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
Notice to Applicant
In response to the communication received on 01/21/2025, the following is a Non-Final Office Action for Application No. 19032728.
Status of Claims
Claims 1-12 are pending.
Drawings
The applicant’s drawings submitted on 01/21/2025 are acceptable for examination purposes.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 01/21/2025 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of applicant’s claim for priority based on: 19032728 filed 01/21/2025 claims foreign priority to 202421004633, filed 01/23/2024.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims fall within statutory class of process or machine or manufacture; hence, the claims fall under statutory category of Step 1.
Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
1. A processor implemented method for inline recovery of airline operations, the method comprising: receiving, by a Stochastic-Minplus-with-State (SMS) agent executed by one or more hardware processors, a current state of an airline network from a digital twin of the airline network, the current state comprising a set of inherited delay of each of a plurality of flights of the airline network currently operating in a time horizon across an airspace covered by the airline network, wherein the inherited delay of each of the plurality of flights is a result of previous actions implemented for inline recovery of each of the plurality of flights; iteratively processing, by the SMS agent, the current state of the airline network to generate an optimized action state vector for inline recovery of the plurality of flights in consideration with a plurality of constraints, wherein an action state vector in each iteration is executed on the digital twin of the airline network to analyze effect of the action state vector on the plurality of flights; wherein the SMS agent processes an undirected coordination graph with a plurality of nodes representing the plurality of flights operating in the time horizon with associated inherited delays and the plurality of nodes connected via the undirected edges between flights if at least a physical aircraft or one or more passengers are shared between the flights, wherein the SMS agent iterates until an objective function is minimized, wherein the objective function is defined by cost incurred per missed passenger (PAX), a cost per unit of departure delay, and a cost of the intervention action for each flight among the plurality of flights, and wherein the SMS agent utilizes a state dependent cost function comprising (i) a node cost incorporating action cost function and (ii) an edge cost function comprising delay and PAX cost, wherein state dependent cost function determines the cost per unit of departure delay, and the cost of the intervention action for each flight; and recommending, by the one SMS agent executed by the one or more hardware processors, the optimized action state vector for implementing in live operations of the airline network, wherein an actual action taken by the airline network for the plurality of flights is fed back to the digital twin as the current state of the airline network.
[or]
5. A system for inline recovery of airline operations, the system comprising: a memory storing instructions; one or more Input/Output (1/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to: receive by a Stochastic-Minplus-with-State (SMS) agent executed by the one or more hardware processors, a current state of an airline network from a digital twin of the airline network, the current state comprising a set of inherited delay of each of a plurality of flights of the airline network currently operating in a time horizon across an airspace covered by the airline network, wherein the inherited delay of each of the plurality of flights is a result of previous actions implemented for inline recovery of each of the plurality of flights; iteratively process by the SMS agent, the current state of the airline network to generate an optimized action state vector for inline recovery of the plurality of flights in consideration with a plurality of constraints, wherein an action state vector in each iteration is executed on the digital twin of the airline network to analyze effect of the action state vector on the plurality of flights; wherein the SMS agent processes an undirected coordination graph with a plurality of nodes representing the plurality of flights operating in the time horizon with associated inherited delays and the plurality of nodes connected via undirected edges between flights if at least a physical aircraft or one or more passengers are shared between the flights, wherein the SMS agent iterates until an objective function is minimized, wherein the objective function is defined by cost incurred per missed passenger (PAX), a cost per unit of departure delay, and a cost of the intervention action for each flight among the plurality of flights, and wherein the SMS agent utilizes a state dependent cost function comprising (i) a node cost incorporating action cost function and (ii) an edge cost function comprising delay and PAX cost, wherein state dependent cost function determines the cost per unit of departure delay, and the cost of the intervention action for each flight; and recommend by the one SMS agent executed by the one or more hardware processors, the optimized action state vector for implementing in live operations of the airline network, wherein an actual action taken by the airline network for the plurality of flights is fed back to the digital twin as the current state of the airline network.
[or]
9. One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: receiving, by a Stochastic-Minplus-with-State (SMS) agent executed by one or more hardware processors, a current state of an airline network from a digital twin of the airline network, the current state comprising a set of inherited delay of each of a plurality of flights of the airline network currently operating in a time horizon across an airspace covered by the airline network, wherein the inherited delay of each of the plurality of flights is a result of previous actions implemented for inline recovery of each of the plurality of flights; iteratively processing, by the SMS agent, the current state of the airline network to generate an optimized action state vector for inline recovery of the plurality of flights in consideration with a plurality of constraints, wherein an action state vector in each iteration is executed on the digital twin of the airline network to analyze effect of the action state vector on the plurality of flights; wherein the SMS agent processes an undirected coordination graph with a plurality of nodes representing the plurality of flights operating in the time horizon with associated inherited delays and the plurality of nodes connected via the undirected edges between flights if at least a physical aircraft or one or more passengers are shared between the flights, wherein the SMS agent iterates until an objective function is minimized, wherein the objective function is defined by cost incurred per missed passenger (PAX), a cost per unit of departure delay, and a cost of the intervention action for each flight among the plurality of flights, and wherein the SMS agent utilizes a state dependent cost function comprising (i) a node cost incorporating action cost function and (ii) an edge cost function comprising delay and PAX cost, wherein state dependent cost function determines the cost per unit of departure delay, and the cost of the intervention action for each flight; and recommending, by the one SMS agent executed by the one or more hardware processors, the optimized action state vector for implementing in live operations of the airline network, wherein an actual action taken by the airline network for the plurality of flights is fed back to the digital twin as the current state of the airline network.
The claim(s) recite(s) the following summarization of the abstract idea which includes recommending the optimized action state vector executed by the additional element(s) of non-transitory computer readable storage medium, computer and/or processor. This falls into at least the Abstract Idea Grouping of Mental Processes since the information can be analyzed by an abstract evaluation judgment process. Thus, per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity since the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion).
Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The memory medium and/or processor is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic memory medium and/or processor limitation is no more than mere instructions to apply the exception using a generic computer component. Further, recommending the optimized action state vector by a memory medium and/or processor is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B.
Per Step 2B, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: memory medium and/or processor. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, recommending the optimized action state vector by a memory medium and/or processor is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶053 wherein “the system 100 comprises one or more data storage devices or the memory 102 operatively coupled to the processor(s) 104 and is configured to store instructions for execution of steps of the method 200 by the processor(s) or one or more hardware processors 104.” Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims).
The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101.
Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. (US 10417614 B2) hereinafter referred to as Johnson in view of Yeh et al. (US 20220014963 A1) hereinafter referred to as Yeh.
Johnson teaches:
Claim 1. A processor implemented method for inline recovery of airline operations, the method comprising:
receiving, by a Stochastic-Minplus-with-State (SMS) agent executed by one or more hardware processors, a current state of an airline network from a digital twin of the airline network, the current state comprising a set of inherited delay of each of a plurality of flights of the airline network currently operating in a time horizon across an airspace covered by the airline network, wherein the inherited delay of each of the plurality of flights is a result of previous actions implemented for inline recovery of each of the plurality of flights (C.6 L.50 In example embodiments, a Digital Twin is a continuously tuned, serial-number specific digital representation available in a scalable manner so that every piece of equipment in a set of assets (e.g., a fleet of aircraft, the engines assigned to the aircraft, or the subsystems within the engines) is individually modeled with respect to its state estimation, including life and efficiency states. Digital Twin Operations Optimization (DTOO) is a logical extension that models the operations with the intent to optimize the rate of state change caused by the purposed operations of the assets and the business systems which support their use and consumption processes so as to improve financial and operating key performance indicator (KPI) objectives. KPIs and functional objectives may collide where a local optimal may detract from a system wide benefit—and a means is created to optimally manage the dynamical business-physical system's impact on, for example, customer asset utilization, fuel consumption, shop cost, capacity and inventory, safe risk adjusted CSA/MSA growth and cash flow from operations C.26 L.20 Further, as depicted in FIGS. 1A-1G, the reliability probability 160 of the entire asset or system may be based on the RUL 172 of each of the subsystems 175 of the asset, and may be developed by one or more methods, such as, for example, cumulative damage models comprised of thermodynamic and materials engineering models, statistical regression, machine learning or subsystem models that are probabilistic and aggregated with techniques, such as Monte Carlo simulation. C.40 L.25 If it is found that the next flight is a missed flight from the past then the aircraft immediately attempts to fly the next flight. First, a check is made to insure the aircraft is “fit-to-fly”, and if so, then the flight is immediately processed (without waiting for a particular time in the simulation). It is possible, that if an aircraft is grounded for an extended time period, there may actually be a backlog of missed flights. These are all processed in sequence as soon as the aircraft is “fit-to-fly”. These delayed flights have consequences on the simulation, particularly in regards to engine removals, shop visits and interactions with other aircraft. For example, if an aircraft is grounded from January to July, then on July 1st, it will begin processing the 6 months of missed flights. If, under the original flight schedule, a LLP component would have met its cycle limit sometime in February, that LLP shop visit event will now occur in August (e.g., a 6 month delay). This delayed LLP shop visit would have subsequent ripple effects on the shop visit scheduling, availability of spares engines, and determination of which engine serial numbers get installed on other aircraft. C.5 L.55 An outer loop optimizer (e.g., stochastic, foil enumeration, or full enumeration with logical cuts) solves individual decision support preferences (e.g., from pricing, work scope, assignment, rotables management, retirement estimation, portfolio membership, and so on) to optimize the ratio of risk and return to one or both of the preferences of an aircraft fleet owner whose assets are under contract or the offer or of the service contract or operations optimization decision support, such as an OEM or third party service provider. Other optimization objectives enabled by the disclosed computing control system include cash consumption, inventory, spares, turnaround time (TAT). C.31 L.27 FIG. 3 starts from the generation of the flight schedule and aircraft-to-route assignment. The flight schedule and aircraft-to-route assignment is provided, as an input. In this manner, the simulation can be used as a scenario-based “what-if” tool where different flight schedules are provided as inputs. The simulation then “flies” the schedule using “flight ops policies” and “environment issues”. Specifically, the flight ops policies include takeoff derate, climb derate and single engine taxi policy per city-pair route. In order to introduce stochasticity in the model, each of these inputs is provided a probability distribution such as a mean and a standard deviation in a normal distribution which is sampled from during the simulation.);
iteratively processing, by the SMS agent, the current state of the airline network to generate an optimized action state vector for inline recovery of the plurality of flights in consideration with a plurality of constraints, wherein an action state vector in each iteration is executed on the digital twin of the airline network to analyze effect of the action state vector on the plurality of flights (C.10 L.37 Engineering models are generated and fed data pertaining to surface temperature, local oxidation, stress, strain, cracks, and other data needed to estimate the physical stale of key components/sub-systems of the engines at a point in time. After the current point in time, simulations play out multiple possible futures. Different possible scenarios are enumerated for relevant parameters, including flight schedules, engine assignments, service work scopes, demand for worn-out parts, and so on. In example embodiments, the analysis is constrained based on contractual term limitations pertaining to the asset. The estimated demand may be used as a demand function for shop scheduling automation, making it possible to automatically make choices about parts inventory and cost calculations pertaining to the engine maintenance (e.g., comparing costs of using a new part vs. using a rebuilt part). C.36 L.65 If replicating a historical time-period, this flight schedule would be the actual historical flights flown. If performing a forecast simulation, then the flight schedule must be generated. It could either be provided by the airline directly, or it could be generated based on historical flight schedules with some subject matter expertise input into future growth or modifications to the schedule. For example, consider that an airline is looking to potentially expand its route network to more harsh environment. In absence of specific information from the airline directly, a notional future schedule is generated by using past year's flights as a basis, and making adjustments to deploy aircraft to these new routes. In example embodiments, columns include departure aircraft code, arrival airport code, aircraft type, distance, departure taxi PE Bits, arrival Taxi PE Bits, To derate mean, to derate halfrange, CI Derate mean CI Derate Halfrang, WeivullServerityf1, WeibullSeverityf2, WeibullServertiyf3.);
wherein the SMS agent processes an undirected coordination graph with a plurality of nodes representing the plurality of flights operating in the time horizon with associated inherited delays and the plurality of nodes connected via the undirected edges between flights if at least a physical aircraft or one or more passengers are shared between the flights,wherein the SMS agent iterates until an objective function is minimized, wherein the objective function is defined by cost incurred per missed passenger (PAX), a cost per unit of departure delay, and a cost of the intervention action for each flight among the plurality of flights, and wherein the SMS agent utilizes a state dependent cost function comprising (i) a node cost incorporating action cost function and (ii) an edge cost function comprising delay and PAX cost, wherein state dependent cost function determines the cost per unit of departure delay, and the cost of the intervention action for each flight (C.51 L.17 During the simulation, as an engine failure occurs, it creates the necessary WSA inputs, runs the WSA as an external module, and then receives the recommended workscope from the WSA algorithm. The simulation then processes the shop visit (using the recommended workscope) in the same manner as any other shop visit. The WSAParameter input table that defines the WSA run-time parameters that are passed to the WSA module during the external call. These parameters are required by WSA and define how the WSA algorithm is run and how it behaves. The Workscopes table may be completed with the additional workscopes defined by WSA (and the flag is set to TRUE, indicating it is used by WSA, and not the native rules-based approach). Only the workscopes flagged TRUE are passed to WSA. This table is provided by the WSA team for the specific engine platform. The WSAWorkscopeFailure map lists the WSA damage modes that could cause an engine removal and identifies the WSA-specific workscope that is subsequently triggered. This is the analog to the WorkscopeFailureMap. The WSA algorithm may interpret this as the minimum workscope that will be considered, and it is likely to propose additional workscopes as part of the optimization. The WSASimulatorDriverMap table defines the mapping between the individual damage modes to the “WSA damage modes”. Some of the modes map 1-to-1 (for example, the “Weibull Combustor” mode maps to the WSA “Combustor” mode), while others are grouped (for example, the “LLP Booster Spool”, “Fan Forward Shaft”, and “Fan Rotor” all map to the WSA “LLP Fan” grouped damage mode). This mapping must be provided by the WSA team and is necessary since, as described earlier, the WSA algorithm uses groupings of damage modes defined specifically for WSA. The WorkscopeResetMap table is populated with the WSA specific information. This table is used in the same manner as described earlier to determine which WSA damage modes need to be reset. In example embodiments, the flow of how the tables are used in a simulation is as follows. First, an engine shop visit is triggered by a particular damage mode. This damage mode is used to identify the workscope to be completed (using the WorkscopeFailureMap). The turn-around-time and cost for the shop visit are specified (using the Workscopes table) and the engine remains in the shop for the specified TAT duration. Upon leaving the shop (into the spares pool), the appropriate Weibulls are reset (using the WorkscopesResetMap). In example embodiments, the flow of how WSA is used when integrated into the simulation is as follows….); and
recommending, by the one SMS agent executed by the one or more hardware processors, the optimized action state vector for implementing in live operations of the airline network, wherein an actual action taken by the airline network for the plurality of flights is fed back to the digital twin as the current state of the airline network (C.62 L.25 Thus, in example embodiments, fuel consumption and asset lifing state changes are computed in response to a candidate duty cycle that differentially degrades engine performance according to those duty cycles. A flight schedule of an aircraft may be beneficially changed to reduce fuel costs and increase asset utilization associated with operating the aircraft, the recommendation based upon the disclosed computing state control system and comparisons of operations data collected from databases of multiple external computer systems corresponding to multiple fleets of aircraft associated with one or multiple customers. A computing control system beneficially simulates a set of physical apparatus through one or more time intervals and interacts with the service systems that interface with them, providing the historical duty cycles, routes, sensor signals, component and configuration identification, maintenance works cope, and other parameters of the system that result in enhanced operations for the owner or operator.).
Although not explicitly taught by Johnson, Yeh teaches in the analogous art of reinforcement learning for multi-access traffic management:
Stochastic-Minplus-with-State (SMS) agent (¶0563 The term “classification” in the context of ML may refer to an ML technique for determining the classes to which various data points belong. Here, the term “class” or “classes” may refer to categories, and are sometimes called “targets” or “labels.” Classification is used when the outputs are restricted to a limited set of quantifiable properties. Classification algorithms may describe an individual (data) instance whose category is to be predicted using a feature vector. As an example, when the instance includes a collection (corpus) of text, each feature in a feature vector may be the frequency that specific words appear in the corpus of text. In ML classification, labels are assigned to instances, and models are trained to correctly predict the pre-assigned labels of from the training examples. ML algorithms for classification may be referred to as a “classifier.” Examples of classifiers include linear classifiers, k-nearest neighbor (kNN), decision trees, random forests, support vector machines (SVMs), Bayesian classifiers, convolutional neural networks (CNNs), among many others (note that some of these algorithms can be used for other ML tasks as well). The terms “regression algorithm” and/or “regression analysis” in the context of ML may refer to a set of statistical processes for estimating the relationships between a dependent variable (often referred to as the “outcome variable”) and one or more independent variables (often referred to as “predictors”, “covariates”, or “features”). Examples of regression algorithms/models include logistic regression, linear regression, gradient descent (GD), stochastic GD (SGD), and the like. The terms “instance-based learning” or “memory-based learning” in the context of ML may refer to a family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training, which have been stored in memory. Examples of instance-based algorithms include k-nearest neighbor, and the like), decision tree Algorithms (e.g., Classification And Regression Tree (CART), Iterative Dichotomiser 3 (ID3), C4.5, chi-square automatic interaction detection (CHAID), etc.), Fuzzy Decision Tree (FDT), and the like), Support Vector Machines (SVM), Bayesian Algorithms (e.g., Bayesian network (BN), a dynamic BN (DBN), Naive Bayes, and the like), and ensemble algorithms (e.g., Extreme Gradient Boosting, voting ensemble, bootstrap aggregating (“bagging”), Random Forest and the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the reinforcement learning for multi-access traffic management of Yeh with the system for controlling aircraft operations and aircraft engine components assignment of Johnson for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Johnson C.1 L.25 teaches that it is desirable to optimizing factors such as a potential number of factors to manage the operations associated with a large, complex industrial system, such as for example, one or more aircraft and their associated apparatuses, such as engines for an enterprise, e.g., for asset utilization, fuel cost reduction, physical inspection, physical damage state assessment, workscope, and shop service capacity;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Johnson Abstract teaches providing a recommendation for optimizing operations of a set of industrial assets, and Yeh Abstract teaches multi-access traffic management in edge computing environments, and in particular, artificial intelligence (AI) and/or machine learning (ML) techniques for multi-access traffic management; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Johnson at least the above cited paragraphs, and Yeh at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the reinforcement learning for multi-access traffic management of Yeh with the system for controlling aircraft operations and aircraft engine components assignment of Johnson. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Johnson teaches:
Claim 2. The processor implemented method of claim 1, wherein the plurality of constraints comprise a set of resource constraints and a set of delay propagation constraints that further explicitly includes external noise from ground and air operations that are non-linear functions of delay (C.37 L.29 In example embodiments the future flight schedule is generated as a pre-processing step by running a specially developed script, such as a script having the following pseudo code: (1) read in the input files; Rename columns in the historical flight data; … (6a) for each aircraft, determine the date of its earliest flight and subtract the first flight data across ail aircraft—this indicates the delay before this aircraft began flying (within this historical time range), (6b) classify each aircraft as to whether its delay to first flight is more or less than a parameter defining the maximum—if its delay is less than the parameter, classify the aircraft as having a valid flight schedule, if delay is greater or equal, classify the aircraft as having an invalid flight schedule, (6c) for each aircraft with an invalid schedule, replace its list of flights with a copy randomly selected from valid aircraft of same type—at this point, optionally save a copy of the “cleaned up” flight list; (7) create the projected schedule, (7a) create a “schedule block” consisting of all necessary columns, (7b), starting from the data indicated in parameter file, copy the schedule block with incremented dates, (7c) repeat until the desired end date is reached and delete any flight beyond the end date, (8) save the schedule (e.g., into separate files for aircraft, airports, and routes).).
Johnson teaches:
Claim 3. The processor implemented method of claim 1, wherein the non-linear functions of delay are addressed using a sample average across forecasted optimization scenarios (C.5 L.27 In example embodiments, the risk of service cost in a contractual service agreement is estimated and priced, such as, for example, where maintenance cost and/or operating cost and/or reliability is to be guaranteed. In various embodiments, the estimating and pricing uses average estimates derived for many historical observations, such as Weibull distributions, and life limited settings for one or more engines in the agreement. In other embodiments, specific historical data is used to create a reliability characterization, such as those historical data that represents a certain operating mode and that that probability distribution is thus used for more specific forecasting by virtue of its basis more closely matching that of a given current engine's life).
Johnson teaches:
Claim 4. The processor implemented method of claim 1, wherein the method iterates over a rolling horizon, wherein the current horizon moves forward with regular time steps (C.4 L.3 In example embodiments, engine state estimation (e.g., including damage state estimation) is determined using cumulative damage models (CDMs) over historical, current, and future time horizons using historical data. The basis for these damage models may be historical observation of engine operations and resultant repairs to create probabilities of life such as Weibull distributions. In other instances. Physics based first principle engineering models are derived with, for example, thermodynamics, chemical and metallurgical analysts. The historical data may be aggregated from a plurality of sources, such as engine sensors, service shops, lab tests (image, chemical, physical measures), airline schedules and weather services, and stored in a structured data environment to create assumptions (e.g., for life cycling of specific parts, specific clustering of parts or subsystems as a function of how they jointly fail, or calculating probabilities of failure for specific parts).).
As per claims 5-8 and 9-12, the system and non-transitory machine-readable information storage medium tracks the method of claims 1-4 and 1-4, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1-4 and 1-4 are applied to claims 5-8 and 9-12, respectively. Johnson discloses that the embodiment may be found as a system and non-transitory machine-readable information storage medium, particularly memory storing instructions;one or more Input/Output (1/O) interfaces; and one or more hardware processors coupled to the memory via the one ormore I/O interfaces, wherein the one or more hardware processors are configured by the instructions (Figs. 1A-G and C.58 L.5).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/KURTIS GILLS/Primary Examiner, Art Unit 3624
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