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 final action is in response to Applicant’s amended filing of 02/19/2025.
Claims 1, 3-13, and 15-24 are currently pending and have been examined. Applicant has added new claims 23-24.
Response to Arguments
Applicant's arguments with respect to claims 1, 3-13, and 15-22 rejected under 35 USC § 103 have been fully considered but they are not persuasive.
The Applicant argues that Ladurini does not suggest a controller recording atmospheric data during refueling operations so artificial intelligence can learn and determine desired environments for future refueling operations. The Examiner respectfully disagrees.
Applicant’s specification ¶ [0055] recites “…the control unit 112 records atmospheric data (including altitude, time or day, cloud cover, precipitation, air turbulence, and/or the like) during each refueling operation...” Without reading the specification into the claims, a broadest reasonable interpretation of “atmospheric data” is taught by Ladurini ¶ [0006]: “The route determination module can be programmed to determine flight route segments based on predicted weather conditions for the aerial vehicle during flight... The ML library module can be programmed to provide the predicted weather conditions for the aerial vehicle during flight based on current weather data.” This is expanded further in parts of ¶ [0013-0016] to monitor and examine weather conditions, windy conditions, and atmospheric conditions during flights all to provide and store data into the ML library module to generate predicted weather conditions for an optimized flight route for a vehicle flight controller to execute (¶ [0072]). The vehicle flight controller adjusts the flight based on weather data (¶ [0074]), and also adjusts the flight in accordance with aerial refueling operations (¶ [0077]). These features at the very least suggest “a controller recording atmospheric data during refueling operations so artificial intelligence can learn and determine desired environments for future refueling operations” enough to render obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate weather condition data and considerations into training and performing aerial refueling operations in the combination of Riley and Lozano. Any further arguments against Ladurini in combination with Riley and Lozano should be addressed to show non-obviousness without attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
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.
Claims 1, 3-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Riley et al. (US 20150293225 A1) in view of Lozano (US 20190118963 A1) and Ladurini et al. (US 20220261012 A1).
Regarding claim 1, Riley discloses a system configured for allowing a first vehicle to refuel a second vehicle (see at least ¶ [0002]), the system comprising:
sensors configured to acquire scan data of the second vehicle (see at least ¶ [0027-0029] disclosing a UAV being scanned for LIDAR information by a tanker);
a control unit in communication with the sensors (see at least ¶ [0037] and [0075-0076] disclosing a laser positioning system (LPS) using LIDAR data to help control the positioning of the aircraft), wherein the control unit is configured to:
receive the scan data of the second vehicle from the sensors (see at least ¶ [0036-0038] disclosing LIDAR data from the LPS being provided to an aircraft flight control computer).
While Riley discloses automatically controlling a refueling boom of the first vehicle based on the monitored data (see at least ¶ [0037] and [0075-0076] disclosing a laser positioning system (LPS) using LIDAR data to help control the positioning of the aircraft), Riley does not disclose associating the scan data with a three-dimensional (3D) model of the second vehicle,
registering the scan data with the 3D model to provide monitored data of the second vehicle,
and automatically controlling the first vehicle, the second vehicle, and the refueling boom based on the monitored data.
However, Lozano suggests associating the scan data with a three-dimensional (3D) model of the second vehicle (see at least ¶ [0064-0074] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model to compare and adjust with a 3D model stored thereon),
registering the scan data with the 3D model to provide monitored data of the second vehicle (see at least ¶ [0064-0074] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model to compare and adjust with a 3D model stored thereon),
and automatically controlling the first vehicle, the second vehicle, and the refueling boom based on the monitored data (see at least ¶ [0010-0011] disclosing data being used to affect control laws and direct a tanker’s refueling boom, the tanker, and a receiving aircraft).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate controlling the refueling boom, tanker, and receiving aircraft and storing 3D models of the receiving aircraft of Lozano into the aerial refueling system of Riley with a reasonable expectation of success because both inventions are directed toward performing refueling operations with the help of laser-collected 3D point cloud data. Collecting and storing 3D models of the receiving aircraft would help train a neural network that would further facilitate automated refueling operations (see Lozano ¶ [0073]). Furthermore, controlling all of the boom, tanker, and receiving aircraft would help coordinate the machinery to ensure smooth refueling operations.
The combination of Riley and Lozano does not explicitly disclose recording atmospheric data during each refueling operation to allow for the artificial intelligence or machine learning system to learn and determine desired environments for future refueling operations.
However, Ladurini suggests recording atmospheric data during each refueling operation to allow for the artificial intelligence or machine learning system to learn and determine desired environments for future refueling operations (see at least abstract and ¶ [0006], [0013-0016], and [0072-0077] disclosing a flight path optimization (FPO) system that compiles various weather data into machine learning (ML) libraries to improve flight pathing operations, including aerial refueling).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the weather data ML library of Ladurini into the combination of Riley and Lozano with a reasonable expectation of success because all inventions are directed toward operating aerial vehicles with the aid of machine learning techniques with consideration for midair refueling operations. This would help the system account for a variety of weather conditions (see Ladurini ¶ [0002-0003]) and improve the neural network perform aerial refueling during those conditions.
Regarding claim 3, Riley does not disclose the 3D model is previously stored in a model database.
However, Lozano suggests the 3D model is previously stored in a model database (see at least ¶ [0064-0074] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model to compare and adjust with a 3D model stored thereon).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate storing 3D models of the receiving aircraft of Lozano into the aerial refueling system of Riley with a reasonable expectation of success because both inventions are directed toward performing refueling operations with the help of laser-collected 3D point cloud data. Collecting and storing 3D models of the receiving aircraft would help train a neural network that would further facilitate automated refueling operations (see Lozano ¶ [0073]).
Regarding claim 4, Riley does not disclose the control unit is further configured to generate the 3D model by recognizing one or more features within the scan data.
However, Lozano suggests the control unit is further configured to generate the 3D model by recognizing one or more features within the scan data (see at least ¶ [0064-0074] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model to compare and adjust with a 3D model stored thereon to recognize the mouth of a receiver aircraft and guide a boom of the tanker aircraft toward it).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate storing 3D models of the receiving aircraft of Lozano into the aerial refueling system of Riley with a reasonable expectation of success because both inventions are directed toward performing refueling operations with the help of laser-collected 3D point cloud data. Collecting and storing 3D models of the receiving aircraft would help train a neural network that would further facilitate automated refueling operations (see Lozano ¶ [0073]).
Regarding claim 5, Riley does not disclose the one or more features are on a fuel port of the second vehicle.
However, Lozano suggests the one or more features are on a fuel port of the second vehicle (see at least ¶ [0064-0074] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model to compare and adjust with a 3D model stored thereon to recognize the mouth of a receiver aircraft and guide a boom of the tanker aircraft toward it).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate storing 3D models of the receiving aircraft of Lozano into the aerial refueling system of Riley with a reasonable expectation of success because both inventions are directed toward performing refueling operations with the help of laser-collected 3D point cloud data. Collecting and storing 3D models of the receiving aircraft would help train a neural network that would further facilitate automated refueling operations (see Lozano ¶ [0073]).
Regarding claim 6, Riley discloses the first vehicle is a first aircraft (see at least abstract and Fig. 2), and the second vehicle is a second aircraft (see at least abstract and Fig. 2).
Regarding claim 7, Riley discloses the first vehicle comprises the sensors (see at least ¶ [0036-0038] and [0075-0076] disclosing a tanker aircraft equipped with a laser positioning system (LPS) using LIDAR sensor data).
Regarding claim 8, Riley discloses the sensors do not include a photographic camera or a video camera (see at least ¶ [0036-0038] and [0075-0076] disclosing a tanker aircraft equipped with a laser positioning system (LPS) using LIDAR sensor data).
Regarding claim 9, Riley discloses the sensors include one or more of light detection and ranging (LIDAR) sensors, lasers, infrared sensors, ultrasonic sensors, radio detection and ranging (RADAR) sensors, or sound navigation ranging (SONAR) sensors (see at least ¶ [0036-0038] and [0075-0076] disclosing a tanker aircraft equipped with a laser positioning system (LPS) using LIDAR sensor data).
Regarding claim 10, Riley discloses a user interface including a display (see at least ¶ [0079]), wherein the control unit is further configured to show information regarding a refueling process on the display (see at least ¶ [0038] and [0079] disclosing displaying refueling operations to an air vehicle operator (AVO)).
Regarding claim 11, Riley suggests the control unit is further configured to show a preferable location for refueling on the display (see at least ¶ [0038] disclosing displaying refueling operations to an air vehicle operator (AVO) using reflectors near the refueling receptacle and positioned with XYZ coordinates).
Regarding claim 12, Riley does not disclose an imaging device that is separate and distinct from the sensors, wherein the imaging device is configured to acquire photographic images or video of the second vehicle.
However, Lozano suggests an imaging device that is separate and distinct from the sensors, wherein the imaging device is configured to acquire photographic images or video of the second vehicle (see at least ¶ [0030-0033] disclosing subsystems that operate according to different cameras, distinctly including two 3D cameras, a time-of-flight (TOF) camera, and a diffractive optical element (DOE) camera).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the distinct cameras of Lozano into the aerial refueling system of Riley with a reasonable expectation of success because both inventions are directed toward performing refueling operations with the help of laser-collected 3D point cloud data. While Riley discloses using LIDAR sensors without the aid of cameras as an improvement (see at least ¶ [0022]), combining the cameras of Lozano with it does not teach away from this feature because Lozano describes multiple cameras with distinct functions separate from 3D data collection, namely the TOF and DOE cameras. Therefore these distinct cameras are used to verify and provide accuracy to the 3D cameras of Lozano, and would similarly be used to improve the LIDAR data collection accuracy of Riley.
Regarding claim 13, Riley does not disclose the control unit comprises the artificial intelligence.
However, Lozano suggests the control unit comprises the artificial intelligence (see at least ¶ [0034] and [0072-0073] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model by using and training a neural network).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the neural network of Lozano into the aerial refueling system of Riley with a reasonable expectation of success because both inventions are directed toward performing refueling operations with the help of laser-collected 3D point cloud data. Collecting and storing 3D models of the receiving aircraft would help train a neural network that would further facilitate automated refueling operations (see Lozano ¶ [0073]).
Regarding claim 15, Riley discloses a system (see at least abstract) comprising:
a first aircraft comprising a refueling boom and sensors (see at least ¶ [0021] disclosing a tanker aircraft equipped with LIDAR sensors);
a second aircraft comprising a fuel port (see at least ¶ [0027] disclosing a UAV with a receptacle for refueling);
a control unit in communication with the sensors (see at least ¶ [0037] and [0075-0076] disclosing a laser positioning system (LPS) using LIDAR data to help control the positioning of the aircraft), and wherein the control unit is configured to:
receive scan data of the second aircraft from the sensors (see at least ¶ [0036-0038] disclosing LIDAR data from the LPS being provided to an aircraft flight control computer),
and a user interface including a display (see at least ¶ [0079]),
wherein the control unit is further configured to show information regarding a refueling process on the display (see at least ¶ [0038] and [0079] disclosing displaying refueling operations to an air vehicle operator (AVO)).
While Riley discloses automatically controlling a refueling boom of the first vehicle based on the monitored data (see at least ¶ [0037] and [0075-0076] disclosing a laser positioning system (LPS) using LIDAR data to help control the positioning of the aircraft),Riley does not disclose the control unit comprises an artificial intelligence or machine learning system, configured to:
associate the scan data with a three-dimensional (3D) model of the second aircraft,
register the scan data with the 3D model to provide monitored data of the second aircraft,
and automatically controlling the first vehicle, the second vehicle, and the refueling boom based on the monitored data.
However, Lozano suggests the control unit comprises an artificial intelligence or machine learning system (see at least ¶ [0034] and [0072-0073] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model by using and training a neural network), configured to:
associate the scan data with a three-dimensional (3D) model of the second aircraft (see at least ¶ [0064-0074] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model to compare and adjust with a 3D model stored thereon),
register the scan data with the 3D model to provide monitored data of the second aircraft (see at least ¶ [0064-0074] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model to compare and adjust with a 3D model stored thereon),
and automatically controlling the first vehicle, the second vehicle, and the refueling boom based on the monitored data (see at least ¶ [0010-0011] disclosing data being used to affect control laws and direct a tanker’s refueling boom, the tanker, and a receiving aircraft).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate controlling the refueling boom, tanker, and receiving aircraft and storing 3D models of the receiving aircraft of Lozano into the aerial refueling system of Riley with a reasonable expectation of success because both inventions are directed toward performing refueling operations with the help of laser-collected 3D point cloud data. Collecting and storing 3D models of the receiving aircraft would help train a neural network that would further facilitate automated refueling operations (see Lozano ¶ [0073]). Furthermore, controlling all of the boom, tanker, and receiving aircraft would help coordinate the machinery to ensure smooth refueling operations.
The combination of Riley and Lozano does not explicitly disclose recording atmospheric data during each refueling operation to allow for the artificial intelligence or machine learning system to learn and determine desired environments for future refueling operations.
However, Ladurini suggests recording atmospheric data during each refueling operation to allow for the artificial intelligence or machine learning system to learn and determine desired environments for future refueling operations (see at least abstract and ¶ [0016] and [0077] disclosing a flight path optimization (FPO) system that compiles various weather data into machine learning (ML) libraries to improve flight pathing operations, including aerial refueling).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the weather data ML library of Ladurini into the combination of Riley and Lozano with a reasonable expectation of success because all inventions are directed toward operating aerial vehicles with the aid of machine learning techniques with consideration for midair refueling operations. This would help the system account for a variety of weather conditions (see Ladurini ¶ [0002-0003]) and improve the neural network perform aerial refueling during those conditions.
Regarding claim 16, Riley discloses the control unit is configured to automatically control the first aircraft, the second aircraft, and the refueling boom based on the monitored data (see at least ¶ [0029] and [0043] disclosing an automated aerial refueling system).
Regarding claim 17, Riley does not disclose the 3D model is previously stored in a model database.
However, Lozano suggests the 3D model is previously stored in a model database (see at least ¶ [0064-0074] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model to compare and adjust with a 3D model stored thereon).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate storing 3D models of the receiving aircraft of Lozano into the aerial refueling system of Riley with a reasonable expectation of success because both inventions are directed toward performing refueling operations with the help of laser-collected 3D point cloud data. Collecting and storing 3D models of the receiving aircraft would help train a neural network that would further facilitate automated refueling operations (see Lozano ¶ [0073]).
Regarding claim 18, Riley does not disclose the control unit is further configured to generate the 3D model by recognizing one or more features within the scan data.
However, Lozano suggests the control unit is further configured to generate the 3D model by recognizing one or more features within the scan data (see at least ¶ [0064-0074] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model to compare and adjust with a 3D model stored thereon to recognize the mouth of a receiver aircraft and guide a boom of the tanker aircraft toward it).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate storing 3D models of the receiving aircraft of Lozano into the aerial refueling system of Riley with a reasonable expectation of success because both inventions are directed toward performing refueling operations with the help of laser-collected 3D point cloud data. Collecting and storing 3D models of the receiving aircraft would help train a neural network that would further facilitate automated refueling operations (see Lozano ¶ [0073]).
Regarding claim 19, Riley does not disclose an imaging device that is separate and distinct from the sensors, wherein the imaging device is configured to acquire photographic images or video of the second aircraft.
However, Lozano suggests an imaging device that is separate and distinct from the sensors, wherein the imaging device is configured to acquire photographic images or video of the second aircraft (see at least ¶ [0030-0033] disclosing subsystems that operate according to different cameras, distinctly including two 3D cameras, a time-of-flight (TOF) camera, and a diffractive optical element (DOE) camera).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the distinct cameras of Lozano into the aerial refueling system of Riley with a reasonable expectation of success because both inventions are directed toward performing refueling operations with the help of laser-collected 3D point cloud data. While Riley discloses using LIDAR sensors without the aid of cameras as an improvement (see at least ¶ [0022]), combining the cameras of Lozano with it does not teach away from this feature because Lozano describes multiple cameras with distinct functions separate from 3D data collection, namely the TOF and DOE cameras. Therefore these distinct cameras are used to verify and provide accuracy to the 3D cameras of Lozano, and would similarly be used to improve the LIDAR data collection accuracy of Riley.
Regarding claim 20, Riley discloses a method for allowing a first vehicle to refuel a second vehicle (see at least ¶ [0002]), the method comprising:
acquiring, by sensors of the first vehicle, scan data of the second vehicle (see at least ¶ [0027-0029] and [0036-0038] disclosing a UAV being scanned for LIDAR data by a tanker with a laser positioning system (LPS) and providing that data to an aircraft flight control computer);
and receiving, by a control unit in communication with the sensors, the scan data of the second vehicle from the sensors (see at least ¶ [0036-0038] disclosing LIDAR data from the LPS being provided to an aircraft flight control computer).
While Riley discloses automatically controlling a refueling boom of the first vehicle based on the monitored data (see at least ¶ [0037] and [0075-0076] disclosing a laser positioning system (LPS) using LIDAR data to help control the positioning of the aircraft), Riley does not disclose associating the scan data with a three-dimensional (3D) model of the second vehicle,
registering the scan data with the 3D model to provide monitored data of the second vehicle,
and automatically controlling, by the control unit, the first vehicle, the second vehicle, and the refueling boom based on the monitored data.
However, Lozano suggests associating the scan data with a three-dimensional (3D) model of the second vehicle (see at least ¶ [0064-0074] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model to compare and adjust with a 3D model stored thereon),
registering the scan data with the 3D model to provide monitored data of the second vehicle (see at least ¶ [0064-0074] disclosing a processing element of a tanker aircraft processing laser data of receiving aircraft into a 3D model to compare and adjust with a 3D model stored thereon),
and automatically controlling, by the control unit, the first vehicle, the second vehicle, and the refueling boom based on the monitored data (see at least ¶ [0010-0011] disclosing data being used to affect control laws and direct a tanker’s refueling boom, the tanker, and a receiving aircraft).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate controlling the refueling boom, tanker, and receiving aircraft and storing 3D models of the receiving aircraft of Lozano into the aerial refueling system of Riley with a reasonable expectation of success because both inventions are directed toward performing refueling operations with the help of laser-collected 3D point cloud data. Collecting and storing 3D models of the receiving aircraft would help train a neural network that would further facilitate automated refueling operations (see Lozano ¶ [0073]). Furthermore, controlling all of the boom, tanker, and receiving aircraft would help coordinate the machinery to ensure smooth refueling operations.
The combination of Riley and Lozano does not explicitly disclose recording atmospheric data during each refueling operation;
and, in response to said recording, determining, by artificial intelligence, environments for future refueling operations.
However, Ladurini suggests recording atmospheric data during each refueling operation (see at least abstract and ¶ [0006], [0013-0016], and [0072-0077] disclosing a flight path optimization (FPO) system that compiles various weather data into machine learning (ML) libraries to improve flight pathing operations, including aerial refueling);
and, in response to said recording, determining, by artificial intelligence, environments for future refueling operations (see at least abstract and ¶ [0016] and [0077] disclosing a flight path optimization (FPO) system that compiles various weather data into machine learning (ML) libraries to improve flight pathing operations, including aerial refueling).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the weather data ML library of Ladurini into the combination of Riley and Lozano with a reasonable expectation of success because all inventions are directed toward operating aerial vehicles with the aid of machine learning techniques with consideration for midair refueling operations. This would help the system account for a variety of weather conditions (see Ladurini ¶ [0002-0003]) and improve the neural network perform aerial refueling during those conditions.
Claims 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Riley et al. in view of Lozano and Ladurini et al., as applied to claims 1 and 20 above, and in further view of Cramblitt (US 20220237822 A1).
Regarding claims 21 and 22, the combination of Riley, Lozano, and Ladurini does not explicitly disclose the control unit is further configured to:
in response to the scan data of the second vehicle not matching the 3D model of the second vehicle, search the scan data of the second vehicle for features stored in a model database that are predetermined to be associated with a fuel port,
in response to finding the features within the model database that are predetermined to be associated with the fuel port, generate a new 3D model for the second vehicle,
and store the new 3D model for the second vehicle in the model database.
However, Cramblitt suggests, in response to the scan data of the second vehicle not matching the 3D model of the second vehicle, searching the scan data of the second vehicle for features stored in a model database that are predetermined to be associated with a fuel port (see at least ¶ [0044-0055] disclosing a control unit that uses a Maximum Mutual Information Correlator (MMIC) matching algorithm to compare detected features to a vehicle model stored in a model database),
in response to finding the features within the model database that are predetermined to be associated with the fuel port, generating a new 3D model for the second vehicle (see at least ¶ [0044-0055] disclosing a control unit with a renderer that renders views of an imaged vehicle and manipulates the 3D model to correspond with renderings),
and storing the new 3D model for the second vehicle in the model database (see at least ¶ [0044-0055] disclosing a control unit storing rendered views and features of the 3D vehicle model into a model database).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the control unit renderer into the combination of Riley, Lozano, and Ladurini with a reasonable expectation of success because all inventions are directed toward operating aerial vehicles with the aid of machine learning techniques with consideration for midair refueling operations. This would help the system maintain relevant and up-to-date model information while also making it more dynamic to different perceived views of the receiving aircraft.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Riley et al. in view of Lozano and Ladurini et al., as applied to claim 1 above, and in further view of Choi (US 20230043519 A1).
Regarding claim 23, while Lozano suggests overlaying the 3D model of the second vehicle onto the scan data of the second vehicle (see at least ¶ [0084] disclosing virtually placing the 3D model in the work scenario over a real image), the combination of Riley, Lozano, and Ladurini does not explicitly disclose moving and manipulating the 3D model of the second vehicle to match the scan data of the second vehicle.
However, Choi suggests moving and manipulating the 3D model of the second vehicle to match the scan data of the second vehicle (see at least ¶ [0062] and [0074-0075] disclosing a global alignment step that aligns scan data of a subject with a generated 3D model of the subject).
While Choi is not explicitly directed toward modeling aircraft and their aerial refueling operations, the ability to manipulate a 3D model to match with and image for the purpose of overlaying one on the other is a feature that can be performed without regard to the subjects being modeled and imaged. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate alignment step of Choi into the combination of Riley, Lozano, and Ladurini with a reasonable expectation of success because all inventions are directed toward collecting, processing, and using image data to generate and implement a corresponding 3D model of the subject. This would help the autonomous system align models to images and facilitate aerial refueling operations by making it more dynamic to different perceived views of the receiving aircraft.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Riley et al. in view of Lozano and Ladurini et al., as applied to claim 11 above, and in further view of Schroeder (US 20070023576 A1), Chamas et al. (US 20020053983 A1), Schwindt (US 20220343771 A1), and Goddemeier et al. (US 20170025023 A1).
Regarding claim 24, the combination of Riley, Lozano, and Ladurini does not explicitly disclose determining a refueling envelope for the second vehicle in relation to the first vehicle, and wherein the control unit is further configured to show:
a no-fly zone on the display, wherein the no-fly zone is based on information received from other aircraft,
an inclement weather location on the display,
and an alternate location for refueling on the display,
wherein the control unit is further configured to provide each of the preferable location, the no-fly zone, the inclement weather location, and the alternate location with a different indicia on the display.
However, Schroeder suggests determining a refueling envelope for the second vehicle in relation to the first vehicle (see at least ¶ [0033] disclosing a signal envelope that is used to position a first aircraft relative to a second aircraft).
Additionally, Chamas suggests showing an inclement weather location and a no-fly zone on the display (see at least ¶ [0045-0046], [0051-0056], and [0063-0064] and Figs. 3-18 disclosing a display showing restricted airspaces and approaching thunderstorms).
Lastly, Schwindt suggests showing an alternate location for refueling on the display (see at least ¶ [0057] disclosing a display listing a selectable menu of refueling locations).
Lastly, Goddemeier suggests the no-fly zone is based on information received from other aircraft (see at least ¶ [0048], [0052], and [0088] disclosing an airspace monitoring system allowing a first and second aircraft to communicate between respective control and detection units and reserve airspace).
While only Schroeder and Schwindt are directed toward features directed toward assisting an aircraft in refueling operations, Schroeder, Chamas, Schwindt, and Goddemeier each demonstrate features that are known in the art of presenting information relevant to flying the aircraft to a display. Furthermore, each of the references provide that displayed information with different means and indicators, and thus suggest the control unit is further configured to provide each of the preferable location, the no-fly zone, the inclement weather location, and the alternate location with a different indicia on the display. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the display and information presenting features of Schroeder, Schwindt, Chamas, and Goddemeier into the combination of Riley, Lozano, and Ladurini with a reasonable expectation of success because all inventions are directed toward operating and aircraft with an availability of situational information to facilitate operations. Securing an area for the second aircraft to approach facilitates refueling formation operations (Schroeder), sharing multiple refueling locations facilitates access and provides the pilot with operation authority (Schwindt), and displaying weather and restricted and/or reserved airspace (Chamas and Goddemeier) further informs the pilot whether certain locations would be appropriate for refueling operations.
Conclusion
THIS ACTION IS MADE FINAL. 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 JARED C BEAN whose telephone number is (571)272-5255. The examiner can normally be reached 7:30AM - 5:00PM.
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, Navid Z Mehdizadeh can be reached at (571) 272-7691. 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.
/J.C.B./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
Prosecution Insights