DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 of US Application No. 19/283,312, filed on 07/29/2025, are currently pending and have been examined.
Information Disclosure Statement
The information Disclosure Statements filed on 07/29/2025 and 10/07/2025 have been considered. An initialed copy of form 1449 for each is enclosed herewith.
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.
Claim(s) 1-10, 14, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over unpatentable over Baughman et al. (US 2019/0246626 A1, “Baughman”) in view of Studnicka (US 2017/0286892 A1, “Studnicka”).
Regarding claims 1, 19, and 20, Baughman discloses wild-life surveillance and protection and teaches:
A control device comprising: (The computer system 155, i.e., a control device, includes a game warden management application 175 which is configured to surveil and protect one or more wild-life species (e.g., lions, elephants, leopards, etc.) by using the drones 110 and 120 and robots 130 and 140, i.e., mobile objects. The game warden management application 175 can collect sensor data from the drones 110 and 120 and/or robots 130 and 140 and issue actions to the drones 110 and 120 and/or robots 130 and 140 based on the observed sensor data – See at least ¶ [0022])
a memory; and (Consistent with various embodiments, the computer system 155 includes a communication interface (I/F) 160, a processor 165, and memory 170 – See at least ¶ [0018])
at least one processor performing operations to: (Consistent with various embodiments, the computer system 155 includes a communication interface (I/F) 160, a processor 165, and memory 170 – See at least ¶ [0018])
determine whether a first mobile object is [closer to] a first standby facility; (When the drones 215 and/or robots 210 get low on battery/fuel, they can be directed (e.g., by the computer system 225) to the nearest charging station 220 to recharge – See at least ¶ [0044])
cause a second mobile object to move from a second standby facility based on a determination result from the determination unit, the second mobile object being on standby at the second standby facility, (some embodiments, standby drones or robots can be implemented to replace the drones 215 or robots 210 that are currently recharging. By cycling out drones 215 and robots 210 with depleted batteries (e.g., batteries with no/low charge) or low fuel, constant surveillance of the herd 205 can be maintained. The standby drones and robots can be directed to a position of the particular drone or robot they are replacing. For example, if drone 215-1 is instructed to recharge at charging station 220-1, a standby drone can be directed to drone 215-1's previous position – See at least ¶ [0045]) the second standby facility being closer to the first mobile object than the first standby facility; and (The number and placement of the charging stations 220 can vary. In some embodiments, the number of charging stations depends on the number of dispatched drones 215 and/or robots 210. For example, 10 drones may only require two charging stations, while 100 drones may require 20 charging stations. In some embodiments, the number and placement of charging stations 220 depends on the area of surveilled land. For example, a larger stretch of land may require a greater number of charging stations 220 to ensure that the drones 215 and robots 210 are proximate to charging stations 220. The charging stations 220 can also be placed such that the drones 215 and robots 210 are proximate to the charging stations 220. For example, the charging stations 220 can be uniformly spaced across the surveilled land to ensure that the drones 215 and robots 210 are always within In a particular distance of the charging stations 220 – See at least ¶ [0046]; Examiner notes that Fig. 2 shows that the charging stations are spread out from one another and that drone 215-4 is closer to charging station 220-2, i.e., a second charging station, than charging station 220-1, i.e., a first charging station. Thus, drone 215-4 would be directed to the nearest station, i.e., a second standby facility being closer to the first mobile object than the first standby facility, when it is battery is below a threshold.)
cause the first mobile object to move to the second standby facility based on the determination result. (The charging stations 220 can be disposed throughout the environment to maintain constant surveillance of the herd 205. When the drones 215 and/or robots 210 get low on battery/fuel, they can be directed (e.g., by the computer system 225) to the nearest charging station 220 to recharge. In some embodiments, upon a threshold battery charge level, signal is automatically transmitted to the computer system 225 that includes an indication that the batter level is low. The computer system 225 can then analyze the position of the drones 215 and/or robots 210 with respect to the charging stations 220 and automatically direct the drones 215 and/or robots 210 to the nearest charging station 220 – See at least ¶ [0044])
Baughman teaches identifying when a drone’s battery level is at or below a specific charge threshold and then suggests the closest charging station to the drone for recharging. Baughman does not explicitly teach that this includes determining whether a first mobile object is capable of reaching a first standby facility. However, Studnicka discloses an unmanned aerial vehicle delivery system and teaches:
determine whether a first mobile object is capable of reaching a first standby facility; (In some examples, the system may maintain specifications for the UAV to determine the maximum travel distance of the UAV based on its carrying load. The system may use the maximum travel distance to determine and ensure that a travel route has charging stations within a threshold travel distance that is a fraction of the determined maximum travel distance. In some examples, the system may receive battery information from the UAV regularly, and the system may update the travel distance for the UAV based on the battery information received. The battery information may be information related to the battery life that the UAV monitors – See at least ¶ [0059])
In summary, Baughman teaches that when the drone’s battery level hits a threshold value the system commands the drone to go to the nearest charging location. Baughman does not disclose that this threshold is determined based on whether a first mobile object is capable o reaching a first standby facility. However, Studnicka discloses an unmanned aerial vehicle delivery system and teaches a drone routing system determines if the drone can reach a charging station based on its current load and battery information.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman to provide for the unmanned aerial vehicle delivery system, as taught in Studnicka, to provide a charging station that confirms the UAV landed at the charging station, and other maintenance check information about the UAV, including how much charge to provide based on travel distance. (At Studnicka ¶ [0111])
Regarding claim 2, Baughman does not explicitly teach, but Studnicka further teaches:
further causing a computer to perform, acquiring a cruising distance of the first mobile object; and (In some examples, the system may maintain specifications for the UAV to determine the maximum travel distance of the UAV based on its carrying load. The system may use the maximum travel distance to determine and ensure that a travel route has charging stations within a threshold travel distance that is a fraction of the determined maximum travel distance. In some examples, the system may receive battery information from the UAV regularly, and the system may update the travel distance for the UAV based on the battery information received – See at least ¶ [0059])
determining whether the first mobile object is capable of reaching the first standby facility by comparing the cruising distance and a distance from the first mobile object to the first standby facility. (In some examples, the system may maintain specifications for the UAV to determine the maximum travel distance of the UAV based on its carrying load. The system may use the maximum travel distance to determine and ensure that a travel route has charging stations within a threshold travel distance that is a fraction of the determined maximum travel distance. In some examples, the system may receive battery information from the UAV regularly, and the system may update the travel distance for the UAV based on the battery information received. The battery information may be information related to the battery life that the UAV monitors – See at least ¶ [0059])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman to provide for the unmanned aerial vehicle delivery system, as taught in Studnicka, to provide a charging station that confirms the UAV landed at the charging station, and other maintenance check information about the UAV, including how much charge to provide based on travel distance. (At Studnicka ¶ [0111])
Regarding claim 3, Baughman further teaches:
wherein the second standby facility is a standby facility closest to the first mobile object. (The computer system 225 can then analyze the position of the drones 215 and/or robots 210 with respect to the charging stations 220 and automatically direct the drones 215 and/or robots 210 to the nearest charging station 220 – See at least ¶ [0044])
Regarding claim 4, Baughman does not explicitly teach, but Studnicka further teaches:
further causing a computer to perform, acquiring information indicating an availability of a standby facility from each of a plurality of standby facilities; and (In some examples, the route path for the UAV may include one or more pit stops for charging if the system determines that the route is beyond the battery power of the UAV. In some examples, the system may store a map of charging stations. The system may instruct the UAV to travel or navigate to a delivery location using a path based on nearby charging stations. In some examples, the UAV delivery route may not be the shortest distance between the UAV and the delivery location if charging stations are unavailable along the shortest distance – See at least ¶ [0058])
selecting a standby facility providing the information indicating an availability of a standby facility as the first standby facility from a plurality of standby facilities. (In some examples, the system may store a map of charging stations. The system may instruct the UAV to travel or navigate to a delivery location using a path based on nearby charging stations – See at least ¶ [0058]; Here the system checks the availability of the charging stations while perform path planning. If the charging stations are not available along the current shortest path, then the system chooses a different path that has charging stations available. Therefore, the system is selecting the charging stations only if they are available, i.e., selecting a standby facility providing the information indicating an availability of a standby facility.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman to provide for the unmanned aerial vehicle delivery system, as taught in Studnicka, to provide a charging station that confirms the UAV landed at the charging station, and other maintenance check information about the UAV, including how much charge to provide based on travel distance. (At Studnicka ¶ [0111])
Regarding claim 5, Baughman further teaches:
further causing a computer to perform, selecting a standby facility, as the first standby facility, being closest to the first mobile object among standby facilities [] (When the drones 215 and/or robots 210 get low on battery/fuel, they can be directed (e.g., by the computer system 225) to the nearest charging station 220 to recharge – See at least ¶ [0044])
Baughman does not explicitly teach, but Studnicka further teaches:
[] providing the information indicating an availability of a standby facility. (In some examples, the route path for the UAV may include one or more pit stops for charging if the system determines that the route is beyond the battery power of the UAV. In some examples, the system may store a map of charging stations. The system may instruct the UAV to travel or navigate to a delivery location using a path based on nearby charging stations. In some examples, the UAV delivery route may not be the shortest distance between the UAV and the delivery location if charging stations are unavailable along the shortest distance – See at least ¶ [0058])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman to provide for the unmanned aerial vehicle delivery system, as taught in Studnicka, to provide a charging station that confirms the UAV landed at the charging station, and other maintenance check information about the UAV, including how much charge to provide based on travel distance. (At Studnicka ¶ [0111])
Regarding claim 6, Baughman does not explicitly teach, but Studnicka further teaches:
further causing a computer to perform, determining, according to detecting an abnormal state of the first mobile object, whether the first mobile object is capable of reaching the first standby facility. (For example, the system may instruct the UAV to proceed to the nearest charging station when communication is lost – See at least ¶ [0063])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman to provide for the unmanned aerial vehicle delivery system, as taught in Studnicka, to provide a charging station that confirms the UAV landed at the charging station, and other maintenance check information about the UAV, including how much charge to provide based on travel distance. (At Studnicka ¶ [0111])
Regarding claim 7, Baughman further teaches:
further causing a computer to perform, determining, according to a monitoring processing of a target performed by the first mobile object being taken over by another mobile object [] (standby drones or robots can be implemented to replace the drones 215 or robots 210 that are currently recharging. By cycling out drones 215 and robots 210 with depleted batteries (e.g., batteries with no/low charge) or low fuel, constant surveillance of the herd 205 can be maintained. The standby drones and robots can be directed to a position of the particular drone or robot they are replacing. For example, if drone 215-1 is instructed to recharge at charging station 220-1, a standby drone can be directed to drone 215-1's previous position. In some embodiments, upon receipt of an indication that a drone or robot is low on battery, the computer system 225 can automatically transmit a signal to a standby drone to replace the low-battery drone or robot. Accordingly, the computer system 225 can automatically dispatch signals to direct low battery drones 215 and robots 210 to nearby charging stations, while simultaneously dispatching signals to standby drones or robots to replace the recharging drones 215 or robots 210 – See at least ¶ [0045])
Baughman does not explicitly teach, but Studnicka further teaches:
[] whether the first mobile object is capable of reaching the first standby facility. (In some examples, the system may maintain specifications for the UAV to determine the maximum travel distance of the UAV based on its carrying load. The system may use the maximum travel distance to determine and ensure that a travel route has charging stations within a threshold travel distance that is a fraction of the determined maximum travel distance. In some examples, the system may receive battery information from the UAV regularly, and the system may update the travel distance for the UAV based on the battery information received. The battery information may be information related to the battery life that the UAV monitors – See at least ¶ [0059])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman to provide for the unmanned aerial vehicle delivery system, as taught in Studnicka, to provide a charging station that confirms the UAV landed at the charging station, and other maintenance check information about the UAV, including how much charge to provide based on travel distance. (At Studnicka ¶ [0111])
Regarding claim 8, Baughman further teaches:
further causing a computer to perform, causing the second mobile object to move from the second standby facility to an available standby facility other than the first standby facility and the second standby facility. (As shown in Fig. 2, the drones may be moved to additional standby facilities, e.g., charging stations 220-3 and 220-4. Therefore, as drone 215-2 would be replaced by drone 215-1 at its charging station, it can be assigned to charging stations 220-3 or 220-4 when its battery hits a threshold – See at least ¶ [0045])
Regarding claim 9, Baughman further teaches:
further causing a computer to perform, selecting the available standby facility other than the first standby facility and the second standby facility among available standby facilities including a charging facility based on distance from the second mobile object and availability of the charging facility. (In Fig. 2, there are 4 charging stations, e.g., 220-1-220-4, and a plurality of drones, e.g., 215-1-215-4. Selection of the charging station is based on proximity to the charging station when the battery charge level hits a threshold value. (¶ [0044]) Thus, when drone 215-2, i.e., the second mobile object, is nearest to charging stations 220-3 or 220-4, i.e., standby facilities other than the first and second, it will select the charging station and move to it to recharge its battery.)
Regarding claim 10, Baughman further teaches:
further causing a computer to perform, in a case where the first mobile object monitoring a target fails to detect the target, (The drone sensor data received in the cautionary mode is then analyzed and a determination is made whether the threat persists. This is illustrated at step 334. Determining whether a threat condition persists can be based on the previously defined thresholds. For example, sensor data collected by the drones can be analyzed and compared to one or more thresholds defined in the normal and cautionary operating mode thresholds. In some embodiments however, addition thresholds can be defined (e.g., additional image classification match certainty thresholds or thermogram temperature thresholds). Further, in some embodiments, the thresholds defined to determine whether the threat persists can differ from the normal and/or cautionary operating mode thresholds. For example, the thresholds to determine whether a threat condition persists in the active operating mode can be heighted as compared to the normal and/or cautionary operating thresholds (e.g., raised by a certain match certainty percentage) – See at least ¶ [0091]; Here the system is determining if a threat exists based on image data. If the threat is not identified then the system has failed to detect a target.) causing the first mobile object to monitor a predetermined range including a position where the target has not been detected; and (If a determination is made that the threat condition does not persist , process 300 ends. In some embodiments, however, a determination that the threat condition does not persist can cause process 300 to return back to steps 304 or 314, where the normal or cautionary operating modes are selected. This can resume normal and/or heighted surveillance of the wild-life in a temporal period proximate to the identification of a threat condition – See at least ¶ [0092]; Here, if the threat isn’t identified then a normal operation occurs, i.e., the drone continues its surveillance of the area despite there not being a target there.)
in a case where the first mobile object does not detect the target within a predetermined time, (This can resume normal and/or heighted surveillance of the wild-life in a temporal period proximate to the identification of a threat condition – See at least ¶ [0092]) []
Baughman does not explicitly teach, but Studnicka further teaches:
[] determining whether the first mobile object is capable of reaching the first standby facility. (In some examples, the system may maintain specifications for the UAV to determine the maximum travel distance of the UAV based on its carrying load. The system may use the maximum travel distance to determine and ensure that a travel route has charging stations within a threshold travel distance that is a fraction of the determined maximum travel distance. In some examples, the system may receive battery information from the UAV regularly, and the system may update the travel distance for the UAV based on the battery information received. The battery information may be information related to the battery life that the UAV monitors – See at least ¶ [0059])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman to provide for the unmanned aerial vehicle delivery system, as taught in Studnicka, to provide a charging station that confirms the UAV landed at the charging station, and other maintenance check information about the UAV, including how much charge to provide based on travel distance. (At Studnicka ¶ [0111])
Regarding claim 14, Baughman does not explicitly teach, but Studnicka further teaches:
further causing a computer to perform, calculating predicted power consumption during movement based on the movement capability information of the first mobile object; and (Determination of an appropriate UAV may also depend on the estimated time requested/needed for delivery and/or the estimated power consumption needed for delivery, including absence or presence of charging stations along the delivery route – See at least ¶ [0053])
calculating the cruising distance based on a remaining battery capacity obtained by subtracting the predicted power consumption from a current battery capacity of the first mobile object. (In some examples, the system may maintain specifications for the UAV to determine the maximum travel distance of the UAV based on its carrying load. The system may use the maximum travel distance to determine and ensure that a travel route has charging stations within a threshold travel distance that is a fraction of the determined maximum travel distance. In some examples, the system may receive battery information from the UAV regularly, and the system may update the travel distance for the UAV based on the battery information received. The battery information may be information related to the battery life that the UAV monitors – See at least ¶ [0059])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman to provide for the unmanned aerial vehicle delivery system, as taught in Studnicka, to provide a charging station that confirms the UAV landed at the charging station, and other maintenance check information about the UAV, including how much charge to provide based on travel distance. (At Studnicka ¶ [0111])
Regarding claim 18, Baughman further teaches:
further causing a computer to perform, acquiring facility type information indicating whether each standby facility includes at least a charging facility, a standby place, or both; and (FIG. 2 is a high-level diagram of a game warden system 200, in accordance with embodiments of the present disclosure. The game warden system 200 includes a plurality of drones 215-1, 215-2, 215-3, 215-4 (herein collectively drones 215), a computer system 225, a plurality of robots 210-1, 210-2, 210-3, and 210-4 (herein collectively robots 210), a radio tower 230, and a plurality of charging stations 220-1, 220-2, 220-3, and 220-4 (herein collectively charging stations 220) – See at least ¶ [0037]; Examiner notes that this limitation only requires that a facilty include a charging facility. Therefore, if there is only one type of facility, e.g., charging, then the facility type information will by default include information that indicates the facility is a charging facility.)
selecting the first standby facility from standby facilities that include a charging facility in a case where a battery capacity of the first mobile object is below a charging threshold. (Accordingly, the computer system 225 can automatically dispatch signals to direct low battery drones 215 and robots 210 to nearby charging stations, while simultaneously dispatching signals to standby drones or robots to replace the recharging drones 215 or robots 210 – See at least ¶ [0045])
Claim(s) 11-13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over unpatentable over Baughman in view of Studnicka, as applied to claim 1, and in further view of Enos (US 2018/0150087 A1, “Enos”).
Regarding claim 11, the combination of Baughman and Studnicka does not explicitly teach further causing a computer to perform, acquiring information on a category of the target related to speed of movement; and selecting, as the second mobile object to be moved from the second standby facility, a standby mobile object from among standby mobile objects waiting at the second standby facility, the standby mobile object having a monitorable time being equal to or greater than a predetermined value and being capable of moving at a speed corresponding to the category of the target, the monitorable time being a time during which the standby mobile object is capable of monitoring the target. However, Enos discloses border surveillance and tagging unauthorized targets using drone aircraft and sensors and teaches:
further causing a computer to perform, acquiring information on a category of the target (Drones in system 100 can also be configured to identify "friendly" people, vehicles, and the like, using identification-friend-foe (IFF) transponders, visual patterns, radio broadcasts, etc. Operators or automated routines can choose to ignore a particular target if identified as "friendly." System 100 can also be configured with thresholds, visual identification, and the like to help avoid normal animals in the environment (for example, wild horses, deer, etc.), but to permit targeting of people, vehicles, and the like moving in the same environment – See at least ¶ [0060]) related to speed of movement; and (Similarly, if conditions related to a tagging operation dynamically change (for example, a human target enters a ground-based vehicle and leaves an area at higher than expected speed) – See at least ¶ [0025]; Here the system acknowledges that there are differences in the speed of the targets based on their type, e.g., a human on foot is slower than a human in a vehicle.)
selecting, as the second mobile object to be moved from the second standby facility, a standby mobile object from among standby mobile objects waiting at the second standby facility, (In some implementations, TD 106 station locations can be positioned near known, suspected, or projected (for example, using machine learning or artificial intelligence) trafficking or travel routes. In some implementations, some station locations can change at random timeframes so drone locations are not known or extremely difficult to determine to border crossers. For example, a drone "fleet" can launch and randomly re-disperse to different locations every day at some determined or random timeframe, based on weather patterns, shifts in border crossing efforts, or using other data consistent with this disclosure. In some implementations, a TD 106 can be configured to operate as an AS 108 while in a parked/sleep state. TD 106s can also be configured to routinely activate, launch, and scan an area for potential targets before returning to a parked/sleep state – See at least ¶ [0056]) the standby mobile object having a monitorable time being equal to or greater than a predetermined value and being capable of moving at a speed corresponding to the category of the target, the monitorable time being a time during which the standby mobile object is capable of monitoring the target. (Drones (particularly TD 106s) may be configured for either generic or specialized purposes. Some drones can have "cookie-cutter"-type configurations and be used for multiple purposes. For specialized configurations, drones can be configured to be of different sizes, with different equipment, for different weather, temperature, and atmospheric conditions, for different speed needs (for example, depending on target 110 types-people, automobiles, planes, boats, animals, etc.), loiter time/range (for example, battery or gasoline powered), altitude capabilities, types of tagging needed (for example, dye, radioactive, paint, spray, etc.), etc. – See at least ¶ [0057]; a different TD 106 (for example, with a longer-range gasoline engine providing higher air speed) can be called into active service to complete a tagging mission of the ground-based vehicle instead of the original human target – See at least ¶ [0025]; Here, Enos describes that some drones maybe generic, however, others may be specialized and used for specific targets. For example, a target that requires higher speeds and more time/range to monitor will require a drone that can match its speed and operate for the appropriate time/range. Therefore, instances such as those describe in ¶ [0025], where a person enters a vehicle, a new drone will be chosen that has the capabilities to effectively monitor the target.)
In summary, Baughman teaches identifying different types of targets, e.g., animals, people, vehicles, and selecting drones from a plurality of stations to surveil the targets. The combination of Baughman and Studnicka does not explicitly teach further causing a computer to perform, acquiring information on a category of the target related to speed of movement; and selecting, as the second mobile object to be moved from the second standby facility, a standby mobile object from among standby mobile objects waiting at the second standby facility, the standby mobile object having a monitorable time being equal to or greater than a predetermined value and being capable of moving at a speed corresponding to the category of the target, the monitorable time being a time during which the standby mobile object is capable of monitoring the target. However, Enos discloses border surveillance and tagging unauthorized targets using drone aircraft and sensors and teaches identifying the type of target, e.g., person, animal, vehicle, etc. and recognizing a speed difference between the targets. Then based on dynamic target requirements, the system may recall one drone and activate another because the specialized speed and range characteristics of the activated drone allow it to effectively monitor the target.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman and Studnicka to provide for border surveillance and tagging unauthorized targets using drone aircraft and sensors, as taught in Enos, to provide drones that are specialized and configured to be of different sizes, with different equipment, for different weather, temperature, and atmospheric conditions, for different speed needs (for example, depending on target 110 types — people, automobiles, planes, boats, animals, etc.), loiter time/range (for example, battery or gasoline powered), altitude capabilities, types of tagging needed (for example, dye, radioactive, paint, spray, etc.), etc. (At Enos ¶ [0056])
Regarding claim 12, Baughman further teaches:
further causing a computer to perform, causing the second mobile object waiting at the second standby facility to move from the second standby facility according to a result of the selecting and a result of the determining. (some embodiments, standby drones or robots can be implemented to replace the drones 215 or robots 210 that are currently recharging. By cycling out drones 215 and robots 210 with depleted batteries (e.g., batteries with no/low charge) or low fuel, constant surveillance of the herd 205 can be maintained. The standby drones and robots can be directed to a position of the particular drone or robot they are replacing. For example, if drone 215-1 is instructed to recharge at charging station 220-1, a standby drone can be directed to drone 215-1's previous position – See at least ¶ [0045])
Regarding claim 13, Baughman further teaches:
further causing a computer to perform, extracting a feature amount of the target from imaging data transmitted from the first mobile object; (In embodiments, video and image data captured by the drones is received by the computer system and analyzed. The video and image data can be analyzed automatically by the computer system or manually by a user. In some embodiments, the video data is analyzed using statistically generated models (e.g., deep learning such as IBM Watson Image Recognition). The models can compare the frames of the videos (e.g., or images from a camera) to a library of pre-classified images. Based on the mapped classifications, the models can output the various classes (e.g., humans, plants, animals, etc.) the video frame or photo includes, and the match certainty to those mapped classifications – See at least ¶ [0064])
collating the extracted feature amount with a feature amount of an object detected by the another mobile object; and (Upon receipt of the drone sensor data, the computer system analyzes the drone sensor data to determine whether normal operating mode threshold (s) are exceeded. This is illustrated at step 312. As described with respect to step 304, the normal operating mode can dictate normal operating mode threshold(s) used to identify potential threat conditions. Accordingly, the analyzed drone sensor data is compared to normal operating mode threshold(s) specified in the normal operating mode instructions. Statistical analysis can be completed at step 312 (e.g., using k-Means or other centroid-based clustering, connectivity-based clustering, distribution-based clustering, density-based clustering, etc.) to determine features or characteristics of the sensor data. For example, the appearance of humans, hunter accessories, vehicles, and the like can be frequently associated with threat conditions, and can useful in identifying threat conditions – See at least ¶ [0062]-[0063]; Examiner notes that the data comes from batches of data sent from other drones – See at least ¶ [0060]-[0061])
in a case where the collating matches, (Referring now to FIG. 3B, the drone configuration with respect to the surveilled target is expanded based on the selection of the cautionary operating mode at step 314, i.e., in a case where the collating matches. This is illustrated at step 316. Expanding the drone configuration can allow the drones to detect sensor data from additional locations. By increasing the range the drones collect sensor data from, potential threats can be more easily identified. For example, increasing the range at which sensor data is collected can capture threat conditions which were not apparent in a condensed configuration. The drone configuration can be expanded in any manner. In some embodiments, the drone configuration is expanded based on the previously selected configuration. For example, the perimeter the drones were previously covering could be multiplied by a particular factor (e.g., the coverage perimeter could be doubled, tripled, etc.) – See at least ¶ [0076]-[007]) []
The combination of Baughman and Enos does not explicitly teach, but Studnicka further teaches:
[] determining whether the first mobile object is capable of reaching the first standby facility. (In some examples, the system may maintain specifications for the UAV to determine the maximum travel distance of the UAV based on its carrying load. The system may use the maximum travel distance to determine and ensure that a travel route has charging stations within a threshold travel distance that is a fraction of the determined maximum travel distance. In some examples, the system may receive battery information from the UAV regularly, and the system may update the travel distance for the UAV based on the battery information received. The battery information may be information related to the battery life that the UAV monitors – See at least ¶ [0059])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman and Enos to provide for the unmanned aerial vehicle delivery system, as taught in Studnicka, to provide a charging station that confirms the UAV landed at the charging station, and other maintenance check information about the UAV, including how much charge to provide based on travel distance. (At Studnicka ¶ [0111])
Regarding claim 15, the combination of Baughman and Studnicka does not explicitly teach, but Enos further teaches:
further causing a computer to perform, acquiring information on the category of the target related to type, the type including at least one of a person, a vehicle, and a crowd; (Drones in system 100 can also be configured to identify "friendly" people, vehicles, and the like, using identification-friend-foe (IFF) transponders, visual patterns, radio broadcasts, etc. Operators or automated routines can choose to ignore a particular target if identified as "friendly." System 100 can also be configured with thresholds, visual identification, and the like to help avoid normal animals in the environment (for example, wild horses, deer, etc.), but to permit targeting of people, vehicles, and the like moving in the same environment – See at least ¶ [0060])
determining a required movement speed based on the type; and (Similarly, if conditions related to a tagging operation dynamically change (for example, a human target enters a ground-based vehicle and leaves an area at higher than expected speed)
selecting the standby mobile object capable of moving at the required movement speed from among standby mobile objects having the monitorable time equal to or greater than the predetermined value. (For specialized configurations, drones can be configured to be of different sizes, with different equipment, for different weather, temperature, and atmospheric conditions, for different speed needs (for example, depending on target 110 types-people, automobiles, planes, boats, animals, etc.), loiter time/range (for example, battery or gasoline powered), altitude capabilities, types of tagging needed (for example, dye, radioactive, paint, spray, etc.), etc. – See at least ¶ [0057]; a different TD 106 (for example, with a longer-range gasoline engine providing higher air speed) can be called into active service to complete a tagging mission of the ground-based vehicle instead of the original human target – See at least ¶ [0025])
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over unpatentable over Baughman in view of Studnicka, as applied to claim 1, and in further view of Chen et al. (US 2019/0369625 A1, “Chen”).
Regarding claim 16, the combination of Baughman and Studnicka does not explicitly teach further causing a computer to perform, in a case where the cruising distance is insufficient for the distance from the first mobile object to the first standby facility, moving the second mobile object from the second standby facility to a third standby facility that is available, and causing the first mobile object to move to the second standby facility after the second mobile object has vacated the second standby facility. However, Chen discloses an automatic charging system for robots and method thereof and teaches:
further causing a computer to perform, in a case where the cruising distance is insufficient for the distance from the first mobile object to the first standby facility, moving the second mobile object from the second standby facility to a third standby facility that is available, and causing the first mobile object to move to the second standby facility after the second mobile object has vacated the second standby facility. (In addition, according to another embodiment of the present invention, during charging one of the robots, when another robot needs to be charged at this charging station (such as the current battery level of the robot is not enough to move to other charging stations), then the first processing unit 111 of the control terminal 110 will cause the charged robot to be charged to a first threshold (such as 35 % of the battery level), notify the charged robot to leave the charging station, and then send the confirmation signal to the robot waiting to be charged. For example, when a robot is being charged, and there is another robot needs to use the charging station, the first processing unit 111 might waits until the charged robot reaches 35% of the power level to send a leaving signal to notify the charged robot to leave, and then send the confirmation signal to the robot that is waiting for moving to the charging station for charging – See at least ¶ [0018]; Examiner notes that the system constantly determines the charge level and whether it is enough to finish a task. Therefore, if the robot that is removed from the charging station does not have enough battery at 35% to finish a task, then it will request a different charging station.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman and Studnicka to provide for the automatic charging system for robot and method thereof, as taught in Chen, to efficiently arrange the charging position of a robot. (At Chen ¶ [0003])
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over unpatentable over Baughman in view of Studnicka, as applied to claim 1, and in further view of Sasaki. (US 2019/0114929 A1, “Sasaki”).
Regarding claim 17, the combination of Baughman and Studnicka does not explicitly teach further causing a computer to perform, calculating a search time duration for monitoring the predetermined range based on remaining battery capacity of the first mobile object; and setting the predetermined time based on the calculated search time duration to ensure sufficient battery capacity remains for the first mobile object to reach a standby facility. However, Sasaki discloses data processing device, drone, and control device method, and processing program thereof and teaches:
further causing a computer to perform, calculating a search time duration for monitoring the predetermined range based on remaining battery capacity of the first mobile object; and (Subsequently, the battery level of the UAV 100 and the battery level consumed when the UAV 100 performs each action in flight are received (Step S104). The estimated battery consumption when returning is calculated according to the flight distance calculated in Step S103 and the battery level consumed by performing each flight action received in Step S104 (Step S105) – See at least ¶ [0051]; Examiner notes the system is determining a distance the aircraft can travel, based on the battery consumption. While the system is discussed in terms of “distances” the battery consumption is determined per time unit: “The battery status acquisition unit 204 acquires the battery level of the UAV 100 or the battery consumption corresponding to each action when the UAV 100 is flying. The battery consumption is defined by, for example, a current amount per time unit” – See at least ¶ [0041] Therefore, the system determines a duration of time according to the distance traveled on the remaining charge.)
setting the predetermined time based on the calculated search time duration to ensure sufficient battery capacity remains for the first mobile object to reach a standby facility. (Subsequently, the battery level of the UAV 100 and the battery level consumed when the UAV 100 performs each action in flight are received (Step S104). The estimated battery consumption when returning is calculated according to the flight distance calculated in Step S103 and the battery level consumed by performing each flight action received in Step S104 (Step S105). The battery level of the UAV 100 received in Step S104 and the estimated battery consumption calculated in Step S105 are compared, and if the estimated battery consumption is equal to or smaller than a specified amount (an amount obtained by adding a surplus a to the estimated battery consumption), it is judged that the UAV 100 should be reminded to return. Moreover, if the estimated battery consumption is above the specified amount, the battery level of the UAV 100 is managed by returning back to Step S102 and repeating the processing (Step S106) – See at least ¶ [0051]-[0052])
In summary, Baughman considers the battery charge level when determining if a drone should stop tracking a target and fly to a charging station. The combination of Baughman and Studnicka does not explicitly teach further causing a computer to perform, calculating a search time duration for monitoring the predetermined range based on remaining battery capacity of the first mobile object; and setting the predetermined time based on the calculated search time duration to ensure sufficient battery capacity remains for the first mobile object to reach a standby facility. However, Sasaki teaches determining a flight distance, based on battery depletion over time, and ensuring that the drone can return or land at a target location without running out of energy.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the wild-life surveillance and protection of Baughman and Studnicka to provide for data processing device, drone, and control device method, and processing program thereof, as taught in Sasaki, to prevent an insufficient battery level that would cause the drone have to directly land on the ground, thus causing risks such as the drones may be inundated or damaged. (At Sasaki ¶ [0004])
Conclusion
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/CHASE L COOLEY/Examiner, Art Unit 3662
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