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
This action is in response to the application filed on 12--/10/2025 for application 18/679,257. Claim 1 – 20 are pending and have been examined.
Claim 1 – 20 are amended.
Claim rejection under 112(b) has been withdrawn in light of applicant’s amendment and remarks.
Response to Amendment
Applicant’s amendment filed on 12/10/2025 has been entered.
Response to Argument
Applicant’s arguments, see page 10 – 12, filed on 12/10/2025, with respect to the rejection(s) under U.S.C. 102 and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Boyle, US12,277,850. Taveira discloses a system that checks propeller guard installation using onboard camera to “ensure safe and proper operation of the arial robotic vehicle”, “in pre-flight testing instructions provided to the aerial robotic vehicle before take-off” (0059). In an explicit use case of palm take-off/landing Taveira teaches “the aerial robotic vehicle 100 does not have propeller guards installed, the processor may set flight parameters that tum off a palm take-off/landing mode”, “without propeller guards, the processor may not allow the aerial robotic vehicle to approach the user closer than a standoff distance” (0028 - 0033). As illustrated in figure 4 – 7, it is clear that Taveira’s system “relying on the set flight parameters to prevent or restrict unsafe activity” (0080), in the case of palm take-off, the installation of propeller guard can determine if the takeoff action should be prevented or restricted. Boyle teaches a system that controls target and focal length (col. 6 ln. 48 – 54) of onboard gimbal cameras on UAV (fig. 18) to perform a variety of complex tasks (col. 1, ln 27 – 41). The operation of the Boyle’s system includes pre-takeoff check (col 6, ln 62 – 67). The combination renders obviousness of the claimed limitation. For further details, refer to the claim rejection under 35 U.S.C. 103 section.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1, 3 – 9, 12, 18 – 20 are rejected under 35 U.S.C. 103 as unpatentable over Taveira et al., (hereinafter Taveira), US20190206267, in view of Boyle, US12277850.
Claim 1. Taveira discloses: An aircraft, comprising: a body, comprising one or more propellers (Fig. 2 & 0035, “body 110 … plurality of rotors (propellers) 120”);
at least one storage medium storing at least one set of instructions for controlling the aircraft (Fig. 2 & 0036 – 0040, “processing device 210 … software, instructions … non-volatile storage memory 230 ”, ); and
at least one processor 210 in communication with the at least one storage medium 230, and a sensor carried by the body (Fig. 2 Sensors 236) wherein during operation, the at least one processor executes the at least one set of instructions to cause the device to (refer to the mapping above) at least:
receive a takeoff control instruction … detect installation of a safety protection device around the one or more propellers on the aircraft … not execute the takeoff control instruction in response to the safety protection device not being installed around the one or more propellers on the aircraft: or execute the takeoff control instruction and control the sensor to turn to a direction that does not face the one or more propellers on the aircraft for detection during flight in response to the safety protection device being installed (0059, “method 400 to detect and respond to anomalies in the propeller guards”, “the processor may repeat the operations of the method 400 continuously or until all propeller guards are checked to ensure safe and proper operation of the aerial robotic vehicle”, “the processor may repeat the operations of the method 400 for a predefined number of iterations indicated in pre-flight testing instructions provided to the aerial robotic vehicle before take-off”; 0080, “The processor may control the one or more motors using currently set … restrictions to certain modes of operation”, “445). In some embodiments, the aerial robotic vehicle may continue to operate normally executing user commands and/or a preloaded flight plan relying on the set flight parameters to prevent or restrict unsafe activity. In some embodiments, the processor of the aerial robotic vehicle may use the visual algorithm settings associated with the determination as to whether the propeller guard is installed, while otherwise operating normally.” 0021, “certain modes of operation of aerial robotic vehicles, such as … the palm take-off/landing mode, assume propeller guards are present making the aerial robotic vehicle safe enough to fly extremely close to people”; i.e., system receives user take off command while the drone is on users hand and determines to take off or restrict this operation based on whether propeller guard is present or not. Examiner notes that the limitation “not execute the takeoff control instruction … or execute the takeoff control instruction” states alternative limitation. Only one of the steps is required to fulfill the limitation. ).
Taveira does not explicitly teach:
control the sensor to turn toward a direction of the one or more propellers on the aircraft to detect installation of a safety protection device around the one or more propellers on the aircraft
Boyle, in the same field of endeavor, explicitly teach:
control the sensor to turn toward a direction of the one or more propellers on the aircraft to detect installation of a safety protection device around the one or more propellers on the aircraft (Boyle, col 3 ln. 58 – col 4, ln. 31, “one or more gimballed camera sensors 15”, “executing a computer vision application which positions the camera sensors 15 at specific angles and collects image frames”, “the computer vision application compares baseline images with 10 collected images to detect anomalies”; col, 6, ln. 48 – 67, “Autogenerate Camera target and focal length”, “Arm Vehicle-Pre-flight Checklist, Self-test”; Boyle teaches control/position gimbal cameras to perform various tasks. Taveira teaches using onboard cameras to perform pre-flight check. The combination renders obviousness of the claimed limitation)
Taveira and Boyle both teach camera drone application and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the gimbal cameras taught by Boyle in the system of Taveira to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification in order for the camera to perform multiple functions during a mission such as object detection and avoidance (Boyle, col. 1, ln. 27 – 40).
Claim 3. Taveira and Boyle combination teaches all the limitation of Claim 1. The combination further teach:
in response to the takeoff control instruction, obtain environmental sensing data collected by the sensor; and determine whether the aircraft is installed with the safety protection device based on the environmental sensing data (Taveira, 0050, “The processor (e.g., 220) of an aerial robotic vehicle ( e.g., 200) may perform monitoring operations ( e.g., data collection and processing) before … flight (in response to takeoff), such as by accessing readings from on-board sensors to determine whether propeller guards are installed”; 0058 – 0059, “In block 405, the processor may initiate a test for setting the flight parameters associated with propeller guards. The test for setting the flight parameters may be initiated … in response to an event”; remote control inputs are events to the system;).
Claim 4. Taveira and Boyle combination teaches all the limitation of Claim 3. The combination further teach:
to obtain the environmental sensing data collected by the sensor, the at least one processor executes the at least one set of instructions to cause the aircraft to at least: control a gimbal of the aircraft to drive the sensor to reach a preset attitude; and obtain the environmental sensing data by the sensor in the preset attitude (refer to the mapping above & Taveira, 0050, “The images (sensor data) received by the processor may show all or part of any propeller guards present during inspection. Camera (sensor) angles (preset attitude) that provide images of the area surrounding each rotor 120 may be preferred for determining whether propeller guards are installed.”)
Claim 5. Taveira and Boyle combination teaches all the limitation of Claim 3. The combination further teach:
the sensor comprises an imaging device, and to determine whether the aircraft is installed with the safety protection device based on the environmental sensing data (Taveira 0027, “a processor of an aerial robotic vehicle may be configured to obtain data from one or more sensors configured to detect whether one or more propeller guards is installed on the aerial robotic vehicle. For example … an image sensor (e.g., a camera)”), the at least one processor executes the at least one set of instructions to cause the aircraft to at least:
obtain a target image collected by the imaging device, wherein the target image comprises at least a part of an installation occupancy area of the safety protection device on the aircraft; and determine whether the aircraft is installed with the safety protection device based on the target image (Taveira, 0029, “the processor may analyze the obtained data (target image) from the sensor(s), such as by comparing the obtained data to previously collected data indicating the propeller guard is installed.”; i.e., camera facing the direction of the prop guard to take images for the determination whether propeller guard is installed).
Claim 6. Taveira and Boyle combination teaches all the limitation of Claim 5. The combination further teach:
obtain a reference comparison image, wherein the reference comparison image is collected by the imaging device when the aircraft is installed with the safety protection device, and the reference comparison image at least includes the part of the installation occupancy area (refer to the mapping in Claim 5 & Taveira, 0029, the previously collected data (reference comparison image) that has the prop guard installed);
determine a similarity between the reference comparison image and the target image; and determine that the aircraft is installed with the safety protection device in response to the similarity being greater than or equal to a preset similarity, or determine that the aircraft is not installed with the safety protection device in response to the similarity less than the preset similarity (refer to the mapping above, compare target images to previously collected image is to determine the similarity. The determination of installed/not installed is based on how much different two images are, i.e., a preset threshold).
Claim 7. Taveira and Boyle combination teaches all the limitation of Claim 5. The combination further teach:
obtain a reference comparison image library, wherein the reference comparison image library comprises a plurality of reference comparison images (Taveira 0054, “images received by the processor of an installed propeller guard may be compared to previously stored images”; i.e., compare a plurality of images/image library );
determine a similarity between the target image and the plurality of reference comparison images in the reference comparison image library; and determine that the aircraft is installed with the safety protection device in response to the similarity between at least one of the plurality of reference comparison images in the reference comparison image library and the target image being greater than or equal to a preset similarity, or determine that the aircraft is not installed with the safety protection device in response to the similarity between none of the plurality of reference comparison images in the reference comparison image library and the target image is greater than or equal to the preset similarity (Taveira 0088 “Determining the location and/or dimensions of objects”; 0089, “a loop to individually investigate each object. Thus, in block 730, the processor may select one of the identified objects, and perform object recognition processing on of the image data for the selected object to determine whether the object in a propeller guard in block 740. As described, such image recognition processing may involve comparing image data to the database of known propeller guards to determine whether there is a close match.”; 0056, “style of propeller guards”; i.e., iteratively load reference images to compare with known propeller guards (including different styles, sizes) to determine how close the match is to the reference image. If any match is close enough (within preset similarity), then the propeller guard is presence. If none match is close enough, then not presence).
Claim 8. Taveira and Boyle combination teaches all the limitation of Claim 5. The combination further teach:
to determine whether the aircraft is installed with the safety protection device based on the target image, the at least one processor executes the at least one set of instructions to cause the aircraft to at least: obtain a first recognition model of the safety protection device; and determine whether the aircraft is installed with the safety protection device based on the first recognition model (Taveira 0089 – 0090, “Such image recognition processes may involve the use of machine learning techniques (recognition model)”; ).
Claim 9. Taveira and Boyle combination teaches all the limitation of Claim 8. The combination further teach:
input the target image into the first recognition model for recognition processing to obtain a classification label of the target image; and determine that the aircraft is installed with the safety protection device in response to the classification label being a first classification label, or determine that the aircraft is not installed with the safety protection device in response to the classification label being a second classification label (refer to the mapping in Claim 8, Taveira teaches using machine learning technique to classify images. The classification model are known to label the input images. In this case the label is either positive/true (with propeller guard) or negative/false (without propeller guard). Such understanding can be easily found online for example: Viso AI, “Evaluate ML Models with Confusion Matrix & Metrics”).
Claim 12. Taveira and Boyle combination teaches all the limitation of Claim 5. The combination further teach:
obtain an image collected by the imaging device in a preset attitude (Taveira fig. 2 camera 236 is installed and pointing to a preset attitude), and determine the image as the target image (Taveira 0037, “a first set of cameras 236 may face a side of each of the rotors 120 in the plane of rotation … second set of cameras 236 …”; many sets of cameras are installed on the drone. The system determines which image data point to which propeller guard (target)); wherein an image collection range of the imaging device in the preset attitude at least partially overlaps with the installation occupancy area of the safety protection device on the aircraft (refer to the mapping above & fig. 2, the cameras are installed so that the field of view (image collection range) includes/overlaps the location of the propeller guards).
Claim 18. Taveira and Boyle combination teaches all the limitation of Claim 1. Taveira further teach:
the aircraft further comprises an installation detection device to detect the safety protection device (Fig. 2, the cameras 236 are the installation detection device that detects the propeller guard), and the at least one processor executes the at least one set of instructions to further cause the aircraft to at least: in response to the takeoff control instruction, obtain installation detection information output by the installation detection device; and determine that the aircraft is installed with the safety protection device in response to the installation detection information meeting a preset condition, or determine that the aircraft is not installed with the safety protection device in response to the installation detection information does not meeting the preset condition (refer to the mapping in Claim 1 & 0080, “In some embodiments, the processor of the aerial robotic vehicle may use the visual algorithm settings associated with the determination as to whether the propeller guard is installed”).
Claim 19. Claim 19 recites all the limitation of Claim 1. In addition, Claim 19 recites the following limitations which are also disclosed by Taveira: not executing the takeoff control instruction to not allow the aircraft to take off includes not executing the takeoff control instruction and not determining surrounding conditions (Fig. 5 – 7 & 0035 – 0086, “a processor (220) within a processing device (e.g., 210) of an aerial robotic vehicle (e.g., 100, 200) to detect obstacles ( e.g., 120) and perform an action in response”; fig. 5 – 7 illustrate the processing and detecting of the surrounding environment while flying. Since the drone does not take off, the determining of surrounding conditions would not happen.).
Regarding Claim 20, it is the corresponding method claim of Claim 1. Thus rejected with same reason.
Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over Taveira et al., (hereinafter Taveira), US20190206267 in view of Boyle, US12277850 as applied to Claim 1, further in view of DJI, “MINI2 360 Propeller Guard”.
Claim 2. Taveira and Boyle combination teaches all the limitation of Claim 1. The combination does not explicitly teach:
the aircraft is a foldable aircraft, and the safety protection device is not installed when the aircraft is in a folded state.
DJI, in the same field of endeavor, explicitly teach:
the aircraft is a foldable aircraft, and the safety protection device is not installed when the aircraft is in a folded state (DJI, page 3, “Make sure all frame arms are unfolded before installing the propeller guard. DO NOT fold the frame arms with the propeller guard installed.”).
Taveira (in view of Boyle) and DJI both teach drone with prop guard and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the foldable arm and propeller taught by DJI in the system of Taveira (in view of Boyle) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to reduce the storage size and prevent damage.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Taveira et al., (hereinafter Taveira), US20190206267, in view of Boyle, US12277850 as applied to Claim 8, further in view of Brownlee, “8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset”.
Claim 10. Taveira and Boyle combination teaches all the limitation of Claim 8. The combination does not explicitly teach: the first recognition model is obtained by iteratively training a neural network model based on a first sample data set, and the first sample data set comprises a plurality of positive sample data and a plurality of negative sample data; the plurality of positive sample data comprises a first sample image and the first classification label, and the first sample image contains the safety protection device; and the plurality of negative sample data comprises a second sample image and the second classification label, and the second sample image does not contain the safety protection device.
Brownlee, in the same field of endeavor, explicitly teach:
the first recognition model is obtained by iteratively training a neural network model based on a first sample data set, and the first sample data set comprises a plurality of positive sample data and a plurality of negative sample data; the plurality of positive sample data comprises a first sample image and the first classification label, and the first sample image contains the safety protection device; and the plurality of negative sample data comprises a second sample image and the second classification label, and the second sample image does not contain the safety protection device (Brownlee, page 3 – 4, “2-class (binary) classification problem”, “train on an imbalanced dataset … 90% of the instances in Class-1”; i.e., both classes are used in training dataset; page 60, “how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks”; machine learning model for image classification includes neural network model).
Taveira (in view of Boyle) and Brownlee both teach the use of machine learning model to perform image classification and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the training details taught by Brownlee in the system of Taveira (in view of Boyle) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to get a good model accuracy (Brownlee, page 4).
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Taveira et al., (hereinafter Taveira), US20190206267 in view of Boyle, US12277850 as applied to Claim 5, further in view of Chen, CN106054737.
Claim 11. Taveira and Boyle combination teaches all the limitation of Claim 5. The combination does not explicitly teach:
prior to obtaining the target image collected by the imaging device, the at least one processor executes the at least one set of instructions to further cause the device to at least: obtain an ambient light intensity; and obtain the target image collected by the imaging device when the ambient light intensity is greater than or equal to a preset light intensity threshold.
Chen, in the same field of endeavor, explicitly teach:
prior to obtaining the target image collected by the imaging device, the at least one processor executes the at least one set of instructions to further cause the device to at least: obtain an ambient light intensity; and obtain the target image collected by the imaging device when the ambient light intensity is greater than or equal to a preset light intensity threshold (Chen, translation page 11, “unmanned vehicle based on light sensor Device for visual identification initialize, light sensor 2 periodically gathers the light intensity of flight environment of vehicle, when ambient light intensity Less than the first threshold preset and more than Second Threshold, in timer the most no N continuous the detection cycle, ambient light intensity All it is less than the first threshold preset and more than Second Threshold, if it is, the data of visual identity module are believable, unmanned Aircraft can be according to this data normal flight, if it is not, light sensor 2 continues periodically to gather the light of flight environment of vehicle Intensity, when ambient light intensity is not less than the first threshold preset and when more than Second Threshold, and vision processing module is defeated Going out data trustless, unmanned vehicle slows down, hovers, transfers manual operation mode to or start short distance avoidance radar”; i.e., if the ambient light is less than the second threshold, the camera data is trustless and the system would not use/capture image data).
Taveira (in view of Boyle) and Chen both teach the control of aerial vehicle using safety detection sensors and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the ambient light detection and sensor remediation taught by Chen in the system of Taveira (in view of Boyle) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to “improve flight safety” (Chen, translation page 6).
Claim(s) 13 – 14 are rejected under 35 U.S.C. 103 as being unpatentable over Taveira et al., (hereinafter Taveira), US20190206267 in view of Boyle, US12277850 as applied to Claim 5, further in view of Weng, WO2021179293A1.
Claim 13. Taveira and Boyle combination teaches all the limitation of Claim 5. Taveira further teach:
obtain a plurality of images collected by the imaging device; and combine the plurality of images to obtain the target image (0037, “a first set of cameras 236 may face a side of each of the rotors 120 in the plane of rotation thereof, such as mounted near a central part of the aerial robotic vehicle 200. Additionally, or alternatively, second set of cameras 236 may be mounted under the rotors 120, such as in a position configured to detect whether propeller guards 250 are installed”; 0050, “the processor may receive and analyze images (i.e., video (plurality of images) or still images) from one or more cameras ( e.g., cameras 236). The images received by the processor may show all or part of any propeller guards present during inspection. Camera angles that provide images of the area surrounding each rotor 120 may be preferred for determining whether propeller guards are installed”; 0077, “the visual algorithm setting may be configured to mask, ignore, or conceal regions in which propeller guards are present, in order to supply imaging (target image) unobstructed by structures”; i.e., the system use a combination of images to supply obstructed area of view.).
The combination does not explicitly teach:
images collected by the imaging device rotating within a first rotation range;
when the imaging device rotates within the first rotation range, an image collection range of the imaging device at least partially overlaps with the installation occupancy area of the safety protection device on the aircraft.
Chen, in the same field of endeavor, explicitly teach:
images collected by the imaging device rotating within a first rotation range (Weng translation page 4, “control the pan/tilt to perform a preset operation”, “Controlling the gimbal to perform preset operations”; pan and tilt of gimbal involves rotate an axis of the gimbal);
when the imaging device rotates within the first rotation range, an image collection range of the imaging device at least partially overlaps with the installation occupancy area of the safety protection device on the aircraft (refer to the mapping above, Weng teaches using gimbal camera that pan and tilt to perform preset operations; Taveira teaches having multiple cameras with field of view that include/overlap the position of propeller guards. The combination renders obviousness of the recited limitations.).
Taveira (in view of Boyle) and Weng both teach the use of drone camera to perform predefined operation and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the image processing technique taught by Weng in the system of Taveira (in view of Boyle) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to increase the usage of one sensor and reduce the necessity of additional sensors and thus reduce the weight of the aircraft.
Claim 14. Taveira and Boyle combination teaches all the limitation of Claim 5. The combination does not explicitly teach:
after controlling the aircraft to take off, control the imaging device to rotate within a second rotation range, wherein when the imaging device rotates within the second rotation range, an image collection range of the imaging device does not overlap with the installation occupancy area of the safety protection device on the aircraft.
Weng, in the same field of endeavor, explicitly teach:
after controlling the aircraft to take off, control the imaging device to rotate within a second rotation range, wherein when the imaging device rotates within the second rotation range, an image collection range of the imaging device does not overlap with the installation occupancy area of the safety protection device on the aircraft (Weng translation page 4, “control the pan/tilt to perform a preset operation”, “Controlling the gimbal to perform preset operations”; Weng teaches having gimbal camera with preset operations that rotates to observe environment for different phase of the flight, Taveira teaches having camera to observe the installation of safety device during test/takeoff and to “supply imaging unobstructed by structures like the propeller guards” (Taveira, 0077); The combination renders obviousness that while not in the test/takeoff phase, the gimble rotates to avoid being obstructed by the propeller guards.).
The reason for combination is same as Claim 13
Claim(s) 15 – 17 are rejected under 35 U.S.C. 103 as being unpatentable over Taveira et al., (hereinafter Taveira), US20190206267 in view of Boyle, US12277850 as applied to Claim 3, further in view of Chen, CN106054737, and further in view of Liu et al., (hereinafter Liu), WO2021012987.
Claim 15. Taveira and Boyle combination teaches all the limitation of Claim 3. The combination does not explicitly teach:
the sensor includes a radar device and to determine whether the aircraft is installed with the safety protection device based on the environmental sensing data, the at least one processor executes the at least one set of instructions to cause the device to at least: obtain point cloud data collected by the radar device, and obtain position coordinates corresponding to an installation position of the safety protection device on the aircraft; obtain, from the point cloud data, target point cloud data matching the position coordinates, and obtain a second recognition model of the safety protection device; and determine whether the aircraft is installed with the safety protection device based on the target point cloud data and the second recognition model.
Chen, in the same field of endeavor, explicitly teach:
the sensor includes a radar device (Chen, translation page 2, “described device for visual identification is provided with radar detected module, when described visual identity module disconnects or stops Time, start described radar detected module.”)
The reason for combining Taveira, Boyle and Chen is same as Claim 11.
Taveira, Boyle and Chen combination does not explicitly teach:
obtain point cloud data collected by the radar device, and obtain position coordinates corresponding to an installation position of the safety protection device on the aircraft; obtain, from the point cloud data, target point cloud data matching the position coordinates, and obtain a second recognition model of the safety protection device; and determine whether the aircraft is installed with the safety protection device based on the target point cloud data and the second recognition model.
Wang, in the same field of endeavor, explicitly teach:
obtain point cloud data collected by the radar device, and obtain position coordinates corresponding to an installation position of the safety protection device on the aircraft; obtain, from the point cloud data, target point cloud data matching the position coordinates, and obtain a second recognition model of the safety protection device; and determine whether the aircraft is installed with the safety protection device based on the target point cloud data and the second recognition model (Liu, translation page 3, “determine at least one object based on at least the three-dimensional coordinate data in the road environment point cloud data”; translation page 17, “the input data of the algorithm (model) may include the point cloud data of each object”; Liu teaches using a machine learning model on radar point cloud data for object recognition. Taveira and Chen combination teaches using radar for the detection of safety protection device. The combination renders obviousness of the recited limitation. )
Taveira (in view of Boyle and Chen) and Liu both teach object detection application using radar on aerial vehicle and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the detail of machine learning model based on radar point cloud data taught by Liu in the system of Taveira (in view of Boyle and Chen) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification in order to “effectively improve the accuracy of object detection. (Liu, translation page 14).
Claim 16. Taveira, Boyle, Chen and Wang combination renders obviousness of all the limitation of Claim 15. The combination further teach:
input the target point cloud data into the second recognition model for recognition processing to obtain a classification label of the target point cloud data; and determine that the aircraft is installed with the safety protection device in response to the classification label of the target point cloud data being a preset classification label, or determine that the aircraft is not installed with the safety protection device in response to the classification label of the target point cloud data not being not the preset classification label (refer to the mapping in Claim 15 and Liu, translation page 18, “network structure of the prediction model may be a two-classifier neural network, including an input layer … The input data of the model is the first point cloud quantity ratio vector (point cloud data)”).
Claim 17. Taveira, Boyle, Chen and Wang combination renders obviousness of all the limitation of Claim 15. The combination further teach:
iteratively training a neural network model based on a second sample data set, the second sample data set comprises a plurality of sample data, and the plurality of sample data comprises sample point cloud data matching the position coordinates and annotated classification labels (refer to the mapping in Claim 15 & Liu, translation page 6, “model involved in this solution needs to be trained from a large amount of annotation data”; translation page 17, “prediction model may be a two-classifier neural network”; translation page 18, “In this embodiment, 106,000 training samples are included, where the number of positive samples is much larger than the number of negative samples”; i.e., iteratively train a neural network model using annotated training sample data).
The reason for combination is same as Claim 15.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: FAA, “Preflight” and AOPA, “Before takeoff checklist”, both teaches to perform safety check before takeoff an aircraft. Wang, CN112698301, which teaches training of a machine learning model for the object detection using radar point cloud data.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 SHIEN MING CHOU whose telephone number is (571)272-9354. The examiner can normally be reached Monday- Friday 9 am - 5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HITECH PATEL can be reached on 517-270-5227. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SHIEN MING CHOU/Examiner, Art Unit 3666
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
2/19/26
Prosecution Insights