CONTROL DEVICE, CONTROL METHOD, AND STORAGE MEDIUM

Application No. 18/866,996

• Filed: Nov 18, 2024
• Examiner: ALSOMAIRY, IBRAHIM ABDOALATIF
• Art Unit: 3667
• Tech Center: 3600 — Transportation & Electronic Commerce
• Assignee: Japan Aerospace Exploration Agency
• OA Round: 1 (Non-Final)

Office Action

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 .
This is a Non-Final Action on the Merits. Claims 1-7 are currently pending and are addressed below.

Preliminary Amendment
The preliminary amendment filed on November 18th, 2024 has been considered and entered. Claims 3-4 and 7 have been amended.

Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 18th, 2024 has been considered and entered.

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.

Claims 1 and 6-7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wada (JP 2021030950 A) (“Wada”) (Translation Attached).

With respect to claim 1, Wada teaches a control device for controlling a user-wearable flight device, the control device comprising:
a processing unit configured to (See at least Wada Paragraph 47 “The control unit 230 is realized by a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit) executing a program stored in the storage unit 206.”)
acquire state data1 related to a state of the flight device and manipulation data2 related to a manipulation of the flight device (See at least Wada FIG. 12 and Paragraphs 63-64 “First, the control unit 230 receives a command from an external device via the communication unit 202 (step S100). The command includes, for example, the attitude that the air vehicle 100 should take, i.e., the target attitude q_ｒ. Next, the control unit 230 calculates the current attitude q_ｃ of the flying object 100 based on the detection result of the detection unit 204, and calculates the deviation q_ｅ between the calculated current attitude q_ｃ and the target attitude q_ｒ (step S102). The deviation q_ｅ includes a quaternion q_{ｅｘ，ｅｙ，ｅｚ} corresponding to the aircraft-fixed coordinate axes X_Ｂ, Y_Ｂ, and Z_Ｂ”),
input the acquired state data and the acquired manipulation data to a model trained using deep reinforcement learning, and control the flight device on the basis of an output result of the model to which the state data and the manipulation data are input (See at least Wada FIG. 13 and Paragraphs 72-92 “The second embodiment differs from the first embodiment described above in that deep reinforcement learning is used to determine the control amounts of the sweep mechanism, twist mechanism, and fold mechanism based on the attitude, speed, etc. of the aircraft 100. The following description will focus on the differences from the first embodiment, and a description of the points in common with the first embodiment will be omitted. In the description of the second embodiment, the same parts as those in the first embodiment will be denoted by the same reference numerals … The model MDL is trained so that, when a state variable s_ｔ is input, it outputs an action value Q(s_ｔ, a_ｔ). The state variable s_ｔ is, for example, the current attitude q_ｃ or the target attitude q_ｒ of the flying object 100 described above, or the deviation therebetween q_ｅ. Furthermore, the state variable s_ｔ may include the velocity of the flying object 100 instead of or in addition to the attitude and deviation. Furthermore, if the detection unit 204 includes an optical fiber sensor that detects strain or a pressure sensor that detects pressure, the state variable s_ｔ may include strain or pressure that can be obtained from these sensors. The state variable s_ｔ, which includes strain and pressure, is an example of "displacement information." The action a_ｔ is, for example, the control amount of the sweep mechanism, the control amount of the twist mechanism, the control amount of the fold mechanism, the rotation speed of the propeller 110, the rudder angle of the elevator, the rudder angle, and the like. That is, the action a_ｔ is the amount of operation of each actuator of the driving unit 210. Furthermore, the behavior a_ｔ may be a proportional gain K_Ｐ of PID control, an integral gain K_Ｉ, a differential gain K_Ｄ, or a correction gain K_ｊ. Furthermore, the behavior a_ｔ may be an index value indicating which of various types of control, such as PID control or hovering control, is to be performed or whether it is not to be performed … The control unit 230 controls the actuators based on the operation amount of each actuator output by the model MDL, thereby causing the flying object 100 to fly.” | Paragraph 40 “The detection unit 204 is, for example, an inertial measurement unit. The inertial measurement unit includes, for example, a three-axis acceleration sensor and a three-axis gyro sensor. The inertial measurement unit outputs the detection values detected by these sensors to the control unit 230 . The values detected by the inertial measurement unit include, for example, acceleration and/or angular velocity in the horizontal, vertical, and depth directions, and velocity (rate) of each axis of pitch, roll, and yaw. The detection unit 204 may further include a radar, a finder, a sonar, a GPS (Global Positioning System) receiver, and the like. Furthermore, the detection unit 204 may further include optical fiber sensors that detect distortions in the vertical stabilizer 120, horizontal stabilizer 130, and morphing wing 140, and pressure sensors that detect pressures acting on these wings.”).

With respect to claim 6, Wada teaches a control method for controlling a user-wearable flight device, the control method comprising:
acquiring state data3 related to a state of the flight device and manipulation data4 related to a manipulation of the flight device (See at least Wada FIG. 12 and Paragraphs 63-64 “First, the control unit 230 receives a command from an external device via the communication unit 202 (step S100). The command includes, for example, the attitude that the air vehicle 100 should take, i.e., the target attitude q_ｒ. Next, the control unit 230 calculates the current attitude q_ｃ of the flying object 100 based on the detection result of the detection unit 204, and calculates the deviation q_ｅ between the calculated current attitude q_ｃ and the target attitude q_ｒ (step S102). The deviation q_ｅ includes a quaternion q_{ｅｘ，ｅｙ，ｅｚ} corresponding to the aircraft-fixed coordinate axes X_Ｂ, Y_Ｂ, and Z_Ｂ”),
inputting the acquired state data and the acquired manipulation data to a model trained using deep reinforcement learning, and controlling the flight device on the basis of an output result of the model to which the state data and the manipulation data are input (See at least Wada FIG. 13 and Paragraphs 72-92 “The second embodiment differs from the first embodiment described above in that deep reinforcement learning is used to determine the control amounts of the sweep mechanism, twist mechanism, and fold mechanism based on the attitude, speed, etc. of the aircraft 100. The following description will focus on the differences from the first embodiment, and a description of the points in common with the first embodiment will be omitted. In the description of the second embodiment, the same parts as those in the first embodiment will be denoted by the same reference numerals … The model MDL is trained so that, when a state variable s_ｔ is input, it outputs an action value Q(s_ｔ, a_ｔ). The state variable s_ｔ is, for example, the current attitude q_ｃ or the target attitude q_ｒ of the flying object 100 described above, or the deviation therebetween q_ｅ. Furthermore, the state variable s_ｔ may include the velocity of the flying object 100 instead of or in addition to the attitude and deviation. Furthermore, if the detection unit 204 includes an optical fiber sensor that detects strain or a pressure sensor that detects pressure, the state variable s_ｔ may include strain or pressure that can be obtained from these sensors. The state variable s_ｔ, which includes strain and pressure, is an example of "displacement information." The action a_ｔ is, for example, the control amount of the sweep mechanism, the control amount of the twist mechanism, the control amount of the fold mechanism, the rotation speed of the propeller 110, the rudder angle of the elevator, the rudder angle, and the like. That is, the action a_ｔ is the amount of operation of each actuator of the driving unit 210. Furthermore, the behavior a_ｔ may be a proportional gain K_Ｐ of PID control, an integral gain K_Ｉ, a differential gain K_Ｄ, or a correction gain K_ｊ. Furthermore, the behavior a_ｔ may be an index value indicating which of various types of control, such as PID control or hovering control, is to be performed or whether it is not to be performed … The control unit 230 controls the actuators based on the operation amount of each actuator output by the model MDL, thereby causing the flying object 100 to fly.” | Paragraph 40 “The detection unit 204 is, for example, an inertial measurement unit. The inertial measurement unit includes, for example, a three-axis acceleration sensor and a three-axis gyro sensor. The inertial measurement unit outputs the detection values detected by these sensors to the control unit 230 . The values detected by the inertial measurement unit include, for example, acceleration and/or angular velocity in the horizontal, vertical, and depth directions, and velocity (rate) of each axis of pitch, roll, and yaw. The detection unit 204 may further include a radar, a finder, a sonar, a GPS (Global Positioning System) receiver, and the like. Furthermore, the detection unit 204 may further include optical fiber sensors that detect distortions in the vertical stabilizer 120, horizontal stabilizer 130, and morphing wing 140, and pressure sensors that detect pressures acting on these wings.”).

With respect to claim 7, Wada teaches a non-transitory computer-readable storage medium storing a program for causing a computer, which controls a user-wearable flight device, to:
acquire state data5 related to a state of the flight device and manipulation data6 related to a manipulation of the flight device (See at least Wada FIG. 12 and Paragraphs 63-64 “First, the control unit 230 receives a command from an external device via the communication unit 202 (step S100). The command includes, for example, the attitude that the air vehicle 100 should take, i.e., the target attitude q_ｒ. Next, the control unit 230 calculates the current attitude q_ｃ of the flying object 100 based on the detection result of the detection unit 204, and calculates the deviation q_ｅ between the calculated current attitude q_ｃ and the target attitude q_ｒ (step S102). The deviation q_ｅ includes a quaternion q_{ｅｘ，ｅｙ，ｅｚ} corresponding to the aircraft-fixed coordinate axes X_Ｂ, Y_Ｂ, and Z_Ｂ”),
input the acquired state data and the acquired manipulation data to a model trained using deep reinforcement learning, and control the flight device on the basis of an output result of the model to which the state data and the manipulation data are input (See at least Wada FIG. 13 and Paragraphs 72-92 “The second embodiment differs from the first embodiment described above in that deep reinforcement learning is used to determine the control amounts of the sweep mechanism, twist mechanism, and fold mechanism based on the attitude, speed, etc. of the aircraft 100. The following description will focus on the differences from the first embodiment, and a description of the points in common with the first embodiment will be omitted. In the description of the second embodiment, the same parts as those in the first embodiment will be denoted by the same reference numerals … The model MDL is trained so that, when a state variable s_ｔ is input, it outputs an action value Q(s_ｔ, a_ｔ). The state variable s_ｔ is, for example, the current attitude q_ｃ or the target attitude q_ｒ of the flying object 100 described above, or the deviation therebetween q_ｅ. Furthermore, the state variable s_ｔ may include the velocity of the flying object 100 instead of or in addition to the attitude and deviation. Furthermore, if the detection unit 204 includes an optical fiber sensor that detects strain or a pressure sensor that detects pressure, the state variable s_ｔ may include strain or pressure that can be obtained from these sensors. The state variable s_ｔ, which includes strain and pressure, is an example of "displacement information." The action a_ｔ is, for example, the control amount of the sweep mechanism, the control amount of the twist mechanism, the control amount of the fold mechanism, the rotation speed of the propeller 110, the rudder angle of the elevator, the rudder angle, and the like. That is, the action a_ｔ is the amount of operation of each actuator of the driving unit 210. Furthermore, the behavior a_ｔ may be a proportional gain K_Ｐ of PID control, an integral gain K_Ｉ, a differential gain K_Ｄ, or a correction gain K_ｊ. Furthermore, the behavior a_ｔ may be an index value indicating which of various types of control, such as PID control or hovering control, is to be performed or whether it is not to be performed … The control unit 230 controls the actuators based on the operation amount of each actuator output by the model MDL, thereby causing the flying object 100 to fly.” | Paragraph 40 “The detection unit 204 is, for example, an inertial measurement unit. The inertial measurement unit includes, for example, a three-axis acceleration sensor and a three-axis gyro sensor. The inertial measurement unit outputs the detection values detected by these sensors to the control unit 230 . The values detected by the inertial measurement unit include, for example, acceleration and/or angular velocity in the horizontal, vertical, and depth directions, and velocity (rate) of each axis of pitch, roll, and yaw. The detection unit 204 may further include a radar, a finder, a sonar, a GPS (Global Positioning System) receiver, and the like. Furthermore, the detection unit 204 may further include optical fiber sensors that detect distortions in the vertical stabilizer 120, horizontal stabilizer 130, and morphing wing 140, and pressure sensors that detect pressures acting on these wings.”).

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 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Wada (JP 2021030950 A) (“Wada”) (Translation Attached) in view of Peng (Sim-to-Real Transfer of Robotic Control with Dynamics Randomization) (“Peng”) (Attached).

With respect to claim 2, Wada teaches a model that is a neural network (See at least Wada Paragraph 75 “The model MDL may be realized, for example, by a neural network including multiple convolutional layers and a fully connected layer that integrates the output results of the multiple convolutional layers into one.”).
Wada, however, fails to explicitly disclose that the neural network is trained by domain randomization.
Peng teaches a neural network is trained by domain randomization (See at least Peng Page 4 “During training, rollouts are organized into episodes of a fixed length. At the start of each episode, a random set of dynamics parameters µ are sampled according to ρµ and held fixed for the duration of the episode. The parameters which we randomize include:”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of Wada to include that the neural network is trained by domain randomization as disclosed above, in order to increase efficiency of the model (Peng Page 1 “The effectiveness of our approach is demonstrated on an object pushing task, where a policy trained exclusively in simulation is able to successfully perform the task with a real robot without additional training on the physical system”).

With respect to claim 3, Wada in view of Peng teach that the model is a recurrent neural network including a memory layer (See at least Peng Page 4 “Constructing such a set of parameters necessarily requires some structural assumptions about the dynamics of a system, which may not hold in the real world. Alternatively, SysID can be implicitly embedded into a policy by using a recurrent model π(at|st, zt, g), where the internal memory zt = z(ht) acts as a summary of past states and actions, thereby providing a mechanism with which the policy can use to infer the dynamics of the system. This model can then be trained end-to-end and the representation of the internal memory can be learned without requiring manual identification of a set of dynamics parameters to be inferred at runtime”).

Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Wada (JP 2021030950 A) (“Wada”) (Translation Attached) in view of Wiegman (US 20230083902 A1) (“Wiegman”) in view of Dmitrowsky (US 4253625 A) (“Dmitrowsky”).

With respect to claim 4, Wada teaches that the state data includes at least one of an attribute, position, speed, and angular velocity of the flight device (See at least Wada FIG. 12 and Paragraphs 63-64) and wherein the processing unit controls an attribute of the flight device on the basis of manipulation data (See at least Wada FIG. 13 and Paragraphs 72-92).
Wada, however, fails to explicitly disclose that the flight device includes a jet engine; wherein the manipulation data includes a thrust force and a thrust direction of the propulsion system, and wherein the processing unit controls an attribute of the flight device on the basis of the thrust force and the thrust direction output by the model.
Wiegman teaches that the manipulation data includes a thrust force and a thrust direction of the propulsion system, and wherein the processing unit controls an attribute of the flight device on the basis of the thrust force and the thrust direction output by the model (See at least Wiegman FIG. 4 and Paragraph 35 “With continued reference to FIG. 1 , sensor datum 108 may include an input datum. An “input datum,” for the purpose of this disclosure, is an element of data describing a manipulation of one or more pilot input controls that correspond to a desire to affect an aircraft's trajectory as a function of the movement of one or more flight components and/or actuators … When angle of attack is not an acceptable input to any system disclosed herein, proxies may be used such as pilot controls, remote controls, or sensor levels, such as true airspeed sensors, pitot tubes, pneumatic/hydraulic sensors, and the like. “Roll” for the purposes of this disclosure, refers to an aircraft's position about its longitudinal axis, that is to say that when an aircraft rotates about its axis from its tail to its nose, and one side rolls upward, like in a banking maneuver. “Yaw”, for the purposes of this disclosure, refers to an aircraft's turn angle, when an aircraft rotates about an imaginary vertical axis intersecting the center of the earth and the fuselage of the aircraft. “Throttle”, for the purposes of this disclosure, refers to an aircraft outputting an amount of thrust from a propulsor. Pilot input, when referring to throttle, may refer to a pilot's desire to increase or decrease thrust produced by at least a propulsor.” | Paragraphs 60-70 “Referring now to FIG. 4 , flow diagram of an exemplary method 400 for flight control for managing actuators for an electric aircraft is provided. Method 400, at step 405, includes receiving, by a controller, a sensor datum from at least a sensor. Sensor datum may include any sensor datum as described herein. Controller may include any controller as described herein. In a non-limiting embodiment, sensor datum may include any data captured by any sensor as described in the entirety of this disclosure … With continued reference to FIG. 4 , step 405 may include receiving an input datum. At least pilot control may be communicatively connected to any other component presented in system, the communicative connection may include redundant connections configured to safeguard against single-point failure. Pilot input may indicate a pilot's desire to change the heading or trim of an electric aircraft. Pilot input may indicate a pilot's desire to change an aircraft's pitch, roll, yaw, or throttle. Aircraft trajectory is manipulated by one or more control surfaces and propulsors working alone or in tandem consistent with the entirety of this disclosure, hereinbelow. Pitch, roll, and yaw may be used to describe an aircraft's attitude and/or heading, as they correspond to three separate and distinct axes about which the aircraft may rotate with an applied moment, torque, and/or other force applied to at least a portion of an aircraft. Sensor datum may include a flight datum … Still referring to FIG. 4 , method 400, at step 420, includes generating an actuator allocation command datum as a function of at least the actuator performance model and at least the identification of the defunct actuator. Generating actuator allocation command datum may include generating actuator allocation command as a function of a machine-learning model … Still referring to FIG. 4 , method 400, at step 420, may include outer loop controller generating rate setpoint as a function of sensor datum and the identification of defunct actuator. Outer loop controller may include circuitry, components, processors, transceivers, or a combination thereof configured to receive and/or send electrical signals. Outer loop controller may include a proportional-integral-derivative (PID) controller.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of Wada to include that the manipulation data includes a thrust force and a thrust direction of the propulsion system, and wherein the processing unit controls an attribute of the flight device on the basis of the thrust force and the thrust direction output by the model, as taught by Wiegman as disclosed above, in order to ensure accurate flight control (Wiegman Paragraph 2 “The present invention generally relates to the field of flight control. In particular, the present invention is directed to methods system for flight control for managing actuators for an electric aircraft.”).
Wada in view of Wiegman, however, fails to explicitly disclose that the flight device includes a jet engine.
Dmitrowsky teaches that the flight device includes a jet engine (See at least Dmitrowsky Col. 3 “The upper part of the saddle provides a pylon or support 16 upon which a jet fuel engine 17 is mounted. Each wing section is fitted internally with a fuel tank 18, one being shown in broken line; and suitable means is provided, not shown, for feeding the fuel from the tanks to and for activating the engine”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of Wada in view of Wiegman to include that the flight device includes a jet engine, as taught by Dmitrowsky as disclosed above, in order to ensure sufficient thrust generated by the flight device (Dmitrowsky “The present invention has been designed to avoid this error. It is directed to the provision of a non-flappable winged craft which is adapted to be attached to the body of a man; it is provided with an engine whereby it may be propelled into space and sustained in flight; and it is provided with manually operable controls whereby it may be directionally controlled in flight and caused to ascend and descend at the will of the pilot.”).

With respect to claim 5, Wada in view of Wiegman in view of Dmitrowsky teach that the flight device further includes a morphing wing (See at least Wada Paragraph 14 “The morphing wings 140 are provided on both the left and right sides of the fuselage of the flying object 100.”), wherein the manipulation data further includes a manipulation quantity of the morphing wing, and wherein the processing unit controls an attitude of the flight device on the basis of the thrust force, the thrust direction, and the manipulation quantity of the morphing wing output by the model (See at least Wada Paragraphs 78-79 “The state variable s_ｔ is, for example, the current attitude q_ｃ or the target attitude q_ｒ of the flying object 100 described above, or the deviation therebetween q_ｅ. Furthermore, the state variable s_ｔ may include the velocity of the flying object 100 instead of or in addition to the attitude and deviation. Furthermore, if the detection unit 204 includes an optical fiber sensor that detects strain or a pressure sensor that detects pressure, the state variable s_ｔ may include strain or pressure that can be obtained from these sensors. The state variable s_ｔ, which includes strain and pressure, is an example of "displacement information." The action a_ｔ is, for example, the control amount of the sweep mechanism, the control amount of the twist mechanism, the control amount of the fold mechanism, the rotation speed of the propeller 110, the rudder angle of the elevator, the rudder angle, and the like. That is, the action a_ｔ is the amount of operation of each actuator of the driving unit 210. Furthermore, the behavior a_ｔ may be a proportional gain K_Ｐ of PID control, an integral gain K_Ｉ, a differential gain K_Ｄ, or a correction gain K_ｊ. Furthermore, the behavior a_ｔ may be an index value that indicates which of various types of control, such as PID control or hovering control, is to be performed, or whether or not to be performed.” | Paragraph 40 “The inertial measurement unit includes, for example, a three-axis acceleration sensor and a three-axis gyro sensor. The inertial measurement unit outputs the detection values detected by these sensors to the control unit 230 . The values detected by the inertial measurement unit include, for example, acceleration and/or angular velocity in the horizontal, vertical, and depth directions, and velocity (rate) of each axis of pitch, roll, and yaw. The detection unit 204 may further include a radar, a finder, a sonar, a GPS (Global Positioning System) receiver, and the like. Furthermore, the detection unit 204 may further include optical fiber sensors that detect distortions in the vertical stabilizer 120, horizontal stabilizer 130, and morphing wing 140, and pressure sensors that detect pressures acting on these wings.”) (See at least Wiegman Paragraph 40 | Paragraphs 60-70).

Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM ABDOALATIF ALSOMAIRY whose telephone number is [phone]. The examiner can normally be reached M-F 7:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faris Almatrahi can be reached at 313-446-4821. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IBRAHIM ABDOALATIF ALSOMAIRY/Examiner, Art Unit 3667 /KENNETH J MALKOWSKI/Primary Examiner, Art Unit 3667

1 There is no limiting definition as to what constitutes “state data” however paragraph 41 of the published specification states “At least one or all of the attitude, position, velocity, and angular velocity at current time t is an example of “state data.””
2 There is no limiting definition as to what constitutes “manipulation data” however paragraph 41 of the published specification states “The thrust force and thrust direction of the thrust device 10 at current time t and the shape and direction of the wings 20 at current time t are examples of “manipulation data.””

3 There is no limiting definition as to what constitutes “state data” however paragraph 41 of the published specification states “At least one or all of the attitude, position, velocity, and angular velocity at current time t is an example of “state data.””
4 There is no limiting definition as to what constitutes “manipulation data” however paragraph 41 of the published specification states “The thrust force and thrust direction of the thrust device 10 at current time t and the shape and direction of the wings 20 at current time t are examples of “manipulation data.””

5 There is no limiting definition as to what constitutes “state data” however paragraph 41 of the published specification states “At least one or all of the attitude, position, velocity, and angular velocity at current time t is an example of “state data.””
6 There is no limiting definition as to what constitutes “manipulation data” however paragraph 41 of the published specification states “The thrust force and thrust direction of the thrust device 10 at current time t and the shape and direction of the wings 20 at current time t are examples of “manipulation data.””

Prosecution Timeline

• Nov 18, 2024: Application Filed
• Jan 18, 2026: Non-Final Rejection — §102, §103 (current)