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 .
Claim Status
This action is in response to the application filed on July 24, 2026. Claims 1-7, 9-17, and 19-20 are pending examination for this application.
Response to Amendments
Applicant’s remarks and amendments filed July 24, 2026, have been entered.
Response to Arguments
Applicant's arguments filed July 24, 2026, have been fully considered but they are not persuasive.
Argument 1: On page 9, the applicant alleges, “Han's Equation (10) does not utilize the bottom height coordinate Yo as recited in the claims. Rather, Han calculates a horizon-line estimate using a different relationship involving vehicle width information. The variable corresponding to the claimed bottom height coordinate is absent from Han's Equation (10). Thus, Han does not disclose the mathematical relationship recited by the claims.”
Response: The examiner respectfully disagrees. The examiner has applied the broadest reasonable interpretation of the claim limitations. The claim mapping provided an explanation and direct equation comparison to the Applicant’s submitted equation.
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In Han, equation 10 and Figure 11, as shown above, vbottom,i is shown as the lower bottom height coordinate of the bounding box around the object and is considered to be the same as Yo the bottom height coordinate. Based on applicant’s drawing, Fig. 5, the Yo is just the bottom height coordinate of the bounding box around the object.
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Argument 2: On pages 9-10, the applicant alleges, “Second, the claims require calculating the horizon height coordinate based on a particular set of inputs, namely:
(1) the installation height of the camera;
(2) the height coordinate of the target object image;
(3) the image width of the target object image; and
(4) the actual physical width of the target object.
The Office Action has not identified where Han teaches a horizon-height calculation based upon all of these recited parameters. In particular, the rejection does not identify any disclosure in Han in which a height coordinate of the target object image is utilized in the claimed calculation.”
Response: The examiner respectfully disagrees. The examiner has applied the broadest reasonable interpretation of the claim limitations. The claim mapping provided an explanation and direct equation comparison to the Applicant’s submitted equation. According to claim 1 limitations:
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In addition to Figure 11 displayed in Argument 1, Figure 12 of Han further details:
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(1) Hc is the installation height which is equivalent to hc (camera height) in equation 10 of Han.
(2) height of coordinate of the target object image
(a) Yh is the horizontal height which is equivalent to v0,VD (estimated vertical coordinate of the horizon line) in equation 10 of Han.
(b) Yo is the bottom height coordinate which is equivalent to vbottom,I (vehicle bottom line) in equation 10 of Han.
(3) Wo is the image width which is equivalent to ωv,i (vehicle image width) in equation 10 of Han.
(4) Wp is the actual width which is equivalent to Wv,I (vehicle physical width) in equation 10 of Han.
The examiner has mapped directly to the defined equation terms in equation 1. If the applicant interpreting the equation terms differently the interpretations and definitions should be included in the claim language.
Argument 3: On pages 10-11, the applicant alleges, “Third, the claims require obtaining an actual physical width of the target object based on the type of the target object. However, Han's discussion of vehicle width estimation concerns estimating vehicle width from image-based information and lane information. The Office Action does not identify any disclosure in Han of obtaining an actual physical width based upon an identified object type as required by the claims.
Therefore, Han does not teach:
(a) obtaining an actual physical width of the target object based on the type;
(b) calculating a horizon height coordinate based on the specific claimed set of
parameters; or
(c) calculating the horizon height coordinate according to the specific mathematical
relationship recited in the claims.”
Response: The examiner respectfully disagrees. The examiner has applied the broadest reasonable interpretation of the claim limitations. The claim mapping provided an explanation and direct equation comparison to the applicant’s submitted equation. A vehicle is considered to be the object and image-based information is based on the vehicle being in the image which is a target object type. Therefore, the actual physical width of the object which in this case is the vehicle is obtained.
(a) obtaining an actual physical width of the target object based on the type; Addressed in Argument 3.
(b) calculating a horizon height coordinate based on the specific claimed set of
parameters; or Addressed in Argument 1.
(c) calculating the horizon height coordinate according to the specific mathematical
relationship recited in the claims.” Addressed in Argument 2.
Argument 4: On page 11, the applicant alleges, “Further still, a closer review closely at Han's Equation (10), it appears that this equation is mathematically closer to:
Yh = Hc - (Wo/Wp)*ω
whereas the claim requires:
Yh = Yo – Hc*Wo/Wp
These are not merely different symbols. The claimed formula uses Yo as the base term, while Han uses Hc as the base term, and Han introduces an entirely different variable (ω) not found in the claim. Simply stated the teachings of Han, including the relied-upon mathematical expression, do not disclose the claimed limitations.”
Response: The examiner respectfully disagrees. This rewrite of the cited equation is deliberately wrong. The actual equation 10 of Han is:
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Even when simplified the equations would read as:
v0,VD = vbottom,i (bottom edge) - hc * (ωv,i )/Wv,i
which equivalates to
Yh = Yo - Hc*Wo/Wp
The variable vbottom,i is the base term which is equivalent to Yo. The variable ω is equivalent to Wo so while a different symbol it does equate to the same variable.
Argument 5: On page 9, the applicant alleges, “The office action stated that the references were all in the field of "object detection." Applicant believes this is far too broad of an field of art characterization. In fact, Han is expressly directed to vehicle distance estimation, not object detection.”
Response: The examiner respectfully disagrees. A vehicle is an object, therefore vehicle distance estimation is considered object detection. In Paragraph [0027] of the Applicant’s Specification, “In summary, the object detection device and the object detection method of the present disclosure may be effectively applied on a vehicle to detect another vehicle in front by means of a real-time image detection, to provide highly reliable functions of front object detection and distance detection.” Therefore the broadest reasonable interpretation applies to the claim limitations.
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.
Claim(s) 1-2, 4, 6-7, 9-12, 14, 16-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kale et al, U.S. Publication No. 2023/0139682 in view of Niesen et al, U.S. Publication No. 2022/0405952 in view of Han et al. (“Vehicle Distance Estimation Using A Mono-Camera For Fcw/Aeb Systems,” 2016).
Regarding claim 1, Kale teaches an object detection device, comprising:
a camera (See Kale Figure 1, Paragraph [0023], a camera component) configured to obtain a plurality of original sensed images (see Kale Paragraph [0023], configured to capture and generate images);
a storage circuit configured to store a plurality of modules (see Kale Paragraph [0037], memory 104);
and a processor (see Kale, Paragraph [0028], a processing device) coupled to the storage circuit, configured to execute the plurality of modules to:
define respective overall image areas of a plurality of first sensed images from the plurality of original sensed images as first regions of interest (see Kale, Paragraph [0028], provide the first image as an input to the model and obtain object data based on one or more outputs of the model. The object data may include an indication of a region of the first image that includes the object (e.g., a bounding box, etc.), an indication of an object class):
define respective partial image areas of a plurality of second sensed images from the plurality of original sensed images as second regions of interest (Paragraph Kale [0028], a second image may be generated that includes a depiction of the environment based on a second set of conditions);
input the plurality of first sensed images (see Kale Paragraph [0028], A processing device (e.g., at the edge device) may provide the first image as an input to the model and obtain object data based on one or more outputs of the model) and the plurality of third sensed images to a deep neural network learning model (see Kale Paragraph [0029], processing device may provide training data associated with the third image to retrain the machine learning model. The training data may include the third image, an indication of a region of the third image that includes the object),
wherein the deep neural network learning model outputs image information of a target object image (see Kale Paragraph [0029], the indication of the object class (e.g., as determined from the object data obtained from the one or more outputs of the machine learning model) in the plurality of first sensed images and the plurality of third sensed images, respectively;
obtain an actual distance to a target object in the target object image based on the image information of the target object image (see Kale Paragraph [0215], an image processing chip that may measure distance from vehicle 1600 to target object).
Kale does not expressively teach
and crop out a plurality of third sensed images based on respective second regions of interest of the plurality of second sensed images;
However, Niesen in a similar invention in the same field of endeavor teaches
and crop out a plurality of third sensed images based on respective second regions of interest of the plurality of second sensed images (see Niesen Paragraph [0036], the detection and tracking system can extract or crop the image area within the determined bounding box (e.g., after determining or increasing the bounding box size based on the estimate uncertainty) to produce or generate a sub-image).
The combination of Kale and Niesen are analogous art because they are both in the same field of endeavor of object detection. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to crop an image area within a bounding box to create a sub-image; to pre-process adjust and scale images before inputting into the machine-learning model; scale sub-image to a predetermined dimension; for image data to include size and position of the target object; and object data to include class information as taught in Niesen in the device of Kale to determine the precise location of the target object (see Niesen, Paragraph [0037]).
Kale in view of Niesen does not expressively teach
wherein the processor is configured to obtain an actual physical width of the target object based on the type, and calculate a horizon height coordinate corresponding to the target object image based on an installation height of the camera, a height coordinate of the target object image, an image width of the target object image, and the actual physical width of the target object, wherein the horizontal height it calculated according to the following formula: wherein Yh is the horizontal height, Yo is the bottom height coordinate, He is the installation height (in cm), Wo is the image width, and Wp is the actual physical width.
However, Han in a similar invention in the same field of endeavor teaches
wherein the processor is configured to obtain an actual physical width of the target object based on the type, and calculate a horizon height coordinate corresponding to the target object image based on an installation height of the camera, a height coordinate of the target object image, an image width of the target object image, and the actual physical width of the target object, wherein the horizontal height it calculated according to the following formula: wherein Yh is the horizontal height, Yo is the bottom height coordinate, He is the installation height (in cm), Wo is the image width, and Wp is the actual physical width (see Han, pg. 487, Fig. 11, Fig. 12, and 5.1. Horizon Line Estimation, “from the i-th vehicle bottom line vbottom,i and the i-th vehicle width Wv,i, the vehicle image height, yi can be obtained by the a pin-hole camera model as shown in Figure 11. Thus, from the N number of the detected vehicles, the horizon line v0,VD can be computed by averaging its estimated horizon line as follows,
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”, v0,VD (estimated horizon line) is considered to be Yh horizontal height, vbottom,i (bottom edge) is considered to be Yo bottom height coordinate, hc (camera height) is considered to be Hc the installation height, ωv,i (vehicle image width) is considered to be Wo image width, and Wv,I (vehicle physical width) is considered to be actual physical width).
The combination of Kale, Niesen, and Han are analogous art because they are all in the same field of endeavor of object detection. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to calculate an estimated horizon line; use Kalman filtering to enhance estimation performance; and use image height to estimate distance as taught in the method of Han in the device of Kale in view of Niesen improve the performance of the FCW/AEB (forward collision warning/autonomous emergency breaking) systems (see Han, Abstract).
Regarding claim 2, Kale in view of Niesen in view of Han further teaches the object detection device according to claim 1,
wherein the processor is configured to adjust the plurality of first sensed images and the plurality of third sensed images to a same image size (see Niesen, Paragraph [0082], the sub-image 408 may be further pre-processed before being provided to the detection model. For instance, the height and/or width of sub-image 408 may be adjusted or scaled, for example to increase or decrease a pixel height and/or width of the sub-image 408 before it is provided to the detection model … Further, because the scale of the tracking object in the image has been normalized (based on the scaling of the sub-image to the fixed size), a machine-learning based object detector can be trained to process images having tracking objects (e.g., tracking vehicles) of that width. The object detection and tracking system can scale the sub-image back to the original size and can account for the sub-image position, which can result in the object detection and tracking system obtaining an accurate bounding box of the target object in an original digital display (e.g., in a full digital display mirror image)),
and input the first sensed images and the third sensed images after being adjusted to the deep neural network learning model (see Niesen, Paragraph [0036], By way of example, the detection and tracking system can scale the sub-image to a predetermined dimension (e.g., with a pre-determined width and/or a predetermined height) that corresponds with the input layer of an object detection model that is (or that includes) a machine-learning (ML) based classifier, such as a deep neural network. The object detection model of the detection and tracking system can then perform object detection on the scaled image in order to detect the position and/or location of the object in the image).
The rationale of claim 1 has been applied herein 1.
Regarding claim 4, Kale in view of Niesen in view of Han further teaches the object detection device according to claim 1,
wherein the processor is configured to execute an image tracking module (see Kale, Fig. 2, Paragraph [0051], object detection engine 210 may include an input image module 212, an inference module 214, an output module 216 and/or a transmission module 218) to track the target object image in the plurality of first sensed images and the target object image in the plurality of third sensed images, respectively (see Kale Paragraph [0052], input image module 212 may be configured to obtain an image (e.g., image 106) and provide the obtained image as input to a trained object detection and/or classification model 222 (referred to as object detection model 222 herein) stored at memory 220 and Niesen Paragraph [0038], Object tracking can be performed across multiple successive images (or frames), for example, that are received by the tracking object, e.g., captured by an image-capture device, such as a camera, Light Detection and Ranging (LiDAR) sensor, and/or a radar sensor of the tracking object)).
The rationale of claim 1 has been applied herein 1.
Regarding claim 6, Kale in view of Niesen in view of Han further teaches the object detection device according to claim 1,
wherein the image information includes image size and position information of the target object image in the plurality of first sensed images and image size and position information of the target object image in the plurality of third sensed images (see Niesen, Paragraph [0036], the detection and tracking system can scale the sub-image to a predetermined dimension (e.g., with a pre-determined width and/or a predetermined height) and see Niesen Paragraph [0068], the tracking object can receive (or capture) sensor data, such as image data … that include velocity, pose, and/or size information for target object 402. The pose can include three-dimensional (3D) position (e.g., including horizontal (x), vertical (y), and depth (z) dimensions) and 3D orientation (e.g., including pitch, roll, and yaw)”. The image can be scaled to a predetermined dimension which implies the image size would be included in the image information of the image data and apply to the first image and the third image).
The rationale of claim 1 has been applied herein 1.
Regarding claim 7, Kale in view of Niesen in view of Han further teaches the object detection device according to claim 1,
wherein the image information further includes a type of the target object image in the plurality of first sensed images and in the plurality of third sensed images (see Kale, Paragraph [0048], Object data 108 may include an indication of a class associated with an object detected in the one or more given input images 106, a region of the one or more given input images 106 that depict the detected object, and/or additional data (e.g., mask data) associated with the detected object).
The rationale of claim 1 has been applied herein 1.
Regarding claim 9, Kale in view of Niesen in view of Han further teaches the object detection device according to claim 1,
wherein the processor is configured to smooth horizon height coordinates corresponding to a plurality of target object images to obtain a current frame horizon height coordinate (see Han, pg. 485, 4. Estimation with lane information, “After that, longitudinal position, velocity, and acceleration of the target vehicles were tracked by applying a Kalman filter with a constant acceleration model,” Fig. 8, and pg. 486, 4.3.2 Kalman Filtering).
The rationale of claim 1 has been applied herein 1.
Regarding claim 10, Kale in view of Niesen in view of Han further teaches the object detection device according to claim 9,
wherein the processor is configured to calculate the actual distance to the target object based on the current frame horizon height coordinate, a focal length of the camera, the installation height of the camera, and the height coordinate of the target object image (see Han, pg. 484, 2.1. Method Using Image Height, “
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”, where Dx (longitudinal distance) is considered to be actual distance to the target object, f (focal length), y (vehicle image height between the horizon line and the bottom edge of the vehicle) is considered to be height coordinate of the target image and current frame horizon coordinate, hc (camera height) is considered to be installation height of camera).
The rationale of claim 1 has been applied herein 1.
As per claim 11, Claim 11 claims a method comprising the same limitations as Claim 1. Therefore, the rejection and rationale are analogous to that made in Claim 1.
Kale further teaches obtaining a plurality of original sensed images by a camera (see Kale Paragraph [0023], “a camera component”)
As per claim 12, Claim 12 claims the same limitations as Claim 2 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 2.
As per claim 14, Claim 14 claims the same limitations as Claim 4 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 4.
As per claim 16, Claim 16 claims the same limitations as Claim 6 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 6.
As per claim 17, Claim 17 claims the same limitations as Claim 7 and is dependent on a similarly rejected dependent claim. Therefore, the rejection and rationale is analogous to that made in Claim 7.
As per claim 19, Claim 19 claims the same limitations as Claim 9 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 9.
As per claim 20, Claim 20 claims the same limitations as Claim 10 and is dependent on a similarly rejected dependent claim. Therefore, the rejection and rationale is analogous to that made in Claim 10.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kale in view Niesen in view of Han et al, (“Vehicle Distance Estimation Using A Mono-Camera For Fcw/Aeb Systems,” 2016) and further in view of Yoon et al, US 20180101178.
Regarding claim 3, Kale in view of Niesen in view of Han does not expressively teach
wherein the second region of interest is a partial image area at a center of the plurality of second sensed images.
However, Yoon, in a similar invention in the same field of endeavor teaches
wherein the second region of interest is a partial image area at a center of the plurality of second sensed images (see Yoon, Paragraph [0051], “a central section (R2 in FIG. 7)”).
The combination of Kale, Niesen, Han, and Yoon are analogous art because they are all in the same field of endeavor of object detection. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have a central section R2 as taught in the apparatus of Yoon in the device of Kale in view of Niesen in view of Han for a moving object to be detected (see Yoon, Paragraph [0023]).
As per claim 13, Claim 13 claims the same limitations as Claim 3 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 3.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kale in view of Niesen in view of Han et al, (“Vehicle Distance Estimation Using A Mono-Camera For Fcw/Aeb Systems,” 2016) and in further view of Kasazumi et al, U.S. Publication No. 2019/0018240.
Regarding claim 5, Kale in view of Niesen in view of Han does not expressively teach
wherein the plurality of first sensed images and the plurality of second sensed images are respectively sensed images of odd frames and sensed images of even frames in the plurality of original sensed images.
However, Kasazumi, in a similar invention in the same field of endeavor teaches
wherein the plurality of first sensed images and the plurality of second sensed images are respectively sensed images of odd frames (see Kasazumi Figure 9A and Paragraph [0028], first image is displayed in an odd-numbered frame of the image data) and sensed images of even frames (see Kasazumi Figure 9B and Paragraph [0028], second image is displayed in an even-numbered frame of the image data) in the plurality of original sensed images.
Kale, Niesen, Han, and Kasazumi are considered to be analogous to the claimed invention because they are in the same field obtaining images for use in vehicular sensing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date for the plurality of first sensed images and second sensed images to be displayed in an odd-numbered frame and even-numbered frame respectively as taught in the device of Kasazumi in the device of Kale in view of Niesen in view of Han in a time-division manner for the plurality of images having different distances from the display medium in the depth direction (see Kasazumi, Paragraph [0004]).
As per claim 15, Claim 15 claims the same limitations as Claim 5 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale is analogous to that made in Claim 5.
Conclusion
THIS ACTION IS MADE FINAL. 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 DOMINIQUE JAMES whose telephone number is (703)756-1655. The examiner can normally be reached 9:00 am - 6:00 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emily Terrell can be reached on ((571)270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DOMINIQUE JAMES/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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