DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to the application filed on January 08, 2026. Claims 1 and 14 have been amended. Claims 1-15 are presently pending and are presented for examination.
Response to Amendments
In response to the Amendments dated January 08, 2026, the Examiner withdraws the prior art rejections.
Response to Arguments
Applicant's arguments filed January 08, 2026 have been fully considered but they are moot in view of the new ground(s) of rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to ATA 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2018/0272941 (hereinafter, "Bliss"; previously of record), in view of U.S. Pub. No. 2020/0079165 (hereinafter, "Niewiadomski"; previously of record), and in further view of U.S. Pub. No. 2018/0312112 (hereinafter, "Lewis"; newly of record).
Regarding claim 1, Bliss discloses a computer-implemented method for localizing a fifth wheel hitch coupler of a motor vehicle, the method comprising:
receiving a camera image depicting the fifth wheel hitch coupler from a camera of the motor vehicle (“receiving an image taken by a rear-facing camera; identifying a coupler of a trailer in the received image” (para 0003));
determining a contour map representing a contour of a coupler throat of the fifth wheel hitch coupler based on the top view image (“The detecting the position of the coupler of the trailer in the image may further include: estimating contour points of the coupler in the image; determining a geometry of the coupler; based on the contour points and the geometry” (para 0006)); and
determining a two-dimensional in-plane position of the coupler throat is determined by fitting a predefined geometric figure to the contour of the coupler throat (“The estimating contour points of the coupler may include determining two-dimensional coordinates of edges of the coupler using a convolutional neural network for detecting a contour of a coupler” para 0007);
However, Bliss does not explicitly teach
wherein the motor vehicle comprises a cargo area and the fifth wheel hitch coupler is mounted on a surface of the cargo area;
generating a top view image by projecting the camera image to a plane, which is perpendicular to a predefined height axis of the motor vehicle.
Lewis, in the same field of endeavor, teaches
wherein the motor vehicle comprises a cargo area and the fifth wheel hitch coupler is mounted on a surface of the cargo area (“The hitch identification module is configured to identify at least one of a type of a trailer hitch attached to the cargo bed and a size of the trailer hitch based on the detected edges of the object” (para 0005), “Some pickup trucks have a trailer hitch mounted to a cargo bed of the pickup truck above a rear axle of the pickup truck. These types are trailer hitches are typically either a fifth wheel hitch or a gooseneck hitch. A fifth wheel hitch typically includes a U-shaped coupling mounted a foot or more above the cargo bed of the pickup truck” (para 0003), and Fig. 1, #26-1).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Lewis in order to identify a type of a trailer hitch attached to the cargo bed and a size of the trailer hitch based on the detected edges of the object; see Lewis at least at [0005];
Niewiadomski, in the same field of endeavor, teaches
generating a top view image by projecting the camera image to a plane, which is perpendicular to a predefined height axis of the motor vehicle (“applies an image undistortion and homography transformation to generate a top-down view of images captured by the imager 40” (para 0081)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Niewiadomski in order to determine the height of hitch ball and/or a length Lbm of ball mount in low visibility conditions; see Niewiadomski at least at [0070].
Regarding claim 2, Bliss discloses and Niewiadomski teaches the computer-implemented method according to claim 1. However, Bliss does not explicitly teach wherein prior to determining the in-plane position, the presence of the fifth wheel hitch coupler in the camera image is validated by applying an object detection algorithm to a predetermined region of interest within the camera image, inside which the fifth wheel hitch coupler is expected.
Niewiadomski, in the same field of endeavor, teaches
wherein prior to determining the in-plane position, the presence of the fifth wheel hitch coupler in the camera image is validated by applying an object detection algorithm to a predetermined region of interest within the camera image, inside which the fifth wheel hitch coupler is expected (“predicting the presence of a predefined object based on detected features” (para 0098) and “The proximity sensors may also be used to detect other various objects proximate the vehicle 12 during operation of the hitch assist system 10 prior to and/or during any hitch assist operations” (para 0057)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Niewiadomski in order to detect other various objects proximate the vehicle during operation of the hitch assist system prior to and/or during any hitch assist operations; see Niewiadomski at least at [0057].
Regarding claim 3, Bliss discloses and Niewiadomski teaches the computer-implemented method according to claim 1. However, Bliss does not explicitly teach wherein one of the preceding claims, generating the contour map comprises applying an edge detection algorithm to at least a part of the top view image.
Niewiadomski, in the same field of endeavor, teaches
wherein one of the preceding claims, generating the contour map (“generating a feature map” (para 0088)) comprises applying an edge detection algorithm (“applying a parametric circle function to locate circular structures within the image patch; comparing an inputted value of a hitch ball diameter to a number of pixels within the circular structure to form a reference length; and utilizing the reference length to determine a ball mount length or a hitch ball height” (para 0005)) to at least a part of the top view image (“The imager 40 may be capable of imaging a top view of the hitch ball 26 and can provide the image data 56 to the controller 14 for use by the image processing routine 58 (by the process described above or by other available processes) to determine the height H.sub.hb of hitch ball 26 and/or a length L.” (para 0070)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Niewiadomski in order to determine a ball mount length or a hitch ball height; see Niewiadomski at least at [0005].
Regarding claim 4, Bliss discloses and Niewiadomski teaches the computer-implemented method according to claim 1. However, Bliss does not explicitly teach wherein the geometric figure comprises at least a part of a circle line.
Niewiadomski, in the same field of endeavor, teaches
wherein the geometric figure comprises at least a part of a circle line (“the processor 124 may apply a Hough circular transform using a parametric circle function to locate circular structures. In so doing, the hitch ball 26, having a circular shape, may be more easily identified and distinguished from other structures proximate the vehicle 12. Upon identifying the circular structure, the controller 14 through the processor 124, applies a filter (e.g., a Kalman filter) to the circular structure. Upon detection of the circular structure, the number of pixels forming a diameter of the structure may be measured” (para 0081)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Niewiadomski in order to determine a ball mount length or a hitch ball height; see Niewiadomski at least at [0005].
Regarding claim 5, Bliss discloses and Niewiadomski teaches the computer-implemented method according to claim 4. However, Bliss does not explicitly teach wherein fitting the geometric figure to the contour of the coupler throat comprises fitting a circle center of the circle line and/or fitting a circle radius of the circle line
Niewiadomski, in the same field of endeavor, teaches
wherein fitting the geometric figure to the contour of the coupler throat comprises fitting a circle center of the circle line and/or fitting a circle radius of the circle line (“applying a parametric circle function to locate circular structures within the image patch” (para 0104) and “controlling a vehicle along a path to align a hitch ball with a coupler of the trailer” (para 0098)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Niewiadomski in order to align a hitch ball with a coupler of the trailer; see Niewiadomski at least at [0098].
Regarding claim 6, Bliss discloses and Niewiadomski teaches the computer-implemented method according to claim 5. However, Bliss does not explicitly teach wherein:
for each of a plurality of predefined values for the circle radius, a scan area is determined depending on the contour of the coupler throat and the respective value for the circle radius, and
fitting the geometric figure to the contour comprises varying the circle center within the respective scan area for each of the plurality of values for the circle radius.
Niewiadomski, in the same field of endeavor, teaches
for each of a plurality of predefined values for the circle radius, a scan area is determined depending on the contour of the coupler throat and the respective value for the circle radius (“The hitch assist system further includes a controller for creating an image patch of a scene rearwardly of the vehicle based on images provided by the imager; applying a parametric circle function to locate circular structures within the image patch; comparing an inputted value of a hitch ball diameter to a number of pixels within the circular structure to form a reference length; and utilizing the reference length to determine a ball mount length or a hitch ball height” (para 0107) and “apply a Hough circular transform using a parametric circle function to locate circular structures. In so doing, the hitch ball 26, having a circular shape, may be more easily identified and distinguished from other structures proximate the vehicle 12. Upon identifying the circular structure, the controller 14 through the processor 124, applies a filter (e.g., a Kalman filter) to the circular structure. Upon detection of the circular structure, the number of pixels forming a diameter of the structure may be measured” (para 0081)), and
fitting the geometric figure to the contour comprises varying the circle center within the respective scan area for each of the plurality of values for the circle radius (“areas in the occupancy grid map 142, or in the image patch 54 in examples that additionally and/or alternatively use imagers 38, 40, 42, 44, are analyzed and features 144 or patterns in the data indicative of an object in the grid map 142 and/or image patch 54 are extracted. The extracted features 144 are then classified according to any number of classifiers. An exemplary classification can include classification as a trailer 18, a coupler 16, a moving object, such as another vehicle, and/or a stationary object, such as a street sign. Data including the classification is then analyzed according to data association in order to form a feature extraction database 146 (FIG. 2). The data of the feature extraction database 146 is then stored for iterative comparison to new data and for prediction of a likelihood that a trailer 18 is proximate the vehicle 12” (para 0062)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Niewiadomski in order to predict a likelihood that a trailer is proximate the vehicle; see Niewiadomski at least at [0062].
Regarding claim 7, Bliss discloses and Niewiadomski teaches the computer-implemented method according to claim 6. However, Bliss does not explicitly teach wherein:
for each of the plurality of values for the circle radius and for each of a plurality of positions for the circle center within the respective scan area, at least one rating score is computed depending on the respective value for the circle radius and the respective position for circle center; and
fitting the geometric figure to the contour comprises selecting one of the plurality of values for the circle radius and one of the plurality of positions for the circle center within the respective scan area depending on the at least one rating score.
Niewiadomski, in the same field of endeavor, teaches
for each of the plurality of values for the circle radius and for each of a plurality of positions for the circle center within the respective scan area, at least one rating score is computed depending on the respective value for the circle radius and the respective position for circle center (“applying a parametric circle function to locate circular structures within the image patch; comparing an inputted value of a hitch ball diameter to a number of pixels within the circular structure to form a reference length” (para 0029)); and fitting the geometric figure to the contour comprises selecting one of the plurality of values for the circle radius and one of the plurality of positions for the circle center within the respective scan area depending on the at least one rating score (“To measure the pixel diameter of the hitch ball 26, the processor 124 applies an image distortion and homography transformation to generate a top-down view of images captured by the imager 40. Then, the processor 124 may apply a Hough circular transform using a parametric circle function to locate circular structures. In so doing, the hitch ball 26, having a circular shape, may be more easily identified and distinguished from other structures proximate the vehicle 12. Upon identifying the circular structure, the controller 14 through the processor 124, applies a filter (e.g., a Kalman filter) to the circular structure. Upon detection of the circular structure, the number of pixels forming a diameter of the structure may be measured. Based on the measured amount of pixels and the inputted value from the user at step 186, the diameter D.sub.hb of the hitch ball 26 may be converted to number of pixels within the image patch 54 (FIG. 12). Likewise, the ball mount 24 may be identified through image processing and the number of pixels along the length of the ball mount 24 may also be measured” (para 0081)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Niewiadomski in order to identify and distinguish the hitch ball from other structures proximate the vehicle; see Niewiadomski at least at [0081].
Regarding claim 13, Bliss discloses and Niewiadomski teaches a method for assisting coupling of a trailer with a fifth wheel hitch coupler of a motor vehicle, wherein:
carrying out a computer-implemented method for localizing the fifth wheel hitch coupler of the motor vehicle (“determining an absolute position of the coupler based on the height and two-dimensional coordinates of the edges of the coupler” (para 0008)) according to claim 1; and
guiding the motor vehicle at least in part automatically towards the trailer depending on a result of the localization of the coupler throat (“controlling to guide the vehicle to the coupler based on at least one from among the information on the determined distance and the information on the detected position of the coupler of the trailer” (para 0010)).
Regarding claim 14, Bliss discloses a system for localizing a fifth wheel hitch coupler of a motor vehicle, the system comprising:
a camera for the motor vehicle, which is configured to generate a camera image depicting the fifth wheel hitch coupler (“a method and apparatus that provides trailer information using a vehicle camera to perform vehicle guidance and that display visual assistance to an operator of vehicle” (para 0034));
at least one computing unit (“a non-transitory computer readable medium” (para 0019)), which is configured to:
determine a contour map representing a contour of a coupler throat of the fifth wheel hitch coupler based on the top view image (“The detecting the position of the coupler of the trailer in the image may further include: estimating contour points of the coupler in the image; determining a geometry of the coupler; based on the contour points and the geometry, determining a height of the coupler” (para 0006)); and
determine a two-dimensional in-plane position of the coupler throat by fitting a predefined geometric figure to the contour of the coupler throat (“The estimating contour points of the coupler may include determining two-dimensional coordinates of edges of the coupler using a convolutional neural network for detecting a contour of a coupler” para 0007);
However, Bliss does not explicitly teach
the motor vehicle comprising a cargo area, wherein the fifth wheel hitch coupler is mounted on a surface of the cargo area;
generate a top view image by projecting the camera image to a plane, which is perpendicular to a predefined height axis of the motor vehicle.
Lewis, in the same field of endeavor, teaches
the motor vehicle comprising a cargo area, wherein the fifth wheel hitch coupler is mounted on a surface of the cargo area (“The hitch identification module is configured to identify at least one of a type of a trailer hitch attached to the cargo bed and a size of the trailer hitch based on the detected edges of the object” (para 0005), “Some pickup trucks have a trailer hitch mounted to a cargo bed of the pickup truck above a rear axle of the pickup truck. These types are trailer hitches are typically either a fifth wheel hitch or a gooseneck hitch. A fifth wheel hitch typically includes a U-shaped coupling mounted a foot or more above the cargo bed of the pickup truck” (para 0003), and Fig. 1, #26-1).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Lewis in order to identify a type of a trailer hitch attached to the cargo bed and a size of the trailer hitch based on the detected edges of the object; see Lewis at least at [0005];
Niewiadomski, in the same field of endeavor, teaches
generate a top view image by projecting the camera image to a plane, which is perpendicular to a predefined height axis of the motor vehicle (“applies an image undistortion and homography transformation to generate a top-down view of images captured by the imager 40” (para 0081)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Niewiadomski in order to determine the height of hitch ball and/or a length Lbm of ball mount in low visibility conditions; see Niewiadomski at least at [0070].
Regarding claim 15, Bliss discloses a computer program product comprising instructions, which, when executed by a data processing device, cause the data processing device to carry out a computer-implemented method according claim 1 (“perform a method including detecting a position of a coupler of a trailer in an image taken by a rear-facing camera by using a convolutional neural network” (para 0019)).
Claims 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2018/0272941 (hereinafter, "Bliss"; previously of record), in view of U.S. Pub. No. 2020/0079165 (hereinafter, "Niewiadomski"; previously of record), in further view of U.S. Pub. No. 2018/0312112 (hereinafter, "Lewis"; newly of record) as applied to claim 7 above, and in further view of U.S. Pub. No. 2017/0177949 (hereinafter, "Hu"; previously of record).
Regarding claim 8, Bliss discloses and Niewiadomski teaches the computer-implemented method according to claim 7. However, Bliss does not explicitly teach wherein the at least one rating score comprises an intensity rating score, which depends on a sum of pixel values of the contour map for all pixel positions, which lie on the part of the respective circle line.
Hu, in the same field of endeavor, teaches
wherein the at least one rating score comprises an intensity rating score, which depends on a sum of pixel values of the contour map for all pixel positions, which lie on the part of the respective circle line (“the controller 38 derives an edge map of the averaged image by calculating the intensity gradient for each pixel of the averaged image 125. The intensity gradient, or edge value, of each pixel may range from 0 to 255” (para 0047) and “Based on the match quality, a confidence score is given to each candidate hitch point location 173a-173d and the candidate hitch point location 173a-173d receiving the highest confidence score is selected as the hitch point. In the event the matching quality associated with each candidate hitch point location 173a-173d is below a predetermined threshold, the controller 38 may define additional candidate hitch point locations (not shown) along the reference line 174 in either or both directions of the candidate hitch point location 173a-173d that received the highest confidence score and execute template matching with respect to each of the additional candidate hitch point locations” (para 0049)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Hu in order to distinguish trailer contour from ground noise in images captured by the imaging device; see Hu at least at [0044].
Regarding claim 9, Bliss discloses and Niewiadomski teaches the computer-implemented method according to claim 8. However, Bliss does not explicitly teach wherein the at least one rating score comprises a symmetry rating score, which depends on a mirror symmetry of the contour of the coupler throat.
Hu, in the same field of endeavor, teaches
wherein the at least one rating score comprises a symmetry rating score, which depends on a mirror symmetry of the contour of the coupler throat (“This process may be iterated as many times as needed until the predetermined threshold has been met. In so doing, the location of the candidate hitch point location that is ultimately selected as the imaged hitch point will closely mirror the location of the actual hitch point 172” (para 0049) and “the controller 38 selects the candidate line (e.g., candidate line 242) having approximately the same number of trailer pixels on each of its sides, or said differently, the candidate line, or centerline, about which the trailer pixels are substantially symmetric” (para 0054)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Hu in order to distinguish trailer contour from ground noise in images captured by the imaging device; see Hu at least at [0044].
Regarding claim 10, Bliss discloses and Niewiadomski teaches the computer-implemented method according to claim 9. However, Bliss does not explicitly teach wherein the at least one rating score comprises a combined score, which is given by a weighted sum of the intensity rating score and the symmetry rating score.
Hu, in the same field of endeavor, teaches
wherein the at least one rating score comprises a combined score, which is given by a weighted sum of the intensity rating score and the symmetry rating score (“Based on the match quality, a confidence score is given to each candidate hitch point location 173a-173d and the candidate hitch point location 173a-173d receiving the highest confidence score is selected as the hitch point. In the event the matching quality associated with each candidate hitch point location 173a-173d is below a predetermined threshold, the controller 38 may define additional candidate hitch point locations (not shown) along the reference line 174 in either or both directions of the candidate hitch point location 173a-173d that received the highest confidence score and execute template matching with respect to each of the additional candidate hitch point locations” (para 0049) and “the controller 38 selects the candidate line (e.g., candidate line 242) having approximately the same number of trailer pixels on each of its sides, or said differently, the candidate line, or centerline, about which the trailer pixels are substantially symmetric” (para 0054)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Hu in order to distinguish trailer contour from ground noise in images captured by the imaging device; see Hu at least at [0044].
Regarding claim 11, Bliss discloses and Niewiadomski teaches the computer-implemented method according to claim 10. However, Bliss does not explicitly teach wherein the one of the plurality of values for the circle radius and the one of the plurality of positions for the circle center within the respective scan area are selected, if at least one predefined condition is fulfilled, wherein the at least one condition is fulfilled only if:
the respective combined score is greater than a predefined first threshold value; and/or
the respective combined score is maximum for all of the plurality of values for the circle radius and all of the plurality of positions for the circle center; and/or
the respective symmetry rating score is greater than a predefined second threshold value; and/or
all pixel values of the contour map within a predefined environment of the respective circle line are smaller than a predefined third threshold value.
Hu, in the same field of endeavor, teaches
wherein the one of the plurality of values for the circle radius and the one of the plurality of positions for the circle center within the respective scan area are selected, if at least one predefined condition is fulfilled, wherein the at least one condition is fulfilled only if:
the respective combined score is greater than a predefined first threshold value; and/or
the respective combined score is maximum for all of the plurality of values for the circle radius and all of the plurality of positions for the circle center; and/or
the respective symmetry rating score is greater than a predefined second threshold value; and/or
all pixel values of the contour map within a predefined environment of the respective circle line are smaller than a predefined third threshold value (“the controller 38 compares the edge value of each pixel of the edge map 135 to a threshold value (e.g., 30). Pixels having an edge value meeting or exceeding the threshold value are identified as trailer pixels whereas pixels having an edge value not meeting or exceeding the threshold value are identified as ground noise pixels” (para 0047), “In the event the matching quality associated with each candidate hitch point location 173a-173d is below a predetermined threshold, the controller 38 may define additional candidate hitch point locations (not shown) along the reference line 174 in either or both directions of the candidate hitch point location 173a-173d that received the highest confidence score and execute template matching with respect to each of the additional candidate hitch point locations. This process may be iterated as many times as needed until the predetermined threshold has been met” (para 0049) and “It should be appreciated that the boundary line 186 may assume other shapes in alternative embodiments. The location and shape of the boundary line 186 may be determined based on various considerations such as, but not limited to, vehicle speed, trailer length, drawbar length, imager characteristics, trailer contour, and vehicle contour” (para 0050)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Hu in order to distinguish trailer contour from ground noise in images captured by the imaging device; see Hu at least at [0044].
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2018/0272941 (hereinafter, "Bliss"; previously of record), in view of U.S. Pub. No. 2020/0079165 (hereinafter, "Niewiadomski"; previously of record), in further view of U.S. Pub. No. 2018/0312112 (hereinafter, "Lewis"; newly of record) as applied to claim 1 above, and in further view of U.S. Pub. No. 2023/0410359 (hereinafter, "Mustafa"; previously of record).
Regarding claim 12, Bliss discloses and Niewiadomski teaches the computer-implemented method according to claim 1. However, Bliss does not explicitly teach wherein a height position of the coupler throat is determined depending on the in-plane position and depending on a predetermined height position of the camera.
Mustafa, in the same field of endeavor, teaches
wherein a height position of the coupler throat is determined depending on the in-plane position and depending on a predetermined height position of the camera (“determining a height of a hitch ball of a vehicle relative to a camera height” (para 0033) and “the disclosure provides for a novel system 8 for detecting a height and position of an interface between the vehicle 12 and the trailer 10” (para 0036)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Bliss with the teachings of Mustafa in order to determine the height and position of the coupler; see Mustafa at least at [0037].
Conclusion
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/ADAM M ALHARBI/Primary Examiner, Art Unit 3663