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
Claims 1-20 of U.S. Application No. 18/395461 filed on 12/23/2023 have been examined.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1-3, 5-12 and 14-19 rejected under 35 U.S.C. 102(a)(1) as being anticipated by Singhal et al. [US 2022/0057213 A1], hereinafter referred to as Singhal et al.
As to Claim 1, 10 and 18, Singhal discloses a method of landing an aircraft without usage of a GPS signal ([see at least 0039]), comprising: receiving an image from a camera attached to an aircraft ([see at least Fig. 2, 0040 and 0042]); determining a correspondence between estimated landmark locations based on the image and predicted landmark locations to produce a plurality of world coordinates of best matches for at least four coplanar points in the image ([see at least 0047 and 0059]); applying a COPOSIT method to the plurality of world coordinates of best matches for the at least four coplanar points to produce an estimated aircraft position and orientation ([see at least 0039, 0047 and 0059]); receiving, by a Kalman filter, the estimated aircraft position and orientation, and further receiving input from an IMU, the IMU input representing acceleration and angular body rates ([see at least 0039, 0047 and 0059]); and generating, by the Kalman filter, a state estimation of the aircraft, the state estimation including a corrected estimated position, a corrected estimated velocity, and a corrected estimated orientation of the aircraft, the Kalman filter operating without using input of the GPS signal, the state estimation configured to be outputted to a flight control system of the aircraft for landing the aircraft without using input of the GPS signal ([see at least 0039, 0047 and 0059]).
As to Claim 2 and 11, Singhal discloses a method, further comprising: detecting features in the image, wherein the features correspond to fiducials ([see at least 0037, 0047 and 0059]); and estimating the landmark locations based on the detected features; and predicting the predicted landmark locations ([see at least 0037, 0047 and 0059]).
As to Claim 3, 12 and 19, Singhal discloses a method, wherein the detecting features is performed by Hough circle detection ([see at least 0033]).
As to Claim 5 and 14, Singhal discloses a method, wherein method is performed by a simulator to simulate aircraft flight using computer-generated terrain and fiducial ([see at least 0022]).
As to Claim 6 and 15, Singhal discloses a method wherein the aircraft position and orientation are an output measurement matrix of the COPOSIT module, and wherein the output measurement matrix is set to zero when no COPOSIT data is available and the zeroed output measurement matrix is input to the Kalman filter ([see at least 0039, 0047 and 0059]).
As to Claim 7 and 16, Singhal discloses a method, wherein the corrected estimated position, the corrected estimated velocity, and the corrected estimated orientation are repeatedly determined over time based on new received images from the camera and are repeatedly inputted into a flight control system to facilitate landing the aircraft ([see at least 0028, 0041, 0047 and 0053]).
As to Claim 8 and 17, Singhal discloses a method, wherein the method is performed in near real time ([see at least abstract, 0004 and 0028]).
As to Claim 9, Singhal discloses a method, further comprising: obtaining real world telemetry data and videos from a drone flight test, and simulating the method using the obtained real world telemetry data and videos as input to the simulation ([see at least Fig. 2, 0039, 0047 and 0059]).
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 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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 4, 13 and 20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Singhal, in view of Zadeh et al. [US 2022/0121884 A1], hereinafter referred to as Zadeh.
As to claims 4, 13 and 20, Singhal discloses all of the limitations of claim 1 as stated above. Singhal does not explicitly disclose, wherein the detecting features is performed by Harris corner detection. However Zadeh, wherein the detecting features is performed by Harris corner detection ([see at least 2950 and 3340], “ In one embodiment, various transformations and/or aggregate functions are used to generate signature from the pixel map of the image, e.g., DCT (discrete cosine transform) wavelet, averaging, contrast, variation measures, and/or intensity measures. In one embodiment, an image is reduced to a thumbnail to take a signature, e.g., by down sampling (fat pixels) or resolution reduction (e.g., spatial and/or color resolution). In one embodiment, an image is fuzzied or unsharpened, e.g., via convolution with for example a Gaussian. In one embodiment, a process uses edge detection (e.g., Canny, Canny-Deriche, Differential, Sobel, Prewitt, and Roberts cross), corner detection (e.g., Moravec corner detection, Harris operator...”). Both Singhal and Zadeh illustrate similar methods using image or video recognition platforms to navigate a vehicle or drone. Singhal does not clear illustrate a corner detention method however Zadeh teaches detecting features is performed by Harris corner detection.
It would have been obvious to one of ordinary skill in the art at the time of the invention was made to have modified Singhal so as to include Harris corner detection. with a reasonable expectation of success. Those having ordinary skill in the art would understand that Harris corner detection in Zadeh, as required by the claim. One of ordinary skill would have been motivated to combine Singhal and Zadeh because this would improve vision based navigation.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YAZAN A SOOFI whose telephone number is (469)295-9189. The examiner can normally be reached on Flex schedule.
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/YAZAN A SOOFI/Primary Examiner, Art Unit 3668
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