DETAILED ACTION
This is in response to applicant's communication filed on 11/14/2024 wherein:
Claim 1-20 are pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claim 1-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-4 and 6-9 of U.S. Patent No. US 12148183 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because their scope are overlapped as presented below:
Claim 1: An apparatus for supporting an aircraft operating on an airfield, the apparatus comprising: a memory configured to store computer-readable program code; and processing circuitry configured to access the memory, and execute the computer-readable program code to cause the apparatus to at least (Claim 1 - “An apparatus for supporting an aircraft approaching a runway on an airfield, the apparatus comprising: a memory configured to store computer-readable program code; and processing circuitry configured to access the memory, and execute the computer-readable program code to cause the apparatus to at least”): receive an image of the airfield, captured by a camera onboard the aircraft (Claim 1- “receive a sequence of images of the airfield, captured by at least one camera onboard the aircraft approaching the runway”); apply the image to a machine learning model trained to predict a pose of the aircraft relative to a runway, wherein the machine learning model has been trained on a training set of labeled images with respective ground truth poses (claim 1 – “for at least one image of the sequence of images, apply the at least one image to a machine learning model trained to predict a pose of the aircraft relative to the runway, the machine learning model configured to map the at least one image to the pose based on a training set of labeled images with respective ground truth poses of the aircraft relative to the runway”); and output the predicted pose of the aircraft (claim 1 – “output the pose as a current pose estimate of the aircraft relative to the runway for use in at least one of monitoring the current pose estimate, generating an alert based on the current pose estimate, or guidance or control of the aircraft on a final approach”).
Claim 2: The apparatus of claim 1, wherein applying the image to the machine learning model comprises: applying the image to the machine learning model to predict a pose of the camera in camera coordinates (claim 2 – “apply the at least one image to the machine learning model trained to predict a pose of the at least one camera in camera coordinates”); and transforming the camera coordinates for the camera to corresponding runway-framed local coordinates, thereby predicting the pose of the aircraft (claim 2 – “transform the camera coordinates for the at least one camera to corresponding runway-framed local coordinates and thereby predict the pose of the aircraft relative to the runway”).
Claim 3: The apparatus of claim 1, wherein the image is in a non-visible light spectrum (claim 3 - “wherein the at least one image and the labeled images are in a non-visible light spectrum”).
Claim 4: The apparatus of claim 1, wherein the labeled images are mono-channel images, the image is a multi-channel image, and the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further convert the image to a mono-channel image that is applied to the machine learning model (claim 4 – “wherein the labeled images are mono-channel images, the at least one image is a multi-channel image, and the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further convert the multi-channel image to a mono-channel image that is applied to the machine learning model”).
Claim 5: The apparatus of claim 1, wherein the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further generate the training set of labeled images by at least: receiving training images; and labeling the training images with the respective ground truth poses to generate the training set of labeled images (claim 6 – “wherein the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further generate the training set of labeled images, including the apparatus caused to at least: receive earlier images of the airfield, captured by the at least one camera onboard the aircraft or a second aircraft approaching the runway, and the respective ground truth poses of the aircraft or the second aircraft relative to the runway; and label the earlier images with the respective ground truth poses of the aircraft to generate the training set of labeled images”).
Claim 6: The apparatus of claim 1, wherein the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further generate the training set of labeled images by at least: executing a flight simulator configured to artificially re-create a flight; capturing synthetic images; determining the respective ground truth poses using the flight simulator; and labeling the synthetic images with the respective ground truth poses to generate the training set of labeled images (claim 7 – “further generate the training set of labeled images, including the apparatus caused to at least: execute a flight simulator configured to artificially re-create flight of the aircraft approaching the runway on the airfield; capture synthetic images of the airfield, and determine the respective ground truth poses of the aircraft relative to the runway, from the flight simulator; and label the synthetic images with the respective ground truth poses of the aircraft to generate the training set of labeled images”).
Claim 7: The apparatus of claim 1, wherein applying the image to the machine learning model comprises: applying the image to neural networks trained to predict respective components of the pose of the aircraft, the neural networks configured to determine values of the components, thereby predicting the pose of the aircraft (claim 8 - “the apparatus caused to apply the at least one image to machine learning models trained to predict respective components of the pose of the aircraft relative to the runway, the machine learning models configured to determine values of the components and thereby the pose of the aircraft relative to the runway”).
Claim 8: The apparatus of claim 1, wherein applying the image to the machine learning model comprises: applying the image to neural networks trained to predict multiple current pose estimates according to different algorithms; determining confidence intervals associated with respective ones of the multiple current pose estimates; and performing a sensor fusion of the multiple current pose estimates using the confidence intervals to predict the pose of the aircraft (claim 9 – “wherein the apparatus caused to apply the at least one image to the machine learning model includes the apparatus caused to apply the at least one image to machine learning models trained to predict multiple current pose estimates according to different algorithms, and the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further at least: determine confidence intervals associated with respective ones of the multiple current pose estimates; and perform a sensor fusion of the multiple current pose estimates using the confidence intervals to determine the current pose estimate of the aircraft relative to the runway”).
Claim 9: the scope and content of the claim recite a method performed by the apparatus of claim 1, therefore, being addressed as in claim 1.
Claim 10: the scope and content of the claim recite a method performed by the apparatus of claim 2, therefore, being addressed as in claim 2.
Claim 11: the scope and content of the claim recite a method performed by the apparatus of claim 3, therefore, being addressed as in claim 3.
Claim 12: the scope and content of the claim recite a method performed by the apparatus of claim 4, therefore, being addressed as in claim 4.
Claim 13: the scope and content of the claim recite a method performed by the apparatus of claim 7, therefore, being addressed as in claim 7.
Claim 14: the scope and content of the claim recite a method performed by the apparatus of claim 8, therefore, being addressed as in claim 8.
Claim 15: A method of generating training data for a machine learning model, the method comprising: receiving a plurality of images of an airfield; determining a respective ground truth pose of an aircraft relative to a runway for each of the plurality of images; and labeling the plurality of images with the respective ground truth poses to generate the training data (claim 6 – “wherein the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further generate the training set of labeled images, including the apparatus caused to at least: receive earlier images of the airfield, captured by the at least one camera onboard the aircraft or a second aircraft approaching the runway, and the respective ground truth poses of the aircraft or the second aircraft relative to the runway; and label the earlier images with the respective ground truth poses of the aircraft to generate the training set of labeled images”).
Claim 16: The method of claim 15, wherein the plurality of images is generated by: executing a flight simulator configured to artificially re-create a flight of the aircraft operating on the runway; and capturing synthetic images of the airfield (claim 7 – “wherein the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further generate the training set of labeled images, including the apparatus caused to at least: execute a flight simulator configured to artificially re-create flight of the aircraft approaching the runway on the airfield; capture synthetic images of the airfield”).
Claim 17: The method of claim 16, wherein the respective ground truth poses are determined using the flight simulator (claim 7 – “determine the respective ground truth poses of the aircraft relative to the runway, from the flight simulator; and label the synthetic images with the respective ground truth poses of the aircraft to generate the training set of labeled images”).
Claim 18: The method of claim 15, wherein the plurality of images is captured by a plurality of cameras onboard the aircraft (claim 6 – “generate the training set of labeled images, including the apparatus caused to at least: receive earlier images of the airfield, captured by the at least one camera onboard the aircraft”).
Claim 19: The method of claim 15, wherein the plurality of images is in a non-visible light spectrum (claim 3 – “wherein the at least one image and the labeled images are in a non-visible light spectrum”).
Allowable Subject Matter
Claim 20 is 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUNG HONG whose telephone number is (571)270-7928. The examiner can normally be reached on Monday-Friday from 8:00 am to 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, JINSONG HU, can be reached on (571) 272-3965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DUNG HONG/
Primary Examiner, Art Unit 2643
Prosecution Insights