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 .
Response to the applicant’s arguments.
A new rejection is made herein based on the applicant’s IDS.
The allowance is withdrawn.
The RCE is now entered.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 7-15 and 16-17, 19, 20-23, and 25 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of United States Patent Application Pub. No.: US 2017/0069214 A1 to Dupray and in view of Chinese Patent Application Pub. No.: CN108566663B to Chongqing that was filed in 2018 and in view of NPL, DeVargas, Patrick et al., Patrolling Strategy for Multiple UAVs with Recharging Stations in Unknown Environments, 2019 Latin American Robotics Symposium (LARS), 2019 Brazilian Symposium on Robotics (SBR) and 2019 Workshop on Robotics in Education (WRE)
(https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9018633) (hereinafter “DeVargas” and in view of European Patent Pub. No.: EP 3842303 B1 to Schwartz filed in 2019 and in view of Liu, UAV Energy Extraction with Incomplete Atmospheric Data using MPC, EEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL.51,NO.2 APRIL2015, (hereinafter “LIU”).
In regard to claim 7, 16, and 25, Dupray discloses “....7. (New) A method for controlling an autonomous sensing vehicle (ASV) comprising: (see paragraph 97 where the drone includes a sensor and an optical and acoustic sensor and radar)
preparing a reward mapping, (see paragraph 176 where the flight component of the UAV can be changed based on 1. Navigational charts.2 flight area limitations, one or more no fly zones, 3. Elevation, d. restrictions e. noise abatements and private property and weather)
where the reward map is a map of the area of interest divided into a geographical grid of grid points; for each grid point storing information about whether the grid point is an observation point, (see paragraph 305 where the landing and take off point is located near a source of energy to recharge the UAV on the route and map)”.
Dupray is silent but Devargas teaches “...a probability of finding energy, .the expected energy amount, and (see parge 3, second paragraph where the detected charging stations can be detected and placed on a map; and provide a long range or a short range messaging)
an indication of the reliability of the probability of finding energy; ( see FIG. 1 where the drone can include 1. Energy spent. 2. Energy available 3. A full recharge 100 percent to the recharging station and 4. A registered new station that can also provide a full recharge on the map)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of DEVARGAS with a reasonable expectation of success since DEVARGAS teaches that a number of drones can search and observe a charging station. They can then share this information of observations with other drones. Then the other drones can share the recharging station information matrix that they also have. A drone can be very low and then navigate to the detected charging station with a minimal energy intensive path with no turns before it crashes. This can result in an extension of the operations of the drone. See page 1-5.
Dupray is silent but Chongqing teaches “..and for each grid point calculating a reward r based on whether the grid point is an observation point, and (See section 2.2 to 2.4 where 1. determined by the link energy consumption, the remaining energy of the next-hop cluster head and the number of member nodes of the next-hop cluster head. The greater the link energy consumption, the lower the remaining energy of the cluster head, the more member nodes in the cluster, the greater the value of ω ij , and the smaller the probability that the cluster head CN j is responsible for data forwarding, thereby saving its own energy and achieving The energy balance of the entire network. After the sensor control server obtains the weight of each link from equation (15), it uses the Dijkstra algorithm to calculate the optimal path for each cluster head to transmit data, generates the flow table entry corresponding to the cluster head and sends it to the corresponding cluster head , a multi-hop route is established).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of CHONGQING with a reasonable expectation of success since CHONGQING teaches that a number of drones can provide a global clustering and grid. A tree of paths can be drawn that provides a 1. Shortest tree, and 2. Minimal link consumption and 3. Remaining energy of the drone and 4. A successful recharging station back to the base station. Each point in the grid can be an observation point to provide this clustering and an energy balance parameter for each of the nodes.
Dupray is silent but Devargas teaches “...a probability of finding energy, the expected energy amount, and
an indication of the reliability of the probability of finding energy; calculating for each grid point a discounted probabilistic reward G is calculated using a weighted combination of the present and future rewards at that point and calculated as below: (see parge 3, second paragraph where the detected charging stations can be detected and placed on a map; and provide a long range or a short range messaging); ( see FIG. 1 where the drone can include 1. Energy spent. 2. Energy available 3. A full recharge 100 percent to the recharging station and 4. A registered new station that can also provide a full recharge on the map)
Schwartz teaches “...
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where t indicates the present time, r is the reward at time t and Y is a value between o and 1 and is the discount value for future reward, and rt is a distribution of rewards” (see paragraph 234-238).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of SHWARTZ with a reasonable expectation of success since SHWARTZ teaches that an algorithm that uses the discounted sum of future rewards can be used to maximize the cumulative reward for a time horizon for a planner to manage a state of action and while using supervised learning. This can provide improved planning of a robotic vehicle. See paragraph 230-237
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Dupray is silent but Devargas teaches “.for moves in a particular direction based on the probability of finding energy, the expected energy amount, and an indication of the reliability of the probability of finding energy, and the value of making an observation; (see FIG. 1 where the first drone and second drone and third drone all have observations of the first charging station or the second charging station and the second drone can be provided that the two charging stations can provide a 100 percent recharge of the battery and the drone can be routed to the closest station without making any turns or alternatively can obtain new information that there is a closer station)
calculating at each grid point an expected value V of the discounted probabilistic reward G at that grid point; and (see page 3 where a charging station can be detected and registered as a moving or stationary charging station within a matrix and within a perimeter; and 2. The charging level of the uav can be determined as nS; and 3. Then a energy spent in a one hour period is determined; 4. Then adjustments are being made for wind, and hovering and turning and drag and horizontal flight; 5 then in page 4, the total amount of available energy without crashing the uav can determined; and 6. The Esafe value can be adjusted and the drone can be made to return to charge if this value goes below a predetermined area; then 7 a path from the current location to the moving or stationary drone charging station is made with the least amount of turns in page 5);
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directing the ASV to the adjacent grid point with the highest expected value V; (see FIG. 2 where the drones exchange a matrix of charging stations and can keep the old matrix or use the new matrix that includes 1. A new charging station 2. A static or moving location and the 3. Shortest path to the station)
and further comprising a variable between 0 and 1 indicating the priority of seeking energy, where 1 indicates that the reward r is entirely based on finding energy and 0 indicates that the reward r is entirely based on making an observation, and increasing the variable as the useful energy of the ASV decreases, and using this variable to either: (see page 3 where a charging station can be detected and registered as a moving or stationary charging station within a matrix and within a perimeter; and 2. The charging level of the uav can be determined as nS; and 3. Then a energy spent in a one hour period is determined; 4. Then adjustments are being made for wind, and hovering and turning and drag and horizontal flight; 5 then in page 4, the total amount of available energy without crashing the uav can determined; and 6. The Esafe value can be adjusted and the drone can be made to return to charge if this value goes below a predetermined area; then 7 a path from the current location to the moving or stationary drone charging station is made with the least amount of turns in page 5);
(a) adjust the calculation of the reward r; or
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(b) modify the calculation of the expected value V. (see page 3 where a charging station can be detected and registered as a moving or stationary charging station within a matrix and within a perimeter; and 2. The charging level of the uav can be determined as nS; and 3. Then a energy spent in a one hour period is determined; 4. Then adjustments are being made for wind, and hovering and turning and drag and horizontal flight; 5 then in page 4, the total amount of available energy without crashing the uav can determined; and 6. The Esafe value can be adjusted and the drone can be made to return to charge if this value goes below a predetermined area; then 7 a path from the current location to the moving or stationary drone charging station is made with the least amount of turns in page 5);
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of DEVARGAS with a reasonable expectation of success since DEVARGAS teaches that a number of drones can search and observe a charging station. They can then share this information of observations with other drones. Then the other drones can share the recharging station information matrix that they also have. A drone can be very low and then navigate to the detected charging station with a minimal energy intensive path with no turns before it crashes. This can result in an extension of the operations of the drone. See page 1-5.
Claim 7 is amended to recite and Liu teaches “…an observational point of interest….for at least one of the grid points, storing information about a
respective probability of finding energy from a free energy source
available in the atmosphere at that grid point;” (See page 1206 and to 1209 where the drone can perform an energy source searching using the MPC controller to find energy using an adaptive grid and a probability function to find energy).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of LIU with a reasonable expectation of success since LIU teaches that a number of drones can search and observe an energy source and then find the energy source for charging using an adaptive search method using a MPC controller. This can provide charging the drone where the drones themselves can search and locate the energy while using less energy for harvesting and located in a grid pattern using an adaptive controller. See page 11 of the PDF.
In regard to claim 8, and 17, Devargas teaches “..8. (New) The method of claim 7, where the formula reflecting whether the grid point is a potential source of energy or an observation point is: B when the grid point is a grid point where the ASV can make an observation, 8 when the grid point is a potential source of energy, and zero in all other cases, and the values of B and 0 are set by a predetermined formula. (see FIG. 2 where the drones exchange a matrix of charging stations and can keep the old matrix or use the new matrix that includes 1. A new charging station 2. A static or moving location and the 3. Shortest path to the station) (see page 3 where a charging station can be detected and registered as a moving or stationary charging station within a matrix and within a perimeter; and 2. The charging level of the uav can be determined as nS; and 3. Then a energy spent in a one hour period is determined; 4. Then adjustments are being made for wind, and hovering and turning and drag and horizontal flight; 5 then in page 4, the total amount of available energy without crashing the uav can determined; and 6. The Esafe value can be adjusted and the drone can be made to return to charge if this value goes below a predetermined area; then 7 a path from the current location to the moving or stationary drone charging station is made with the least amount of turns in page 5)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of DEVARGAS with a reasonable expectation of success since DEVARGAS teaches that a number of drones can search and observe a charging station. They can then share this information of observations with other drones. Then the other drones can share the recharging station information matrix that they also have. A drone can be very low and then navigate to the detected charging station with a minimal energy intensive path with no turns before it crashes. This can result in an extension of the operations of the drone. See page 1-5.
Claim 8 and 17 is amended to recite and Liu teaches “…an observational point of interest…. (See page 1206 and to 1209 where the drone can perform an energy source searching using the MPC controller to find energy using an adaptive grid and a probability function to find energy).”
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of LIU with a reasonable expectation of success since LIU teaches that a number of drones can search and observe an energy source and then find the energy source for charging using an adaptive search method using a MPC controller. This can provide charging the drone where the drones themselves can search and locate the energy while using less energy for harvesting and located in a grid pattern using an adaptive controller. See page 11 of the PDF.
In regard to claim 9 and 19, Devargas teaches “..9. (New) The method of claim 7, where the value of Y is 0. (see FIG. 2 where the drones exchange a matrix of charging stations and can keep the old matrix or use the new matrix that includes 1. A new charging station 2. A static or moving location and the 3. Shortest path to the station) (see page 3 where a charging station can be detected and registered as a moving or stationary charging station within a matrix and within a perimeter; and 2. The charging level of the uav can be determined as nS; and 3. Then a energy spent in a one hour period is determined; 4. Then adjustments are being made for wind, and hovering and turning and drag and horizontal flight; 5 then in page 4, the total amount of available energy without crashing the uav can determined; and 6. The Esafe value can be adjusted and the drone can be made to return to charge if this value goes below a predetermined area; then 7 a path from the current location to the moving or stationary drone charging station is made with the least amount of turns in page 5)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of DEVARGAS with a reasonable expectation of success since DEVARGAS teaches that a number of drones can search and observe a charging station. They can then share this information of observations with other drones. Then the other drones can share the recharging station information matrix that they also have. A drone can be very low and then navigate to the detected charging station with a minimal energy intensive path with no turns before it crashes. This can result in an extension of the operations of the drone. See page 1-5.
In regard to claim 10 and 20, Devargas teaches “...10. (New) The method of claim 7, where the step of modifying the expected value V for each grid point further comprises incorporating biasing. (see FIG. 2 where the drones exchange a matrix of charging stations and can keep the old matrix or use the new matrix that includes 1. A new charging station 2. A static or moving location and the 3. Shortest path to the station and where the drone can reject the new matrix and keep the old matrix as it is stale) (see page 3 where a charging station can be detected and registered as a moving or stationary charging station within a matrix and within a perimeter; and 2. The charging level of the uav can be determined as nS; and 3. Then a energy spent in a one hour period is determined; 4. Then adjustments are being made for wind, and hovering and turning and drag and horizontal flight; 5 then in page 4, the total amount of available energy without crashing the uav can determined; and 6. The Esafe value can be adjusted and the drone can be made to return to charge if this value goes below a predetermined area; then 7 a path from the current location to the moving or stationary drone charging station is made with the least amount of turns in page 5)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of DEVARGAS with a reasonable expectation of success since DEVARGAS teaches that a number of drones can search and observe a charging station. They can then share this information of observations with other drones. Then the other drones can share the recharging station information matrix that they also have. A drone can be very low and then navigate to the detected charging station with a minimal energy intensive path with no turns before it crashes. This can result in an extension of the operations of the drone. See page 1-5.
In regard to claim 11, and 21, Devargas teaches “..11. (New) The method of claim 7, where the step of modifying the expected value V for each grid point comprises alternating between periods of time where the expected value V only reflects energy sources and period of time where the expected value V only reflects observations. (see FIG. 2 where the drones exchange a matrix of charging stations and can keep the old matrix or use the new matrix that includes 1. A new charging station 2. A static or moving location and the 3. Shortest path to the station and where the drone can reject the new matrix and keep the old matrix as it is stale) (see page 3 where a charging station can be detected and registered as a moving or stationary charging station within a matrix and within a perimeter; and 2. The charging level of the uav can be determined as nS; and 3. Then a energy spent in a one hour period is determined; 4. Then adjustments are being made for wind, and hovering and turning and drag and horizontal flight; 5 then in page 4, the total amount of available energy without crashing the uav can determined; and 6. The Esafe value can be adjusted and the drone can be made to return to charge if this value goes below a predetermined area; then 7 a path from the current location to the moving or stationary drone charging station is made with the least amount of turns in page 5)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of DEVARGAS with a reasonable expectation of success since DEVARGAS teaches that a number of drones can search and observe a charging station. They can then share this information of observations with other drones. Then the other drones can share the recharging station information matrix that they also have. A drone can be very low and then navigate to the detected charging station with a minimal energy intensive path with no turns before it crashes. This can result in an extension of the operations of the drone. See page 1-5.
Claim 11 is amended to recite and Liu teaches “…an observational point of interest…. (See page 1206 and to 1209 where the drone can perform an energy source searching using the MPC controller to find energy using an adaptive grid and a probability function to find energy).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of LIU with a reasonable expectation of success since LIU teaches that a number of drones can search and observe an energy source and then find the energy source for charging using an adaptive search method using a MPC controller. This can provide charging the drone where the drones themselves can search and locate the energy while using less energy for harvesting and located in a grid pattern using an adaptive controller. See page 11 of the PDF.
Devargas teaches “..12. (New) The method of claim 7, further comprising updating the stored information about whether a grid point is an observation point, the probability of finding energy, the expected energy amount, and an indication of the reliability of the probability of finding energy of the map of energy sources. (see FIG. 2 where the drones exchange a matrix of charging stations and can keep the old matrix or use the new matrix that includes 1. A new charging station 2. A static or moving location and the 3. Shortest path to the station and where the drone can reject the new matrix and keep the old matrix as it is stale) (see page 3 where a charging station can be detected and registered as a moving or stationary charging station within a matrix and within a perimeter; and 2. The charging level of the uav can be determined as nS; and 3. Then a energy spent in a one hour period is determined; 4. Then adjustments are being made for wind, and hovering and turning and drag and horizontal flight; 5 then in page 4, the total amount of available energy without crashing the uav can determined; and 6. The Esafe value can be adjusted and the drone can be made to return to charge if this value goes below a predetermined area; then 7 a path from the current location to the moving or stationary drone charging station is made with the least amount of turns in page 5)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of DEVARGAS with a reasonable expectation of success since DEVARGAS teaches that a number of drones can search and observe a charging station. They can then share this information of observations with other drones. Then the other drones can share the recharging station information matrix that they also have. A drone can be very low and then navigate to the detected charging station with a minimal energy intensive path with no turns before it crashes. This can result in an extension of the operations of the drone. See page 1-5.
Claim 12 is amended to recite and Liu teaches “…an observational point of interest…. (See page 1206 and to 1209 where the drone can perform an energy source searching using the MPC controller to find energy using an adaptive grid and a probability function to find energy).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of LIU with a reasonable expectation of success since LIU teaches that a number of drones can search and observe an energy source and then find the energy source for charging using an adaptive search method using a MPC controller. This can provide charging the drone where the drones themselves can search and locate the energy while using less energy for harvesting and located in a grid pattern using an adaptive controller. See page 11 of the PDF.
In regard to claim 13 and 22, Devargas teaches “..13. (New) The method of claim 7, further comprising updating the stored information about whether a grid point is an observation point, the probability of finding energy, the expected energy amount, and an indication of the reliability of the probability of finding energy of the map of energy sources, using sensor information from at least one sensor on the ASV. (see FIG. 2 where the drones exchange a matrix of charging stations and can keep the old matrix or use the new matrix that includes 1. A new charging station 2. A static or moving location and the 3. Shortest path to the station and where the drone can reject the new matrix and keep the old matrix as it is stale) (see page 3 where a charging station can be detected and registered as a moving or stationary charging station within a matrix and within a perimeter; and 2. The charging level of the uav can be determined as nS; and 3. Then a energy spent in a one hour period is determined; 4. Then adjustments are being made for wind, and hovering and turning and drag and horizontal flight; 5 then in page 4, the total amount of available energy without crashing the uav can determined; and 6. The Esafe value can be adjusted and the drone can be made to return to charge if this value goes below a predetermined area; then 7 a path from the current location to the moving or stationary drone charging station is made with the least amount of turns in page 5)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of DEVARGAS with a reasonable expectation of success since DEVARGAS teaches that a number of drones can search and observe a charging station. They can then share this information of observations with other drones. Then the other drones can share the recharging station information matrix that they also have. A drone can be very low and then navigate to the detected charging station with a minimal energy intensive path with no turns before it crashes. This can result in an extension of the operations of the drone. See page 1-5.
Claim 13 is amended to recite and Liu teaches “…an observational point of interest…. (See page 1206 and to 1209 where the drone can perform an energy source searching using the MPC controller to find energy using an adaptive grid and a probability function to find energy).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of LIU with a reasonable expectation of success since LIU teaches that a number of drones can search and observe an energy source and then find the energy source for charging using an adaptive search method using a MPC controller. This can provide charging the drone where the drones themselves can search and locate the energy while using less energy for harvesting and located in a grid pattern using an adaptive controller. See page 11 of the PDF.
In regard to claim 14 and 23, Dupray discloses “..14. (New) The method of claim 13, where the sensor information comprises one of more of the following: the airspeed of the ASV, the heading of the ASV, the geographical location of the ASV, the inertial measurements of the ASV, the ambient temperature, ambient pressure, ambient humidity”. (see paragraph 103)
In regard to claim 15 and 24, Devargas teaches “..15. (New) The method of claim 7, where r is further determined by one of more of the following inputs: the type of energy harvesting, the purpose of the flight, data from past flights, identification of no-fly zones, the aircraft parameters, the energy capabilities of the UAV, meteorological forecast, and importance factors for the observations; meteorological forecast, maps, waypoints that the ASV must cross, and the importance factor for observation”. (see FIG. 2 where the drones exchange a matrix of charging stations and can keep the old matrix or use the new matrix that includes 1. A new charging station 2. A static or moving location and the 3. Shortest path to the station and where the drone can reject the new matrix and keep the old matrix as it is stale) (see page 3 where a charging station can be detected and registered as a moving or stationary charging station within a matrix and within a perimeter; and 2. The charging level of the uav can be determined as nS; and 3. Then a energy spent in a one hour period is determined; 4. Then adjustments are being made for wind, and hovering and turning and drag and horizontal flight; 5 then in page 4, the total amount of available energy without crashing the uav can determined; and 6. The Esafe value can be adjusted and the drone can be made to return to charge if this value goes below a predetermined area; then 7 a path from the current location to the moving or stationary drone charging station is made with the least amount of turns in page 5)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of DEVARGAS with a reasonable expectation of success since DEVARGAS teaches that a number of drones can search and observe a charging station. They can then share this information of observations with other drones. Then the other drones can share the recharging station information matrix that they also have. A drone can be very low and then navigate to the detected charging station with a minimal energy intensive path with no turns before it crashes. This can result in an extension of the operations of the drone. See page 1-5.
Claim 15 is amended to recite and Liu teaches “…an observational point of interest…. (See page 1206 and to 1209 where the drone can perform an energy source searching using the MPC controller to find energy using an adaptive grid and a probability function to find energy).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of LIU with a reasonable expectation of success since LIU teaches that a number of drones can search and observe an energy source and then find the energy source for charging using an adaptive search method using a MPC controller. This can provide charging the drone where the drones themselves can search and locate the energy while using less energy for harvesting and located in a grid pattern using an adaptive controller. See page 11 of the PDF.
Claim 18 is rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of United States Patent Application Pub. No.: US 2017/0069214 A1 to Dupray and in view of Chinese Patent Application Pub. No.: CN108566663B to Chongqing that was filed in 2018 and in view of NPL, DeVargas, Patrick et al., Patrolling Strategy for Multiple UAVs with Recharging Stations in Unknown Environments, 2019 Latin American Robotics Symposium (LARS), 2019 Brazilian Symposium on Robotics (SBR) and 2019 Workshop on Robotics in Education (WRE)
(https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9018633) (hereinafter “DeVargas”) and Schwartz and Liu.
Devargas teaches “....18. | (New) The method of claim 16, where modifying the expected value V comprises increasing the weighting of energy sources versus the weighting of making an observation in the reward r by a variable between o and 1, where 1 indicates that the reward r is entirely based on finding energy and o indicates that the reward r is entirely based on making an observation, and increasing the variable as the useful energy of the ASV decreases. (see FIG. 2 where the drones exchange a matrix of charging stations and can keep the old matrix or use the new matrix that includes 1. A new charging station 2. A static or moving location and the 3. Shortest path to the station and where the drone can reject the new matrix and keep the old matrix as it is stale) (see page 3 where a charging station can be detected and registered as a moving or stationary charging station within a matrix and within a perimeter; and 2. The charging level of the uav can be determined as nS; and 3. Then a energy spent in a one hour period is determined; 4. Then adjustments are being made for wind, and hovering and turning and drag and horizontal flight; 5 then in page 4, the total amount of available energy without crashing the uav can determined; and 6. The Esafe value can be adjusted and the drone can be made to return to charge if this value goes below a predetermined area; then 7 a path from the current location to the moving or stationary drone charging station is made with the least amount of turns in page 5)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of DEVARGAS with a reasonable expectation of success since DEVARGAS teaches that a number of drones can search and observe a charging station. They can then share this information of observations with other drones. Then the other drones can share the recharging station information matrix that they also have. A drone can be very low and then navigate to the detected charging station with a minimal energy intensive path with no turns before it crashes. This can result in an extension of the operations of the drone. See page 1-5.
Claims 24 and 26 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of United States Patent Application Pub. No.: US 2017/0069214 A1 to Dupray and in view of Chinese Patent Application Pub. No.: CN108566663B to Chongqing that was filed in 2018 and in view of NPL, DeVargas, Patrick et al., Patrolling Strategy for Multiple UAVs with Recharging Stations in Unknown Environments, 2019 Latin American Robotics Symposium (LARS), 2019 Brazilian Symposium on Robotics (SBR) and 2019 Workshop on Robotics in Education (WRE)
(https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9018633) (hereinafter “DeVargas”)
in view of European Patent Application Pub. No.: EP3251108B1 to Taveira (US 20160253907A1) that was filed in 2015 (hereinafter “Taveira”) and Schwartz and Liu.
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Taviera teaches “...24. (New) The method of claim 16, where r is further determined by one of more of the following inputs: the type of energy harvesting, the purpose of the flight, data from past flights, identification of no-fly zones, the aircraft parameters, the energy capabilities of the UAV, meteorological forecast, and importance factors for the observations; meteorological forecast, maps, waypoints that the ASV must cross, and the importance factor for observation”. (see paragraph 45) (see station 214 and 256, 230 that can communicate with the drone 100) (see claims 1-9; see paragraph 44-46 where the drone receives navigation signals and beacon signals and restricted area signals from the ground station) (see paragraph 45) (see station 214 and 256, 230 that can communicate with the drone 100) (see claims 1-9; see paragraph 44-46 where the drone receives navigation signals and beacon signals and restricted area signals from the ground station) (see paragraph 55-56 where the drone can be provided a conditional access to the restricted area) (see paragraph 55 where the server can provide a periodic heart beat check for the drone to indicate if the navigation unit and the drone system is still functioning; see paragraph 26 where the drone is controlled to include that the video/photo recording is disabled and the drone must fly in a silent mode) (See FIG. 3a to 3b where the restriction area node can provide signals 320a-b so the drone takes a path 311c to avoid the first, second and n restricted areas) (see claim 1-2 where the ground operator can provide corrective action to the drone including 1. Landing in or 2. Moving to a designated area; 3. Returning to the designated location, 4. Preventing take off; 5. A third party taking control of the drone; 6. Restricting use of the drone, and 7. Waiting for a period of time) (See FIG. 3a to 3b where the restriction area node can provide signals 320a-b so the drone takes a path 311c to avoid the first, second and n restricted areas) (see paragraph 63 where the base station in the restricted area provides signals 321a to 321c and where the drone receives these signals and knows to maintain an elevation to not enter this area or alternatively turn around and move away from the restricted Area) (see drones 100 in FIG. 2c that include a flight plan from the ground station from the drone base 260 to the destination 210 and that includes a first restricted area 260a and a second restricted area 260b) (see paragraph 55 where the server can provide a periodic heart beat check for the drone to indicate if the navigation unit and the drone system is still functioning; see paragraph 26 where the drone is controlled to include that the video/photo recording is disabled and the drone must fly in a silent mode) (see paragraph 63 where the base station in the restricted area provides signals 321a to 321c and where the drone receives these signals and knows to maintain an elevation to not enter this area or alternatively turn around and move away from the restricted Area)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of TAVIERA with a reasonable expectation of success since TAVIERA teaches that a central control unit can control a number of drones and provide restricted areas where the drone cannot enter to protect the area and/or the drone from being harmed.
Claim 24 is amended to recite and Liu teaches “…an observational point of interest…. ” (See page 1206 and to 1209 where the drone can perform an energy source searching using the MPC controller to find energy using an adaptive grid and a probability function to find energy).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of LIU with a reasonable expectation of success since LIU teaches that a number of drones can search and observe an energy source and then find the energy source for charging using an adaptive search method using a MPC controller. This can provide charging the drone where the drones themselves can search and locate the energy while using less energy for harvesting and located in a grid pattern using an adaptive controller. See page 11 of the PDF.
Taviera teaches “...26. (New) The system of claim 25 where the computing system further comprises an off-board computing system. (see paragraph 45) (see station 214 and 256, 230 that can communicate with the drone 100) (see claims 1-9; see paragraph 44-46 where the drone receives navigation signals and beacon signals and restricted area signals from the ground station) (see paragraph 55-56 where the drone can be provided a conditional access to the restricted area) (see paragraph 55 where the server can provide a periodic heart beat check for the drone to indicate if the navigation unit and the drone system is still functioning; see paragraph 26 where the drone is controlled to include that the video/photo recording is disabled and the drone must fly in a silent mode) (See FIG. 3a to 3b where the restriction area node can provide signals 320a-b so the drone takes a path 311c to avoid the first, second and n restricted areas) (see claim 1-2 where the ground operator can provide corrective action to the drone including 1. Landing in or 2. Moving to a designated area; 3. Returning to the designated location, 4. Preventing take off; 5. A third party taking control of the drone; 6. Restricting use of the drone, and 7. Waiting for a period of time) (See FIG. 3a to 3b where the restriction area node can provide signals 320a-b so the drone takes a path 311c to avoid the first, second and n restricted areas) (see paragraph 63 where the base station in the restricted area provides signals 321a to 321c and where the drone receives these signals and knows to maintain an elevation to not enter this area or alternatively turn around and move away from the restricted Area) (see drones 100 in FIG. 2c that include a flight plan from the ground station from the drone base 260 to the destination 210 and that includes a first restricted area 260a and a second restricted area 260b) (see paragraph 55 where the server can provide a periodic heart beat check for the drone to indicate if the navigation unit and the drone system is still functioning; see paragraph 26 where the drone is controlled to include that the video/photo recording is disabled and the drone must fly in a silent mode) (see paragraph 63 where the base station in the restricted area provides signals 321a to 321c and where the drone receives these signals and knows to maintain an elevation to not enter this area or alternatively turn around and move away from the restricted Area)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the disclosure of DUPRAY with the teachings of TAVIERA with a reasonable expectation of success since TAVIERA teaches that a central control unit can control a number of drones and provide restricted areas where the drone cannot enter to protect the area and/or the drone from being harmed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN PAUL CASS whose telephone number is (571)270-1934. The examiner can normally be reached Monday to Friday 7 am to 7 pm; Saturday 10 am to 12 noon.
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/JEAN PAUL CASS/Primary Examiner, Art Unit 3666
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