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 .
DETAILED ACTION
Status of Claims
The following is a Final Office Action in response to Applicant’s amendment received 08/03/2026.
In accordance with Applicant’s amendment, claims 1 and 3 are amended. Claims 1 and 3 are currently pending.
Response to Amendment
The 35 U.S.C. §112(b) rejection of claim 3 is withdrawn in response to applicant’s amendment.
The 35 U.S.C. §103 rejection of claims 1 and 3 is withdrawn in response to applicant’s amendment.
Response to Arguments
Response to §101 arguments: Applicant’s remarks (Remarks at pgs. 6-7) with respect to the §101 rejection of claims 1 and 3 have been considered, however the arguments are primarily raised in support of the amendments to claims 1 and 3, which are believed to be fully addressed in the updated §101 rejection set forth below.
Response to §103 arguments: Applicant’s remarks (Remarks at pgs. 8-10) with respect to the §103 rejection of claims 1 and 3 have been considered, but are moot in view of withdrawal of the §103 rejection.
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 and 3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1 and 3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the subject matter eligibility guidance set forth in MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106.03), it is first noted that the claimed device (claim 1) and method/device (claim 3) are each directed to at least one potentially eligible category of subject matter (i.e., machine and process/machine). Accordingly, claims 1 and 3 satisfy Step 1 of the eligibility inquiry.
With respect to Step 2A Prong One of the eligibility inquiry (as explained in MPEP 2106.04), it is next noted that the claims recite an abstract idea that falls under the “Mental Processes” abstract idea grouping by reciting limitations that, but for the generic computer implementation, may be implemented mentally by a human (e.g., observation, evaluation, judgment, or opinion). The limitations reciting the abstract idea as set forth in independent claim 1 are identified in bold text below, whereas the additional elements are presented in plain text and are separately evaluated under Step 2A Prong Two and Step 2B:
a processor; and a memory storing a program, upon executed by the processor, to perform a process (These are additional elements evaluated below under Step 2A Prong Two and Step 2B) comprising:
acquiring first schedule information including estimated times of arrival for a plurality of aircraft (The acquiring of first schedule information is activity that, but for the generic processor implementation, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion such as by a human observing the first schedule information. In addition, the “acquiring” step may be considered insignificant extra-solution activity, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution activity has also been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network));
determining a first allocation result at a first time based on the first schedule information; after determining the first allocation result, acquiring second schedule information including a change in the estimated time of arrival of at least one aircraft of the plurality of aircraft (The determining of a first allocation result and acquiring of second schedule information are activities that, but for the generic processor implementation, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion such as by a human observing the first schedule information);
determining, based on the second schedule information including the change in the estimated time of arrival, a second allocation result at a second time later than the first time as an allocation result indicating an allocation order for a plurality of objects to be allocated, and calculating a change cost, which is an amount of increase in at least one of a fuel cost and a physical or mental burden on a pilot cause by delayed landing time, when the allocation result is changed from the first allocation result to the second allocation result (The determining of a second allocation result and calculating a change cost are activities that, but for the generic processor implementation, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion such as by a human observing the results and mentally evaluating the change cost, even if aided by pen and paper to perform the calculation);
giving each of the first allocation result and the second allocation result to a machine learning model for reward value prediction, acquiring a first reward value indicating a degree of quality of the first allocation result and a second reward value indicating a degree of quality of the second allocation result from the machine learning model, and predicting a reward value difference between the first reward value and the second reward value by subtracting the first reward value from the second reward value (The step for giving of the first/second allocation results to the model is activity that, but for the generic processor implementation and high-level generic recitation of a machine learning model, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion, such as by a human writing the results with the aid of pen and paper);
selecting the first allocation result or the second allocation result on a basis of a change cost calculated; calculating the first reward value by giving the first allocation result to a reward function, and calculating the second reward value by giving the second allocation result to the reward function, and calculate a reward value difference between the first reward value and the second reward value by subtracting the first reward value from the second reward value, wherein each of the first allocation result and the second allocation result indicates a different sequence at which the plurality of aircraft are allocated use of a runway for landing, the process calculates the change cost independently of the first reward value, the second reward value, and the reward value difference, the process calculates each of the first reward value, the second reward value, and the reward value difference independently of the change cost (The selecting of the first/second allocation result and calculation of the first/second reward values and a difference between them are activities that, but for the generic processor implementation, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion, such as with the aid of pen and paper),
the process utilizes supervised learning or reinforcement learning to update weights of the machine learning model so as to minimize a square of a difference between the reward value difference that has been predicted and the reward value difference calculated (The updating of weights of a model is activity that, but for the generic processor and high-level generic recitation of supervised or reinforcement learning, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion, such as with the aid of pen and paper),
the process selects the second allocation result when the reward value difference is larger than 0 and the change cost is smaller than or equal to a cost threshold, and selects the first allocation result otherwise (The selecting of the second allocation result or the first allocation result are considered activities that, but for the generic processor implementation, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion to perform the selection based on the value difference), and
the plurality of aircraft are allocated use of the runway for landing according to the sequence indicated by the selected one of the first allocation and the second allocation (The allocating step is activity that, but for the generic processor, could be implemented as mental activity such as by observation, evaluation, judgment, or opinion, such as with the aid of pen and paper to depict a schedule/sequence of runway landings).
Independent claim 3 recites similar limitations as those set forth in claim 1 as discussed above, and have therefore been determined to recite the same abstract idea as claim 1.
With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP 2106.04(d)), the judicial exception is not integrated into a practical application. Independent claims 1 and 3 include additional elements directed to a processor, memory storing a program executed by the processor, machine learning, and supervised learning or reinforcement learning. The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring activities are considered additional elements, this activity at most amounts to insignificant extra-solution data gathering activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g). Lastly, the machine learning and supervised learning or reinforcement learning are recited at a high level of generality and involve steps that, as recited, could performed mentally or with the aid of pen and paper, do not include sufficient details of actual machine-learning, supervised, learning, or reinforcement learning that tend to show these activities requires anything other than generic computer implementation, nor have these activities been shown to improve upon machine-learning, the computer, or any technology or otherwise integrate the claim into a practical application.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry (as explained in MPEP 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent claims 1 and 3 include additional elements directed to a processor, memory storing a program executed by the processor, machine learning, and supervised learning or reinforcement learning. These additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions/software to perform the abstract idea, which merely serves to tie the abstract idea to a particular technological environment (generic computing environment), similar to adding the words “apply it” (or an equivalent) (See, e.g., Spec. at par. [0021],” noting for example that “The computer means hardware that executes the program, and may be, for example, a central processing unit (CPU), central processor, processing unit, computing unit, microprocessor, microcomputer, processor, or digital signal processor (DSP)”). Accordingly, the generic computer implementation merely serves to link the use of the judicial exception to a particular technological environment and therefore does not amount to significantly more than the abstract idea itself. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Even if the acquiring activities are considered additional elements, this activity at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is well-understood, routine, and conventional activity and thus insufficient to add significantly more to the claims. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Lastly, the machine learning and supervised learning or reinforcement learning are recited at a high level of generality and involve steps that, as recited, could otherwise be performed mentally or with the aid of pen and paper. These elements merely serve to tie the judicial exception to a particular technological environment (machine learning) and therefore do not amount to significantly more than the abstract idea itself. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Nevertheless, it is noted that machine learning along with supervised and reinforcement learning are considered well-understood, routine, and conventional in the art, and therefore do not add significantly more to the claims. See, e.g., You et al, US 2012/0191531 (par. 37: “model 514 may comprise, for example, a model obtained using any of a variety of well-known machine learning techniques”). See also, Chickering et al., US Pat. No. 6,831,663 (col. 9, lines 53-58: “obtaining a probabilistic model 300, such as by learning or creating one using conventional machine learning techniques”). See also, Gansner, US Pat. No. 8,447,713 (col. 8 lines 34-37: “There are three major learning paradigms for an artificial neural network--supervised learning, unsupervised learning and reinforcement learning--all of which are well known in the art”).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Allowable over the prior art
Claims 1 and 3 are allowable over the prior art. The closest prior art reference of record, Wulf et al. (US 2023/0273624), is directed to computer implemented features for managing assignments of tasks using machine learning. Wulf et al. and the other prior art of record teach several features of claims 1/3, including for example: acquiring first schedule information; determining a first allocation result at a first time based on the first schedule information; acquiring second schedule information including a change (Wulf et al. at pars. 8-10 and 24); calculating a change cost, and selecting the first allocation result or the second allocation result on a basis of the change cost calculated (Wulf et al. at pars. 10, 20, 58, 88, and 100); selects the second allocation result when the reward value difference is larger than 0 and the change cost is smaller than or equal to a cost threshold, and selects the first allocation result otherwise (Wulf et al. at par. 20); and the process utilizes supervised learning or reinforcement learning to update weights of the machine learning model (Wulf et al. at pars. 8-10, 20, and claim 17). However, Wulf et al. and the other prior art references of record do not teach or render obvious the claim limitations directed to wherein each of the first allocation result and the second allocation result indicates a different sequence at which the plurality of aircraft are allocated use of a runway for landing, the process calculates the change cost independently of the first reward value, the second reward value, and the reward value difference, the process calculates each of the first reward value, the second reward value, and the reward value difference independently of the change cost; the plurality of aircraft are allocated use of the runway for landing according to the sequence indicated by the selected one of the first allocation and the second allocation, as recited and arranged in independent claim 1 and as similarly encompassed by independent claim 3, thereby rendering independent claims 1 and 3 as allowable over the prior art. Claims 1 and 3 are not allowed, however, because they stand rejected under 35 USC §101, as discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sasaki et al. (US 2019/0087751): discloses reinforcement learning techniques for controlling objects in accordance with a minimization/maximization value function and costs related thereto (at least pars. 3-4).
Kim (US 2021/0211111): discloses features for adjusting information through reinforcement learning, including adjusting weights/bias of a learning model (par. 235).
Lopez Leones et al. (US 2022/0139232): discloses features for predicting flight data, including updating models to improve prediction accuracy of temporal data (pars. 49-52).
D. S. Kieckbusch et al., "Negotiation Approach by Reinforcement Learning for Takeoff Sequencing Decision in Airports," 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 2019, pp. 4477-4482: discloses features for employing reinforcement learning techniques to manage air traffic flow.
C. Strottmann Kern et al., "Data-driven aircraft estimated time of arrival prediction," 2015 Annual IEEE Systems Conference (SysCon) Proceedings, Vancouver, BC, Canada, 2015, pp. 727-733: discloses features for enhancing aircraft ETA predictions.
S. Khanmohammadi et al., "A systems approach for scheduling aircraft landings in JFK airport," 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, 2014, pp. 1578-1585: discloses features for scheduling airport landings, including a framework that integrates computational intelligence techniques using an adaptive network based fuzzy inference system to predict flight delays and a fuzzy decision making procedure for scheduling the aircraft landings.
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 extension fee 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 date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Timothy A. Padot whose telephone number is 571.270.1252. The Examiner can normally be reached on Monday-Friday, 8:30 - 5:30. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Brian Epstein can be reached at 571.270.5389. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
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/TIMOTHY PADOT/
Primary Examiner, Art Unit 3625
08/26/2026
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