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 .
Oath/Declaration
The receipt of the Oath/Declaration is acknowledged.
Drawings
The drawing(s) filed on 10/31/2024 are accepted by the Examiner.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/09/2026 are in compliance with the provisions on 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
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 (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 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, 10, 11, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Schiffer et al. (US 2025/0131729 A1) (hereinafter known as Schiffer) in view of Wang (US 10,586,456 B2).
Regarding claim 1, Schiffer teaches a computer-implemented method for displaying attributes of vehicle images (Schiffer, Abstract and paragraphs [0001] and [0007]), the method comprising:
providing a digital test image including a racing vehicle (Schiffer, paragraphs [0086] and [0106-0108], especially paragraph [0108]);
passing the digital test image to a first machine-learning model configured to identify an attribute of the racing vehicle (Schiffer, paragraphs [0086] and [0106-0108], especially paragraph [0108]),
passing the cropped digital test image to a second machine-learning model (Schiffer, paragraph [0103]).
However, Schiffer does not explicitly teach automatically measuring and displaying attributes of the vehicle images, the method comprising:
passing the digital test image to a first machine-learning model configured to identify an attribute of the racing vehicle, wherein a first attribute comprises a component of a racing vehicle with a known distance d;
identifying, by the first machine-learning model, the first attribute and cropping the digital test image to emphasize the first attribute;
passing the cropped digital test image to a second machine-learning model configured to predict coordinates of at least four properties of the first attribute of the racing vehicle;
calculating a first measurement of the first attribute using the coordinates of the at least four properties of the attribute and known distance d;
calculating a second measurement of a second attribute using the first measurement as an input;
displaying the calculated value of the second measurement on a graphical user interface; and
changing a component of a racing vehicle corresponding to the second attribute based on the calculated value of the second measurement.
In reference to Wang, Wang teaches, a computer-implemented method for automatically measuring and displaying attributes of vehicle images (Wang, Col 11, line 22 – line 67; Fig. 3, Col, 12, line 1 – line 48; Wang discloses a computer-implemented method for determining car-to-lane distance from vehicle images and outputting distance values) the method comprising:
providing a digital test image including a racing vehicle (Wang, Fig. 3, Col 12, line 12 – line 27; The system of receives an image from a camera and processes vehicles in the image.);
passing the digital test image to a first machine-learning model configured to identify an attribute of the racing vehicle, wherein a first attribute comprises a component of a racing vehicle with a known distance d (Wang, Col 12, line 12 – line 27, Wang uses deep learning-based object detection and wheel segmentation.);
identifying, by the first machine-learning model, the first attribute and cropping the digital test image to emphasize the first attribute (Wang, Col 13, line 52 – Col 14, line 16; Wang describes associating wheels with bounding boxes and cropping an area corresponding to a selected bounding box.);
passing the cropped digital test image to a second machine-learning model configured to predict coordinates of at least four properties of the first attribute of the racing vehicle (Wang, Fig. 7, Col 13, line 34 – Col 16, line 29; Wang uses wheel/lane segmentation and geometric processing to yield the at least four properties of the first attribute.);
calculating a first measurement of the first attribute using the coordinates of the at least four properties of the attribute and known distance d (Wang, Col 15, line 3 – Col 16, line 38; Wang computes distance between wheel and lane, and then vehicle-to-lane distance based on that geometry.);
calculating a second measurement of a second attribute using the first measurement as an input (Wang, Col 13, line 5 – line 18, Wang predicts behavior based on vehicle lane distance.);
displaying the calculated value of the second measurement on a graphical user interface (Wang, Col 13, line 5 – line 18, Wang states distance values by be overlaid on the lane segmentation map or output by the system.);
and
changing a component of a racing vehicle corresponding to the second attribute based on the calculated value of the second measurement (Wang, Col 9, line 39 – line 58; Col 15, line 3 – Col 16, line 38; Wang computes distance between wheel and lane, and then vehicle-to-lane distance based on that geometry.).
These arts are analogous since they are both related to imaging devices. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the invention of Schiffer with the method of automatically measuring and displaying attributes of the vehicle images of Wang to provide a practical, camera-based way to better estimate car-to-lane distance using wheel and lane segmentation as seen in Wang, Col 11, lines 22 -39.
Independent claims 10 and 17 are rejected for the same reasons as claim 1.
Regarding claim 3, the combination of Schiffer and Wang teaches the method of claim 1, and also teaches wherein the first attribute is a first wheel of the racing vehicle (Wang, Col 11, line 22 – line 39; Col 13, line 34 – Col 15, line 39; Wang describes associating detected wheels with a vehicle and using wheel locations in the calculation.).
Claims 11 and 18 are rejected for the same reasons as claim 3.
Allowable Subject Matter
Claims 2, 4-9, 12-16 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/TWYLER L HASKINS/ Supervisory Patent Examiner, Art Unit 2639
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