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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/27/2026 has been entered.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-11, 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tulloch (United Kingdom Patent GB 2588893 A) in view of Haskin et. al. (United States Patent Application Publication US 2026/0133051 A1).
Regarding claim 1, Tulloch discloses an obstacle detection method implemented by an obstacle detection system on-board an aircraft, the aircraft including at least one on-board camera having an associated field of view, configured to acquire full-field digital images, the method being implemented by a processor of a computation platform (Figures 1a-1b and 2; Summary [0003]), said method comprising:
a)receiving at least one full-field digital image captured by said at least one on-board camera (Summary, [0024-26] e.g., image sensors equipped on aircraft; also, processors equipped within fuselage of aircraft; “first” and “second” captured images are full-field digital image),
b)determining a spatial position of a zone of interest of predetermined size in said full-field digital image, and extraction of said zone of interest from said full-field digital image, and (Tulloch Summary, Figure 5, [0007], [0040] i.e., “target space” is extracted zone of interest)
c)implementing an obstacle detection module on said extracted zone of interest, and implementing a neural network previously trained to detect and classify obstacles in sub-images having said predetermined size, wherein each obstacle detected is located in said zone of interest (Tulloch [0004] one or more stored representations of aerial vehicles is determined by a classifier which is trained to recognize different types of aerial vehicles using supervised training procedures based on images from a library of aerial vehicle images).
However, Tulloch fails to disclose the full-field digital image having a number L of rows and number C of columns, as a sub-image of the full field digital image, said predetermined size being defined by a number M of rows and a number N of columns, M being strictly smaller than L and N being strictly smaller than C.
Haskin et. al. teaches the full-field digital image having a number L of rows and number C of columns, as a sub-image of the full field digital image, said predetermined size being defined by a number M of rows and a number N of columns, M being strictly smaller than L and N being strictly smaller than C (Haskin et. al. Abstract, Fig. 8, image location with coordinates, [0373], [0386]-[0387] The coordinates provide dimensions for the captured images).
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This is important to the claimed invention because the sub-image size and coordinates improves the accuracy of target detection. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Tulloch and Haskin et. al. so that the dimensions of the sub-image and full digital image captured are specified.
Regarding claim 2, Tulloch further discloses the method according to claim 1, further comprising a post-processing comprising a computation of a distance between the aircraft and the or each detected obstacle and/or a storage of a geo-referenced position, in a fixed terrestrial reference frame, for each detected obstacle belonging to a class of fixed obstacles (Tulloch [0036]-[0037], Figure 4).
Regarding claim 3, Tulloch further discloses the method according to claim 1, further comprising repeating the determination of a spatial position of a zone of interest in a same wide field digital image, serving to obtain a plurality of zones of interest in said wide field digital image (Tulloch [0042], Figure 5).
Regarding claim 4, Tulloch further discloses the method according to claim 1 further comprising, before determining a spatial position of a zone of interest, acquisition of at least one avionic information item relating to a parameter of motion of the aircraft, and wherein the determination of a spatial position is a function of at least one avionic information item (Tulloch [0037]-[0040], Figure 6, identifying UAVs).
Regarding claim 5, Tulloch further discloses the method according to claim 4, wherein said avionic information item comprises a path vector of the aircraft; the zone of interest being centered on a point indicated by the direction of said path vector (Tulloch, [0038]-[0039], triangulation performed based on altitude and ground velocity of the aircraft).
Regarding claim 6, Tulloch further discloses the method according to claim 1, wherein the determining a spatial position of a zone of interest comprises receiving a spatial position of a zone of interest using a communication interface with an external system (Tulloch, [0027] sensors may be interconnected and be in communication with one another either directly or via a central system using wireless protocol).
Regarding claim 7, Tulloch further discloses the method according to claim 6, wherein the external system is another aircraft (Tulloch, [0041], Figure 5, there is a second aircraft 170 with a plurality of image sensors mounted in similar positions as aircraft 102).
Regarding claim 8, Tulloch further discloses the method according to claim 1, wherein the determining a spatial position of a zone of interest comprises receiving a spatial position of a zone of interest from another sensor on-board the aircraft (Tulloch, [0042], Figure 5 and 6).
Regarding claim 9, Tulloch further discloses the method according to claim 1, wherein the camera is configured to acquire a succession of full-field digital images forming a video, wherein a) to c) are performed on a subset of acquired digital images spaced apart in time by a given time, and wherein the method further comprises a time tracking of the detected obstacles (Tulloch, [0026] The processor(s) are arranged to process images and/or video captured by the plurality of image sensors to identify external aerial vehicle candidates, such as UAVs. [0034]-[0037] Figure 4 illustrates two overhead images of the ground 138 captured at two different times. The method also includes calculating the velocity of the moving objects based on the difference between reference and their positions in the images relative to static objections, and with the knowledge of the ground velocity and altitude of the aircraft).
Regarding claim 10, Tulloch further discloses the method according to claim 1, wherein the spatial position of a zone of interest is determined randomly, and wherein a pseudo-random draw is used to determine respective coordinates of a predetermined point of the zone of interest (Tulloch, [0034]-[0037] non-moving objects in the zone of interest are used as reference to the satellite images and maps of a respective landscape).
Regarding claim 11, Tulloch further discloses a computer program comprising software instructions which, when executed by a programmable electronic system, implement the obstacle detection method according to claim 1 (Tulloch, claim 14).
Regarding claim 13, Tulloch further discloses the obstacle detection system according to claim 10, further comprising a processor configured to calculate a distance between the aircraft and the or each detected obstacle and/or to store a geo-referenced position, in a fixed terrestrial reference frame, for each detected obstacle belonging to a class of fixed obstacles (Tulloch [0036]-[0037], Figure 4).
Regarding claim 14, the rejection analysis of claim 1 is substantially incorporated herein. Furthermore, Tulloch further discloses the spatial position being expressed in a reference- frame associated to said full field digital image, and wherein each obstacle detected is located in said zone of interest, wherein the method further comprises, for each detected obstacle, a computation of a geo-referenced position of said detected obstacle, in a fixed terrestrial reference frame, of coordinates of the said detected obstacle in the reference frame associated to the full field image and of coordinates of the aircraft in said fixed terrestrial reference frame at the time of detection of said detected obstacle, and storage of the geo-referenced position of the detected obstacle to obtain or enrich a mapping of fixed obstacles (Tulloch [0008], [0032], [0034]-[0035], Figure 4 Optionally, a location of the external aerial vehicle is triangulated using the first and second captured images. Beneficially, this provides location data about the external aerial vehicle which can be used to help minimize the risk the external aerial vehicle poses. Non-moving objects may be identified by reference to the respective locations in the consecutive images and with knowledge of the ground velocity and altitude of the aircraft.).
Regarding claim 15, Haskin et. al. further discloses the obstacle detection method according to claim 14, further comprising displaying the classification and geo-referenced position of the detected obstacles on a human-machine interface (Haskin et. al. Fig. 8, 8a).
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Regarding claim 16, Haskin et. al. further discloses the obstacle detection method according to claim 14, further comprising transmitting, for each detected obstacle, the classification and geo-referenced position of said detected obstacle to a ground control center (Haskin et. al. Fig. 8, 84: Send features descriptors and image locations to ground station).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Tulloch (United Kingdom Patent GB 2588893 A) in view of Haskin et. al. (United States Patent Application Publication US 2026/0133051 A1) and Patel et. al. (United States Patent US 2022/0198788 A1).
Regarding claim 12, Tulloch discloses an obstacle detection system, suitable for being taken on-board an aircraft, the aircraft comprising at least one on-board camera having an associated field of view, configured to acquire full-field digital images, the obstacle detection system comprising a computation platform comprising at least one processor (See rejection of claim 1) configured to implement: - a receiver for receiving at least one full-field digital image captured by said at least one on-board camera, - a spatial positioner for determining a spatial position of a zone of interest of predetermined size in said full-field digital image, and for extracting said zone of interest from said full-field digital image, and (Tulloch Summary, Figures 5 and 6, [0040]). However, Tulloch fails to disclose:
- an obstacle detector, taking as input, said zone of interest and implementing a neural network previously trained to detect and classify obstacles in images having said predetermined size, wherein each obstacle detected is located in said zone of interest, the full-field digital image having a number L of rows and number C of columns, as a sub-image of the full field digital image, said predetermined size being defined by a number M of rows and a number N of columns, M being strictly smaller than L and N being strictly smaller than C.
Haskin et. al. teaches the full-field digital image having a number L of rows and number C of columns, as a sub-image of the full field digital image, said predetermined size being defined by a number M of rows and a number N of columns, M being strictly smaller than L and N being strictly smaller than C (Haskin et. al. Abstract, Fig. 8, image location with coordinates, [0373], [0386]-[0387] The coordinates provide the dimensions for the captured images).
Patel teaches an obstacle detection module, taking as input, said zone of interest and implementing a neural network previously trained to detect and classify obstacles in images having said predetermined size, each obstacle detected being located in said zone of interest (Patel et. al. US 2022/0198788 A1, [0041], Figure 3, using machine learning (CNN) that regresses parameters for an equation that best fits a line to detect the horizon. [0062] the detection proposals are input into a neural network (CNN) for further processing to detect and classify dynamic objects and to train the neural network). The obstacle detector using machine learning in the form of convolutional neural network (CNN) is the key component of the claimed invention. This allows the obstacles to be detected more efficiently and accurately to avoid aircraft collision. Additionally, the specific sub-image size and coordinates improves the accuracy of target detection. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Tulloch, Haskin et. al. and Patel et. al. so that the CNN algorithm is included in the solution of the claimed invention.
Conclusion
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 October 3, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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