fusion data generation unit 120 may project the camera data to a radar coordinate system of the radar apparatus 10 and match the camera data and the radar data projected to the radar coordinate system for each target to generate fusion data. For example, referring to FIG. 2C, the fusion data generation unit 120 may transform the location information of the object included in the camera data into a radar coordinate system of the radar apparatus 10 installed in a vehicle 205 (for example, an XYZ coordinate system around the vehicle 205) and check whether the coordinates of the object included in the camera data transformed into the radar coordinate system are similar to the coordinates of the object included in the radar data.”).
Matsunaga (US 12125237 B2) discloses the fusion of radar and camera data to improve the resolution of object detection (see Col. 22, lines 15-26, “The camera 201 is superior to the millimeter-wave radar 202 in the items of a non-interference performance, an independence from material, and a horizontal angular resolution. On the other hand, the millimeter-wave radar 202 is superior to the camera 201 in the items of the distance accuracy, the accuracy in recognition during bad weather, and the accuracy in recognition during a nighttime driving. Thus, when both the camera 201 and the millimeter-wave radar 202 are used to fuse recognition results, this makes it possible to compensate for weaknesses of each other. This results in improving the accuracy in recognizing a target object.”).
KIM et al. (US 20210309217 A1) discloses the determination of an own vehicle speed based on the fusion of radar and camera data (see paragraph 0083, “In this case, the autonomous driving control apparatus 100 may determine the lane on which the vehicle is currently travelling, by combining a detailed map, a front camera, or a surround view monitor, and may determine the safety of each lane by combining the detailed map and the information from the camera 210, the Lidar 220, and the radar 230. In detail, the autonomous driving control apparatus 100 calculates a relative distance and a relative speed to a moving object or a stationary object on each lane, and may calculate a time taken until the ego vehicle stops without colliding with the moving object or the stationary object on each lane.”).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAZRA N. WAHEED whose telephone number is (571)272-6713. The examiner can normally be reached M-F (8 AM - 4:30 PM).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vladimir Magloire can be reached at (571)270-5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAZRA NUR WAHEED/Primary Examiner, Art Unit 3648
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