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 1-18 and 20-22 are pending in this application.
Claims 1, 8, 11 and 22 are presented as currently amended claims.
Claim 22 is newly presented.
No claims are newly cancelled.
Examiner's Note
Examiner has cited particular paragraphs / columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to Applicants’ definition which is not specifically set forth in the claims.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 4, 10-12, 14, 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Mazzei (US 20230202321 A1) in view of Maury et al. (US 20220072962 A1) (the combination of which is referenced as “combination Maury” hereinafter). As regards the individual claims:
Regarding claim 1, Mazzei explicitly teaches, an :
apparatus (¶49; battery display 404) for determining available energy (¶50; remaining battery indicator 416) for an electric aircraft (¶40; A system and method for providing battery displays of an electric aircraft battery system), comprising: a processor; and a memory (¶122; processor circuit and a memory) communicatively connected to the processor, the memory containing instructions configuring the processor (¶119; the memory comprises machine-readable instructions) to: receive sensor data indicative of a battery state for a battery of the electric aircraft (¶3 at least one sensor connected to the battery bank); determine, based on a flight plan, reserve energy (¶27; maintaining a reserve can also help to protect the batteries; ¶0100; computing a reserve energy required to provide a reserve flight time) indicative of a predetermined value of energy stored in the battery (“indicative of a predetermined value of energy stored in the battery” is given little weight in that this phrase only describes the outcome once the reserve energy amount is determined. It is inherent that you must know the predetermined value of energy that can be stored in a battery before you can determine the reserve amount of energy that will exist given/based on a flight plan); and determine, based on the sensor data (¶3) and the reserve energy (¶27), the available energy indicative of energy remaining (¶32; this again leads to the requirement for maintaining certain reserves, and for onboard computers to be aware of the flight profile and the flight plan so that sufficient energy can be reserved to support the landing maneuver, preferably without dipping into the regulatory reserve or damaging the aircraft battery; ¶33; The single battery display may illustrate the overall state of charge of the battery bank and thus indicate the overall power available to continue operating the aircraft. This display may be computed by measuring information about the batteries, such as the terminal voltage, the output current, the operating temperature, and other factors that may affect the operation or that that may affect the ability of the battery to deliver charge. Furthermore, the display may account for the state of health of the batteries, which may be known before flight, and may also account for failures or irregularities) in the battery in excess of the reserve energy (¶34; flight time remaining may exclude battery required to maintain a statutory or regulatory reserve. For example if the regulatory reserve is 25 minutes, then when the battery reads “0%” this may actually indicate that an estimated 25 minutes of flight time is remaining for the aircraft).
Mazzei does not explicitly teach:
and causing the electric aircraft to perform a power saving flight plan in response to the available energy being below a threshold level, wherein in the power saving flight plan comprises a set of actions to reduce power usage of the electric aircraft; however, Maury does teach:
A power management system (Maury: ¶ 002) that can be applied to electric drone vehicles (Maury: ¶ 065) that calculates a planned route based on predicted battery usage and monitors for a battery threshold limit (Maury: ¶ 112; when the energy available for completion of a planned route falls below a first threshold . . . power conservation steps can be implemented.) and that when the threshold is exceeded a series of prioritized steps (Maury: ¶ 112) can be taken to reduce power consumption (Maury: ¶ 114; can be managed in an effort to extend vehicle range if reaching the planned destination becomes questionable at current operating conditions is the motor management system. This will cause vehicle operating conditions to change, thus impacting all of the other power saving calculations) including the reduction of speed (Maury: ¶ 114; Vehicle speed could be reduced to a speed where aerodynamic drag is greatly reduced and thus vehicle range is extended)
Therefore a person of ordinary skill in the art would be taught or suggested:
and causing the electric aircraft to perform a power saving flight plan in response to the available energy being below a threshold level, wherein in the power saving flight plan comprises a set of actions to reduce power usage of the electric aircraft
because, before the effective filling date of the claimed invention, applying an improved power management system for electric drones to electric aircraft would have been obvious to a person of ordinary skill in the art because prior art Mazzei teaches the concept of applying battery management systems to electric aircraft and Maury is merely teaching an improvement to a power management system already taught by Mazzei. Furthermore, before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Maury with the teachings of Mazzei because doing so would result in the predicable benefit of facilitating improved vehicle travel range. (Maury: ¶ 014).
Regarding claim 2, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 1. Mazzei further teaches:
wherein the processor is further configured to communicate the available energy to a pilot indicator communicatively connected to the processor, wherein the pilot indicator is configured to display the available energy to a user. (Mazzei: ¶ 050; Remaining battery indicator . . . may display composite information derived from a bank of batteries. Remaining battery indicator 416 may also provide a numerical readout of the composite battery charge remaining,) (Mazzei: Fig. 004; [])
Regarding claim 4, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 1. Mazzei further teaches:
wherein the at least a parameter of the battery of the electric aircraft comprises a current charge of the battery. (Mazzei: ¶ 050; indicator 416 may also provide a numerical readout of the composite battery charge remaining,)
Regarding claim 10, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 1. Mazzei further teaches:
wherein the processor is configured to alert a user when the available energy approaches zero a predetermined threshold. (Mazzei: ¶ 099; display a “land as soon as possible” warning if the nominal flight time remaining is substantially zero.)
Regarding claim 11, Mazzei teaches a method for:
determining a resource remaining datum (Mazzei: ¶ 027; ¶27; maintaining a reserve can also help to protect the batteries:) of an electric aircraft, (Mazzei: ¶ 040; ¶40; A system and method for providing battery displays of an electric aircraft battery system) comprising: receiving, by a processor, (Mazzei: ¶ 122; ¶122; processor circuit and a memory) sensor data from a sensing device, wherein the sensing device is configured to measure a parameter of a battery pack of the electric aircraft indicative of a battery state; (Mazzei: ¶ 003; ¶3 at least one sensor connected to the battery bank) determining, by the processor and based on a flight plan, (Mazzei: ¶ 027; ¶27; maintaining a reserve can also help to protect the batteries:) (Mazzei: ¶ 100; ¶0100; computing a reserve energy required to provide a reserve flight time) wherein the reserve energy comprises an amount of energy (Mazzei: ¶ 100; computing a reserve energy required to provide a reserve flight time;) in excess of a resource remaining datum (Mazzei: ¶ 099; display a “land as soon as possible” warning if the nominal flight time remaining is substantially zero.) determining, by the processor and based on the sensor data and the reserve energy, the resource remaining datum indicative of energy remaining in the battery in excess of the reserve energy. (Mazzei: ¶ 100; computing a reserve energy required to provide a reserve flight time;) (Mazzei: ¶ 099; display a “land as soon as possible” warning if the nominal flight time remaining is substantially zero.)
Mazzei does not explicitly teach:
and causing the electric aircraft to perform a power saving flight plan in response to the resource remaining datum being below a threshold level, wherein in the power saving flight plan comprises a set of actions to reduce power usage of the electric aircraft; however, Maury does teach:
A power management system (Maury: ¶ 002) that can be applied to electric drone vehicles (Maury: ¶ 065) that calculates a planned route based on predicted battery usage and monitors for a battery threshold limit (Maury: ¶ 112; when the energy available for completion of a planned route falls below a first threshold . . . power conservation steps can be implemented.) and that when the threshold is exceeded a series of prioritized steps (Maury: ¶ 112) can be taken to reduce power consumption (Maury: ¶ 114; can be managed in an effort to extend vehicle range if reaching the planned destination becomes questionable at current operating conditions is the motor management system. This will cause vehicle operating conditions to change, thus impacting all of the other power saving calculations) including the reduction of speed (Maury: ¶ 114; Vehicle speed could be reduced to a speed where aerodynamic drag is greatly reduced and thus vehicle range is extended)
Therefore a person of ordinary skill in the art would be taught or suggested:
and causing the electric aircraft to perform a power saving flight plan in response to the resource remaining datum being below a threshold level, wherein in the power saving flight plan comprises a set of actions to reduce power usage of the electric aircraft
because, before the effective filling date of the claimed invention, applying an improved power management system for electric drones to electric aircraft would have been obvious to a person of ordinary skill in the art because prior art Mazzei teaches the concept of applying power management systems to electric aircraft and Maury is merely teaching an improvement to a power management system already taught by Mazzei. Furthermore, before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Maury with the teachings of Mazzei because doing so would result in the predicable benefit of facilitating improved vehicle travel range. (Maury: ¶ 014).
Regarding claim 12, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 11. Mazzei further teaches:
further comprising: communicating the resource remaining datum to a pilot indicator in communication with the processor; and displaying, using the pilot indicator, the resource remaining datum to a user. (Mazzei: ¶ 050; Remaining battery indicator . . . may display composite information derived from a bank of batteries. Remaining battery indicator 416 may also provide a numerical readout of the composite battery charge remaining,) (Mazzei: Fig. 004; [])
Regarding claim 14, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 11. Mazzei further teaches:
wherein the at least a parameter of the battery pack of the electric aircraft comprises a current charge of the battery pack. (Mazzei: ¶ 050; indicator 416 may also provide a numerical readout of the composite battery charge remaining,)
Regarding claim 20, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 11. Mazzei further teaches:
further comprising alerting a user when the resource remaining datum approaches zero. (Mazzei: ¶ 079; If the pilot reaches the statutory or regulatory reserve, then the display may switch)
Regarding claim 21, combination Mazzei teaches a method for:
determining available energy (Mazzei: ¶ 050; ¶50; remaining battery indicator 416) for an electric aircraft, (Mazzei: ¶ 040; ¶40; A system and method for providing battery displays of an electric aircraft battery system) comprising: receiving, by a processor, (Mazzei: ¶ 122; ¶122; processor circuit and a memory) sensor data from a sensing device, wherein the sensing device is configured (Mazzei: ¶ 003; ¶3 at least one sensor connected to the battery bank) to measure a parameter of a battery pack of the electric aircraft indicative of a battery state (Mazzei: ¶ 040; ¶40; A system and method for providing battery displays of an electric aircraft battery system) determining, by the processor and based on a flight plan, reserve energy for flight modes of the flight plan, (Mazzei: ¶ 027; ¶27; maintaining a reserve can also help to protect the batteries:) (Mazzei: ¶ 100; ¶0100; computing a reserve energy required to provide a reserve flight time) wherein the reserve energy is indicative of a predetermined value of energy stored in the battery; and (Mazzei: ¶ 000; (“indicative of a predetermined value of energy stored in the battery” is given little weight in that this phrase only describes the outcome once the reserve energy amount is determined. It is inherent that you must know the predetermined value of energy that can be stored in a battery before you can determine the reserve amount of energy that will exist given/based on a flight plan) (Mazzei: ¶ 100; ¶0100; computing a reserve energy required to provide a reserve flight time) determining, by the processor and based on the sensor data (Mazzei: ¶ 003; ¶3) and the reserve energy, (Mazzei: ¶ 027; ¶27) available energy for the aircraft indicative of energy remaining (Mazzei: ¶ 032; ¶32; this again leads to the requirement for maintaining certain reserves, and for onboard computers to be aware of the flight profile and the flight plan so that sufficient energy can be reserved to support the landing maneuver, preferably without dipping into the regulatory reserve or damaging the aircraft battery;) (Mazzei: ¶ 033; ¶33; The single battery display may illustrate the overall state of charge of the battery bank and thus indicate the overall power available to continue operating the aircraft. This display may be computed by measuring information about the batteries, such as the terminal voltage, the output current, the operating temperature, and other factors that may affect the operation or that that may affect the ability of the battery to deliver charge. Furthermore, the display may account for the state of health of the batteries, which may be known before flight, and may also account for failures or irregularities) in the battery in excess of the reserve energy.◄ (Mazzei: ¶ 034; ¶34; flight time remaining may exclude battery required to maintain a statutory or regulatory reserve. For example if the regulatory reserve is 25 minutes, then when the battery reads “0%” this may actually indicate that an estimated 25 minutes of flight time is remaining for the aircraft)
Mazzei does not explicitly teach:
and causing the electric aircraft to perform a power saving flight plan in response to the available energy being below a threshold level, wherein in the power saving flight plan comprises a set of actions to reduce power usage of the electric aircraft; however, Maury does teach:
A power management system (Maury: ¶ 002) that can be applied to electric drone vehicles (Maury: ¶ 065) that calculates a planned route based on predicted battery usage and monitors for a battery threshold limit (Maury: ¶ 112; when the energy available for completion of a planned route falls below a first threshold . . . power conservation steps can be implemented.) and that when the threshold is exceeded a series of prioritized steps (Maury: ¶ 112) can be taken to reduce power consumption (Maury: ¶ 114; can be managed in an effort to extend vehicle range if reaching the planned destination becomes questionable at current operating conditions is the motor management system. This will cause vehicle operating conditions to change, thus impacting all of the other power saving calculations) including the reduction of speed (Maury: ¶ 114; Vehicle speed could be reduced to a speed where aerodynamic drag is greatly reduced and thus vehicle range is extended)
Therefore a person of ordinary skill in the art would be taught or suggested:
and causing the electric aircraft to perform a power saving flight plan in response to the available energy being below a threshold level, wherein in the power saving flight plan comprises a set of actions to reduce power usage of the electric aircraft
because, before the effective filling date of the claimed invention, applying an improved power management system for electric drones to electric aircraft would have been obvious to a person of ordinary skill in the art because prior art Mazzei teaches the concept of applying power management systems to electric aircraft and Maury is merely teaching an improvement to a power management system already taught by Mazzei. Furthermore, before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Maury with the teachings of Mazzei because doing so would result in the predicable benefit of facilitating improved vehicle travel range. (Maury: ¶ 014).
Regarding claim 22, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 11. Mazzei further teaches:
wherein the processor being configured to cause the electric aircraft to perform the power saving flight plan comprises: providing a recommendation via a pilot indicator; and wherein causing the electric aircraft to perform the power saving flight plan is in response to an input received through the pilot indicator (Mazzei : ¶ 037; an indicator or a marker may be provided on the battery display with a “land as soon as possible”)
Claims 3, 5, 8, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Mazzei (US 20230202321 A1) in view of Maury et al. (US 20220072962 A1) as applied to claims 1 and 11 respectively above, and in further view of Geng (US 20210181766 A1) As regards the individual claims:
Regarding claim 3, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 1. Mazzei does not explicitly teach:
wherein the reserve energy comprises a first reserve energy associated with a horizontal flight mode and a second reserve energy associated with a vertical flight mode; however, Geng does teach:
wherein the reserve energy comprises a first reserve energy associated with a horizontal flight mode and a second reserve energy associated with a vertical flight mode. (Geng: ¶ 058; movement state information may include at least one of the horizontal flight speed, vertical flight speed, and altitude information) (Geng: ¶ 101; UAV return flight power estimation device can determine the movement state information of the UAV during the return flight process, and estimate the return flight power based on the determined movement state information. By using this method to estimate the return flight power, the error in estimation can be reduced).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mazzei with the teachings of Geng with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Mazzei's and Geng's base methods are similar electric aircraft battery monitoring and prediction methods however, the combined device would predictably be improved because it would more accurately predict the battery life expected (Geng: ¶ 061).
Regarding claim 5, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 1. Mazzei does not explicitly teach:
wherein the processor is configured to determine a plurality of available energy levels based on a plurality of flight modes; however, Geng does teach:
wherein the processor is configured to determine a plurality of available energy levels based on a plurality of flight modes. (Geng: ¶ 058; movement state information may include at least one of the horizontal flight speed, vertical flight speed, and altitude information) (Geng: ¶ 101; UAV return flight power estimation device can determine the movement state information of the UAV during the return flight process, and estimate the return flight power based on the determined movement state information. By using this method to estimate the return flight power, the error in estimation can be reduced).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mazzei with the teachings of Geng with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Mazzei's and Geng's base methods are similar electric aircraft battery monitoring and prediction methods however, the combined device would predictably be improved because it would more accurately predict the battery life expected (Geng: ¶ 061).
Regarding claim 8, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 1. Mazzei does not explicitly teach:
wherein the processor and the memory communicatively connected to the processor is further configured to generate a power saving flight plan as a function of the available energy; however, Geng does teach:
wherein the processor and the memory communicatively connected to the processor is further configured to generate a power saving flight plan as a function of the available energy. (Geng: ¶ 058; movement state information may include at least one of the horizontal flight speed, vertical flight speed, and altitude information) (Geng: ¶ 101; UAV return flight power estimation device can determine the movement state information of the UAV during the return flight process, and estimate the return flight power based on the determined movement state information. By using this method to estimate the return flight power, the error in estimation can be reduced).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mazzei with the teachings of Geng with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Mazzei's and Geng's base methods are similar electric aircraft battery monitoring and prediction methods however, the combined device would predictably be improved because it would allow the aircraft to “control the UAV to perform forced landing and return flight based on the first predetermined horizontal speed control value” (Geng: ¶ 111).
Regarding claim 13, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 11. Mazzei does not explicitly teach:
wherein the flight modes of the flight plan comprise a horizontal flight mode and a vertical flight mode; however, Geng does teach:
wherein the flight modes of the flight plan comprise a horizontal flight mode and a vertical flight mode. (Geng: ¶ 058; movement state information may include at least one of the horizontal flight speed, vertical flight speed, and altitude information) (Geng: ¶ 101; UAV return flight power estimation device can determine the movement state information of the UAV during the return flight process, and estimate the return flight power based on the determined movement state information. By using this method to estimate the return flight power, the error in estimation can be reduced).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mazzei with the teachings of Geng with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Mazzei's and Geng's base methods are similar electric aircraft battery monitoring and prediction methods however, the combined device would predictably be improved because it would more accurately predict the battery life expected (Geng: ¶ 061).
Regarding claim 15, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 11. Mazzei does not explicitly teach:
wherein determining the resource remaining datum comprises determining a plurality of resource remaining data based on a plurality of flight modes. ; however, Geng does teach:
wherein determining the resource remaining datum comprises determining a plurality of resource remaining data based on a plurality of flight modes. (Geng: ¶ 058; movement state information may include at least one of the horizontal flight speed, vertical flight speed, and altitude information) (Geng: ¶ 101; UAV return flight power estimation device can determine the movement state information of the UAV during the return flight process, and estimate the return flight power based on the determined movement state information. By using this method to estimate the return flight power, the error in estimation can be reduced).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mazzei with the teachings of Geng with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Mazzei's and Geng's base methods are similar electric aircraft battery monitoring and prediction methods however, the combined device would predictably be improved because it would more accurately predict the battery life expected (Geng: ¶ 061).
Claims 6-7 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Mazzei in view of Maury as applied to claims 1 and 11 respectively above, and further in view of Nishio (JP 2022045029 A) and Geng (US 20210181766 A1).
Regarding claim 6, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 1. Mazzei does not explicitly teach:
wherein the processor is configured to determine the reserve energy by utilizing a machine learning model trained to output the reserve energy as a function of training data and a flight mode, wherein the training data comprises correlating reserve energy labels to flight mode labels; however, Nishio does teach:
wherein the processor is configured to determine the reserve energy by utilizing a machine learning model trained to output the reserve energy as a function of training data . . . , wherein the training data comprises correlating reserve energy labels . . . (Nishio: ¶ 037; method of predicting the remaining amount of the storage battery [may use a] neural network, etc., using a data set that inputs the remaining amount of storage battery, model, total weight, etc. of flights operated in the past before a certain period of landing and outputs the remaining amount of storage battery at the time of landing as teacher data. A machine learning model may be generated using the machine learning of the above and stored in the storage unit 17. Then, the remaining amount prediction unit 13 predicts the remaining amount of the storage battery at the time of landing by inputting the remaining amount, the model, and the total weight of the storage battery before the landing of the aircraft to be processed into this machine learning model.).
However, neither Mazzei nor Nishio teach applying a battery capacity machine learning method to and a flight mode . . . to flight mode labels; however, Geng does teach:
A process in which battery capacity is calculated based upon a flight mode used (Geng: ¶ 058; movement state information may include at least one of the horizontal flight speed, vertical flight speed, and altitude information) (Geng: ¶ 101; UAV return flight power estimation device can determine the movement state information of the UAV during the return flight process, and estimate the return flight power based on the determined movement state information. By using this method to estimate the return flight power, the error in estimation can be reduced);
therefore before the effective filing date of the claimed invention, a person of ordinary skill in the art would be taught or suggested:
wherein the processor is configured to determine the reserve energy by utilizing a machine learning model trained to output the reserve energy as a function of training data and a flight mode, wherein the training data comprises correlating reserve energy labels to flight mode labels
because before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Nishio with the teachings of Geng because simple substitution of one known element for another to obtain predictable results is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 416; MPEP § 2143(I)(B)). In the instant case, Nishio contains a method which differs from the claimed limitation by the substitution of considering flight mode’s impact on battery life, but Geng shows that considering flight mode’s impact on battery life was known in the art and one of ordinary skill in the art could have substituted one known element for another, and the results of the substitution would have been predictable. Consequently, the combination is obvious to a person of ordinary skill in the art.
Furthermore, before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mazzei with the teachings of Geng and Nishio with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Mazzei's and Geng's base methods are similar electric aircraft battery monitoring and prediction methods however, the combined device would predictably be improved because it would more accurately predict the battery life expected (Geng: ¶ 061) and reduce “an environmental load at an airport” (Nishio: ¶ 006).
Regarding claim 7, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 1. Mazzei does not explicitly teach:
wherein the processor is configured to determine the available energy by utilizing a machine learning model trained to output available energy as a function of training data, the aircraft data, and the reserve energy, wherein the training data comprises correlating reserve energy labels, flight mode labels, and battery labels; however, Nishio does teach:
wherein the processor is configured to determine the available energy by utilizing a machine learning model trained to output available energy as a function of training data, the aircraft data, and the reserve energy, wherein the training data comprises correlating reserve energy labels, . . . , and battery labels. (Nishio: ¶ 037; method of predicting the remaining amount of the storage battery [may use a] neural network, etc., using a data set that inputs the remaining amount of storage battery, model, total weight, etc. of flights operated in the past before a certain period of landing and outputs the remaining amount of storage battery at the time of landing as teacher data. A machine learning model may be generated using the machine learning of the above and stored in the storage unit 17. Then, the remaining amount prediction unit 13 predicts the remaining amount of the storage battery at the time of landing by inputting the remaining amount, the model, and the total weight of the storage battery before the landing of the aircraft to be processed into this machine learning model.).
However, neither Mazzei nor Nishio teach applying a battery capacity machine learning method to flight mode labels; however, Geng does teach:
A process in which battery capacity is calculated based upon a flight mode used (Geng: ¶ 058; movement state information may include at least one of the horizontal flight speed, vertical flight speed, and altitude information) (Geng: ¶ 101; UAV return flight power estimation device can determine the movement state information of the UAV during the return flight process, and estimate the return flight power based on the determined movement state information. By using this method to estimate the return flight power, the error in estimation can be reduced);
therefore before the effective filing date of the claimed invention, a person of ordinary skill in the art would be taught or suggested:
wherein the processor is configured to determine the reserve energy by utilizing a machine learning model trained to output the reserve energy as a function of training data and a flight mode, wherein the training data comprises correlating reserve energy labels to flight mode labels.
because before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Nishio with the teachings of Geng because simple substitution of one known element for another to obtain predictable results is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 416; MPEP § 2143(I)(B)). In the instant case, Nishio contains a method which differs from the claimed limitation by the substitution of considering flight mode’s impact on battery life, but Geng shows that considering flight mode’s impact on battery life was known in the art and one of ordinary skill in the art could have substituted one known element for another, and the results of the substitution would have been predictable. Consequently, the combination is obvious to a person of ordinary skill in the art.
Furthermore, before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mazzei with the teachings of Geng and Nishio with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Mazzei's and Geng's base methods are similar electric aircraft battery monitoring and prediction methods however, the combined device would predictably be improved because it would more accurately predict the battery life expected (Geng: ¶ 061) and reduce “an environmental load at an airport” (Nishio: ¶ 006).
Regarding claim 16, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 11. Mazzei does not explicitly teach:
wherein determining the reserve energy comprises utilizing a machine learning model trained to output reserve energy as a function of training data and flight mode, wherein the training data comprises correlating reserve energy labels and flight mode labels; however, Nishio does teach:
wherein determining the reserve energy comprises utilizing a machine learning model trained to output reserve energy as a function of training data . . . wherein the training data comprises correlating reserve energy labels . . . (Nishio: ¶ 037; method of predicting the remaining amount of the storage battery [may use a] neural network, etc., using a data set that inputs the remaining amount of storage battery, model, total weight, etc. of flights operated in the past before a certain period of landing and outputs the remaining amount of storage battery at the time of landing as teacher data. A machine learning model may be generated using the machine learning of the above and stored in the storage unit 17. Then, the remaining amount prediction unit 13 predicts the remaining amount of the storage battery at the time of landing by inputting the remaining amount, the model, and the total weight of the storage battery before the landing of the aircraft to be processed into this machine learning model.).
However, neither Mazzei nor Nishio teach applying a battery capacity machine learning method to and a flight mode . . . and flight mode labels; however, Geng does teach:
A process in which battery capacity is calculated based upon a flight mode used (Geng: ¶ 058; movement state information may include at least one of the horizontal flight speed, vertical flight speed, and altitude information) (Geng: ¶ 101; UAV return flight power estimation device can determine the movement state information of the UAV during the return flight process, and estimate the return flight power based on the determined movement state information. By using this method to estimate the return flight power, the error in estimation can be reduced);
therefore before the effective filing date of the claimed invention, a person of ordinary skill in the art would be taught or suggested:
wherein determining the reserve energy comprises utilizing a machine learning model trained to output reserve energy as a function of training data and flight mode, wherein the training data comprises correlating reserve energy labels and flight mode labels.
because before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Nishio with the teachings of Geng because simple substitution of one known element for another to obtain predictable results is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 416; MPEP § 2143(I)(B)). In the instant case, Nishio contains a method which differs from the claimed limitation by the substitution of considering flight mode’s impact on battery life, but Geng shows that considering flight mode’s impact on battery life was known in the art and one of ordinary skill in the art could have substituted one known element for another, and the results of the substitution would have been predictable. Consequently, the combination is obvious to a person of ordinary skill in the art.
Furthermore, before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mazzei with the teachings of Geng and Nishio with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Mazzei's and Geng's base methods are similar electric aircraft battery monitoring and prediction methods however, the combined device would predictably be improved because it would more accurately predict the battery life expected (Geng: ¶ 061) and reduce “an environmental load at an airport” (Nishio: ¶ 006).
Regarding claim 17, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 11. Mazzei does not explicitly teach:
wherein determining the resource remaining datum comprises utilizing a machine learning model trained to output resource remaining datum as a function of training data, the aircraft data, and the reserve energy, wherein the training data comprises correlating reserve energy labels, flight mode labels, and battery labels; however, Nishio does teach:
wherein determining the resource remaining datum comprises utilizing a machine learning model trained to output resource remaining datum as a function of training data, the aircraft data, and the reserve energy, wherein the training data comprises correlating reserve energy labels, . . . , and battery labels. (Nishio: ¶ 037; method of predicting the remaining amount of the storage battery [may use a] neural network, etc., using a data set that inputs the remaining amount of storage battery, model, total weight, etc. of flights operated in the past before a certain period of landing and outputs the remaining amount of storage battery at the time of landing as teacher data. A machine learning model may be generated using the machine learning of the above and stored in the storage unit 17. Then, the remaining amount prediction unit 13 predicts the remaining amount of the storage battery at the time of landing by inputting the remaining amount, the model, and the total weight of the storage battery before the landing of the aircraft to be processed into this machine learning model.).
However, neither Mazzei nor Nishio teach applying a battery capacity machine learning method to flight mode labels; however, Geng does teach:
A process in which battery capacity is calculated based upon a flight mode used (Geng: ¶ 058; movement state information may include at least one of the horizontal flight speed, vertical flight speed, and altitude information) (Geng: ¶ 101; UAV return flight power estimation device can determine the movement state information of the UAV during the return flight process, and estimate the return flight power based on the determined movement state information. By using this method to estimate the return flight power, the error in estimation can be reduced);
therefore before the effective filing date of the claimed invention, a person of ordinary skill in the art would be taught or suggested:
wherein determining the resource remaining datum comprises utilizing a machine learning model trained to output resource remaining datum as a function of training data, the aircraft data, and the reserve energy, wherein the training data comprises correlating reserve energy labels, flight mode labels, and battery labels.
because before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Nishio with the teachings of Geng because simple substitution of one known element for another to obtain predictable results is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 416; MPEP § 2143(I)(B)). In the instant case, Nishio contains a method which differs from the claimed limitation by the substitution of considering flight mode’s impact on battery life, but Geng shows that considering flight mode’s impact on battery life was known in the art and one of ordinary skill in the art could have substituted one known element for another, and the results of the substitution would have been predictable. Consequently, the combination is obvious to a person of ordinary skill in the art.
Furthermore, before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mazzei with the teachings of Geng and Nishio with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Mazzei's and Geng's base methods are similar electric aircraft battery monitoring and prediction methods however, the combined device would predictably be improved because it would more accurately predict the battery life expected (Geng: ¶ 061) and reduce “an environmental load at an airport” (Nishio: ¶ 006).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Mazzei in view of Maury in view of Geng as applied to claim 8 above, and further in view of Nishio (JP 2022045029 A).
Regarding claim 9, as detailed above, combination Mazzei in view of Geng teaches the invention as detailed with respect to claim 8. However, Mazzei in view of Geng does not explicitly teach:
wherein the processor is further configured to display the power saving flight plan on a pilot indicator communicatively connected to the processor; however, Nishio does teach:
wherein the processor is further configured to display the power saving flight plan on a pilot indicator communicatively connected to the processor. (Nishio: ¶ 046; planning unit 14 may preferentially select the vehicle 2 having a short distance from the parking place of the aircraft as the vehicle 2 to be charged while considering the remaining amount of the storage battery of the vehicle) (Nishio: ¶ 042; amount of electric power that can be discharged is calculated based on the predicted value of the remaining amount of SOC, the capacity of the storage battery, and the lower limit value in operation of SOC.) (Nishio: ¶ 019; device (not shown) may display the flight plan received from the management center).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mazzei with the teachings of Nishio with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Mazzei's and Nishio's base methods are similar are similar electric aircraft battery monitoring and prediction methods however, the combined device would predictably be improved because displaying a power shortage allows the operator to adjust the loading of the aircraft (Nishio: ¶ 097).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over combination Mazzei as applied to claim 11 above, and further in view of Nishio (JP 2022045029 A).
Regarding claim 18, as detailed above, combination Mazzei teaches the invention as detailed with respect to claim 11. Mazzei does not explicitly teach:
further comprising generating a power saving flight plan as a function of the resource remaining datum, and further comprising displaying the power saving flight plan on an indicator communicatively connected to the processor; however, Nishio does teach:
further comprising generating a power saving flight plan as a function of the resource remaining datum, (Nishio: ¶ 046; planning unit 14 may preferentially select the vehicle 2 having a short distance from the parking place of the aircraft as the vehicle 2 to be charged while considering the remaining amount of the storage battery of the vehicle) (Nishio: ¶ 042; amount of electric power that can be discharged is calculated based on the predicted value of the remaining amount of SOC, the capacity of the storage battery, and the lower limit value in operation of SOC.) and further comprising displaying the power saving flight plan on an indicator communicatively connected to the processor. (Nishio: ¶ 019; device (not shown) may display the flight plan received from the management center)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mazzei with the teachings of Nishio with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Mazzei's and Nishio's base methods are similar are similar electric aircraft battery monitoring and prediction methods however, the combined device would predictably be improved because displaying a power shortage allows the operator to adjust the loading of the aircraft (Nishio: ¶ 097).
Response to Arguments
Applicant's remarks filed July 24, 2025 have been fully considered.
Applicant’s argument and amendments with respect to the previous applied 35 U.S.C. § 101 rejection is persuasive and the rejection is hereby withdrawn.
Applicant’s arguments with respect to claims 1-18 and 20-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant argues that:
Mazzei does not disclose all of the elements of claim 1, at least because it does not teach or suggest to "cause the electric aircraft to perform a power saving flight plan in response to the available energy being below a threshold level." Consequently, Mazzei does not disclose at least these elements of claim 1. Accordingly, Applicant submits that Mazzei does not anticipate claim 1, and respectfully requests that the Office withdraw the § 102 rejection of claim 1. (Applicant’s Arguments filed July 24, 2025, pg. PP).
In response to Applicant’s amendments and arguments, new art Maury et al. (US 20220072962 A1) has been applied. Maury teaches a power management system (Maury: ¶ 002) that can be applied to electric drone vehicles (Maury: ¶ 065) that calculates a planned route based on predicted battery usage and monitors for a battery threshold limit (Maury: ¶ 112) and that when the threshold is exceeded a series of prioritized steps (Maury: ¶ 112) can be taken to reduce power consumption (Maury: ¶ 114) including the reduction of speed (Maury: ¶ 114).
Before the effective filling date of the claimed invention, Maury’s method of improving a power management system for electric drones could have been applied to Mazzei’s electric aircraft battery management system because combining prior art elements according to known methods to yield predictable results is obvious if the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. (KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 416; MPEP § 2143(I)). In the instant case, Mazzei teaches an electric aircraft power management system and Maury teaches adding route and speed control to that aircraft power management system. Further, the combination of these elements results in the predictable benefit of increasing range and safer operations; consequently, the combination is obvious to a person of ordinary skill in the art. Therefore the claims are obvious over the combination of Mazzei and Maury.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure Wiegman (US 20200277080 A1) which discloses a method of in-flight operational assessment for an electric aircraft comprising detecting by a sensor an electrical parameter of an energy source. The method further includes receiving by a controller the electrical parameter from the sensor and determining a power-production capability of the energy source, using the electrical parameter.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.P./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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