DETAILED ACTION
Introduction
Claims 1-15 have been examined in this application. Claims 1, 2, 4, 5, 7, 10, 14, and 15 are amended. Claims 3, 6, 8, 9, and 11-13 are original.
This is a final office action in response to the arguments and amendments filed 4/23/2026. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Office Action Formatting
The following is an explanation of the formatting used in the instant Office Action:
• [0001] – Indicates a paragraph number in the most recent, previously cited source;
• [0001, 0010] – Indicates multiple paragraphs (in example: paragraphs 1 and 10) in the most recent, previously cited source;
• [0001-0010] – Indicates a range of paragraphs (in example: paragraphs 1 through 10) in the most recent, previously cited source;
• 1:1 – Indicates a column number and a line number (in example: column 1, line 1) in the most recent, previously cited source;
• 1:1, 2:1 – Indicates multiple column and line numbers (in example, column 1, line 1 and column 2, line 2) in the most recent, previously cited source;
• 1:1-10 – Indicates a range of lines within one column (in example: all lines spanning, and including, lines 1 and 10 in column 1) in the most recent, previously cited source;
• 1:1-2:1 – Indicates a range of lines spanning several columns (in example: column 1, line 1 to column 2, line 1 and including all intervening lines) in the most recent, previously cited source;
• p. 1, ln. 1 – Indicates a page and line number in the most recent, previously cited source;
• ¶1 – The paragraph symbol is used solely to refer to Applicant's own specification (further example: p. 1, ¶1 indicates first paragraph of page 1); and
• BRI – the broadest reasonable interpretation.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on application EP23306636.4 filed in Europe on 09/29/2023. It is noted, however, that applicant has not filed a certified copy of the application as required by 37 CFR 1.55.
Response to Arguments
Applicant's arguments, filed 4/23/2026, have been fully considered.
Regarding the arguments pertaining to the claim rejections under 112 (presented on p. 7-8), the arguments and amendments are persuasive. Therefore, the rejections have been withdrawn.
Regarding the arguments pertaining to the claim rejections under 101 (presented on p. 8-9), the arguments and amendments are partially persuasive. Claim 14 now recite a statutory category of subject matter and thus the previous rejection is withdrawn. However, Claims 1-15 remain rejected as reciting an abstract idea without significantly more. The arguments state that the added “communicating” limitation in Claims 1 and 15 integrate the abstract idea into a practical application. However, upon further review of the claims, the communication to an aircraft which stores the mission plan is merely the transmitting of data between two computers, recited at a high level of generality. The “application” of the mission plan is only recited as intended use and is not positively claimed as control of an aircraft. Thus, while the “communicating” limitation is an additional element, it is not determined to integrate the abstract idea into a practical application or amount to significantly more as it amounts to mere handling of data of the result of the abstract idea (see the rejection below for complete detail).
Regarding the arguments pertaining to the claim rejections under 103 (presented on p. 9-12), the arguments and amendments are partially persuasive. Particularly, the arguments state that Publication US2021/0103860A1 (de Oliveira et al.) does not show or suggest feature (F1), however no reasoned arguments have been provided as to why Applicant asserts that the mapped subject matter in the previous rejection does not read on the limitation. The office maintains that desirable factors being programmed/applied as a scoring function reads on the broadest reasonable interpretation of encoding user preferences as a multi-criteria scoring scheme because those factors/preferences are then part of the scoring scheme (see the complete rejection below for further detail). The arguments further state that de Oliveira et al. does not show or suggest feature (F2), because it describes how energy consumption varies based on context as opposed to selecting energy consumption models. The office respectfully disagrees and believes the determination of energy consumption based on the context (and representing such energy consumption as data) is equivalent to selecting of a model, because a particular representation of the physical world in data (i.e. a model) is selected/used for the calculation. The arguments regarding feature F3 are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of the additional prior art of Publication US2021/0060774A1 (Butterfoss et al.), Publication US2020/0342770A1 (Shinya), Published Application US2024/0177618A1 (Khan et al.), Publication US2014/0081569A1 (Agrawal et al.), and Published Application US2024/0353574A1 (Sbeity et al.) as well as the previously relied upon prior art of Publication US2021/0103860A1 (de Oliveira et al.), Published Application US2023/0256859A1 (Palombini), and Published Application US2024/0054903A1 (Mollahan et al.).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1 and 15, the communicating of “the output mission plan” to the at least one electric or hybrid electric aircraft renders the claims indefinite. The claims each recite a plurality of multi-criteria constrained optimization algorithms, and state that “each multi-criteria constrained optimization algorithm providing as output a mission plan.” In other words, there appears to be a plurality of mission plans that are output by the algorithms. It is not clear whether the communicating of “the output mission plan” is the communicating of all output mission plans, or only the communication of a single plan (and if so, which one), or whether all output mission plans from the plural algorithms are required to be the same, or something else. The scope of the claims is therefore indefinite. For the purposes of examination, the limitation is interpreted as the communicating of any mission plan.
Claims 2-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being dependent on rejected Claim 1 and for failing to cure the deficiencies listed above.
Regarding Claim 7, the limitation of the modified mission plan score being assured to be greater than the first mission plan score “while the updated scoring scheme stays within a predetermined distance according to a chosen metric of the original scoring scheme” renders the claim indefinite. It is unclear how a “distance” between scoring schemes is determined or calculated, and what the chosen metric is, or how the distance is “according to” the metric. Upon review of the specification for clarity, ¶00120 appears to recite that the distance itself (e.g. a Manhattan or Euclidian distance between scoring parameters) is the metric. However it is not clear whether the metric in the claim should be interpreted to be the distance, or should be interpreted to be a scoring parameter or weight that the distance is based on, or something else. The scope of the claim is therefore indefinite. For the purposes of examination, the limitation is interpreted as any evaluation of distance function based on weights or coefficients of a scoring scheme.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(101 Analysis - Step 1 - Statutory Category) Regarding Claims 1-15, the claims are directed to one of the statutory categories of subject matter as the claims recite a process, machine, manufacture or composition of matter.
(101 Analysis - Step 2A, Prong I - Judicial Exception) Regarding Independent Claim 1, the claim recites a method for computing a mission plan for at least one aircraft powered by electric energy or by electric and fuel generated energy, the mission being defined by mission specifications including a multi-flight path between a plurality of successive mission terminals, the method being implemented in a system comprising a processing unit and a user interface, the method comprising:
receiving input information including aircraft information, infrastructure information and flight routes information;
acquiring the mission specifications;
acquiring user preferences;
encoding the user preferences as a multi-criteria scoring scheme;
selecting at least one electric energy consumption model and one electric energy charge model, based on the mission specifications;
executing a plurality of multi-criteria constrained optimization algorithms, trained by machine learning to optimize a result based on an objective, for mission planning, wherein the multi-criteria scoring scheme is the objective of each multi-criteria constrained optimization algorithm, each multi-criteria constrained optimization algorithm being further provided with the input information, mission specifications and the selected energy models, each multi-criteria constrained optimization algorithm providing as output a mission plan comprising, for each aircraft, an electricity charging duration at each mission terminal; and
communicating, via a wireless communication interface, the output mission plan to the at least one electric or hybrid electric aircraft, the output mission plan being further recorded in an on-board flight management system of the at least one electric or hybrid electric aircraft for application.
The limitations indicated in BOLD above, under their broadest reasonable interpretation, are an abstract idea of a mental process, capable of being performed in a human mind or manually, using pen and paper (see MPEP 2106.04(a)(2)(III)).
Particularly, a person is capable of mentally or manually perform a method for computing a mission plan (for example a pilot mentally deciding on or manually writing out or drawing out a flight plan) for at least one aircraft powered by electric energy or by electric and fuel generated energy (the flight plan intended for such a particular aircraft), the mission being defined by mission specifications including a multi-flight path between a plurality of successive mission terminals (the missing being a series of legs between airports/waypoints), the method comprising:
receiving input information including aircraft information (the pilot knowing or looking up aircraft information such as range or fuel/battery consumption per distance), infrastructure information (knowing or looking up airport locations) and flight routes information (knowing or looking up segments/routes between airports);
acquiring the mission specifications (the pilot desiring or having a written copy of the airports/waypoints);
acquiring user preferences;
encoding the user preferences as a multi-criteria scoring scheme (the pilot knowing or receiving written preferences such as safety margin of range and cost and weighting the preferences such as by setting up a weighted average scoring function);
selecting at least one electric energy consumption model and one electric energy charge model, based on the mission specifications (for example the pilot selecting an energy consumption value for environmental controls and energy charging value for a particular airport’s available chargers); and
executing a plurality of multi-criteria constrained optimization algorithms, to optimize a result based on an objective, for mission planning (the pilot considering two ways to generate fueling plans, e.g. one based on always filling the fuel/battery and one based on only filling minimum amounts if fuel/charging cost is higher), wherein the multi-criteria scoring scheme is the objective of each multi-criteria constrained optimization algorithm (the score being the output parameter of the scoring function), each multi-criteria constrained optimization algorithm being further provided with the input information, mission specifications and the selected energy models (the evaluation of the fueling plans based on the aircraft and airport and flight route data and waypoints and energy use model), each multi-criteria constrained optimization algorithm providing as output a mission plan comprising, for each aircraft, an electricity charging duration at each mission terminal (e.g. the pilot using charge amount at each airport to further determine duration required based on known charging rates)
Thus, the claim recites an abstract idea.
(101 Analysis - Step 2A, Prong II - Practical Application) This judicial exception is not integrated into a practical application. The limitations indicated with underlining above are additional elements in the claim. That is, the additional elements in the claim are the method being implemented in a system comprising a processing unit and a user interface, the multi-criteria constrained optimization algorithms “trained by machine learning,” and the limitation of communicating, via a wireless communication interface, the output mission plan to the at least one electric or hybrid electric aircraft, the output mission plan being further recorded in an on-board flight management system of the at least one electric or hybrid electric aircraft for application.
For the method being implemented in a system comprising a processing unit and a user interface, and the communicating, via a wireless communication interface, the output mission plan to the at least one electric or hybrid electric aircraft, the output mission plan being further recorded in an on-board flight management system of the at least one electric or hybrid electric aircraft for application, these elements are all recitations of generic computer components and their use, recited at a high level of generality. The claims do not provide an improvement in computer hardware or computing technology. Therefore, the claims act as mere instructions to “apply” the abstract idea using generic computer components as tools to perform the functions. Additionally, communicating is the use of a computer in its ordinary capacity for basic tasks (e.g., to receive, store, or transmit data) and the “application” of the mission plan is recited as intended use and not positively claimed as a step of the method.. This does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
For the multi-criteria constrained optimization algorithms, “trained by machine learning,” the phrase is recited broadly, without any details of how the machine learning is used or trained. Therefore, the claim merely generally ties the abstract idea to the field of machine learning, and does not limit the claim or abstract idea in any meaningful way. Therefore, it does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
Additionally, the ordered combination of additional elements and claim as a whole are not determined to integrate the abstract idea into a practical application as the ordered combination does not add anything already present when the elements are considered separately and merely recites input and output of data to/from a processor and an application of machine learning at a high level of generality.
(101 Analysis - Step 2B - Significantly More / Inventive Concept) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As above, the additional elements in the claim are the method being implemented in a system comprising a processing unit and a user interface, the multi-criteria constrained optimization algorithms “trained by machine learning,” and the limitation of communicating, via a wireless communication interface, the output mission plan to the at least one electric or hybrid electric aircraft, the output mission plan being further recorded in an on-board flight management system of the at least one electric or hybrid electric aircraft for application.
For the method being implemented in a system comprising a processing unit and a user interface, and communicating, via a wireless communication interface, the output mission plan to the at least one electric or hybrid electric aircraft, the output mission plan being further recorded in an on-board flight management system of the at least one electric or hybrid electric aircraft for application, for the same reasons as presented above, these elements are all recitations of generic computer components and their use, at a high level of generality, such that the claims act as mere instructions to “apply” the functions using a generic computer components as tools to perform the functions, and the use of computers for their basic tasks (e.g. transmitting and storing data). This does not amount to significantly more than the abstract idea (see MPEP 2106.05(f)). Additionally, such elements are well-understood, routine, and conventional in the art (see MPEP 2106.05(d) computer functions which are recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity include: ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199; Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012), and see also MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)).
For the at least one multi-criteria constrained optimization algorithm, “trained by machine learning,” for the same reasons as above, the machine learning is recited broadly such that the claim merely generally ties the abstract idea to the field of machine learning, and does not limit the claim or abstract idea in a meaningful way. This does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)). Additionally, such elements are well-understood, routine, and conventional in the art (see e.g. US20120263376A1, [0002-0005]).
Additionally, the ordered combination of additional elements and claim as a whole are not determined to amount to significantly more as the ordered combination does not add anything already present when the elements are considered separately and merely recites input and output of data to/from a processor and an application of machine learning at a high level of generality.
Thus, the claim is not patent eligible.
Regarding Independent Claim 15, the claim recites the same abstract idea. The additional elements are the system comprising a processing unit configured to receive data and implement the various “modules” to perform the functions. These are recitations of generic computer components at a high level of generality and therefore the additional elements do not integrate the abstract idea into a practical application or amount to significantly more for the same reasons as presented above with respect to Claim 1.
Dependent Claims 2-14 do not recite further limitations that integrate the judicial exception into a practical application or amount to significantly more.
Claims 2 and 3 recites further details of the mission plan, which are further details of the abstract idea of a mental process, as a person can include mission plan components for scoring/consideration such as how much charging occurs at each airport and target cruising speed and idle time. The claim does not add any new additional elements.
Claim 4 recites particulars of the aircraft being considered and selecting one fuel consumption model, which are further details of the abstract idea of a mental process, as a person can plan a mission for a specific hybrid aircraft and select a fuel consumption model (e.g. select most efficient flight speeds or fastest flight speeds that consumes more fuel). The claim further recites wherein each mission plan further comprises an energy provision amount at each mission terminal and an indication of an energy type to use among fuel, electricity, or both during each flight leg. These are additionally further details of the abstract idea of a mental process, as a person can include mission plan components for scoring/consideration fueling/charging amount, and a decision associated with each leg specifying the type of energy used. The claim does not add any new additional elements.
Claim 5 recites a plurality of algorithms executed in parallel, and evaluation and ranking by satisfaction. These are further details of the abstract idea of a mental process, as a person can perform a plurality of a series of steps or functions (i.e. algorithms) in parallel such as by switching between the steps of each series, and subsequently evaluating some satisfaction evaluation such as by scoring whether the results meet the most preferred criteria. The claim does not add any new additional elements.
Claim 6 recites the display of results on the user interface, and receiving user input for selection, which are additional elements in the claim, but do not integrate the abstract idea into a practical application or amount to significantly more as these steps are insignificant post-solution activity and are well-understood, routine, and conventional in the art (see e.g. US20150294223A1 at [0003]).
Claim 7 recites receiving a modification and updating the scoring scheme. These are further details of the abstract idea of a mental process, as a person can think of or manually receive a request to modify the scoring scheme and update the scoring scheme. The recitation of the “automatic learning method” only generally ties the abstract idea to machine learning and therefore does not integrate the abstract idea into a practical application or amount to significantly more for the same reasons presented above with respect to Claim 1.
Claim 8 recites details of the scoring scheme which are further details of the abstract idea of a mental process, as a person can figure out a scoring scheme based on a hierarchy, using configurable utility and aggregation functions (such as configurable weights or coefficients for a mission plan’s utility/effectiveness and an aggregation of factors). The claim does not add any new additional elements.
Claim 9 recites receiving and updating which are further details of the abstract idea of a mental process, as a person can think of or manually receive an update and repeat the selecting and executing. The claim does not add any new additional elements.
Claims 10 and 11 recite the aircraft being a hybrid aircraft and the method comprising selecting multiple energy models. These are further details of the abstract idea of a mental process, as a person can consider a hybrid aircraft as the intended aircraft for the mission plan and can select or designate a plurality of provided or memorized models for use. The claim does not add any new additional elements, as the models being based on the fine-tuned function only describes how the models were created and are not a step of the method.
Claim 12 recites performing of online supervised machine learning. This is an additional element in the claim but does not integrate the abstract idea into a practical application or amount to significantly more for the same reasons presented above with respect to Claim 1.
Claim 13 details the input information which is a further detail of the abstract idea of a mental process, able to be considered by a person. The claim does not add any new additional elements.
Claim 14 recites a non-transitory computer readable medium for the method of Claim 1. This is an additional element but is the recitation of generic computer components/code and therefore does not integrate the abstract idea into a practical application or amount to significantly more for the same reasons presented above with respect to Claim 1.
Thus, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 4, 8, 9, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Publication US2021/0103860A1 (de Oliveira et al.) in view of Published Application US2023/0256859A1 (Palombini), further in view of Publication US2021/0060774A1 (Butterfoss et al.), further in view of Publication US2020/0342770A1 (Shinya).
Regarding Claim 1, de Oliveira et al. discloses a method for computing a mission plan (see Figure 2, [0043-0044] determination of fueling plan with each landing site of flight plan) for at least one aircraft powered by electric energy (see [0003, 0069] battery-powered aircraft or [0067] Claim 8, hybrid system using battery array) or by electric and fuel generated energy (see [0067] e.g. aircraft with hybrid propulsion system such as liquid fuel and battery array), the mission being defined by mission specifications including a multi-flight path between a plurality of successive mission terminals (see [0037, 0044] flight plan with landing sites), the method being implemented in a system comprising a processing unit (see Figure 1, [0031]) and a user interface (see Figure 1, [0033]), the method comprising:
receiving input information including aircraft information (see [0040] block 210 determining energy load as aircraft information or [0042] using aircraft information e.g. weight, passenger count, cargo), infrastructure information (see [0038-0039] operational conditions including e.g. landing sites, fuel type availability) and flight routes information (see [0039] flight legs known);
acquiring the mission specifications (see [0037] block 202, initiating of the flight plan with sequence of landing sites);
acquiring user preferences (see [0043] desirable factors e.g. low fueling time, and low-cost);
encoding the user preferences as a multi-criteria scoring scheme (see [0040, 0043] used as factors in fuel value scoring calculation);
selecting at least one electric energy consumption model (see [0040] determination of energy load model at block 208, and [0067] energy loads can be determined for use of each fuel type) and one replenishing model (see [0042-0043] in block 214 refueling represented as data (i.e. use/selection of particular model) and [0067] battery as a “fuel”), based on the mission specifications (see Figure 2, based on previous block 204);
executing a multi-criteria constrained optimization algorithm (see Figure 2, algorithm blocks 212, 216, [0040] at block 212 determining the fuel value score, which [0043] is based on plural criteria, constrained by particular landing sites, optimizing for desirable places), trained by machine learning (see [0043] calculating the fuel value score may involve a cost function and/or a machine learning model) to optimize a result based on an objective, for mission planning (see [0043, 0044] optimize fueling plan, based on fuel value score), wherein the multi-criteria scoring scheme is the objective of the multi-criteria constrained optimization algorithm (see [0043-0044] fuel value score calculation as the objective, which is the multi-criteria scoring scheme), each multi-criteria constrained optimization algorithm being further provided with the input information (see [0043] the expected energy load provided as part of the fuel value score calculation, which evaluates landing sites (infrastructure) for given flight leg (route)), mission specifications (see Figure 2, mission specifications from block 202 as input to algorithm) and the selected energy models (see Figure 2, energy models from blocks 208 and 214 as input to algorithm), the multi-criteria constrained optimization algorithm providing as output a mission plan (see Figure 2, [0044] determination of fueling plan) comprising, for each aircraft, an electricity replenishing duration at each mission terminal (see [0043] time for refueling at each landing site as a known feature [0067] battery as “fuel”).
de Oliveira et al. does not explicitly recite selecting:
one electric energy charge model,
and does not explicitly recite the mission plan comprising:
an electricity charging duration at each mission terminal.
Examiner’s note: in other words, de Oliveira et al. recites “refueling” and recites fuels including battery power, but does not explicitly recite electrical charging (as opposed to e.g. battery replacement).
However, Palombini teaches a technique to evaluate electrical replenishment for an aircraft (see e.g. [0018]), including:
selecting: one electric energy charge model (see [0028] recharge estimation evaluated by server (data representation of the recharging, i.e. a model, selected/retrieved for use)), and
determination of: an electricity charging duration (see [0028] generate recharge time).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the determination of refueling time at each mission terminal of de Oliveira et al. to include a recharging time as taught by Palombini, with a reasonable expectation of success, with the motivation of enhancing the robustness and flexibility of the method to include battery recharging and improving accuracy by taking additional parameters such as cool-down into account (see Palombini, [0028]).
de Oliveira et al. does not explicitly recite
executing a plurality of multi-criteria constrained optimization algorithms.
However, Butterfoss et al. teaches a technique to generate vehicle plans (see e.g. [0002]), including:
executing a plurality of multi-criteria constrained optimization algorithms (see [0086] perform the process depicted in FIG. 2 multiple times to generate multiple different candidate schedules, by using different sequences of transformations).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the evaluation of fueling plans (schedules) in de Oliveira et al. to evaluate plans generated by a plurality of algorithms to select the final best plan, as taught by Butterfoss et al., with a reasonable expectation of success, with the motivation of ensuring completeness and accuracy by searching additional possible plans/schedules (see Butterfoss et al., [0086]).
de Oliveira et al. does not explicitly recite:
communicating, via a wireless communication interface, the output mission plan to the at least one electric or hybrid electric aircraft, the output mission plan being further recorded in an on-board flight management system of the at least one electric or hybrid electric aircraft for application.
However, Shinya teaches a method for a handling flight plans (see e.g. [0006]), including:
communicating, via a wireless communication interface, the output mission plan (see [0076] created flight plan transferred to UAV, [0039] wireless) to the at least one electric or hybrid electric (see [0042]) aircraft, the output mission plan being further recorded in an on-board flight management system of the at least one electric or hybrid electric aircraft for application (see [0076] stored as the flight plan route data, and the UAV can be caused to fly in accordance with the flight plan route data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the generation of a flight plan of de Oliveira et al. to occur on a flight route setting system and include communication to the aircraft, as taught by Shinya, with a reasonable expectation of success, with the motivation of extending the usefulness and flexibility of the method to apply to unmanned aircraft while improving safety by ensuring obstacle avoidance (see Shinya, [0013-0016]).
Regarding Claim 3, de Oliveira et al. discloses the method according to claim 1, wherein the mission plan further comprises an idle time at each mission terminal (see [0043] wait time prior to refueling as part of known parameters of mission plan affecting the fuel score value).
Regarding Claim 4, de Oliveira et al. discloses the method according to claim 1, wherein at least one aircraft is a hybrid electric aircraft powered by electric and fuel generated energy (see [0067]), wherein the method further comprises selecting one fuel consumption model (see [0040] determination of energy load model at block 208, and [0067] energy loads can be determined for use of each fuel type), and wherein each mission plan further comprises an energy provision amount at each mission terminal (see [0059] amount of each fuel type for refueling the aircraft at each landing site) and an indication of an energy type to use among fuel, electricity, or both during each flight leg (see [0067] “use of one fuel type and not the other, or alternating to some degree between each fuel type”).
Regarding Claim 8, de Oliveira et al. discloses the method according to claim 1, wherein the multi-criteria scoring scheme representative of the user preferences (see [0043] fuel value score calculation based on desired factors) is constructed hierarchically (see [0063] “fuel value score… based on relative weights associated with time for refueling and fuel-related costs” i.e. ranking/hierarchy of factors) using configurable utility (see [0043] desirable factors such as fuel type or low cost being “useful” i.e. having utility. It is noted that any programmable factor has been programmed i.e. “configured”) and aggregation functions (see [0043] factor being several available fuel types, i.e. aggregation of fuel type information).
Regarding Claim 9, de Oliveira et al. discloses the method according to claim 1, further comprising receiving an update of at least one input among the mission specifications (see Figure 2, [0038] for a second iteration of 204, update of e.g. delays, estimated times of departure) or user preferences, and further repeating said selecting and said executing using the at least one updated input (see Figure 2, the second iterations of blocks 208 through 216, based on the updated block 204).
Regarding Claim 13, de Oliveira et al. discloses the method according to 1, wherein the input information further comprises information relative to predicted weather conditions (see [0041] predicted flight conditions e.g. weather).
Regarding Claim 14, de Oliveira et al. discloses a non-transitory computer readable medium storing instructions which, when executed by a programmable electronic device, cause the device to implement a method for computing a mission plan for at least one aircraft powered by electric energy or by electric and fuel generated energy according to claim 1 (see [0031-0032] and the mapping of Claim 1, above).
Regarding Claim 15, all limitations as recited have been analyzed with respect to Claim 1. Claim 15 pertains to an apparatus corresponding to the method of Claim 1. Claim 15 does not teach or define any new limitations beyond Claim 1, and therefore is rejected under the same rationale.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Publication US2021/0103860A1 (de Oliveira et al.) in view of Published Application US2023/0256859A1 (Palombini), further in view of Publication US2021/0060774A1 (Butterfoss et al.), further in view of Publication US2020/0342770A1 (Shinya), further in view of Published Application US2024/0177618A1 (Khan et al.).
Regarding Claim 2, de Oliveira et al. discloses wherein the mission plan further comprises an amount of electricity charging at each mission terminal (see [0034, 0059] amount of fuel of each fuel type for refueling at each landing site, amount of charging per the combination with Palombini as described in the rejection of Claim 1).
de Oliveira et al. does not explicitly recite the method of claim 1, wherein the mission plan further comprises:
a target aircraft speed during each flight leg.
However, Khan et al. teaches a fight plan comprising:
a target aircraft speed during each flight leg (see [0055] each leg including speed).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the flight plan of de Oliveira et al. to include speed data as taught by Khan et al., with a reasonable expectation of success, with the motivation of improving safety and compliance by integrating speed restriction data into flight plans (see Khan et al., [0055, 0070]).
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Publication US2021/0103860A1 (de Oliveira et al.) in view of Published Application US2023/0256859A1 (Palombini), further in view of Publication US2021/0060774A1 (Butterfoss et al.), further in view of Publication US2020/0342770A1 (Shinya), further in view of Publication US2014/0081569A1 (Agrawal et al.).
Regarding Claim 5, de Oliveira et al. discloses the multi-criteria constrained optimization algorithm outputs being evaluated by a score representative of the satisfaction of the objective (see [0044] fuel value scores for each landing site as a collective score of the plan, for satisfaction of objectives as evaluated in [0043]).
de Oliveira et al. does not explicitly recite the method according to claim 1, wherein the plurality of multi-criteria constrained optimization algorithms are executed in parallel.
However, Butterfoss et al. teaches the technique as above,
wherein the plurality of multi-criteria constrained optimization algorithms are executed in parallel (see [0086]).
The motivation to combine de Oliveira et al. and Butterfoss et al. was provided in the rejection of Claim 1.
de Oliveira et al. does not explicitly recite the method according to claim 1, wherein:
each multi-criteria constrained optimization algorithm outputs being evaluated by a score representative of the satisfaction of the objective, and wherein the mission plans are ranked according to their scores.
However, Agrawal et al. teaches a technique for evaluating flights (see Claim 1), wherein:
each flight being evaluated by a score representative of the satisfaction of the objective (see Claim 1, performance of the aircraft on the route), and wherein the mission plans are ranked according to their scores (see Claim 1, ranking routes based on the performance).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the fueling plan score of de Oliveira et al. to be ranked as taught by Agrawal et al., with a reasonable expectation of success, with the motivation of ensuring the most desirable flight plan (see Agrawal et al., [0004-0008]).
Regarding Claim 6, de Oliveira et al. does not explicitly recite the method according to claim 5, wherein information on the multi-criteria constrained optimization algorithms, including their ranking, is displayed on the user interface, the system being further configured to receive a user input for selecting one of the mission plans as a baseline mission plan.
However, Agrawal et al. teaches the technique as above,
wherein information on the flights, including their ranking, is displayed on the user interface (see Claim 1, [0043] displaying portion of ranked routes), the system being further configured to receive a user input for selecting one of the mission plans as a baseline mission plan (see Claim 2, receiving selection of route).
The motivation to combine de Oliveira et al. and Agrawal et al. was provided in the rejection of Claim 5.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Publication US2021/0103860A1 (de Oliveira et al.) in view of Published Application US2023/0256859A1 (Palombini), further in view of Publication US2021/0060774A1 (Butterfoss et al.), further in view of Publication US2020/0342770A1 (Shinya), further in view of Publication US2014/0081569A1 (Agrawal et al.), further in view of Published Application US2024/0353574A1 (Sbeity et al.).
Regarding Claim 7, de Oliveira et al. discloses the method: further comprising receiving a user modified mission plan, comprising at least one user modification of a first mission plan provided by one multi-criteria constrained optimization algorithm (see Figure 2, [0038] changing conditions at a repeated iteration of block 204, which [0034, 0039] can be e.g. fuel cost input by the user).
de Oliveira et al. does not explicitly recite the method according to claim 6, the method further comprising updating the multi-criteria scoring scheme using an automatic learning method, such that the modified mission plan score is assured to be greater than the first mission plan score while the updated scoring scheme stays within a predetermined distance according to a chosen metric of the original scoring scheme.
However, Sbeity et al. teaches a technique to optimize a scoring scheme (see [0086] weighting coefficients for residuals)
comprising updating the multi-criteria scoring scheme using an automatic learning method (see [0085] training AI model), such that the modified mission plan score is assured to be greater than the first mission plan score while the updated scoring scheme stays within a predetermined distance according to a chosen metric of the original scoring scheme (see [0085] so as to minimize distance between weighting coefficients and reference weighting coefficients – see also the interpretation based on the rejection under 112(b), above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the updating of the multi-criteria scoring scheme of de Oliveira et al. to use a training and distance minimization as taught by Sbeity et al., with a reasonable expectation of success, with the motivation of further improving the precision of the scheme (see Sbeity et al., [0078]).
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Publication US2021/0103860A1 (de Oliveira et al.) in view of Published Application US2023/0256859A1 (Palombini), further in view of Publication US2021/0060774A1 (Butterfoss et al.), further in view of Publication US2020/0342770A1 (Shinya), further in view of Published Application US2024/0054903A1 (Mollahan et al.).
Regarding Claim 10, de Oliveira et al. discloses wherein the mission involves at least one aircraft powered by electric and fuel generated energy (see [0067] hybrid propulsion system), the method further comprising selecting a plurality of energy models comprising an electric energy consumption model, a fuel consumption model (see [0040] determination of energy load model at block 208, and [0067] energy loads can be determined for use of each fuel type) and an electric energy charge model (see [0043] refueling represented as data, i.e. model, and charging per the combination with Palombini as described in the rejection of Claim 1).
de Oliveira et al does not explicitly recite the method according to claim 1, wherein:
each of the energy models being based on a theoretical function fine-tuned by supervised machine learning training.
However, Mollahan et al. teaches a technique for a vehicle’s energy model (see [0169] battery modeling algorithm),
the energy model being based on a theoretical function fine-tuned by supervised machine learning training (see [0169] use of current battery conditions, and machine-learned model, trained by supervised training techniques).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the models of de Oliveira et al. to be fine-tuned by machine learning, as taught by Mollahan et al., with a reasonable expectation of success, with the motivation of improving accuracy by using of specific training data (see Mollahan et al., [0169]).
Regarding Claim 11, de Oliveira et al. further discloses the flight being multi-flight missions (see [0037]).
de Oliveira et al. does not explicitly recite the method according to claim 10, wherein the supervised machine learning training is based on stored historical data relative to executed multi-flight missions.
However, Mollahan et al. teaches the technique as above,
wherein the supervised machine learning training is based on stored historical data relative to executed flight (see[0169] historical or real-time flight observations (real-time being historical by the time they are received/used)).
The motivation to combine de Oliveira et al. and Mollahan et al. was provided above in the rejection of Claim 10.
Regarding Claim 12, de Oliveira et al. does not explicitly recite the method according to claim 11, further comprising:
triggering online supervised machine learning training of fine- tuned energy models automatically after historical data is received; and
automatically optimizing the duration of a training window of historical data to be used.
However, Mollahan et al. teaches the technique as above, comprising:
triggering online supervised machine learning training of fine-tuned energy models automatically after historical data is received (see [0169] training with real-time being immediately training upon receiving the data (which is historical based on the delay to receive the data)); and
automatically optimizing the duration of a training window of historical data to be used (see [0168] using specific conditions, i.e. selecting a subset of the data (particular window) to be used).
The motivation to combine de Oliveira et al. and Mollahan et al. was provided above in the rejection of Claim 10.
Conclusion
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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/P.A./Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
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