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 .
Response to Arguments
Applicant’s arguments filed May 13, 2026 have been fully considered but are not persuasive.
Applicant argues that the rejection does not 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,” as recited in amended claim 1.
See the new rejection below that relies on Chaney (10360458). Chaney teaches sensing an image of a tow vehicle and towed vehicle, identifying a feature of the tow vehicle in the sensed image, referencing data files that may be stored in memory of the controller and that include known dimensions of the tow vehicle, correlating the size of the tow-vehicle feature in the sensed image to the known size of the tow-vehicle feature, determining the scale of the sensed image, and determining dimensions of the towed vehicle (see claim 1 and col. 5, line 64 through col. 6, line 31).
As such this rejection is made final.
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, 4-14, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Herman (20190347498) in view of Chaney (US 10,360,458) in further view of El-sawah (20220147742).
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.
Chaney teaches that data files, possibly stored in the memory of the controller, include dimensions for a specific make and model of the tow vehicle, including overall length, overall width, wheelbase, and height. Chaney further teaches that the dimension determination algorithm identifies the appropriate feature of the tow vehicle in the sensed image that correlates to the known dimension of the tow vehicle, correlates the size of the feature of the tow vehicle in the sensed image to the known size of the feature of the tow vehicle, determines the scale of the sensed image, and then determines dimensions of the towed vehicle (col. 5, line 64 through col. 6, line 31).
Chaney also claims sensing an image of a tow vehicle and towed vehicle, identifying a feature of the tow vehicle having a known dimension saved in memory of a controller, and determining a dimension of the towed vehicle using the known dimension of the feature of the tow vehicle identified in the sensed image (abstract and claim 1).
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 Chaney.
Herman already teaches using a stored factory image and/or 3D model data of the vehicle to calibrate a generated 3D model and estimate trailer geometries. Chaney teaches that a known dimension of a tow vehicle feature stored in memory can be correlated with the depicted size of that feature in a sensed image to determine the image scale and calculate towed vehicle dimensions. The reason is to improve the accuracy of automated image-based trailer dimension estimation and reduce the need for manual entry of trailer measurements.
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 Chaney 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.
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 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). Chaney 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 (col. 5, line 64 through col. 6, line 31 and claim 1).
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 14, Chaney teaches vehicle reference features having known dimensions, including overall length, overall width, wheelbase, and height of the tow vehicle (col. 5, line 64 through col. 6, line 31). Chaney further teaches using a known size of a tailgate saved in memory of a controller to determine scale of the sensed image and then determine dimensions of the towed vehicle.
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).
Chaney teaches identifying a reference feature of the tow vehicle having a known dimension stored in memory and correlating the size of the vehicle feature in the sensed image to the known size to determine the image scale and calculate dimensions of the towed vehicle (col. 5, line 64 through col. 6, line 31 and claim 1).
El-sawah teaches a portable/remote device comprising a camera for capturing image data depicting the trailer (pars. 22-23 and 75).
It would have been obvious to combine Herman’s three-dimensional point cloud and trailer property estimation system with Chaney’s known vehicle-feature scaling technique and El-sawah’s portable device camera for the reasons discussed above with respect to claim 1.
Regarding claim 17, Herman teaches using three-dimensional point-location data/point cloud data in measuring trailer properties/geometric properties (pars. 19, 22, 39 and Fig. 7).
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).
Claim(s) 2-3, 15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Herman in view of Chaney in view of El-sawah, and in further view of Pliefke (20140160276).
Regarding claim 2, Chaney teaches known dimensions and scale for a vehicle feature as discussed above.
Pliefke teaches that trailer features may include a taillight/visible trailer feature and that a vision system may determine trailer dimensions by trigonometric size comparing of known size to unknown size image features (pars. 28 and claims 7).
It would have been obvious prior to the effective filing date of the invention to include, as the reference feature, a taillight or other commonly visible vehicle/trailer feature as taught by Pliefke, because such features are readily identifiable in vehicle/trailer image data and are useful for image-based dimension calculations.
Regarding claim 3, Pliefke teaches determining trailer total length by trigonometric size comparing of known size to unknown size image features and determining physical trailer characteristics for trailer driving aid systems (par. 28 and claims 7).
Regarding claim 15, Pliefke teaches determining physical trailer characteristics and trailer length/width for use in trailer driving aid systems (par. 28 and claims 7).
Regarding claim 19, Pliefke teaches trailer/code/feature recognition and use of known-size-to-unknown-size image feature comparison for trailer dimension determination (par. 28 and claims 7).
Regarding claim 20, Pliefke teaches determining trailer length and width and physical trailer characteristics for trailer driving aid systems (par. 28 and claims 7).
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HADI AKHAVANNIK whose telephone number is (571)272-8622. The examiner can normally be reached 9 AM - 5 PM Monday to Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at (571) 272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HADI AKHAVANNIK/Primary Examiner, Art Unit 2676
Prosecution Insights