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 Office Action on the merits. Claims 1-20 are currently pending and are addressed below.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 04/07/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
[0006] recites “…generation of set of control instructions…”, which appears to be grammatically incorrect.
[0033] recites “…includes at least one of thermal sensor…”, which appears to be grammatically incorrect.
[0045] recites “…identify a control aerial vehicle from the set of the set of aerial vehicles”, in which the underlined portion appears to be grammatically incorrect.
[0049] recites “The computer program product includes program instructions stored on the one or more computer-readable storage media to perform operations includes…”, which appears to be grammatically incorrect.
[0069] recites “The AI model 202A analyzes complex datasets, making particularly valuable in applications like fire management”, in which the underlined portion appears to be grammatically incorrect.
[0077] recites “…may be less suitable due to, such as remote locations, large areas of fire, and the like”, in which the underlined portion appears to be grammatically incorrect.
[0080] recites “…while few aerial vehicles…”, which appears to be grammatically incorrect.
[0094] recites “…each aerial vehicles…”, in which the underlined portion appears to be grammatically incorrect.
[0101] recites “…controls the flight paths, and altitude settings and adjusts dynamically…”, in which the comma after “paths” should be deleted.
[0103] recites “The fire-bound area 304 data include…”, in which the underlined portion appears to be grammatically incorrect.
[0103] recites “…plurality of aerial vehicle…”, which appears to be grammatically incorrect.
[0106] recites “The features include, but is not limited to…”, in which the underlined portion appears to be grammatically incorrect.
[0123] recites “…the operation the operation…”, which appears to be grammatically incorrect.
[0127] recites “…each aerial vehicle of the set of aerial vehicles 306 includes an acoustic generation module is configured to…”, which appears to be grammatically incorrect.
[0128] recites “…adjusts the frequency, amplitude, or emission direction of the acoustic waves accordingly to extinguishing the fire…”, in which the underlined portion appears to be grammatically incorrect.
[0128] recites “…surround infrastructure…”, in which the underlined portion appears to be a typographical error.
[0131] recites “…the present at the ground station 302”, in which the underlined portion appears to be grammatically incorrect.
[0143] recites “…is ensures…”, which appears to be grammatically incorrect.
[0145] recites “In an embodiment, Based…”, in which the underlined portion appears to be a typographical error.
[0150] recites “In an example, where the control aerial vehicle 308 is deployed to monitor a wildfire”, which appears to be grammatically incorrect.
[0160] recites “…the at least the part…”, which appears to be grammatically incorrect.
[0163] recites “…enhancing the overall the…”, which appears to be grammatically incorrect.
[0169] recites “…wind strength, and direction…”, in which the comma after “strength” should be deleted.
[0171] recites “As the AI model 202A learns continuously refines…”, which appears to be grammatically incorrect.
Appropriate correction is required.
Claim Objections
Claim 16 objected to because of the following informalities:
Claim 16 recites “…from the set of the set of aerial vehicles…”, in which the underlined portion appears to be a typographical error.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because it is nominally directed to a computer-readable storage media and is therefore directed to non-statutory subject matter. The claims broadly cover transient, propagating signals. Since a claim to a "computer-readable storage media" reasonably broadly covers both forms of non-transitory tangible media (e.g. memory, disk, tape) and transient, propagating signals (e.g. signals, carrier waves), it necessarily covers non-statutory subject matter. This is so because transient, propagating signals are not patentable subject matter. See In re Nuijten, 500 F.3d 1346, 1356 (Fed. Cir. 2007).
NOTE: Applicant can amend the claim to cover only statutory embodiments by adding the limitation “non-transitory” (e.g., “…one or more non-transitory computer-readable storage media…”). Such an amendment would not raise the issue of new matter, even when the specification is silent, unless the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-2, 6, 11-13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pogorelskij of RU2744324C1, published 03/05/2021, hereinafter “Pogorelskij”, in view of Brinkschulte of US 20250209898 A1, filed 03/21/2023, hereinafter “Brinkschulte”.
Regarding claim 1, Pogorelskij teaches:
A computer-implemented method, comprising: receiving, by a computer, fire-bound area data associated with a fire within a fire-bound area, wherein the fire-bound area data comprises image data, environment data, geospatial data, and fire magnitude data; (See at least [0024-0025]: “A reconnaissance aircraft, and in some cases, as described above, a reconnaissance UAV, is located near the flight control module and can be free to fly or be attached at a height of up to 100 m to a cable or other device, such as an electrical cable, that provides it with continuous power supply. The reconnaissance aircraft contains equipment installed on it that monitors the terrain and identifies fire sources, determines the nature of the fire and its exact coordinates, as well as the direction of the fire, its strength, and wind direction, and then transmits all data to the fire suppression control module in order to adjust the flight path of the supporting aircraft and UAVs. A reconnaissance aircraft can be implemented on the basis of any known aircraft, including, in the particular case, a UAV, equipped with the required communication equipment, as well as sensors and monitoring equipment, such as video cameras, wind direction and force sensors, pressure sensors, temperature sensors, etc., which are well known to a specialist and do not require special explanations.”)
applying, by the computer, an artificial intelligence (AI) model to the fire-bound area data; (See at least [0022-0023]: “The fire suppression control module ensures interaction with all aircraft and UAVs, and also has the ability to adjust the algorithms for constructing the spatial location of the UAV swarm in real time, taking into account the constantly updated information received from the reconnaissance aircraft about the progress of the fire spread and extinguishment. The construction of algorithms and the implementation of aerodynamic calculations based on them in order to determine the required configuration of a swarm of unmanned aerial vehicles and its implementation, the ability to adjust the constructed algorithms in real time, as well as the processing of the obtained information are realized primarily through the use of convolutional neural networks, namely, artificial intelligence.”)
determining, by the computer, requirement data to (See at least [0030-0031]: “Due to the aerodynamic shape of the body and the presence of an electric motor, as well as a control system, the UAVs are designed with the ability to form a swarm from them, the spatial geometric shape of which and the number of UAVs in a section perpendicular to the axis of symmetry of the geometric figure of the swarm are set by the fire extinguishing control module and transmitted to the UAVs through the control module and the carrier aircraft. P1 - has the form of a cone with a base in the form of an ellipse (in particular a circle) and an axis of symmetry in the form of a straight line, perpendicular to the base, or inclined to it. This type is used to extinguish fires in city buildings, industrial premises, and detached houses. The characteristic parameters of a swarm of type P1 are the dimensions of the axes of the base ellipse, the length and the angle of inclination of the straight axis of symmetry. The top of the cone is directed towards the point of maximum combustion temperature, and the tip of the cone is “pressed” into the fire source. The technical result from the use of this form of swarm is achieved by directing the apex of the cone towards the center of the fire and then striking with carbon dioxide charges in diverging ellipses (circles) towards the periphery of the fire, thereby ensuring maximum efficiency in extinguishing the fire…”. See also [0032-0035] regarding other swarm configurations.)
generating, by the computer, a set of control instructions based on the requirement data, wherein the set of control instructions is associated with an operation of each aerial vehicle of the set of aerial vehicles to (See at least [0012]: “The fire extinguishing control module is designed with the capability of two-way continuous interaction with the reconnaissance aircraft and the carrier aircraft, continuous receipt of information from the reconnaissance aircraft and the carrier aircraft, processing the information received from the reconnaissance aircraft and the carrier aircraft, forming a fire extinguishing algorithm and adjusting the fire extinguishing algorithm depending on the constantly incoming information from the reconnaissance aircraft and the carrier aircraft, transmitting to the carrier aircraft the fire extinguishing algorithm and the configuration of the location of the carrier aircraft and the swarm of unmanned aerial vehicles during fire extinguishing…” & [0022]: “The fire suppression control module ensures interaction with all aircraft and UAVs, and also has the ability to adjust the algorithms for constructing the spatial location of the UAV swarm in real time, taking into account the constantly updated information received from the reconnaissance aircraft about the progress of the fire spread and extinguishment.” See also [0018] regarding controlling the aircraft and UAVs until the fire source is completely eliminated & [0030-0035] regarding the different UAV swarm configurations.)
outputting, by the computer, the set of control instructions associated with each aerial vehicle of the set of aerial vehicles. (See at least [0012]: “…transmitting to the carrier aircraft the fire extinguishing algorithm and the configuration of the location of the carrier aircraft and the swarm of unmanned aerial vehicles during fire extinguishing…The unmanned aerial vehicle (UAV) contains a highly flammable aerodynamically shaped body with an electric motor, a control system, and a charge in the form of solid carbon dioxide, and is designed with the ability to interact with a fire extinguishing control module and carrier aircraft to receive commands for the formation of a swarm of a certain spatial configuration and its modification depending on the type of fire, as well as changing the parameters of the UAV swarm based on the information received.” See also [0018] regarding controlling the aircraft and UAVs until the fire source is completely eliminated & [0030-0035] regarding the different UAV swarm configurations.)
However, Pogorelskij does not explicitly teach that the UAVs acoustically extinguish the fire.
Brinkschulte teaches a drone that uses an acoustic cannon to produce sound waves for fighting a fire (See at least [0247]).
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij’s method with Brinkschulte’s technique of using ultrasound waves to extinguish a fire. Doing so would be obvious since “Extinguishing using an acoustic cannon is particularly sustainable, does not produce any waste during extinguishing, does not require water or chemicals that may be problematic for forest soil, and can be carried out as long as the energy storage of the forest fire detection unit 300 has energy” (See [0247] of Brinkschulte).
Regarding claim 2, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 1 as discussed above.
Pogorelskij additionally teaches:
further comprising: controlling, by the computer, the operation of each aerial vehicle of the set of aerial vehicles, wherein the operation of each aerial vehicle of the set of aerial vehicles is controlled based on the set of control instructions; and (See at least [0030-0031]: “Due to the aerodynamic shape of the body and the presence of an electric motor, as well as a control system, the UAVs are designed with the ability to form a swarm from them, the spatial geometric shape of which and the number of UAVs in a section perpendicular to the axis of symmetry of the geometric figure of the swarm are set by the fire extinguishing control module and transmitted to the UAVs through the control module and the carrier aircraft. P1 - has the form of a cone with a base in the form of an ellipse (in particular a circle) and an axis of symmetry in the form of a straight line, perpendicular to the base, or inclined to it. This type is used to extinguish fires in city buildings, industrial premises, and detached houses. The characteristic parameters of a swarm of type P1 are the dimensions of the axes of the base ellipse, the length and the angle of inclination of the straight axis of symmetry. The top of the cone is directed towards the point of maximum combustion temperature, and the tip of the cone is “pressed” into the fire source. The technical result from the use of this form of swarm is achieved by directing the apex of the cone towards the center of the fire and then striking with carbon dioxide charges in diverging ellipses (circles) towards the periphery of the fire, thereby ensuring maximum efficiency in extinguishing the fire…”. See also [0032-0035] regarding other swarm configurations.)
Brinkschulte additionally teaches:
generating, by the computer, an acoustic wave to acoustically extinguish the fire, wherein the acoustic wave is generated based on the controlling of the operation of each aerial vehicle of the set of aerial vehicles. (See at least [0230]: “The network server NS has a first control in the form of a software program on a memory, by means of which the position of a fire source can be determined” & [0247-0249]: “Alternatively, the forest fire detection unit 300 can have an acoustic cannon as an extinguishing agent 313, which fights a fire by means of the air pressure fluctuations caused by the sound pressure. The sound waves with a frequency of 30 to 60 Hz trigger mechanical vibrations in the area around the fire, which affect both the burning material and the oxygen supply…To detect a forest fire, after the first detection and locating of the fire source by a stationary first forest fire detection sensor ED (see FIG. 1) the second forest fire detection sensor 330 and thus the forest fire detection unit 300 is moved to the fire source for the purpose of the second detection, the second locating and the forest fire detection. To this end a route is determined on the network server NS. The route includes the current position of the forest fire detection unit 300 as part of the forest fire detection station 200 as well as the position of the target area, in particular the position of the fire source…The forest fire detection unit 300 then moves in a motorized manner along the calculated route to the target area of the fire source…In the target area, the second detection and the second locating of the fire source as well as the detection or extinguishing of the fire source takes place by means of the forest fire detection unit 300 by ejecting the extinguishing agent 313. The second control of the forest fire detection unit 300 generates and/or executes control commands for the second detection of a fire source, for second locating a fire source, for moving the forest fire detection unit 300, for navigating the forest fire detection unit 300, for steering the forest fire detection unit 300 and/or for ejecting extinguishing agents 313.”)
Regarding claim 6, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 1 as discussed above.
Pogorelskij additionally teaches:
wherein the one or more operating parameters associated with each aerial vehicle of the set of aerial vehicles comprises at least one of a geographical location for a deployment of each aerial vehicle of the set of aerial vehicles, or an acoustic frequency for the operation of each aerial vehicle of the set of aerial vehicles. (See at least [0018]: “…transmit to the aircraft the fire extinguishing algorithm, the trajectory of the aircraft movement and the configuration of the location of the swarm of UAVs…” & [0038]: “The fire extinguishing control algorithm also provides for the instantaneous self-destruction of the UAV at a certain altitude, including when it deviates from the designated flight path. Self-destruction means detonation of the unmanned aerial vehicle with carbon dioxide briquettes at a given altitude and in given coordinates.”. See also [0031-0035] regarding the different swarm configurations.)
Regarding claim 11, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 1 as discussed above.
Pogorelskij additionally teaches:
wherein the environment data comprises at least one of temperature data, wind speed data, wind direction data, or humidity data. (See at least [0026]: “In one embodiment, the fire sensor poles include sensors configured to gather wind speed and direction data, as well as temperature data.”)
Regarding claim 12, Pogorelskij teaches:
A computer system, comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media, the program instructions executable by the processor set to cause the processor set to: (See at least [0020-0021]: “The fire extinguishing control module is a station designed with the ability to receive and transmit signals via satellite guidance channels and is located on the ground, on a vehicle, in a helicopter, on board a ship, on an aircraft carrier or other vehicle. The control module is also designed with the capability of servicing and managing groups of aircraft, including aircraft and UAVs, modeling the development of various types of fires, developing a fire extinguishing scenario and monitoring its implementation, monitoring and managing airspace, and providing information support. The control module can be implemented using any known software and hardware, such as a personal computer or a more powerful workstation, including an industrial design, with the appropriate software to perform its functions and implement the claimed method, as well as equipped with the required means of communication with all aircraft and UAVs.”)
receive fire-bound area data associated with a fire within a fire-bound area, wherein the fire-bound area data comprises image data, environment data, geospatial data, and fire magnitude data; (See at least [0024-0025]: “A reconnaissance aircraft, and in some cases, as described above, a reconnaissance UAV, is located near the flight control module and can be free to fly or be attached at a height of up to 100 m to a cable or other device, such as an electrical cable, that provides it with continuous power supply. The reconnaissance aircraft contains equipment installed on it that monitors the terrain and identifies fire sources, determines the nature of the fire and its exact coordinates, as well as the direction of the fire, its strength, and wind direction, and then transmits all data to the fire suppression control module in order to adjust the flight path of the supporting aircraft and UAVs. A reconnaissance aircraft can be implemented on the basis of any known aircraft, including, in the particular case, a UAV, equipped with the required communication equipment, as well as sensors and monitoring equipment, such as video cameras, wind direction and force sensors, pressure sensors, temperature sensors, etc., which are well known to a specialist and do not require special explanations.”)
apply an Al model to the fire-bound area data; (See at least [0022-0023]: “The fire suppression control module ensures interaction with all aircraft and UAVs, and also has the ability to adjust the algorithms for constructing the spatial location of the UAV swarm in real time, taking into account the constantly updated information received from the reconnaissance aircraft about the progress of the fire spread and extinguishment. The construction of algorithms and the implementation of aerodynamic calculations based on them in order to determine the required configuration of a swarm of unmanned aerial vehicles and its implementation, the ability to adjust the constructed algorithms in real time, as well as the processing of the obtained information are realized primarily through the use of convolutional neural networks, namely, artificial intelligence.”)
determine requirement data to (See at least [0030-0031]: “Due to the aerodynamic shape of the body and the presence of an electric motor, as well as a control system, the UAVs are designed with the ability to form a swarm from them, the spatial geometric shape of which and the number of UAVs in a section perpendicular to the axis of symmetry of the geometric figure of the swarm are set by the fire extinguishing control module and transmitted to the UAVs through the control module and the carrier aircraft. P1 - has the form of a cone with a base in the form of an ellipse (in particular a circle) and an axis of symmetry in the form of a straight line, perpendicular to the base, or inclined to it. This type is used to extinguish fires in city buildings, industrial premises, and detached houses. The characteristic parameters of a swarm of type P1 are the dimensions of the axes of the base ellipse, the length and the angle of inclination of the straight axis of symmetry. The top of the cone is directed towards the point of maximum combustion temperature, and the tip of the cone is “pressed” into the fire source. The technical result from the use of this form of swarm is achieved by directing the apex of the cone towards the center of the fire and then striking with carbon dioxide charges in diverging ellipses (circles) towards the periphery of the fire, thereby ensuring maximum efficiency in extinguishing the fire…”. See also [0032-0035] regarding other swarm configurations.)
generate a set of control instructions based on the requirement data, wherein the set of control instructions is associated with an operation of each aerial vehicle of the set of aerial vehicles to (See at least [0012]: “The fire extinguishing control module is designed with the capability of two-way continuous interaction with the reconnaissance aircraft and the carrier aircraft, continuous receipt of information from the reconnaissance aircraft and the carrier aircraft, processing the information received from the reconnaissance aircraft and the carrier aircraft, forming a fire extinguishing algorithm and adjusting the fire extinguishing algorithm depending on the constantly incoming information from the reconnaissance aircraft and the carrier aircraft, transmitting to the carrier aircraft the fire extinguishing algorithm and the configuration of the location of the carrier aircraft and the swarm of unmanned aerial vehicles during fire extinguishing…” & [0022]: “The fire suppression control module ensures interaction with all aircraft and UAVs, and also has the ability to adjust the algorithms for constructing the spatial location of the UAV swarm in real time, taking into account the constantly updated information received from the reconnaissance aircraft about the progress of the fire spread and extinguishment.” See also [0018] regarding controlling the aircraft and UAVs until the fire source is completely eliminated & [0030-0035] regarding the different UAV swarm configurations.)
output the set of control instructions associated with each aerial vehicle of the set of aerial vehicles. (See at least [0012]: “…transmitting to the carrier aircraft the fire extinguishing algorithm and the configuration of the location of the carrier aircraft and the swarm of unmanned aerial vehicles during fire extinguishing…The unmanned aerial vehicle (UAV) contains a highly flammable aerodynamically shaped body with an electric motor, a control system, and a charge in the form of solid carbon dioxide, and is designed with the ability to interact with a fire extinguishing control module and carrier aircraft to receive commands for the formation of a swarm of a certain spatial configuration and its modification depending on the type of fire, as well as changing the parameters of the UAV swarm based on the information received.” See also [0018] regarding controlling the aircraft and UAVs until the fire source is completely eliminated & [0030-0035] regarding the different UAV swarm configurations.)
However, Pogorelskij does not explicitly teach that the UAVs acoustically extinguish the fire.
Brinkschulte teaches a drone that uses an acoustic cannon to produce sound waves for fighting a fire (See at least [0247]).
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij’s method with Brinkschulte’s technique of using ultrasound waves to extinguish a fire. Doing so would be obvious since “Extinguishing using an acoustic cannon is particularly sustainable, does not produce any waste during extinguishing, does not require water or chemicals that may be problematic for forest soil, and can be carried out as long as the energy storage of the forest fire detection unit 300 has energy” (See [0247] of Brinkschulte).
Regarding claim 13, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 12 as discussed above.
Pogorelskij additionally teaches:
wherein the program instructions further cause the processor set to: control the operation of each aerial vehicle of the set of aerial vehicles, wherein the operation of the set of aerial vehicles is controlled based on the set of control instructions; and (See at least [0030-0031]: “Due to the aerodynamic shape of the body and the presence of an electric motor, as well as a control system, the UAVs are designed with the ability to form a swarm from them, the spatial geometric shape of which and the number of UAVs in a section perpendicular to the axis of symmetry of the geometric figure of the swarm are set by the fire extinguishing control module and transmitted to the UAVs through the control module and the carrier aircraft. P1 - has the form of a cone with a base in the form of an ellipse (in particular a circle) and an axis of symmetry in the form of a straight line, perpendicular to the base, or inclined to it. This type is used to extinguish fires in city buildings, industrial premises, and detached houses. The characteristic parameters of a swarm of type P1 are the dimensions of the axes of the base ellipse, the length and the angle of inclination of the straight axis of symmetry. The top of the cone is directed towards the point of maximum combustion temperature, and the tip of the cone is “pressed” into the fire source. The technical result from the use of this form of swarm is achieved by directing the apex of the cone towards the center of the fire and then striking with carbon dioxide charges in diverging ellipses (circles) towards the periphery of the fire, thereby ensuring maximum efficiency in extinguishing the fire…”. See also [0032-0035] regarding other swarm configurations.)
Brinkschulte additionally teaches:
generate an acoustic wave to acoustically extinguish the fire, wherein the acoustic wave is generated based on the control of the operation of each aerial vehicle of the set of aerial vehicles. (See at least [0230]: “The network server NS has a first control in the form of a software program on a memory, by means of which the position of a fire source can be determined” & [0247-0249]: “Alternatively, the forest fire detection unit 300 can have an acoustic cannon as an extinguishing agent 313, which fights a fire by means of the air pressure fluctuations caused by the sound pressure. The sound waves with a frequency of 30 to 60 Hz trigger mechanical vibrations in the area around the fire, which affect both the burning material and the oxygen supply…To detect a forest fire, after the first detection and locating of the fire source by a stationary first forest fire detection sensor ED (see FIG. 1) the second forest fire detection sensor 330 and thus the forest fire detection unit 300 is moved to the fire source for the purpose of the second detection, the second locating and the forest fire detection. To this end a route is determined on the network server NS. The route includes the current position of the forest fire detection unit 300 as part of the forest fire detection station 200 as well as the position of the target area, in particular the position of the fire source…The forest fire detection unit 300 then moves in a motorized manner along the calculated route to the target area of the fire source…In the target area, the second detection and the second locating of the fire source as well as the detection or extinguishing of the fire source takes place by means of the forest fire detection unit 300 by ejecting the extinguishing agent 313. The second control of the forest fire detection unit 300 generates and/or executes control commands for the second detection of a fire source, for second locating a fire source, for moving the forest fire detection unit 300, for navigating the forest fire detection unit 300, for steering the forest fire detection unit 300 and/or for ejecting extinguishing agents 313.”)
Regarding claim 20, Pogorelskij teaches:
A computer-program product for generation of a set of control instructions to acoustically extinguish a fire, the computer-program product comprising: one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to perform operations comprising: (See at least [0020-0021]: “The fire extinguishing control module is a station designed with the ability to receive and transmit signals via satellite guidance channels and is located on the ground, on a vehicle, in a helicopter, on board a ship, on an aircraft carrier or other vehicle. The control module is also designed with the capability of servicing and managing groups of aircraft, including aircraft and UAVs, modeling the development of various types of fires, developing a fire extinguishing scenario and monitoring its implementation, monitoring and managing airspace, and providing information support. The control module can be implemented using any known software and hardware, such as a personal computer or a more powerful workstation, including an industrial design, with the appropriate software to perform its functions and implement the claimed method, as well as equipped with the required means of communication with all aircraft and UAVs.”)
receiving fire-bound area data associated with the fire within a fire-bound area, wherein the fire-bound area data comprises image data, environment data, geospatial data, and fire magnitude data; (See at least [0024-0025]: “A reconnaissance aircraft, and in some cases, as described above, a reconnaissance UAV, is located near the flight control module and can be free to fly or be attached at a height of up to 100 m to a cable or other device, such as an electrical cable, that provides it with continuous power supply. The reconnaissance aircraft contains equipment installed on it that monitors the terrain and identifies fire sources, determines the nature of the fire and its exact coordinates, as well as the direction of the fire, its strength, and wind direction, and then transmits all data to the fire suppression control module in order to adjust the flight path of the supporting aircraft and UAVs. A reconnaissance aircraft can be implemented on the basis of any known aircraft, including, in the particular case, a UAV, equipped with the required communication equipment, as well as sensors and monitoring equipment, such as video cameras, wind direction and force sensors, pressure sensors, temperature sensors, etc., which are well known to a specialist and do not require special explanations.”)
applying an Al model to the fire-bound area data; (See at least [0022-0023]: “The fire suppression control module ensures interaction with all aircraft and UAVs, and also has the ability to adjust the algorithms for constructing the spatial location of the UAV swarm in real time, taking into account the constantly updated information received from the reconnaissance aircraft about the progress of the fire spread and extinguishment. The construction of algorithms and the implementation of aerodynamic calculations based on them in order to determine the required configuration of a swarm of unmanned aerial vehicles and its implementation, the ability to adjust the constructed algorithms in real time, as well as the processing of the obtained information are realized primarily through the use of convolutional neural networks, namely, artificial intelligence.”)
determining requirement data to (See at least [0030-0031]: “Due to the aerodynamic shape of the body and the presence of an electric motor, as well as a control system, the UAVs are designed with the ability to form a swarm from them, the spatial geometric shape of which and the number of UAVs in a section perpendicular to the axis of symmetry of the geometric figure of the swarm are set by the fire extinguishing control module and transmitted to the UAVs through the control module and the carrier aircraft. P1 - has the form of a cone with a base in the form of an ellipse (in particular a circle) and an axis of symmetry in the form of a straight line, perpendicular to the base, or inclined to it. This type is used to extinguish fires in city buildings, industrial premises, and detached houses. The characteristic parameters of a swarm of type P1 are the dimensions of the axes of the base ellipse, the length and the angle of inclination of the straight axis of symmetry. The top of the cone is directed towards the point of maximum combustion temperature, and the tip of the cone is “pressed” into the fire source. The technical result from the use of this form of swarm is achieved by directing the apex of the cone towards the center of the fire and then striking with carbon dioxide charges in diverging ellipses (circles) towards the periphery of the fire, thereby ensuring maximum efficiency in extinguishing the fire…”. See also [0032-0035] regarding other swarm configurations.)
generating the set of control instructions based on the requirement data, wherein the set of control instructions is associated with an operation of each aerial vehicle of the set of aerial vehicles to (See at least [0012]: “The fire extinguishing control module is designed with the capability of two-way continuous interaction with the reconnaissance aircraft and the carrier aircraft, continuous receipt of information from the reconnaissance aircraft and the carrier aircraft, processing the information received from the reconnaissance aircraft and the carrier aircraft, forming a fire extinguishing algorithm and adjusting the fire extinguishing algorithm depending on the constantly incoming information from the reconnaissance aircraft and the carrier aircraft, transmitting to the carrier aircraft the fire extinguishing algorithm and the configuration of the location of the carrier aircraft and the swarm of unmanned aerial vehicles during fire extinguishing…” & [0022]: “The fire suppression control module ensures interaction with all aircraft and UAVs, and also has the ability to adjust the algorithms for constructing the spatial location of the UAV swarm in real time, taking into account the constantly updated information received from the reconnaissance aircraft about the progress of the fire spread and extinguishment.” See also [0018] regarding controlling the aircraft and UAVs until the fire source is completely eliminated & [0030-0035] regarding the different UAV swarm configurations.)
outputting the set of control instructions associated with each aerial vehicle of the set of aerial vehicles. (See at least [0012]: “…transmitting to the carrier aircraft the fire extinguishing algorithm and the configuration of the location of the carrier aircraft and the swarm of unmanned aerial vehicles during fire extinguishing…The unmanned aerial vehicle (UAV) contains a highly flammable aerodynamically shaped body with an electric motor, a control system, and a charge in the form of solid carbon dioxide, and is designed with the ability to interact with a fire extinguishing control module and carrier aircraft to receive commands for the formation of a swarm of a certain spatial configuration and its modification depending on the type of fire, as well as changing the parameters of the UAV swarm based on the information received.” See also [0018] regarding controlling the aircraft and UAVs until the fire source is completely eliminated & [0030-0035] regarding the different UAV swarm configurations.)
However, Pogorelskij does not explicitly teach that the UAVs acoustically extinguish the fire.
Brinkschulte teaches a drone that uses an acoustic cannon to produce sound waves for fighting a fire (See at least [0247]).
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij’s method with Brinkschulte’s technique of using ultrasound waves to extinguish a fire. Doing so would be obvious since “Extinguishing using an acoustic cannon is particularly sustainable, does not produce any waste during extinguishing, does not require water or chemicals that may be problematic for forest soil, and can be carried out as long as the energy storage of the forest fire detection unit 300 has energy” (See [0247] of Brinkschulte).
Claim(s) 3-4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pogorelskij in view of Brinkschulte and further in view of Beecham of US 20190176987 A1, published 06/13/2019, hereinafter “Beecham”.
Regarding claim 3, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 1 as discussed above.
Pogorelskij additionally teaches:
further comprising: receiving, by the computer, location data associated with the fire within the fire-bound area; (See at least [0012]: “The reconnaissance aircraft is designed with the capability of two-way continuous interaction with the fire extinguishing control module, detecting the source of fire, determining the type of fire, its coordinates and parameters, the direction and speed of fire movement, the strength and direction of the wind, and transmitting data to the fire extinguishing control module.”)
generating, by the computer, control data for at least one aerial vehicle of the plurality of aerial vehicles, wherein the control data is generated based on the location data; and (See at least [0012]: “The fire extinguishing control module is designed with the capability of two-way continuous interaction with the reconnaissance aircraft and the carrier aircraft, continuous receipt of information from the reconnaissance aircraft and the carrier aircraft, processing the information received from the reconnaissance aircraft and the carrier aircraft, forming a fire extinguishing algorithm and adjusting the fire extinguishing algorithm depending on the constantly incoming information from the reconnaissance aircraft and the carrier aircraft, transmitting to the carrier aircraft the fire extinguishing algorithm and the configuration of the location of the carrier aircraft and the swarm of unmanned aerial vehicles during fire extinguishing, receiving information from the reconnaissance aircraft and the carrier aircraft about the progress of fire extinguishing and about new sources of fire, changing the parameters of the swarm of unmanned aerial vehicles based on the information received.”)
controlling, by the computer, the at least one aerial vehicle based on the control data(See at least [0012]: “The unmanned aerial vehicle (UAV) contains a highly flammable aerodynamically shaped body with an electric motor, a control system, and a charge in the form of solid carbon dioxide, and is designed with the ability to interact with a fire extinguishing control module and carrier aircraft to receive commands for the formation of a swarm of a certain spatial configuration and its modification depending on the type of fire, as well as changing the parameters of the UAV swarm based on the information received.”)
Pogorelskij and Brinkschulte in combination do not explicitly teach:
…wherein the at least one aerial vehicle is controlled to collect the fire-bound area data.
Beecham teaches:
…wherein the at least one aerial vehicle is controlled to collect the fire-bound area data. (See at least [0005]: “…(a) collecting, via one or more sensors, a first dataset from a first location; (b) analyzing, via an artificial intelligence enabled computer command system, the first dataset from the first location at a first time; (c) identifying, via the artificial intelligence enabled computer command system, the first location as having a likely wildfire when the analysis at the first time sufficiently via an algorithm matches data in a data repository representative of an active wildfire; (d) dispatching, via the artificial intelligence enabled computer command system, a fire suppression drone having onboard sensors and a reservoir configured to release a fire suppression substance to the first location at a second time; (e) releasing, via the artificial intelligence enabled computer command system, the fire suppression substance on the likely wildfire; (e) collecting, via the onboard sensors, a second dataset from the first location…”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij and Brinkschulte’s method with Beecham’s technique of controlling the aerial vehicle to collect fire-bound area data. Doing so would be obvious “for predicting wildfire spreading locations, as well as a method for suppressing spot fires and protecting homes downwind from a wildfire” (See Abstract of Beecham).
Regarding claim 4, Pogorelskij, Brinkschulte, and Beecham in combination teach all the limitations of claim 3 as discussed above.
Pogorelskij additionally teaches:
wherein the at least one aerial vehicle of the plurality of aerial vehicles comprises a plurality of sensors for collecting the fire-bound area data, and wherein the plurality of sensors comprises at least one of a thermal sensor, a gyroscope, an image sensor, or an anemometer. (See at least [0025]: “A reconnaissance aircraft can be implemented on the basis of any known aircraft, including, in the particular case, a UAV, equipped with the required communication equipment, as well as sensors and monitoring equipment, such as video cameras, wind direction and force sensors, pressure sensors, temperature sensors, etc., which are well known to a specialist and do not require special explanations.”)
Regarding claim 14, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 12 as discussed above.
Pogorelskij additionally teaches:
wherein the program instructions further cause the processor set to: receive location data associated with the fire within the fire-bound area; (See at least [0012]: “The reconnaissance aircraft is designed with the capability of two-way continuous interaction with the fire extinguishing control module, detecting the source of fire, determining the type of fire, its coordinates and parameters, the direction and speed of fire movement, the strength and direction of the wind, and transmitting data to the fire extinguishing control module.”)
generate control data for at least one aerial vehicle of the plurality of aerial vehicles, wherein the control data is generated based on the location data; and (See at least [0012]: “The fire extinguishing control module is designed with the capability of two-way continuous interaction with the reconnaissance aircraft and the carrier aircraft, continuous receipt of information from the reconnaissance aircraft and the carrier aircraft, processing the information received from the reconnaissance aircraft and the carrier aircraft, forming a fire extinguishing algorithm and adjusting the fire extinguishing algorithm depending on the constantly incoming information from the reconnaissance aircraft and the carrier aircraft, transmitting to the carrier aircraft the fire extinguishing algorithm and the configuration of the location of the carrier aircraft and the swarm of unmanned aerial vehicles during fire extinguishing, receiving information from the reconnaissance aircraft and the carrier aircraft about the progress of fire extinguishing and about new sources of fire, changing the parameters of the swarm of unmanned aerial vehicles based on the information received.”)
control the at least one aerial vehicle based on the control data(See at least [0012]: “The unmanned aerial vehicle (UAV) contains a highly flammable aerodynamically shaped body with an electric motor, a control system, and a charge in the form of solid carbon dioxide, and is designed with the ability to interact with a fire extinguishing control module and carrier aircraft to receive commands for the formation of a swarm of a certain spatial configuration and its modification depending on the type of fire, as well as changing the parameters of the UAV swarm based on the information received.”)
Pogorelskij and Brinkschulte in combination do not explicitly teach:
…wherein the at least one aerial vehicle is controlled to collect the fire-bound area data.
Beecham teaches:
…wherein the at least one aerial vehicle is controlled to collect the fire-bound area data. (See at least [0005]: “…(a) collecting, via one or more sensors, a first dataset from a first location; (b) analyzing, via an artificial intelligence enabled computer command system, the first dataset from the first location at a first time; (c) identifying, via the artificial intelligence enabled computer command system, the first location as having a likely wildfire when the analysis at the first time sufficiently via an algorithm matches data in a data repository representative of an active wildfire; (d) dispatching, via the artificial intelligence enabled computer command system, a fire suppression drone having onboard sensors and a reservoir configured to release a fire suppression substance to the first location at a second time; (e) releasing, via the artificial intelligence enabled computer command system, the fire suppression substance on the likely wildfire; (e) collecting, via the onboard sensors, a second dataset from the first location…”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij and Brinkschulte’s method with Beecham’s technique of controlling the aerial vehicle to collect fire-bound area data. Doing so would be obvious “for predicting wildfire spreading locations, as well as a method for suppressing spot fires and protecting homes downwind from a wildfire” (See Abstract of Beecham).
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pogorelskij in view of Brinkschulte and further in view of Arksey of US 20230177968 A1, published 06/08/2023, hereinafter “Arksey”.
Regarding claim 5, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 1 as discussed above.
Pogorelskij and Brinkschulte in combination do not explicitly teach:
further comprising: obtaining, by the computer, aerial vehicle data associated with each aerial vehicle of the plurality of aerial vehicles, wherein the aerial vehicle data comprises one or more operating characteristics of each aerial vehicle of the plurality of aerial vehicles;
applying, by the computer, the Al model to the aerial vehicle data; and
determining, by the computer, the requirement data based on the application of the Al model to the aerial vehicle data.
Arksey teaches:
further comprising: obtaining, by the computer, aerial vehicle data associated with each aerial vehicle of the plurality of aerial vehicles, wherein the aerial vehicle data comprises one or more operating characteristics of each aerial vehicle of the plurality of aerial vehicles; (See at least [0044-0045]: “Drones may be put into resilient swarm configurations based on the types of payload that they have. Each drone has a capability list since some can be multifunctional, but this allows different drones to perform different roles…Each drone type is categorized in the system by its basic characteristics including power, size, and the specific payload it may carry. As new types of payloads are created, different drone types are marked in a database with different characteristics in an extensible schema such as a YAML document or equivalent.”)
applying, by the computer, the Al model to the aerial vehicle data; and determining, by the computer, the requirement data based on the application of the Al model to the aerial vehicle data. (See at least [0076]: “When a resilient swarm is created, the Initialization software may have configurations loaded as part of the resilient swarm mission planning performed by array planning module 622. The array planning module 622 may take input from the overall mission plan for all the drones in the system and will define the number and type of sensors or other systems that are required and generate a list of individual drones that may be required. The array configure and optimize module 626 will then take available drones and create arrays based on the fault tolerance and parallelism requirements from the planning system. These systems may then go through a detailed path planning process using path planning module 628 that provides additional data for resilient swarms and what specific paths drones may take that are exceptions to the default rules. The resilient swarm configurators 620 may dynamically change a resilient swarm configuration while drones are in flight and send updates to the resilient swarm piloting system 630 for reconfiguration in flight,” [0184]: “For example, machine learning can be applied by taking similar missions and seeing what actions actually work” & [0188]: “Additionally, with every mission run there is an opportunity to improve the system by automatic learning from previous missions. Everything from RF models to path planning can have a continuous feedback loop between the actual execution in the physical world and the simulated world.” See also [0087] regarding determining the optimal number and types of drones based on mission requirements & [0131] regarding using AI machine learning and training to improve path planning, piloting, and processing of data.)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij and Brinkschulte’s method with Arksey’s technique of obtaining, by the computer, aerial vehicle data associated with each aerial vehicle of the plurality of aerial vehicles, wherein the aerial vehicle data comprises one or more operating characteristics of each aerial vehicle of the plurality of aerial vehicles, applying, by the computer, the Al model to the aerial vehicle data, and determining, by the computer, the requirement data based on the application of the Al model to the aerial vehicle data. Doing so would be obvious to “allows different drones to perform different roles”, where “the advantage is that it makes each drone lighter and smaller which is key to safety” (See [0044] of Arksey).
Regarding claim 15, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 12 as discussed above.
Pogorelskij and Brinkschulte in combination do not explicitly teach:
wherein the program instructions further cause the processor set to: obtain aerial vehicle data associated with each aerial vehicle of the plurality of aerial vehicles, wherein the aerial vehicle data comprises one or more operating characteristics of each aerial vehicle of the plurality of aerial vehicles;
apply the Al model to the aerial vehicle data; and
determine the requirement data based on the application of the Al model to the aerial vehicle data.
Arksey teaches:
wherein the program instructions further cause the processor set to: obtain aerial vehicle data associated with each aerial vehicle of the plurality of aerial vehicles, wherein the aerial vehicle data comprises one or more operating characteristics of each aerial vehicle of the plurality of aerial vehicles; (See at least [0044-0045]: “Drones may be put into resilient swarm configurations based on the types of payload that they have. Each drone has a capability list since some can be multifunctional, but this allows different drones to perform different roles…Each drone type is categorized in the system by its basic characteristics including power, size, and the specific payload it may carry. As new types of payloads are created, different drone types are marked in a database with different characteristics in an extensible schema such as a YAML document or equivalent.”)
apply the Al model to the aerial vehicle data; and determine the requirement data based on the application of the Al model to the aerial vehicle data. (See at least [0076]: “When a resilient swarm is created, the Initialization software may have configurations loaded as part of the resilient swarm mission planning performed by array planning module 622. The array planning module 622 may take input from the overall mission plan for all the drones in the system and will define the number and type of sensors or other systems that are required and generate a list of individual drones that may be required. The array configure and optimize module 626 will then take available drones and create arrays based on the fault tolerance and parallelism requirements from the planning system. These systems may then go through a detailed path planning process using path planning module 628 that provides additional data for resilient swarms and what specific paths drones may take that are exceptions to the default rules. The resilient swarm configurators 620 may dynamically change a resilient swarm configuration while drones are in flight and send updates to the resilient swarm piloting system 630 for reconfiguration in flight,” [0184]: “For example, machine learning can be applied by taking similar missions and seeing what actions actually work” & [0188]: “Additionally, with every mission run there is an opportunity to improve the system by automatic learning from previous missions. Everything from RF models to path planning can have a continuous feedback loop between the actual execution in the physical world and the simulated world.” See also [0087] regarding determining the optimal number and types of drones based on mission requirements & [0131] regarding using AI machine learning and training to improve path planning, piloting, and processing of data.)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij and Brinkschulte’s method with Arksey’s technique of obtaining, by the computer, aerial vehicle data associated with each aerial vehicle of the plurality of aerial vehicles, wherein the aerial vehicle data comprises one or more operating characteristics of each aerial vehicle of the plurality of aerial vehicles, applying, by the computer, the Al model to the aerial vehicle data, and determining, by the computer, the requirement data based on the application of the Al model to the aerial vehicle data. Doing so would be obvious to “allows different drones to perform different roles”, where “the advantage is that it makes each drone lighter and smaller which is key to safety” (See [0044] of Arksey).
Claim(s) 7-9 and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pogorelskij in view of Brinkschulte and further in view of Zhang of CN111209294B, published 10/24/2023, hereinafter “Zhang”.
Regarding claim 7, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 1 as discussed above.
Pogorelskij additionally teaches:
receiving, by the computer, operation data associated with the operation of each aerial vehicle of the set of aerial vehicles, wherein the operation data is received from the control aerial vehicle; (See at least [0012]: “…receiving information from the reconnaissance aircraft and the carrier aircraft about the progress of fire extinguishing and about new sources of fire…The carrier aircraft is designed with the capability of two-way continuous interaction with the fire extinguishing control module and the unmanned aerial vehicle (UAV), sending information from the fire site to the fire extinguishing control module and receiving a fire extinguishing algorithm from it…” & [0027]: “If necessary, for example, to increase the speed of receiving information about a fire and current external conditions, the carrier aircraft can also be equipped with the required sensors and monitoring tools, such as video cameras, wind direction and force sensors, pressure sensors, temperature sensors, etc., which are well known to a specialist and do not require special explanations.”)
applying, by the computer, the Al model to the operation data; updating, by the computer, the requirement data based on the application of the Al model to the operation data; (See at least [0012]: “The fire extinguishing control module is designed with the capability of two-way continuous interaction with the reconnaissance aircraft and the carrier aircraft, continuous receipt of information from the reconnaissance aircraft and the carrier aircraft, processing the information received from the reconnaissance aircraft and the carrier aircraft, forming a fire extinguishing algorithm and adjusting the fire extinguishing algorithm depending on the constantly incoming information from the reconnaissance aircraft and the carrier aircraft, transmitting to the carrier aircraft the fire extinguishing algorithm and the configuration of the location of the carrier aircraft and the swarm of unmanned aerial vehicles during fire extinguishing, receiving information from the reconnaissance aircraft and the carrier aircraft about the progress of fire extinguishing and about new sources of fire, changing the parameters of the swarm of unmanned aerial vehicles based on the information received.”)
updating, by the computer, the set of control instructions based on the updated requirement data; and transmitting, by the computer, the updated set of control instructions to the control aerial vehicle. (See at least [0012]: “The unmanned aerial vehicle (UAV) contains a highly flammable aerodynamically shaped body with an electric motor, a control system, and a charge in the form of solid carbon dioxide, and is designed with the ability to interact with a fire extinguishing control module and carrier aircraft to receive commands for the formation of a swarm of a certain spatial configuration and its modification depending on the type of fire, as well as changing the parameters of the UAV swarm based on the information received” & [0018]: “…form a fire extinguishing algorithm, the trajectory of the aircraft movement, adjust the fire extinguishing algorithm depending on the constantly incoming information about the fire, transmit to the aircraft the fire extinguishing algorithm, the trajectory of the aircraft movement and the configuration of the location of the swarm of UAVs, receive information on the progress of fire extinguishing and new fire sources, change the parameters of the swarm of UAVs on this basis, monitor and control the airspace, monitor the fire extinguishing process, control the aircraft and UAVs until the fire source is completely eliminated.” See also [0040] regarding the carrier aircraft receiving coordinates of the fire source from the reconnaissance aircraft then flying toward the reconnaissance aircraft and monitoring the area until the fire is extinguished.)
Pogorelskij and Brinkschulte in combination do not explicitly teach:
further comprising: identifying, by the computer, a control aerial vehicle from the set of aerial vehicles, wherein each aerial vehicle of the set of aerial vehicles selects the control aerial vehicle;
Zhang teaches:
further comprising: identifying, by the computer, a control aerial vehicle from the set of aerial vehicles, wherein each aerial vehicle of the set of aerial vehicles selects the control aerial vehicle; (See at least [n0031-n0034]: “S41. Initialize the cluster. Each drone has a unique ID Uid, election round Times, and EpochID indicating the dynasty of the leader when performing a task. S42. During the initial voting, each drone will cast its own vote; S43. The drones communicate with each other to update the voting information of the drones in the current round. If the Uid of another drone is found to be greater than its own Uid, the vote is cast for the drone with the larger Uid. Only one vote can be cast per round. S44. Vote Exchange: If a drone receives more than N<sub>U</sub>/2 votes, it is elected as the Leader. Otherwise, proceed to the next round until a Leader is elected.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij and Brinkschulte’s method with Zhang’s technique of identifying, by the computer, a control aerial vehicle from the set of aerial vehicles, wherein each aerial vehicle of the set of aerial vehicles selects the control aerial vehicle. Doing so would be obvious so that “other drones only need to communicate with the Leader, reducing redundant communication between drones and minimizing resource waste” (See [n0129] of Zhang).
Regarding claim 8, Pogorelskij, Brinkschulte, and Zhang in combination teach all the limitations of claim 7 as discussed above.
Pogorelskij additionally teaches:
further comprising: controlling, by the computer, the control aerial vehicle based on the updated set of control instructions, (See at least [0018]: “…form a fire extinguishing algorithm, the trajectory of the aircraft movement, adjust the fire extinguishing algorithm depending on the constantly incoming information about the fire, transmit to the aircraft the fire extinguishing algorithm, the trajectory of the aircraft movement and the configuration of the location of the swarm of UAVs, receive information on the progress of fire extinguishing and new fire sources, change the parameters of the swarm of UAVs on this basis, monitor and control the airspace, monitor the fire extinguishing process, control the aircraft and UAVs until the fire source is completely eliminated” & [0026]: “The carrier aircraft, and in some cases the carrier UAV, is primarily free-flying and continuously interacts with both the reconnaissance aircraft and the fire suppression control module. This interaction is intended to precisely guide the carrier aircraft and the UAV to the fire site, as well as to obtain the required (calculated) spatial arrangement of the UAV swarm from the fire suppression control module.”)
wherein the control aerial vehicle is controlled to transmit at least a part of the updated set of control instructions to each aerial vehicle of the set of aerial vehicles. (See at least [0030]: “Due to the aerodynamic shape of the body and the presence of an electric motor, as well as a control system, the UAVs are designed with the ability to form a swarm from them, the spatial geometric shape of which and the number of UAVs in a section perpendicular to the axis of symmetry of the geometric figure of the swarm are set by the fire extinguishing control module and transmitted to the UAVs through the control module and the carrier aircraft.”)
Regarding claim 9, Pogorelskij, Brinkschulte, and Zhang in combination teach all the limitations of claim 7 as discussed above.
Pogorelskij additionally teaches:
further comprising: receiving, by the computer, update data associated with the fire within the fire-bound area, wherein the update data is received from at least one of the set of aerial vehicles or the control aerial vehicle; (See at least [0012]: “…receiving information from the reconnaissance aircraft and the carrier aircraft about the progress of fire extinguishing and about new sources of fire…” & [0027]: “If necessary, for example, to increase the speed of receiving information about a fire and current external conditions, the carrier aircraft can also be equipped with the required sensors and monitoring tools, such as video cameras, wind direction and force sensors, pressure sensors, temperature sensors, etc., which are well known to a specialist and do not require special explanations.”)
applying, by the computer, the Al model to the update data; and (See at least [0023]: “The construction of algorithms and the implementation of aerodynamic calculations based on them in order to determine the required configuration of a swarm of unmanned aerial vehicles and its implementation, the ability to adjust the constructed algorithms in real time, as well as the processing of the obtained information are realized primarily through the use of convolutional neural networks, namely, artificial intelligence.”)
updating, by the computer, the set of control instructions based on the application of the Al model to the update data. (See at least [0012]: “The unmanned aerial vehicle (UAV) contains a highly flammable aerodynamically shaped body with an electric motor, a control system, and a charge in the form of solid carbon dioxide, and is designed with the ability to interact with a fire extinguishing control module and carrier aircraft to receive commands for the formation of a swarm of a certain spatial configuration and its modification depending on the type of fire, as well as changing the parameters of the UAV swarm based on the information received” & [0018]: “…form a fire extinguishing algorithm, the trajectory of the aircraft movement, adjust the fire extinguishing algorithm depending on the constantly incoming information about the fire, transmit to the aircraft the fire extinguishing algorithm, the trajectory of the aircraft movement and the configuration of the location of the swarm of UAVs, receive information on the progress of fire extinguishing and new fire sources, change the parameters of the swarm of UAVs on this basis, monitor and control the airspace, monitor the fire extinguishing process, control the aircraft and UAVs until the fire source is completely eliminated.”)
Regarding claim 16, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 12 as discussed above.
Pogorelskij additionally teaches:
receive operation data associated with the operation of each aerial vehicle of the set of aerial vehicles, wherein the operation data is received from the control aerial vehicle; (See at least [0012]: “…receiving information from the reconnaissance aircraft and the carrier aircraft about the progress of fire extinguishing and about new sources of fire…The carrier aircraft is designed with the capability of two-way continuous interaction with the fire extinguishing control module and the unmanned aerial vehicle (UAV), sending information from the fire site to the fire extinguishing control module and receiving a fire extinguishing algorithm from it…” & [0027]: “If necessary, for example, to increase the speed of receiving information about a fire and current external conditions, the carrier aircraft can also be equipped with the required sensors and monitoring tools, such as video cameras, wind direction and force sensors, pressure sensors, temperature sensors, etc., which are well known to a specialist and do not require special explanations.”)
apply the Al model to the operation data; update the requirement data based on the application of the Al model to the operation data; (See at least [0012]: “The fire extinguishing control module is designed with the capability of two-way continuous interaction with the reconnaissance aircraft and the carrier aircraft, continuous receipt of information from the reconnaissance aircraft and the carrier aircraft, processing the information received from the reconnaissance aircraft and the carrier aircraft, forming a fire extinguishing algorithm and adjusting the fire extinguishing algorithm depending on the constantly incoming information from the reconnaissance aircraft and the carrier aircraft, transmitting to the carrier aircraft the fire extinguishing algorithm and the configuration of the location of the carrier aircraft and the swarm of unmanned aerial vehicles during fire extinguishing, receiving information from the reconnaissance aircraft and the carrier aircraft about the progress of fire extinguishing and about new sources of fire, changing the parameters of the swarm of unmanned aerial vehicles based on the information received.”)
update the set of control instructions based on the updated requirement data; and transmit the updated set of control instructions to the control aerial vehicle. (See at least [0012]: “The unmanned aerial vehicle (UAV) contains a highly flammable aerodynamically shaped body with an electric motor, a control system, and a charge in the form of solid carbon dioxide, and is designed with the ability to interact with a fire extinguishing control module and carrier aircraft to receive commands for the formation of a swarm of a certain spatial configuration and its modification depending on the type of fire, as well as changing the parameters of the UAV swarm based on the information received” & [0018]: “…form a fire extinguishing algorithm, the trajectory of the aircraft movement, adjust the fire extinguishing algorithm depending on the constantly incoming information about the fire, transmit to the aircraft the fire extinguishing algorithm, the trajectory of the aircraft movement and the configuration of the location of the swarm of UAVs, receive information on the progress of fire extinguishing and new fire sources, change the parameters of the swarm of UAVs on this basis, monitor and control the airspace, monitor the fire extinguishing process, control the aircraft and UAVs until the fire source is completely eliminated.” See also [0040] regarding the carrier aircraft receiving coordinates of the fire source from the reconnaissance aircraft then flying toward the reconnaissance aircraft and monitoring the area until the fire is extinguished.)
Pogorelskij and Brinkschulte in combination do not explicitly teach:
wherein the program instructions further cause the processor set to: identify a control aerial vehicle from the set of the set of aerial vehicles, wherein each aerial vehicle of the set of aerial vehicles selects the control aerial vehicle from the set of aerial vehicles;
Zhang teaches:
wherein the program instructions further cause the processor set to: identify a control aerial vehicle from the set of the set of aerial vehicles, wherein each aerial vehicle of the set of aerial vehicles selects the control aerial vehicle from the set of aerial vehicles; (See at least [n0031-n0034]: “S41. Initialize the cluster. Each drone has a unique ID Uid, election round Times, and EpochID indicating the dynasty of the leader when performing a task. S42. During the initial voting, each drone will cast its own vote; S43. The drones communicate with each other to update the voting information of the drones in the current round. If the Uid of another drone is found to be greater than its own Uid, the vote is cast for the drone with the larger Uid. Only one vote can be cast per round. S44. Vote Exchange: If a drone receives more than N<sub>U</sub>/2 votes, it is elected as the Leader. Otherwise, proceed to the next round until a Leader is elected.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij and Brinkschulte’s method with Zhang’s technique of identifying, by the computer, a control aerial vehicle from the set of aerial vehicles, wherein each aerial vehicle of the set of aerial vehicles selects the control aerial vehicle. Doing so would be obvious so that “other drones only need to communicate with the Leader, reducing redundant communication between drones and minimizing resource waste” (See [n0129] of Zhang).
Regarding claim 17, Pogorelskij, Brinkschulte, and Zhang in combination teach all the limitations of claim 16 as discussed above.
Pogorelskij additionally teaches:
wherein the program instructions further cause the processor set to: control the control aerial vehicle based on the updated set of control instructions, (See at least [0018]: “…form a fire extinguishing algorithm, the trajectory of the aircraft movement, adjust the fire extinguishing algorithm depending on the constantly incoming information about the fire, transmit to the aircraft the fire extinguishing algorithm, the trajectory of the aircraft movement and the configuration of the location of the swarm of UAVs, receive information on the progress of fire extinguishing and new fire sources, change the parameters of the swarm of UAVs on this basis, monitor and control the airspace, monitor the fire extinguishing process, control the aircraft and UAVs until the fire source is completely eliminated” & [0026]: “The carrier aircraft, and in some cases the carrier UAV, is primarily free-flying and continuously interacts with both the reconnaissance aircraft and the fire suppression control module. This interaction is intended to precisely guide the carrier aircraft and the UAV to the fire site, as well as to obtain the required (calculated) spatial arrangement of the UAV swarm from the fire suppression control module.”)
wherein the control aerial vehicle is controlled to transmit at least a part of the updated set of control instructions to each aerial vehicle of the set of aerial vehicles. (See at least [0030]: “Due to the aerodynamic shape of the body and the presence of an electric motor, as well as a control system, the UAVs are designed with the ability to form a swarm from them, the spatial geometric shape of which and the number of UAVs in a section perpendicular to the axis of symmetry of the geometric figure of the swarm are set by the fire extinguishing control module and transmitted to the UAVs through the control module and the carrier aircraft.”)
Regarding claim 18, Pogorelskij, Brinkschulte, and Zhang in combination teach all the limitations of claim 16 as discussed above.
Pogorelskij additionally teaches:
wherein the program instructions further cause the processor set to: receive update data associated with the fire within the fire-bound area, wherein the update data is received from at least one of the set of aerial vehicles or the control aerial vehicle; (See at least [0012]: “…receiving information from the reconnaissance aircraft and the carrier aircraft about the progress of fire extinguishing and about new sources of fire…” & [0027]: “If necessary, for example, to increase the speed of receiving information about a fire and current external conditions, the carrier aircraft can also be equipped with the required sensors and monitoring tools, such as video cameras, wind direction and force sensors, pressure sensors, temperature sensors, etc., which are well known to a specialist and do not require special explanations.”)
apply the Al model to the update data; and (See at least [0023]: “The construction of algorithms and the implementation of aerodynamic calculations based on them in order to determine the required configuration of a swarm of unmanned aerial vehicles and its implementation, the ability to adjust the constructed algorithms in real time, as well as the processing of the obtained information are realized primarily through the use of convolutional neural networks, namely, artificial intelligence.”)
update the set of control instructions based on the application of the Al model to the update data. (See at least [0012]: “The unmanned aerial vehicle (UAV) contains a highly flammable aerodynamically shaped body with an electric motor, a control system, and a charge in the form of solid carbon dioxide, and is designed with the ability to interact with a fire extinguishing control module and carrier aircraft to receive commands for the formation of a swarm of a certain spatial configuration and its modification depending on the type of fire, as well as changing the parameters of the UAV swarm based on the information received” & [0018]: “…form a fire extinguishing algorithm, the trajectory of the aircraft movement, adjust the fire extinguishing algorithm depending on the constantly incoming information about the fire, transmit to the aircraft the fire extinguishing algorithm, the trajectory of the aircraft movement and the configuration of the location of the swarm of UAVs, receive information on the progress of fire extinguishing and new fire sources, change the parameters of the swarm of UAVs on this basis, monitor and control the airspace, monitor the fire extinguishing process, control the aircraft and UAVs until the fire source is completely eliminated.”)
Claim(s) 10 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pogorelskij in view of Brinkschulte and further in view of Beecham and Taner of TR 201922168 A2, published 03/23/2020, hereinafter “Taner”.
Regarding claim 10, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 1 as discussed above.
Pogorelskij and Brinkschulte in combination do not explicitly teach:
further comprising: obtaining, by the computer, training fire data associated with each training fire event of a plurality of training fire events, wherein the training fire data comprises training intensity data, historical location data, historical environment data, historical extinguishing acoustic wave data, and historical deployment data; and training, by the computer, the Al model based on the training fire data.
Beecham teaches:
further comprising: obtaining, by the computer, training fire data associated with each training fire event of a plurality of training fire events, wherein the training fire data comprises training intensity data, historical location data, historical environment data(See at least [0054]: “Referring now to FIG. 6, a method 200 includes step 201, data gathered from sensors. In one embodiment, the data is training data referring to datasets gathered from sensors disposed on terrain wherein fire approached in past, such data linked to map coordinates of said earlier fire event. In step 202, the datasets are stored in a data repository and serve as training data wherein fire-fighting by similar drones were successful in suppressing the past earlier fire. In step 203, the machine learning algorithm uses said training datasets to match to ongoing input data sets from simulations to form a machine action set of instructions to drones. The machine action set is modelled in computer as simulated response to simulated fire approach, and results are viewable such as how many drones are routed to which sector of the map to fight fire. The machine learning algorithm provides an initial prediction to ‘success’ as defined as correct routing of drones to proper locations such as hotspots of fire approaching. Where incorrect routing is noted, the corrective nature of machine learning intervenes. In this case, the machine is prompted to replay the data simulation with instruction to route drones mostly to simulated hotspots. The prompting can be via human intervention, as in supervised machine learning, or by machine self-prompting, as in machine-self-correcting. A combination of such means is also envisioned” & [0052]: “For instance, prior to a formal warning from a fire department, these mobile sensor poles are sent out to locations and can detect smoke, embers, temperature, wind direction, and intensity…”. See also [0026] regarding fire sensor poles for collecting data related to a fire.)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij and Brinkschulte’s method with Beecham’s technique of obtaining, by the computer, training fire data associated with each training fire event of a plurality of training fire events, wherein the training fire data comprises historical location data, historical environment data, and historical deployment data, and training, by the computer, the Al model based on the training fire data. Doing so would be obvious for “correct routing of drones to proper locations such as hotspots of fire approaching” (See [0054] of Beecham).
Pogorelskij, Brinkschulte, and Beecham in combination do not explicitly teach:
…historical extinguishing acoustic wave data…
Taner teaches:
…historical extinguishing acoustic wave data… (See at least pg. 5, lines 21-25: “Extinguisher UAV (3) is configured to communicate with the management server (4) and to have a low-frequency sound emitter on it that is used to extinguish and/or prevent the spread of fire. The extinguisher UAV is configured to intervene in the fire by using the information learned by the (3) management server (4) from the past fire and response data” & pg. 6, lines 6-13: “It is configured to be trained to determine the 5 types of intervention and the number of extinguishing UAVs (3) required. The management server (4) analyzes the fire zone data received from the leading UAV (2) with the help of sensors with historical data and is configured to determine the number of extinguisher UAVs (3) required and the type of fire intervention. In this way, how many extinguishers are fired UAV (3) and which fire With the 1O intervention type, it is determined instantly with the arrival of data from the fire area.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij, Brinkschulte, and Beecham’s method with Taner’s technique of using past fire and response data to determine the type of intervention and number of extinguisher UAVs required. Doing so would be obvious “to learn more effective fire extinguishing by using artificial intelligence among unmanned aerial vehicles (UAVs) used for fire extinguishing” and to “enables the fire to be extinguished in a shorter time with a learning structure” (See pg. 1, lines 1-3 & pg. 4, lines 10-11 of Taner).
Regarding claim 19, Pogorelskij and Brinkschulte in combination teach all the limitations of claim 12 as discussed above.
Pogorelskij and Brinkschulte in combination do not explicitly teach:
wherein the program instructions further cause the processor set to: obtain training fire data associated with each training fire event of a plurality of training fire events, wherein the training fire data comprises training intensity data, historical location data, historical environment data, historical extinguishing acoustic wave data, and historical deployment data; and train the Al model based on the training fire data.
Beecham teaches:
wherein the program instructions further cause the processor set to: obtain training fire data associated with each training fire event of a plurality of training fire events, wherein the training fire data comprises training intensity data, historical location data, historical environment data(See at least [0054]: “Referring now to FIG. 6, a method 200 includes step 201, data gathered from sensors. In one embodiment, the data is training data referring to datasets gathered from sensors disposed on terrain wherein fire approached in past, such data linked to map coordinates of said earlier fire event. In step 202, the datasets are stored in a data repository and serve as training data wherein fire-fighting by similar drones were successful in suppressing the past earlier fire. In step 203, the machine learning algorithm uses said training datasets to match to ongoing input data sets from simulations to form a machine action set of instructions to drones. The machine action set is modelled in computer as simulated response to simulated fire approach, and results are viewable such as how many drones are routed to which sector of the map to fight fire. The machine learning algorithm provides an initial prediction to ‘success’ as defined as correct routing of drones to proper locations such as hotspots of fire approaching. Where incorrect routing is noted, the corrective nature of machine learning intervenes. In this case, the machine is prompted to replay the data simulation with instruction to route drones mostly to simulated hotspots. The prompting can be via human intervention, as in supervised machine learning, or by machine self-prompting, as in machine-self-correcting. A combination of such means is also envisioned” & [0052]: “For instance, prior to a formal warning from a fire department, these mobile sensor poles are sent out to locations and can detect smoke, embers, temperature, wind direction, and intensity…”. See also [0026] regarding fire sensor poles for collecting data related to a fire.)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij and Brinkschulte’s method with Beecham’s technique of obtaining, by the computer, training fire data associated with each training fire event of a plurality of training fire events, wherein the training fire data comprises historical location data, historical environment data, and historical deployment data, and training, by the computer, the Al model based on the training fire data. Doing so would be obvious for “correct routing of drones to proper locations such as hotspots of fire approaching” (See [0054] of Beecham).
Pogorelskij, Brinkschulte, and Beecham in combination do not explicitly teach:
…historical extinguishing acoustic wave data…
Taner teaches:
…historical extinguishing acoustic wave data… (See at least pg. 5, lines 21-25: “Extinguisher UAV (3) is configured to communicate with the management server (4) and to have a low-frequency sound emitter on it that is used to extinguish and/or prevent the spread of fire. The extinguisher UAV is configured to intervene in the fire by using the information learned by the (3) management server (4) from the past fire and response data” & pg. 6, lines 6-13: “It is configured to be trained to determine the 5 types of intervention and the number of extinguishing UAVs (3) required. The management server (4) analyzes the fire zone data received from the leading UAV (2) with the help of sensors with historical data and is configured to determine the number of extinguisher UAVs (3) required and the type of fire intervention. In this way, how many extinguishers are fired UAV (3) and which fire With the 1O intervention type, it is determined instantly with the arrival of data from the fire area.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Pogorelskij, Brinkschulte, and Beecham’s method with Taner’s technique of using past fire and response data to determine the type of intervention and number of extinguisher UAVs required. Doing so would be obvious “to learn more effective fire extinguishing by using artificial intelligence among unmanned aerial vehicles (UAVs) used for fire extinguishing” and to “enables the fire to be extinguished in a shorter time with a learning structure” (See pg. 1, lines 1-3 & pg. 4, lines 10-11 of Taner).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190100311 A1 is directed to a fire fighting dual drone system.
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/NIKKI MARIE M MOLINA/Examiner, Art Unit 3662
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