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 .
Specification
The abstract of the disclosure is objected to because of the following:
Line 1 – “ordnance” is misspelled. Please correct to “ordinance”.
Line 2 – “UAV” should first be written out as “unmanned aerial vehicle” and then abbreviated to “UAV”. Please correct to “The system combines a customized unmanned aerial vehicle (UAV)…”
Line 3 – “and flies terrain with centimeter precision” should be corrected to “and flies above terrain with centimeter precision” for clarity.
Line 5 – “GUIs” should first be written out as “graphical user interfaces” and then abbreviated. Please correct to “including numerous graphical user interfaces (GUIs)”.
Lines 7-10 – “The data is fully compatible with numerous geophysical software formats and is capable of precisely following real world terrain at distances as close as 20 cm, and is able to avoid obstacles in its flight path.” Given that the subject of this sentence is “the data”, how is it capable of precisely following real-world terrain? If it is the UAV that is capable of precisely following real-world terrain, the sentence needs to reflect that capability. Please correct to “The data is fully compatible with numerous geophysical software formats and enables the UAV to precisely follow real world terrain at distances as close as 20 cm and to avoid obstacles in its flight path.”
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 2, 4-7, 11, 12, 14, 15, and 17 are objected to because of the following informalities:
Claim 2 – “an flight plan should be corrected to “a flight plan”.
Claim 2 – “the user-defined area” should be corrected to “the user-defined boundary” for consistency.
Claim 4 – “the UAV autonomously executing a flight plan and simultaneously storing data the UXO data collected by the metal detector” should be corrected to “the UAV autonomously executing a flight plan and simultaneously storing UXO data collected by the metal detector”.
Claim 4 – “the UAV flying that planned mission while precisely following the ground-contour and simultaneously flagging UXO information” should be corrected to “wherein executing a flight plan includes following a ground contour and simultaneously flagging UXO information” as a flight plan is already established in previous claim limitations.
Claim 4 – “after that mission is completed, the UAV flying back to a predetermined (but changeable) “home” location and lands (verb) onto the ground” should be corrected to “after the mission is completed, the UAV flies back to a predetermined but changeable “home” location and lands”.
Claim 4 – “at completion of mission, automatically transferring all UXO and other mission data is to a storage device attached to the GCS” should be corrected to “at completion of the mission, automatically transferring all UXO and other mission data is to a storage device attached to the GCS”.
Claim 5 – “the system precisely following ground terrain at a distance of 20 cm or higher and avoiding obstacles in its flight path while doing so” should be corrected to “the system precisely following ground terrain at a distance of 20 cm or higher and avoiding obstacles in its flight path”.
Claim 6 – “the UAV stabilizing itself and, when appropriate, flying to a predetermined GPS location” should be corrected to “the UAV stabilizing itself and, when appropriate, flying to a predetermined global positioning system (GPS) location”.
Claim 6 – “the GCS controlling a payload within the UAV the payload comprising the metal detector, camera, GPS” should be corrected to “the GCS controlling a payload within the UAV the payload comprising the metal detector, camera, and GPS”.
Claim 6 – “the GCS receiving all sensor data from the UAV in real-time and factoring all info as a basis for navigation decisions” should be corrected to “the GCS receiving all sensor data from the UAV in real-time and factoring all collected information as a basis for navigation decisions”.
Claim 7 – “the sensor hub maintaining a stable distance of UAV from ground thereby increasing accuracy of the metal detector”. The UAV performs the action of maintaining a stable distance utilizing data acquired by the sensor hub. Please correct to “the UAV maintaining a stable distance to the ground utilizing sensor data from the sensor hub thereby increasing accuracy of the metal detector”.
Claim 7 – “the sensor hub and laser range finder following real world terrain including avoiding obstacles in its flight path”. The UAV performs the action utilizing sensor information. Please correct to “the UAV, utilizing sensor information from the sensor hub and laser rangefinder, follows real-world terrain and avoid obstacles in its flight path”.
Claim 11 – “reading and processing output from the metal detector and converting the output into a MAVLINK-compatible message” should be corrected to “reading and processing an output from the metal detector and converting the output into a MAVLINK-compatible message”.
Claim 12 – “the metal detector controller emitting serial data messages that are buffered, split and analyzed” should be corrected to” the metal detector controller emitting serial data messages that are buffered, split, and analyzed”.
Claim 12 – “the control box creating timestamps that are added to the averaging-data and the system using the timestamps synchronize data from various sources comprising the metal detector, and GPS/GNSS” should be corrected to “a control box creating timestamps that are added to averaging-data and the system using the timestamps to synchronize data from various sources comprising the metal detector, and GPS”.
Claim 14 – “if the metal detector data flow stops for any reason, a set of pre-configured logic automatically re-initializing the metal detector after a timeout” should be corrected to “if the metal detector data flow stops for any reason, a set of pre-configured logic automatically reinitializes the metal detector after a timeout”.
Claim 15 – “the mobile app providing GUIs for a user to plan UXO detection missions ahead of time either off-location or on-location” should be corrected to “the mobile app providing GUIs for the user to plan UXO detection missions ahead of time either off-location or on-location”.
Claim 17 – “equipping the UAV with one or more stereo vision cameras for calculating the depth of field” should be corrected to “equipping the UAV with one or more stereo vision cameras for calculating a depth of field”.
Claim 17 – “the two cameras performing obstacle detection partly by forming a 3D image of what's in front of the UAV and conveying that information to an obstacle detection module” should be corrected to the two cameras performing obstacle detection partly by forming a 3D image of what is in front of the UAV and conveying that information to an obstacle detection module”.
Claim 17 – all instances of “obstacle detection\avoidance” should be corrected to “obstacle detection and avoidance”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the metal detector data". There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation “the time". There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation “the reading". There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation “the user". There is insufficient antecedent basis for this limitation in the claim.
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 (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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lathrop et al. (US 20240134085 A1), hereinafter Lathrop.
Regarding claim 1, Lathrop discloses:
A method of constructing and configuring a ground control system for a customized UAV to achieve detection of UXO, comprising (Abstract, Geophysical anomalies in an area or region can be detected, characterized, or otherwise mapped using a combination of environmental and geophysical sensor data acquired using a vehicle, which may be a remotely operated or autonomously controlled unmanned vehicle. Anomalies are detected by processing the environmental sensor data and geophysical sensor data using artificial intelligence algorithms, programs, or models. As an example, the artificial intelligence algorithms, programs, or models can include machine learning algorithms, programs, or models. The environmental sensor data are processed to generate control parameters for controlling the acquisition and/or processing of the geophysical sensor data optimized for environmental conditions in the area; [0032], As one non-limiting example, the vehicle-based geophysical sensor system can include an aerial vehicle, such as an unmanned aerial vehicle ("UAV") with an integrated multi-sensor package. The UAV may include a quadcopter, a drone, or the like. The UAV-based system uses machine learning to evaluate environmental conditions (e.g., weather and terrain), and from those data determines the optimal blend of geophysical sensor data from on-board instruments that will yield the most accurate anomaly detection and/or characterization results, such as the most accurate UXO detection results. The UAV-based system performs an initial aerial survey to measure environmental conditions, such as humidity, temperature, vegetation density, albedo, and soil moisture content in the targeted area or region using suitable environmental sensor types):
locating and configuring a tablet and laptop within the ground control system ([0038], FIG. 1 illustrates an example vehicle-based anomaly mapping system 100. The vehicle-based anomaly mapping system 100 includes a vehicle 102, an external device 104, a server 106, and a network 108. The vehicle 102 includes a sensor system 120 containing various sensors (e.g., environmental sensors and geophysical sensors) and devices that collect environmental sensor data, geophysical sensor data, and other measurement data from an area or region within which the vehicle 102 is operated; [0041], the illustrated embodiment, the vehicle 102 communicates with the external device 104. The external device 104 may include, for example, a smartphone, a tablet computer, a cellular phone, a laptop computer, a smart watch, and the like. The vehicle 102 communicates with the external device 104, for example, to transmit at least a portion of the environmental sensor data, geophysical sensor data, or other measurement data or output data; to receive configuration information for the vehicle 102, machine learning control(s) or related parameters for processing data onboard the vehicle 102; or a combination thereof. In some embodiments, the external device 104 may include a shortrange transceiver to communicate with the vehicle 102, and a long-range transceiver to communicate with the server 106. In the illustrated embodiment, the vehicle 102 also includes a transceiver to communicate with the external device 104 via, for example, a short-range communication protocol such as Bluetooth®, analog or digital modes of very high frequency ("VHF"), analog or digital modes of ultra-high frequency ("UHF"), IEEE 802.11, and the like. In some embodiments, the external device 104 bridges the communication between the vehicle 102 and the server 106);
configuring the tablet with pre-configured logic for operating the UAV, including selecting a region for performing UXO detection ([0047], In some embodiments, the vehicle 102 may communicate directly with both the server 106 and the external device 104. In such embodiments, the external device 104 may, for example, generate a graphical user interface to facilitate control and programming of the vehicle 102, while the server 106 may store and analyze larger amounts of data (e.g., environmental sensor data, geophysical sensor data) for future processing or analysis of the environmental and/or geophysical sensor data to detect anomalies in the area or region from which the data were acquired);
configuring the laptop to manage, format, store, and display UXO information within the selected region ([0044], The server 106 may maintain a database (e.g., on the server memory 160) for containing environmental sensor data, geophysical sensor data trained machine learning controls (e.g., trained machine learning algorithms, programs, and/or models) artificial intelligence controls (e.g., rules and/or other control logic implemented in an artificial intelligence model and/or algorithm), and the like; [0146], In some embodiments, computing device 1250 and/or server 1252 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, and so on);
and fusing the metal detector data with position data from the UAV at the time the reading was taken (Fig. 4; Fig. 6; [0039], artificial intelligence (e.g., machine learning) for geophysical detection and/or characterization of anomalies, which may include UXO detection, geophysical detection, civil engineering site mapping, geophysics exploration (e.g., lava tube mapping, cave mapping, mineral resource mapping and identification, hydrologic resource tracking), and the like. The sensor system 120 of the vehicle 102 includes environmental sensors 122 and geophysical sensors 124. As described below in more detail, the vehicle-based anomaly mapping system 100 also includes data workflow components, data analytics components, hardware data bus(es), components for implementing software fusion of data, and controls subsystems. The environmental and geophysical sensor data are collectively used to generate an anomaly map that depicts anomalies within the mapped area or region. For instance, the anomaly map may be a high-dimensional map having the ability to mark UXO or other geophysical anomalies).
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.
Claims 2-9 are rejected under 35 U.S.C. 103 as being unpatentable over Lathrop in view of Bridges (US 20240015690 A1), and further in view of Qian et al. (US 20200393593 A1), hereinafter Qian.
Regarding claim 2, Lathrop discloses:
A method of structuring and configuring a system for detecting unexploded ordnance (UXO), comprising (Abstract, Geophysical anomalies in an area or region can be detected, characterized, or otherwise mapped using a combination of environmental and geophysical sensor data acquired using a vehicle, which may be a remotely operated or autonomously controlled unmanned vehicle. Anomalies are detected by processing the environmental sensor data and geophysical sensor data using artificial intelligence algorithms, programs, or models. As an example, the artificial intelligence algorithms, programs, or models can include machine learning algorithms, programs, or models. The environmental sensor data are processed to generate control parameters for controlling the acquisition and/or processing of the geophysical sensor data optimized for environmental conditions in the area; [0032], As one non-limiting example, the vehicle-based geophysical sensor system can include an aerial vehicle, such as an unmanned aerial vehicle ("UAV") with an integrated multi-sensor package. The UAV may include a quadcopter, a drone, or the like. The UAV-based system uses machine learning to evaluate environmental conditions (e.g., weather and terrain), and from those data determines the optimal blend of geophysical sensor data from on-board instruments that will yield the most accurate anomaly detection and/or characterization results, such as the most accurate UXO detection results. The UAV-based system performs an initial aerial survey to measure environmental conditions, such as humidity, temperature, vegetation density, albedo, and soil moisture content in the targeted area or region using suitable environmental sensor types):
configuring a ground control system (GCS) to work with a pre-configured unmanned aerial vehicle (UAV), the UAV comprising a flight controller, a metal detector, and a sensor hub ([0038], FIG. 1 illustrates an example vehicle-based anomaly mapping system 100. The vehicle-based anomaly mapping system 100 includes a vehicle 102, an external device 104, a server 106, and a network 108. The vehicle 102 includes a sensor system 120 containing various sensors (e.g., environmental sensors and geophysical sensors) and devices that collect environmental sensor data, geophysical sensor data, and other measurement data from an area or region within which the vehicle 102 is operated; [0041], the illustrated embodiment, the vehicle 102 communicates with the external device 104. The external device 104 may include, for example, a smartphone, a tablet computer, a cellular phone, a laptop computer, a smart watch, and the like. The vehicle 102 communicates with the external device 104, for example, to transmit at least a portion of the environmental sensor data, geophysical sensor data, or other measurement data or output data; to receive configuration information for the vehicle 102, machine learning control(s) or related parameters for processing data onboard the vehicle 102; or a combination thereof. In some embodiments, the external device 104 may include a shortrange transceiver to communicate with the vehicle 102, and a long-range transceiver to communicate with the server 106. In the illustrated embodiment, the vehicle 102 also includes a transceiver to communicate with the external device 104 via, for example, a short-range communication protocol such as Bluetooth®, analog or digital modes of very high frequency ("VHF"), analog or digital modes of ultra-high frequency ("UHF"), IEEE 802.11, and the like. In some embodiments, the external device 104 bridges the communication between the vehicle 102 and the server 106);
configuring a tablet with a mobile app loaded thereupon for facilitating human usage of the GCS ([0047], In some embodiments, the vehicle 102 may communicate directly with both the server 106 and the external device 104. In such embodiments, the external device 104 may, for example, generate a graphical user interface to facilitate control and programming of the vehicle 102, while the server 106 may store and analyze larger amounts of data (e.g., environmental sensor data, geophysical sensor data) for future processing or analysis of the environmental and/or geophysical sensor data to detect anomalies in the area or region from which the data were acquired);
all data-acquisition and data-management tasks performed by the UAV, including data and communications related to geographic data, flight plan, all terrain data obtained, and all UXO data obtained ([0032], The UAV-based system uses machine learning to evaluate environmental conditions (e.g., weather and terrain), and from those data determines the optimal blend of geophysical sensor data from on-board instruments that will yield the most accurate anomaly detection and/or characterization results, such as the most accurate UXO detection results. The UAV-based system performs an initial aerial survey to measure environmental conditions, such as humidity, temperature, vegetation density, albedo, and soil moisture content in the targeted area or region using suitable environmental sensor types; [0033], One or more machine learning algorithms (which may be local to the UAV or remote to the UAV) can be used to process the environmental sensor data to determine the optimal weighted combination of equipped sensing methods that is most likely to yield accurate results. The optimal weighted combination may, for example, be determined based on recorded or simulated data under various environmental and geophysical conditions, previous surveys, and one or more trained neural network or other machine learning data repositories. The UAV-based system can then switch from an environmental evaluation and/or sensing mode to an anomaly (e.g., UXO) detection mode and perform a second sweep of the same area);
and the GCS controlling the UAV using a specific predetermined methodology of obtaining UXO information available in the user-defined area ([0032], The UAV-based system uses machine learning to evaluate environmental conditions (e.g., weather and terrain), and from those data determines the optimal blend of geophysical sensor data from on-board instruments that will yield the most accurate anomaly detection and/or characterization results, such as the most accurate UXO detection results. The UAV-based system performs an initial aerial survey to measure environmental conditions, such as humidity, temperature, vegetation density, albedo, and soil moisture content in the targeted area or region using suitable environmental sensor types; [0033], One or more machine learning algorithms (which may be local to the UAV or remote to the UAV) can be used to process the environmental sensor data to determine the optimal weighted combination of equipped sensing methods that is most likely to yield accurate results. The optimal weighted combination may, for example, be determined based on recorded or simulated data under various environmental and geophysical conditions, previous surveys, and one or more trained neural network or other machine learning data repositories. The UAV-based system can then switch from an environmental evaluation and/or sensing mode to an anomaly (e.g., UXO) detection mode and perform a second sweep of the same area).
However, Lathrop does not specifically state:
the GCS assisting the user in defining land boundaries to be searched for UXO;
the GCS controlling an flight plan of the UAV;
Bridges teaches:
the GCS assisting the user in defining land boundaries to be searched for UXO ([0148], The user 101X who may be the leader of the team 600, and/or a quality control or quality assurance personnel, may review the graphical user interface (GUI) 609 on the tablet to see the area map 318 of the geospatial area 106, along with an overlay of the geospatial data generated by each operator 100 of the team 600; [0052], FIG. 1 is a geospatial tracking system 190, according to one or more embodiments. In the embodiment of FIG. 1, an operator 100 may utilize a detector 102 (which may also be referred to as an object detector) to search for one or more objects 104 within a geospatial area 106. Alternatively, or in addition, the detector 102 may be intended to inspect and/or determine a condition of an object 104. The operator 100 may be a person (e.g., the user 101), a vehicle, a remotely controlled vehicle or drone (e.g., an unmanned autonomous vehicle, or UAV), and/or an autonomous vehicle that does not need human remote control for some or all of its operation time. The detector is a detector suitable for detecting the intended object 104. In one or more embodiments, the detector may be a magnetometer for detecting metal instances of the object 104, and the object 104 may be, for example, ancient metal relics or UXO. The geospatial area 106 may be any area, including without limitation a predefined geospatial search area. The geospatial area 106 may have been physically marked (e.g., with flags, ropes, or survey pins), and/or may be defined digitally);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Bridges into the invention of Lathrop to include defining a search area using a graphical user interface as Bridges discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that allows a user to define a search area and paths of travel (Bridges: [0188]). Additionally, the claimed invention is merely a combination of old, well-known elements of a UAV for autonomously detecting UXO using machine learning as disclosed by Lathrop and a GUI for defining search boundaries for UXO detection missions as taught by Bridges. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
However, Lathrop in view of Bridges does not specifically state:
the GCS controlling an flight plan of the UAV;
Qian teaches:
the GCS controlling an flight plan of the UAV (Fig. 2; [0026], Based on any of the above examples, the system further includes a fluctuation flight unit and a flight control unit, where the processing unit is connected to the fluctuation flight unit and the flight control unit; the fluctuation flight unit is configured to input the current location and current geological and geophysical data transmitted by the processing unit into a path prediction model to obtain fluctuation flight data output by the path prediction model, where the path prediction model is obtained through training based on sample locations, sample geological and geophysical data, and sample fluctuation flight data; and the flight control unit is configured to control the flight of the UAV based on the fluctuation flight data; [0045], Based on any of the above examples, in this system, the communication unit is further configured to transmit, to the processing unit, a control instruction sent by the ground unit);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Qian into the invention of Lathrop in view of Bridges to include a ground station that can control a UAV as Qian discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that allows control instructions to be sent from the ground unit to the UAV (Qian: [0045]). Additionally, the claimed invention is merely a combination of old, well-known elements of a UAV for autonomously detecting UXO using machine learning as disclosed by Lathrop and ground-unit-to-UAV communication(s) as taught by Qian. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 3, Lathrop in view of Bridges and Qian teaches:
configuring the UAV to coordinate signals and electrical flow between the metal detector and the flight controller (FC), an RFD modem, a remote control (RC) receiver, a position-location unit, a front sensor hub, and a main battery (Lathrop: Fig. 1; Fig. 2; Fig. 3; Fig. 4; Fig. 6; Fig. 12; Fig. 13; [0005], Another general aspect of the present disclosure includes a method for generating an anomaly map depicting geophysical anomalies in an area. This method includes acquiring environmental sensor data using environmental sensors coupled to a vehicle and processing the environmental sensor data using an electronic processor housed on the vehicle to generate geophysical sensing control parameter data for controlling a geophysical sensing mode of the vehicle. Geophysical sensor data are then acquired using geophysical sensors coupled to the vehicle while in the geophysical sensing mode, and the geophysical sensor data are processed using the electronic processor to generate an anomaly map that depicts at least one anomaly within the area, or otherwise indicating that no anomalies are present; [0006], It is another general aspect of the present disclosure to provide a vehicle-based system for mapping geophysical anomalies within an area. The vehicle-based system includes a vehicle; a plurality of environmental sensors coupled to the vehicle; a plurality of geophysical sensors coupled to the vehicle; and an electronic processor housed within the vehicle and in communication with the plurality of environmental sensors and the plurality of geophysical sensors. The electronic processor is configured to acquire environmental sensor data with the plurality of environmental sensors; acquire geophysical sensor data with the plurality of geophysical sensors; and generate an anomaly map that depicts at least one anomaly within an area ( or otherwise indicating that no anomalies are present) using the environmental sensor data and the geophysical sensor data; [0031], described in the present disclosure implement a vehicle-based geophysical sensor system for detecting, characterizing, and mapping anomalies in an area or region. The vehicle can include multiple sensor subsystems (e.g., environmental sensors, geophysical sensors, combinations thereof), which may work together via a central control system to optimize detection and/or characterization of anomalies as described above. For example, the vehicle may utilize cameras, forward- looking infrared ("FLIR") cameras, and other optical sensors; fluxgate magnetometers and other magnetometer technologies (e.g., excitation coils and sensing coils); ground penetrating radar ("GPR"); light detection and ranging ("LiDAR"); environmental sensors; among others. In certain embodiments, the central control system may utilize machine learning to better process the various data collected by the sensor subsystems, as described above. In certain embodiments, the anomaly detection and/or characterization may be performed autonomously and provide alerts to a human pilot or may directly influence the vehicle's autonomous control system; [0071], In some embodiments, the power source 254 can be a battery or other direct current ("DC") power source, such as a photovoltaic cell (e.g., a solar panel). As another example, the power source 254 can include hydrogen cell conversion units (e.g., dual hydrogen cell conversion units). As described above, employing hydrogen cells allows for the vehicle 102 to travel significantly longer than other vehicle-based geophysical instruments (e.g., on the order of hours) depending on the payload weight. The vehicle 102 distributes the power from the power source 254 (i.e., battery) to provide power to one or more components of the vehicle-based anomaly mapping system 100).
Regarding claim 4, Lathrop in view of Bridges and Qian teaches:
planning out a UXO-detection mission using the mobile app located on the tablet (Lathrop: [0047], In some embodiments, the vehicle 102 may communicate directly with both the server 106 and the external device 104. In such embodiments, the external device 104 may, for example, generate a graphical user interface to facilitate control and programming of the vehicle 102, while the server 106 may store and analyze larger amounts of data (e.g., environmental sensor data, geophysical sensor data) for future processing or analysis of the environmental and/or geophysical sensor data to detect anomalies in the area or region from which the data were acquired);
the UAV autonomously executing a flight plan and simultaneously storing data the UXO data collected by the metal detector (Lathrop: [0023], Described here are systems and methods for detecting anomalies in an area or region from which various sensor data (e.g., environmental sensor data and geophysical sensor data) have been acquired using a vehicle, which may be a remotely operated or autonomously controlled unmanned vehicle. One or more anomalies in the area or region are detected by processing the environmental sensor data and geophysical sensor data using artificial intelligence algorithms, programs, or models. As an example, the artificial intelligence algorithms, programs, or models can include machine learning algorithms, programs, or models; [0031], In certain embodiments, the systems and methods described in the present disclosure implement a vehicle-based geophysical sensor system for detecting, characterizing, and mapping anomalies in an area or region. The vehicle can include multiple sensor subsystems (e.g., environmental sensors, geophysical sensors, combinations thereof), which may work together via a central control system to optimize detection and/or characterization of anomalies as described above. For example, the vehicle may utilize cameras, forward- looking infrared ("FLIR") cameras, and other optical sensors; fluxgate magnetometers and other magnetometer technologies (e.g., excitation coils and sensing coils); ground penetrating radar ("GPR"); light detection and ranging ("LiDAR"); environmental sensors; among others. In certain embodiments, the central control system may utilize machine learning to better process the various data collected by the sensor subsystems, as described above. In certain embodiments, the anomaly detection and/or characterization may be performed autonomously and provide alerts to a human pilot or may directly influence the vehicle's autonomous control system);
the UAV flying that planned mission while precisely following the ground-contour and simultaneously flagging UXO information (Lathrop: [0101], The method 700 includes operating the vehicle (e.g., vehicle 102) to switch into an environmental sensing mode, as indicated at step 702. In this mode, the vehicle scans the survey area or region using the onboard environmental sensors to acquire environmental sensor data, as indicated at step 704. As described above, the environmental sensor data can include measurements of air moisture, air temperature, ground temperature, soil moisture, albedo, and so on. The environmental sensor data are then processed (whether onboard the vehicle or at a remote user station, such as external device 104 or server 106) at step 706 to generate geophysical sensing control parameter data for controlling the handling of the geophysical sensor data acquisition and/or processing. For example, the geophysical sensing control parameter data can include control parameters for operating the vehicle (e.g., controlling the motion of an aerial vehicle depending on the environmental conditions and geophysical sensor type(s)), operating the geophysical sensors (e.g., turning various geophysical sensors on or off depending on the environmental conditions), and/or controlling the processing of the geophysical sensor data (e.g., providing confidence weights to hidden layers of a neural network, otherwise weighting the contributions of various geophysical sensor types); [0032], As one non-limiting example, the vehicle-based geophysical sensor system can include an aerial vehicle, such as an unmanned aerial vehicle ("UAV") with an integrated multi-sensor package. The UAV may include a quadcopter, a drone, or the like. The UAV-based system uses machine learning to evaluate environmental conditions (e.g., weather and terrain), and from those data determines the optimal blend of geophysical sensor data from on-board instruments that will yield the most accurate anomaly detection and/or characterization results, such as the most accurate UXO detection results. The UAV-based system performs an initial aerial survey to measure environmental conditions, such as humidity, temperature, vegetation density, albedo, and soil moisture content in the targeted area or region using suitable environmental sensor types);
after that mission is completed, the UAV flying back to a predetermined (but changeable) “home” location and lands (verb) onto the ground (Lathrop: [0047], In some embodiments, the vehicle 102 may communicate directly with both the server 106 and the external device 104. In such embodiments, the external device 104 may, for example, generate a graphical user interface to facilitate control and programming of the vehicle 102, while the server 106 may store and analyze larger amounts of data (e.g., environmental sensor data, geophysical sensor data) for future processing or analysis of the environmental and/or geophysical sensor data to detect anomalies in the area or region from which the data were acquired);
and at completion of mission, automatically transferring all UXO and other mission data is to a storage device attached to the GCS (Lathrop: [0047], In some embodiments, the vehicle 102 may communicate directly with both the server 106 and the external device 104. In such embodiments, the external device 104 may, for example, generate a graphical user interface to facilitate control and programming of the vehicle 102, while the server 106 may store and analyze larger amounts of data (e.g., environmental sensor data, geophysical sensor data) for future processing or analysis of the environmental and/or geophysical sensor data to detect anomalies in the area or region from which the data were acquired).
Regarding claim 5, Lathrop in view of Bridges and Qian teaches:
the system precisely following ground terrain at a distance of 20 cm or higher and avoiding obstacles in its flight path while doing so (Lathrop: [0034], Thus, in some embodiments, the presently disclosed system and methods seek to address concerns raised by existing technologies for identifying UXOs and landmines for humanitarian, civilian, and military applications. In these instances, the geophysical sensor-fused, non-invasive, airborne platform may be referred to as an AVAILD (Aerial Vehicle using Artificial Intelligence for Landmine Detection) system. Combining environmental sensors with GPR, IR, optical, LiDAR, and one or more 3-component magnetometers as an integrated sensor package, the aerial platform can fly 1-3 meters above ground and leverage machine learning techniques with onboard computing to produce rapid-response information about detected munitions in a field area).
Regarding claim 6, Lathrop in view of Bridges and Qian teaches:
the GCS controlling flight of the UAV while communicating with the tablet app and also while analyzing and mapping UXO data (Lathrop: [0047], In some embodiments, the vehicle 102 may communicate directly with both the server 106 and the external device 104. In such embodiments, the external device 104 may, for example, generate a graphical user interface to facilitate control and programming of the vehicle 102, while the server 106 may store and analyze larger amounts of data (e.g., environmental sensor data, geophysical sensor data) for future processing or analysis of the environmental and/or geophysical sensor data to detect anomalies in the area or region from which the data were acquired);
the GCS controlling a payload within the UAV the payload comprising the metal detector, camera, GPS (Lathrop: [0047], In some embodiments, the vehicle 102 may communicate directly with both the server 106 and the external device 104. In such embodiments, the external device 104 may, for example, generate a graphical user interface to facilitate control and programming of the vehicle 102, while the server 106 may store and analyze larger amounts of data (e.g., environmental sensor data, geophysical sensor data) for future processing or analysis of the environmental and/or geophysical sensor data to detect anomalies in the area or region from which the data were acquired);
and the GCS receiving all sensor data from the UAV in real-time and factoring all info as a basis for navigation decisions (Lathrop: [0047], In some embodiments, the vehicle 102 may communicate directly with both the server 106 and the external device 104. In such embodiments, the external device 104 may, for example, generate a graphical user interface to facilitate control and programming of the vehicle 102, while the server 106 may store and analyze larger amounts of data (e.g., environmental sensor data, geophysical sensor data) for future processing or analysis of the environmental and/or geophysical sensor data to detect anomalies in the area or region from which the data were acquired).
Bridges further teaches:
and, when appropriate, flying to a predetermined GPS location (Bridges: [0233], FIG. 15 illustrates a deviation warning process flow 1550, according to one or more embodiments. Operation 1500 receives a transect data. The transect data may specify one or more transect vectors 142 associated with the geospatial area 106, and as may be plotted on the area map 318. An operation 1501, not shown, may optionally set a transect deviation parameter, for example within the project configuration file. Operation 1502 associates a geolocation unit 200 with a transect vector 142. The association may be predetermined ( e.g., color coded transect vectors 142 assigned to a corresponding colored geolocation unit 200), may be manually defined (e.g., a user 101.2 selecting a transect vector 142 on the support device 400 which they are about to traverse, and/or a user 101.1 selecting a transect vector 142 on the coordination device 300 to assign the user 101.2 to traverse a transect vector 142), and/or may be automatically determined ( e.g., by determining the user 101.2 has begun traveling along the transect vector 142 for a threshold distance and/or time); [0188], Operation 1000 may occur off-site ( e.g., pre-project setup), and/or may be completed or supplemented solely in the field. For example, in one or more embodiments the user may access a user interface (e.g., on the coordination device 300) where the user 101 can select an aerial or satellite photograph, define broad geospatial boundaries 140, and then set transect vectors 142. In one or more embodiments, one advantage is setup can occur solely in the field, including adapting to any present site conditions);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the additional teachings of Bridges into the invention of Lathrop in view of Bridges and Qian to include defining travel paths as GPS coordinates as Bridges discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that allows a user to define a search area and paths of travel (Bridges: [0188]). Additionally, the claimed invention is merely a combination of old, well-known elements of a UAV for autonomously detecting UXO using machine learning as disclosed by Lathrop and a GUI for defining search boundaries for UXO detection missions as taught by Bridges. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Qian further teaches:
the GCS constantly controlling UAV-flight in real-time (Qian: Fig. 1; Fig. 2; [0026], Based on any of the above examples, the system further includes a fluctuation flight unit and a flight control unit, where the processing unit is connected to the fluctuation flight unit and the flight control unit; the fluctuation flight unit is configured to input the current location and current geological and geophysical data transmitted by the processing unit into a path prediction model to obtain fluctuation flight data output by the path prediction model, where the path prediction model is obtained through training based on sample locations, sample geological and geophysical data, and sample fluctuation flight data; and the flight control unit is configured to control the flight of the UAV based on the fluctuation flight data; Specifically, the conventional UAV-based geological and geophysical survey technologies mostly implement fixed-altitude flight for UAVs. In this case, the flying height of the UAVs must be increased at the cost of the survey accuracy, so as to ensure the secure flight of the UAVs. In the present invention, the fluctuation flight of the UAV is realized through the fluctuation flight unit and the flight control unit; [0045], Based on any of the above examples, in this system, the communication unit is further configured to transmit, to the processing unit, a control instruction sent by the ground unit);
the UAV stabilizing itself (Qian: Fig. 1; Fig. 2; [0026], Based on any of the above examples, the system further includes a fluctuation flight unit and a flight control unit, where the processing unit is connected to the fluctuation flight unit and the flight control unit; the fluctuation flight unit is configured to input the current location and current geological and geophysical data transmitted by the processing unit into a path prediction model to obtain fluctuation flight data output by the path prediction model, where the path prediction model is obtained through training based on sample locations, sample geological and geophysical data, and sample fluctuation flight data; and the flight control unit is configured to control the flight of the UAV based on the fluctuation flight data; Specifically, the conventional UAV-based geological and geophysical survey technologies mostly implement fixed-altitude flight for UAVs. In this case, the flying height of the UAVs must be increased at the cost of the survey accuracy, so as to ensure the secure flight of the UAVs. In the present invention, the fluctuation flight of the UAV is realized through the fluctuation flight unit and the flight control unit; [0045], Based on any of the above examples, in this system, the communication unit is further configured to transmit, to the processing unit, a control instruction sent by the ground unit)
the GCS providing thrust and tilt angle commands to the UAV (Qian: [0014], The attitude acquisition unit 140 may be a gyroscope capable of acquiring an angular velocity of the UAV, an accelerometer capable of acquiring the acceleration of the UAV, or the like, and the attitude data may be the angular velocity, the acceleration, or the like of the UAV, which are not specifically limited in the examples of the present invention; Fig. 1; Fig. 2; [0026], Based on any of the above examples, the system further includes a fluctuation flight unit and a flight control unit, where the processing unit is connected to the fluctuation flight unit and the flight control unit; the fluctuation flight unit is configured to input the current location and current geological and geophysical data transmitted by the processing unit into a path prediction model to obtain fluctuation flight data output by the path prediction model, where the path prediction model is obtained through training based on sample locations, sample geological and geophysical data, and sample fluctuation flight data; and the flight control unit is configured to control the flight of the UAV based on the fluctuation flight data; Specifically, the conventional UAV-based geological and geophysical survey technologies mostly implement fixed-altitude flight for UAVs. In this case, the flying height of the UAVs must be increased at the cost of the survey accuracy, so as to ensure the secure flight of the UAVs. In the present invention, the fluctuation flight of the UAV is realized through the fluctuation flight unit and the flight control unit; [0045], Based on any of the above examples, in this system, the communication unit is further configured to transmit, to the processing unit, a control instruction sent by the ground unit);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the additional teachings of Qian into the invention of Lathrop in view of Bridges and Qian to include a ground station that can control a UAV as Qian discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that allows control instructions to be sent from the ground unit to the UAV (Qian: [0045]). Additionally, the claimed invention is merely a combination of old, well-known elements of a UAV for autonomously detecting UXO using machine learning as disclosed by Lathrop and ground-unit-to-UAV communication(s) as taught by Qian. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 7, Lathrop in view of Bridges and Qian teaches:
configuring the sensor hub to have a laser range finder (Lathrop: Fig. 4; [0031], In certain embodiments, the systems and methods described in the present disclosure implement a vehicle-based geophysical sensor system for detecting, characterizing, and mapping anomalies in an area or region. The vehicle can include multiple sensor subsystems (e.g., environmental sensors, geophysical sensors, combinations thereof), which may work together via a central control system to optimize detection and/or characterization of anomalies as described above. For example, the vehicle may utilize cameras, forward- looking infrared ("FLIR") cameras, and other optical sensors; fluxgate magnetometers and other magnetometer technologies (e.g., excitation coils and sensing coils); ground penetrating radar ("GPR"); light detection and ranging ("LiDAR"); environmental sensors; among others. In certain embodiments, the central control system may utilize machine learning to better process the various data collected by the sensor subsystems, as described above. In certain embodiments, the anomaly detection and/or characterization may be performed autonomously and provide alerts to a human pilot or may directly influence the vehicle's autonomous control system);
the sensor hub maintaining a stable distance of UAV from ground thereby increasing accuracy of the metal detector (Lathrop: [0034], Thus, in some embodiments, the presently disclosed system and methods seek to address concerns raised by existing technologies for identifying UXOs and landmines for humanitarian, civilian, and military applications. In these instances, the geophysical sensor-fused, non-invasive, airborne platform may be referred to as an AVAILD (Aerial Vehicle using Artificial Intelligence for Landmine Detection) system. Combining environmental sensors with GPR, IR, optical, LiDAR, and one or more 3-component magnetometers as an integrated sensor package, the aerial platform can fly 1-3 meters above ground and leverage machine learning techniques with onboard computing to produce rapid-response information about detected munitions in a field area);
Qian further teaches:
and the sensor hub and laser range finder following real world terrain including avoiding obstacles in its flight path (Fig. 2; [0035], The preset flight attitude is a preset flying height of the UAV based on the ground surface. After obtaining the terrain data, the fluctuation flight unit obtains the fluctuation flight data that can realize a smoothest flight path under the fluctuation terrain based on the current location, the terrain data, and the preset flying height, to ensure that the UAV can successfully avoid obstacles while maintaining the preset flying height from the ground surface to make accurate geological and geophysical surveys. For example, the terrain data is a terrain fluctuation curve. After the terrain fluctuation curve is obtained, the terrain fluctuation curve is raised to the preset flight attitude, and fluctuation flight data is determined based on the current location, a preset flight path, the raised flight curve, and a current flight orientation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the additional teachings of Qian into the invention of Lathrop in view of Bridges and Qian to include a sensor suite to map geological and geophysical features to alter flight plans as Qian discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that realize a smoothest flight path under the fluctuation terrain based on the current location and successfully avoiding obstacles while maintaining a preset flying height from the ground surface to make accurate geological and geophysical surveys (Qian: [0035]). Additionally, the claimed invention is merely a combination of old, well-known elements of a UAV for autonomously detecting UXO using machine learning as disclosed by Lathrop and following real-world terrain while maintaining flight altitude as taught by Qian. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 8, Lathrop in view of Bridges and Qian teaches:
arranging that all communications between the GCS and the UAV occur using a single communication channel (Lathrop: [0041], In the illustrated embodiment, the vehicle 102 communicates with the external device 104. The external device 104 may include, for example, a smartphone, a tablet computer, a cellular phone, a laptop computer, a smart watch, and the like. The vehicle 102 communicates with the external device 104, for example, to transmit at least a portion of the environmental sensor data, geophysical sensor data, or other measurement data or output data; to receive configuration information for the vehicle 102, machine learning control(s) or related parameters for processing data onboard the vehicle 102; or a combination thereof. In some embodiments, the external device 104 may include a shortrange transceiver to communicate with the vehicle 102, and a long-range transceiver to communicate with the server 106. In the illustrated embodiment, the vehicle 102 also includes a transceiver to communicate with the external device 104 via, for example, a short-range communication protocol such as Bluetooth®, analog or digital modes of very high frequency ("VHF"), analog or digital modes of ultra-high frequency ("UHF"), IEEE 802.11, and the like. In some embodiments, the external device 104 bridges the communication between the vehicle 102 and the server 106. That is, the vehicle 102 transmits data (e.g., environmental sensor data, geophysical sensor data) to the external device 104, and the external device 104 forwards the data from the vehicle 102 to the server 106 over the network 108).
Regarding claim 9, Lathrop in view of Bridges and Qian teaches:
a metal detector controller operating and updating the metal detector (Lathrop: [0084], As illustrated in FIG. 4, the geophysical sensors 274 can be integrated using a hardware fusion bus, which in some instances may be implemented using microcontroller technology. As described above, to integrate all sensor electronics, microcontrollers and other related hardware can be used to handle power consumption across each sensor instrument. Data output from each geophysical sensor 274 can be converted into a single format to perform a composite inversion for the survey area. The single data output format can be based on each sensor's electrical and signal output design, and can be used to leverage electronics integration best practices to synchronize all data toward a multi-sensor package that will be equipped on the vehicle 102. Wireless communications can also be included in the sensor fusion design; [0089], As yet another example, the geophysical sensors 274 can include one or more magnetometers, such may include a magnetometer array. For example, a magnetometer array can be used and configured to measure magnetic fields, magnetic field gradients, and perform auto calibrations, space weather subtraction, and detection of geophysical objects in the shallow earth subsurface. As a non-limiting example, the magnetometer can include a 3-component fluxgate magnetometer, which is able to resolve not only the intensity of a magnetic anomaly, but also its direction. These types of magnetometers are able to detect objects with even trace amounts of metal).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lathrop in view of Bridges and Qian, and further in view of Hill et al. (US 20200011961 A1), hereinafter Hill.
Regarding claim 10, Lathrop in view of Bridges and Qian teaches:
storing pre-configured logic within the metal detector controller on customized hardware platform ([0095], In some embodiments, as schematically illustrated in FIG. 5, the vehicle 102 includes components for implementing hardware data fusion, including but not limited to a data bus or busses (e.g., data bus 276), shared system power (e.g., power source 254), environmental sensor-fused data streams (as described above), geophysical sensor-fused data streams (as described above) and the necessary data processing hardware and machine learning hardware accelerators. Machine learning hardware accelerators can include GPUs, TPUs, beyond van-Neumann accelerators (e.g., those described in U.S. Patent Application Publication No. US 2021/0295144, which is herein incorporated by reference in its entirety), and FPGA-based machine learning accelerators (e.g., those described in PCT Application Publication No. WO/2018/213399, which is herein incorporated by reference in its entirety));
However, Lathrop in view of Bridges and Qian does not specifically state:
configuring the customized hardware platform from an ATMEGA328 on a predetermined type of PCB.
Hill teaches:
configuring the customized hardware platform from an ATMEGA328 on a predetermined type of PCB ([0045], Finally, IMDS subsystem 10 may also consist of a complete integrated solution, as exemplified by the Razor IMU for Sparkfun Electronics, a 9 degree-of-freedom system that incorporates three devices-an InvenSense ITG- 3200 (triple-axis gyro), Analog Devices ADXL345 (tripleaxis accelerometer), and a Honeywell HMC5883L (tripleaxis magnetometer). The outputs of all sensors 11, 12, 13 are processed by an on-board Atmel ATmega328 RISC processor 14 and the navigation solution, which is represented by the corrected position and orientation block 40 is output over a serial interface).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Hill into the invention of Lathrop in view of Bridges and Qian to include using an ATMEGA328 microcontroller as Hill discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system uses a common, well-documented, and power efficient microcontroller. Additionally, the claimed invention is merely a combination of old, well-known elements of a UAV for autonomously detecting UXO using machine learning as disclosed by Lathrop in view of Bridges and Qian and using an ATMEGA328 microcontroller as taught by Bridges. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Documents Considered but Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Jones (US 20160359986 A1) is directed to methods and systems for querying a database of geofences, with each geofence in the database being associated with a plurality of geographic designators, wherein each of the plurality of geographic designators is associated with an IP address, preferably an IPv6 address. Dondoneau et al. (US 20210160781 A1) discloses an aviation connectivity gateway module for remote access to an aircraft's systems and remotely offloading its aircraft data. The module broadly comprises a CPU, a first set of communication elements, a second set of communication elements, a memory, a battery, an IMU, a GPS module, and a number of antennas. The module responds to remote prompts and offloads aircraft data when the aircraft is powered off. An aviation connectivity gateway module for complete BVLOS cellular network connectivity broadly comprises a CPU, a set of electronic connectors, a memory, an IMU, a GPS module, a first cellular connectivity element, a second cellular connectivity element, and a number of antennas. The module switches between the first cellular communication element and the second communication element based a status of the aircraft.
Conclusion
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/I.A.R./Examiner, Art Unit 3666 /SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666