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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/10/26 has been entered.
Response to Arguments
Applicant’s arguments filed on 8/10/26 have been considered. Regarding claim 1, please see the new combination below that relies upon Bleicher (20230386065) to teach determining a scaling factor from the metadata that includes the focal length and camera lens properties in pars. 54, 59 and 62-65.
Regarding claim 9, see Karnowski which analyzes front or back vehicle images for window size in pars. 55, 80, 87 and 94.
Regarding claim 16, Beicher teaches lidar depth sensors in pars. 36-37 and 56-62/
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 1-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Herman (20190347498) in view of Karnowski (20080095404) in view of El-sawah (20220147742) in further view of Bleicher (20230386065).
Regarding claim 1, Herman teaches a trailer measurement system, comprising: a processor (pars. 27-31 and Fig. 6);
receiving a first image data set obtained from a first imager (pars. 33-39 and Fig. 7, capturing host images and receiving images from target vehicles/roadside units);
identifying the vehicle and a trailer coupled with the vehicle in the first image data set (pars. 19, 33-39 and Fig. 7, detecting the trailer, performing semantic segmentation, identifying objects, and generating a 3D point cloud of the host vehicle and attached trailer); and
using a factor/model to derive at least one trailer feature dimension from the trailer identified in the first image data set (pars. 19, 22, 39 and Fig. 7, estimating trailer properties/geometric properties using a 3D point cloud, structure from motion, sensor fusion, and a stored 3D model/factory image).
Herman teaches using a three-dimensional model of the host vehicle and/or attached trailer stored in memory to construct a three-dimensional point cloud of the vehicle and attached trailer (par. 19).
Herman further teaches combining structure from motion techniques with a three-dimensional model of the vehicle, such as a factory image and/or 3D model data of the vehicle stored in memory, to measure or estimate geometric properties of the trailer, and using a factory stored image of the vehicle to calibrate the generated three-dimensional model (par. 22).
Herman does not expressly teach identifying a reference feature of the vehicle having at least one known dimension stored in a memory associated with the processor and deriving a scaling factor for the first image data set by comparing a size of the reference feature of the vehicle as depicted in the first image data set and the at least one known dimension stored in the memory.
Karnowski teaches identifying a reference feature of the vehicle having at least one known dimension stored in a memory associated with the processor in the first image data set; deriving a scaling factor for the first image data set by comparing a size of the reference feature of the vehicle as depicted in the first image data set and the at least one known dimension (see pars. 35, 55-68 and 80, stored reference to pixel ratio scales image).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to modify Herman to include the use of a known vehicle feature dimension stored in memory and image scaling as taught by Karnowski. The reason is to improve trailer dimension accuracy.
El-sawah teaches the portable electronic device external to the vehicle. El-sawah teaches a remote device comprising a camera, wherein image data depicting the trailer is captured via the camera of the remote device (pars. 22-23).
El-sawah further teaches that the remote device may guide the user to capture image data using the camera of the remote device and may transmit/process image data for trailer detection training (pars. 75-76).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Herman and Karnowski a portable electronic device external to the vehicle as taught by El-sawah, in order to allow the system to capture trailer image data from additional perspectives and operate in various environments.
Herman, Karnowski and El-sawah do not teach the first image data set including camera metadata comprising at least focal length and lens characteristic of the first imager or the scaling factor derived by using camera metadata.
Bleicher teaches this in pars. 38, 62 and 65.
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Herman, Karnowski and El-sawah the ability to derive image scale as taught by Bleicher. The reason is to compensate for portable camera lens characteristics.
Regarding claim 2, see pars. 55, 80, 87 and 94 of Karnowski which teaches storage of back view window sizes.
Regarding claim 3, see Herman par. 43, which teaches estimating trailer to wheel to hitch distance.
Regarding claim 4, El-sawah teaches a portable electronic device comprising a mobile/remote device with a camera that captures image data depicting the trailer (pars. 22-23 and 75).
Regarding claim 5, Herman teaches that the image data comprises a plurality of images including the vehicle and trailer from corresponding locations, and that structure from motion techniques are used to generate a three-dimensional point cloud/model of the host vehicle and attached trailer before estimating trailer properties (pars. 19, 22, 33-39 and Fig. 7).
Regarding claim 5, see pars. 19, 22 and 33-39 of Herman which teaches multi view structure from motion builds.
Regarding claim 6, Herman teaches multiple images/image sequences and structure from motion processing of collected images (pars. 33-39 and Fig. 7).
Regarding claim 7, Herman teaches image sequences and collected images, and El-sawah teaches a camera of a remote device and capturing image data from multiple perspectives (El-sawah pars. 75-76).
Regarding claim 8, Herman teaches sensor fusion and three-dimensional point cloud data, including use of sensor data such as LiDAR, radar, or ultrasonic data, to further define the three-dimensional structure of the host vehicle and attached trailer (pars. 19 and 39).
Regarding claim 9, see the rejection of claims 1 and 5. Herman teaches receiving a plurality of images, using structure from motion to construct a three-dimensional model/point cloud of the vehicle and trailer, identifying the vehicle and trailer, and estimating trailer geometric properties (pars. 19, 22, 33-39 and Fig. 7).
Karnowski teaches identifying a vehicle feature in a sensed image, using known dimensions of the tow vehicle stored in controller memory/data files, correlating the depicted size of the vehicle feature to the known size, determining scale, and determining dimensions of the towed vehicle and wherein the feature is rear window opening width or height or distance from a wheel well (see pars. 35, 55-68 and 80,j 87 and 94 which stores back window size references)
Regarding claim 10, see the rejection of claim 6.
Regarding claim 11, see the rejection of claim 7.
Regarding claim 12, El-sawah teaches a remote/mobile device and camera for capturing trailer image data and communicating the image data (pars. 22-23 and 75).
Regarding claim 13, El-sawah teaches directing/guiding the user to capture image data from multiple perspectives and providing graphical/written instructions for alignment (pars. 75-76).
Regarding claim 15, see Herman, par. 43, estimating trailer wheel to hitch distance.
Regarding claim 16, Herman teaches a trailer measurement system for use in connection with a vehicle, comprising a first processor, receiving image data, identifying a vehicle and trailer, generating a three-dimensional point cloud using structure from motion, performing sensor fusion, and estimating trailer properties/geometric properties (pars. 19, 22, 33-39 and Fig. 7). Herman further teaches using three-dimensional sensor data, including LiDAR/radar/ultrasonic data, to further define the three-dimensional structure of the host vehicle and attached trailer (pars. 19 and 39).
Karnowski teaches having at least one known dimension stored in a memory associated with the first processor, in the first image data set; and deriving at least one trailer feature dimension from the trailer identified in the first image data set including using the first image data set in combination with the three-dimensional point- location data to scale the first image data set based on the known dimension of the vehicle reference feature stored in the memory, the at least one trailer feature dimension being measured in at least the scaled first image data set (see pars. 35, 55-68 and 80).
El-sawah teaches a portable/remote device comprising a camera for capturing image data depicting the trailer (pars. 22-23 and 75).
Bleicher teaches 3d point data from a lidar sensor in pars. 36-37 and 56-62.
Regarding claim 17, see Bleicher in the rejection of claim 16 above and see claims 14-16 of Bleicher.
Regarding claim 18, El-sawah teaches a remote/mobile device with a camera, user guidance for capturing image data from multiple perspectives, and transmission/processing of the image data for trailer detection (pars. 22-23 and 75-76).
Bliecher teaches lidar sensor in pars. 22, 36-37 and 56-62.
Regarding claim 19, see Karnowski, pars. 34-37 which teaches wheel radius dimensions.
Regarding claim 20, see Herman par. 43, estimating wheel to hitch distance.
Conclusion
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/HADI AKHAVANNIK/Primary Examiner, Art Unit 2676
