DETAILED ACTION
This is a FINAL Action on the merits and is responsive to the papers filed on 07/28/2025. Claims 1-20 are currently pending and are examined below.
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 .
Response to Amendments
The amendments filed on 7/8/2025 in response to the initial rejection made on 03/27/2025 have been acknowledged and entered. Claims 5, 12, and 18 have been canceled. Claims 1, 4, 6, 8, 10, 13-14, 16, and 19 have been amended. Rejections necessitated in response to the amendments made to the claims have been made. Responses to the Applicant’s arguments are written below. Claims 1-4, 6-11, 13-17, and 19-20 are now currently pending.
Contingent Limitations
Claims 1, 8, and 14 contain contingent limitations:
Claim 1: “one or more conditional maneuvers that are executed in response to an emergency” in line 13 of the claim.
Claim 1: “in response to the evaluating, determining a score” in line 37 of the claim.
Claim 8: “one or more conditional maneuvers that are executed in response to an emergency” in lines 14-15 of the claim.
Claim 8: “in response to the evaluating, determining a score” in line 38 of the claim.
Claim 14: “one or more conditional maneuvers that are executed in response to an emergency” in lines 16-17 of the claim.
Claim 14: “in response to the evaluating, determining a score” in line 41 of the claim.
The MPEP recites the following guides for Contingent Clauses:
“The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. For example, assume a method claim requires step A if a first condition happens and step B if a second condition happens. If the claimed invention may be practiced without either the first or second condition happening, then neither step A or B is required by the broadest reasonable interpretation of the claim.” See MPEP 2111.04 II.
“The broadest reasonable interpretation of a system (or apparatus or product) claim having structure that performs a function, which only needs to occur if a condition precedent is met, requires structure for performing the function should the condition occur. The system claim interpretation differs from a method claim interpretation because the claimed structure must be present in the system regardless of whether the condition is met and the function is actually performed.” See MPEP 2111.04 II.
Accordingly, a structure capable of performing limitation (3)-(6) as noted above, is sufficient to disclose this limitation. A claim containing a "recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus" if the prior art apparatus teaches all the structural limitations of the claim. Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987).
Claim limitation (1)-(2) as noted above, is a process claim and Ex Parte Schulhauser applies to that limitation. See MPEP 2111.04 II
"‘[i]f the condition for performing a contingent step is not satisfied, the performance recited by the step need not be carried out in order for the claimed method to be performed…’ Therefore ‘[t]he Examiner did not need to present evidence of the obviousness of the [ ] method steps of claim 1 that are not required to be performed under a broadest reasonable interpretation of the claim (e.g., instances in which the electrocardiac signal data is not within the threshold electrocardiac criteria such that the condition precedent for the determining step and the remaining steps of claim 1 has not been met);’ however to render the claimed system obvious, the prior art must teach the structure that performs the function of the contingent step along with the other recited claim limitations”
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 1-4, 6-11, 13-17, and 19-20 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.
Claim 1 is directed to “a computer-implemented method” (i.e. a process); claim 8 is directed to “a non-transitory computer-readable storage medium ” (i.e. a machine); and claim 14 is directed to “a system” (i.e. a machine). The claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 1 of the subject-matter eligibility analysis is: Yes.
However, the claims are drawn to an abstract idea of “### abstract idea ###,” either in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion) which are “performed on a computer” (per MPEP 2106(III)(C) “A Claim That Requires a Computer May Still Recite a Mental Process”).
The claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes.” Independent claim 1, analyzed as the representative of the claimed subject matter, is reproduced below. The limitations determined to be abstract ideas are in italics. The additional elements recited at a high level of generality are shown in bold. The limitation(s) determined to be extra-solution activity are underlined.
Independent claim 1: A computer-implemented method executed using a first computer and comprising:
receiving, from a flight simulator computer that is separate from the first computer, over a digital electronic computer network, session data indicating flight variables of a flight session occurring on the flight simulator computer in real-time;
inputting the session data, in real-time, into a trained machine learning time-series classifier that has been trained to identify a plurality of maneuvers from the session data and partition the session data into a plurality of segments,
identifying in real-time, by the trained machine learning time-series classifier, the plurality of maneuvers from the session data wherein the plurality of maneuvers includes flight maneuvers and one or more conditional maneuvers that are executed in response to an emergency, unanticipated, or abnormal condition during the flight session, wherein the trained machine learning time-series classifier has been trained to automatically recognize the conditional maneuvers based on one or more changes in flight variables of airspeed, altitude, heading, rate of climb, rate of descent, bank angle, pitch, roll, yaw, application of propulsion system thrust, application of propulsion system power, application of brakes, application of landing gear, application of one or more control surfaces, application of one or more flight control inceptors, application of one or more of flight control devices, aircraft malfunctions, or external environmental conditions;
partitioning the session data into the plurality of segments, each segment corresponding to a maneuver of a plurality of maneuvers identified by the trained machine learning time-series classifier, wherein each maneuver is associated with a plurality of performance metrics;
generating, from historical session data from one or more historical flight sessions, a set of abnormal detection data using a second machine learning algorithm that has been trained to determine evaluation criteria, wherein the set of abnormal detection data is associated with a deviation from the maneuver;
using stored program instructions executed in the first computer, evaluating the plurality of performance metrics associated with the plurality of maneuvers by, for each of the plurality of maneuvers, the evaluating comprising:
identifying one or more of the plurality of performance metrics associated with each of the plurality of maneuvers;
evaluating the identified one or more performance metrics associated with each of the plurality of maneuvers using a corresponding segment of the session data and the set of abnormal detection data;
in response to the evaluating, determining a score indicating a measurement of pilot proficiency for each of the plurality of maneuvers; and
sending the determined scores of the plurality of maneuvers to a client device that is coupled via a network to the first computer.
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea.
Step 2A, Prong 1 of the subject-matter eligibility analysis is: Yes.
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that, e.g., “a flight simulator computer” and “a first computer,” are claimed, as these are merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., data gathering) and/or do no more than generally link the use of a judicial exception to a particular technological environment or field of use. In other words, the claimed “identifying one or more of the plurality of performance metrics…, determining a score” is not providing a practical application.
Step 2A, Prong 2 of the subject-matter eligibility analysis is: No.
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g. “a flight simulator computer” and “a first computer,” are claimed these are all generic, well-known, and conventional computing elements. As evidence that these are generic, well-known, and conventional computing elements, Applicant’s specification discloses them in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a), which satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the Applicant’s claimed “a flight simulator computer” is described in para. [0037], as follows:
“[0030] In an embodiment, the flight simulator computer 102 represents a workstation, mobile computing device, smartphone, laptop computer, or desktop computer that is associated with a flight simulation environment. In some embodiments, the flight simulator computer 102 is programmed with an operating system having an internet software stack that can communicate with the data network 101 using protocols such as HTTP over TCP/IP, and has a connection to the internet that can be used to communicate with the adaptive training server 108 using HTTP, application protocols implemented using apps on the flight simulator computer 102, or other techniques. The flight simulator computer 102 may be one source of introducing flight simulation data into the processes described herein, but other embodiments may the flight simulation data and other types of data appropriate for pilot training through programmatic calls or other systems or computers.”
The Specification is silent regarding what “a first computer” would be so for the purposes of examination it is a computer that operates similar to a well-known computer, or similar to the flight simulator computer that is described in the above paragraph.
This element is reasonably interpreted as a generic computer which provides no details of anything beyond ubiquitous standard equipment. As such, the claimed limitation of “a flight simulator computer” is reasonably understood as not providing anything significantly more.
Additionally, the fact that the Specification does not further describe the training of the machine learning time-series classifier to have a new technique in training the machine learning system that will result in different and improved technology, but instead uses general terms to describe an algorithm (see originally filed Specification (at least ¶ 17, 36-37, and 42)). Without describing the particulars, indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Hence, the additional elements are generic, well-known, and conventional computing elements. The use of the additional element(s) either alone or in combination amounts to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept, and thus the claim is patent ineligible.
Step 2B, of the subject-matter eligibility analysis is: No.
In addition, dependent claims 2-4, 6-7, 9-11, 13, 15-17, and 19-20 not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-4, 6-7, 9-11, 13, 15-17, and 19-20 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to independent claims 1, 8, and 14.
Therefore, claims 1-4, 6-11, 13-17, and 19-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
Claims 1-4, 6-11, 13-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Thomas R. Kurtz (US 20150079545 A1; hereinafter Kurtz) in view of Duncan Gregory Wayne et al. (US 20190324988 A1; hereinafter Wayne et al.) in further view of Jean-Francois Delisle et al. (US 20190304324 A1; hereinafter Delisle et al.) and Bruno Jorge Correia Gracio et al., (US 20180165979 A1; hereinafter Correia Gracio et al.) and Marco Del Pra (“Time Series Classification with Deep Learning” (September 8, 2020); hereinafter Marco).
Regarding claim 1, Kurtz discloses A computer-implemented method executed using a first computer and comprising (see at least: Kurtz [Abstract]):
receiving, from a flight simulator computer (flight simulator 112 [Kurtz; 0020]) that is separate from the first computer (software modules 102 [Kurtz; 0020]), over a digital electronic computer network (internet (network) 116 [Kurtz; 0028]), session data indicating flight variables of a flight session occurring on the flight simulator computer in real-time Kurtz discloses that the system can include real-time guidance to the pilot during the performance of the flight tasks (See at least: Kurtz [0004]). The data/results from the flight test are also stored and recorded and can be verified by the instructor (See at least: Kurtz [0004]). The system is automated and stores the appropriate data for review;
inputting the session data, in real-time (the pilot is prompted for permission to stream data to the VFIS in real time. In a configuration, the pilot can receive a copy of the forensic record and other data that can be uploaded to the VFIS after the flight simulation (see at least: Kurtz paragraph [0062]).
However, Kurtz does not explicitly teach that the session data is inputted into a trained machine learning time-series classifier that has been trained to identify a plurality of maneuvers from the session data and partition the session data into a plurality of segments,
Wayne et al. teaches a neutral network/machine learning models that are trained to predict outcomes based on the environment and training data. Environments can include flight simulation data and the agent (See at least: Wayne et al. [0027]). The neural network/machine learning model is used to predict observations based on the data inputted. Marco also teaches in that there are types of time-series architectures (for example: Recurrent Neural Networks) that are designed mainly to predict an output for each element in the time series (see at least: attached NPL page 37).
It would have been obvious to one having ordinary skill in the art to use a machine learning system like the one taught in Wayne et al. or Marco to take the data inputted from the simulated test from Kurtz for the added benefit of organizing maneuvers or other session data metrics from the agent (test taker). Organizing these automatically and put into a learning system could help prepare the instructor to provide better assistance and guidance when teaching the student further and help show the student their estimated results vs the actual results
Kurtz, Wayne and Marco do not explicitly teach using stored program instructions executed in the first computer, evaluating the plurality of performance metrics associated with the plurality of maneuvers by, for each of the plurality of maneuvers, the evaluating comprising: identifying one or more of the plurality of performance metrics associated with each of the plurality of maneuvers; evaluating the identified one or more performance metrics associated with each of the plurality of maneuvers using a corresponding segment of the session data and the set of abnormal detection data; in response to the evaluating, determining a score indicating a measurement of pilot proficiency for each of the plurality of maneuvers; and sending the determined scores of the plurality of maneuvers to a client device that is coupled via a network to the first computer identifying in real-time, by the trained machine learning time-series classifier, the plurality of maneuvers from the session data wherein the plurality of maneuvers includes flight maneuvers and one or more conditional maneuvers that are executed in response to an emergency, unanticipated, or abnormal condition during the flight session, wherein the trained machine learning time-series classifier has been trained to automatically recognize the conditional maneuvers based on one or more changes in flight variables of airspeed, altitude, heading, rate of climb, rate of descent, bank angle, pitch, roll, yaw, application of propulsion system thrust, application of propulsion system power, application of brakes, application of landing gear, application of one or more control surfaces, application of one or more flight control inceptors, application of one or more of flight control devices, aircraft malfunctions, or external environmental conditions; partitioning the session data into the plurality of segments, each segment corresponding to a maneuver of a plurality of maneuvers identified by the trained machine learning time-series classifier, wherein each maneuver is associated with a plurality of performance metrics; generating, from historical session data from one or more historical flight sessions, a set of abnormal detection data using a second machine learning algorithm that has been trained to determine evaluation criteria, wherein the set of abnormal detection data is associated with a deviation from the maneuver;
Delisle teaches identifying one or more of the plurality of performance metrics that are associated with the maneuver. The processor module computes the plurality of performance metric datasets to identify actual maneuvers of the virtual element during the training activity (See at least: Delisle et al. [Abstract]); evaluating the identified one or more performance metrics associated with the maneuver based on the corresponding segment of the session data; Delisle et al. teaches obtaining a plurality of expected maneuvers of the virtual element during the training activity… computing the plurality of performance metric datasets to identify actual maneuvers of the virtual element during the training activity, identifying and grades one or more actual nested maneuvers against corresponding ones of the expected nested maneuvers… (See at least: Delisle [0113]) determining a score for the maneuver based on the evaluated one or more performance metrics associated with the maneuver, the score indicating a measurement of pilot proficiency for the maneuver Delisle et al. further teaches receiving the plurality of performance metric datasets from a simulation mapping system that determines a plurality of performance metric values in relation to the training activity performed by the user in the interactive computer simulation (See at least Delisle et al. [0120]). sending the determined scores of the plurality of maneuvers to a client device that is coupled via the network to the first computer The plurality of performance metric datasets related to the virtual element being simulated may be used to provide a grading scorecard for the actual maneuvers. The grading scorecard for the actual maneuvers may then be provided for display in the interactive computer simulation (See at least Delisle [0123]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have used the method and system for evaluating and determining scores flight simulated tests from Delisle with the machine learning model Wayne et al. for the added benefit of quickly organizing and categorizing the results based on what will help the student learn the most. A person having ordinary skill in the art would combine Delisle with Kurtz to further categorize the types of tests (maneuvers) for the real-time feedback to help instruct student pilots in a safer way and quicker way.
Kurtz in view of Wayne, Marco, and Delisle do not explicitly teach Identifying in real-time, by the trained machine learning time-series classifier, the plurality of maneuvers from the session data wherein the plurality of maneuvers includes flight maneuvers and one or more conditional maneuvers that are executed in response to an emergency, unanticipated, or abnormal condition during the flight session, wherein the trained machine learning time-series classifier has been trained to automatically recognize the conditional maneuvers based on one or more changes in flight variables of airspeed, altitude, heading, rate of climb, rate of descent, bank angle, pitch, roll, yaw, application of propulsion system thrust, application of propulsion system power, application of brakes, application of landing gear, application of one or more control surfaces, application of one or more flight control inceptors, application of one or more of flight control devices, aircraft malfunctions, or external environmental conditions; partitioning the session data into the plurality of segments, each segment corresponding to a maneuver of a plurality of maneuvers identified by the trained machine learning time-series classifier, wherein each maneuver is associated with a plurality of performance metrics; generating, from historical session data from one or more historical flight sessions, a set of abnormal detection data using a second machine learning algorithm that has been trained to determine evaluation criteria, wherein the set of abnormal detection data is associated with a deviation from the maneuver;
Gracio teaches Identifying in real-time, the plurality of maneuvers from the session data wherein the plurality of maneuvers includes flight maneuvers (a maneuver, a procedure, or other scenario event can be performed again in the same training session depending on the feedback received (see at least: Gracio paragraph [0076])) and one or more conditional maneuvers that are executed in response to an emergency, unanticipated, or abnormal condition during the flight session (the scenario can be an event happening in a scenario script that is planned by the assessor when creating the scenario script. For example, a scenario event can be an engine outage, a maneuver, weather approaching the airport before takeoff, a go-around, or some other suitable event (see at least: Gracio paragraph [0060]), wherein the system has been trained to automatically recognize the conditional maneuvers based on one or more altitude (output data computed by the flight simulator (e.g. altitude and aircraft speed) (see at least: Gracio paragraph [0040]); partitioning the session data into the plurality of segments, each segment corresponding to a maneuver of a plurality of maneuvers identified by the trained machine learning time-series classifier, wherein each maneuver is associated with a plurality of performance metrics (The storing module stores real-time data of the training session in a data repository, wherein the real-time data includes the time-stamped training session data and the time-stamped instructor rating data. The debriefing module retrieves real-time data of a training session from the data repository; integrates time-stamped instructor rating data with video data of the training session; replays the integrated data on a display; and updates the instructor rating data in the data repository with updated instructor rating data received from an instructor during the replay. The real-time instructor rating module is configured to retrieve a checklist box including actions to be performed by the trainee during a pre-defined scenario event; analyze the training device data received from the training device; based on the analysis of the training device data, automatically check execution of at least one action included in the checklist box performed by the trainee during the training session; and provide real-time feedback to the instructor regarding the execution of the at least one action (see at least: Gracio paragraph [0009]); generating, from historical session data from one or more historical flight sessions, a set of abnormal detection data using a second machine learning algorithm that has been trained to determine evaluation criteria, wherein the set of abnormal detection data is associated with a deviation from the maneuver (The instructor 140 could also mark any performance indicator 810 displayed if needed to define the comment in terms of the more detailed performance indicators. Then, the instructor 140 can submit the comment using the “Submit Good Comment” or “Submit Bad Comment” button. Finer rating scales are possible, e.g. a 5-point scale. This comment is then saved and associated with the associated competency 702 and/or performance indicators 810, and with a time stamp that is related to the video being recorded, so that the instructor can later retrieve this comment associated with the right moment of the video recording. In addition, the instructor rating module 210 may also provide means to identify whether the recorded comment or the scenario event relates to the performance of an individual crew member or to the entire crew (e.g., captain, first officer, both) (see at least: Gracio paragraph [0065]);
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have added the machine learning system of Gracio to the one of Kurtz in view of Wayne, Delisle and Marco for the added benefit of creating actual scenarios that Pilots should be prepared for and testing their readiness.
Regarding claim 2, and similarly claims 9 and 15, Kurtz in view of Wayne in further view of Delisle and Gracio teach the claimed matter as stated above and Delisle further teaches sending, to the client device that is coupled via the network to the first computer, the determined scores The plurality of performance metric datasets related to the virtual element being simulated may be used to provide a grading scorecard for the actual maneuvers. The grading scorecard for the actual maneuvers may then be provided for display in the interactive computer simulation (See at least Delisle et al. [0123]). However, Delisle doesn’t explicitly teach that the scores are associated with the plurality of competency indicators.
Correia Gracio, teaches determining, for each of the plurality of competency indicators (see at least Correia Gracio et al., When selecting a competency 702, a list of performance indicators 810 is automatically filled-in with the respective performance indicators [0064]), a score for the competency indicator based on one or more of the evaluated plurality of performance metrics that are associated with the competency indicator; the Standard Operation Procedures across scenario events can also be aggregated since this competency looks into whether the procedures are being followed correctly. These procedures could be take-off checklists, landing checklist and briefings, instrumented landing procedures, and other suitable procedures. Therefore, all International Civil Aviation Organization (ICAO) competencies can be aggregated across scenario events ending up with an overall competency score instead of a scenario-based score (See at least Correia Gracio et al., paragraph [0130]).
Regarding claim 3, and similarly claims 10 and 16, Kurtz in view of Wayne in further view of Delisle and Gracio teach the claimed matter as stated above and Gracio further teaches the plurality of competency indicators being associated with one or more demonstrations of: an ability to control an aircraft flight path through manual control; Gracio et al., teaches that a competency test scenario example is in a go-around, a possible group of actions are making a callout and setting the airplane in go-around (GA) thrust, making a callout for flaps and retracting the flaps, making a callout for positive climb and then performing actions for a gear up and retracting the gear using the correct control. One having ordinary skill in the art will know that if a pilot being tested can pass this scenario, they would it would be a demonstration of a competency indicator.
Regarding claim 4, and similarly claims 11 and 17, Kurtz in view of Wayne in further view of Delisle and Gracio teach the claimed matter as stated above and Delisle further teaches, the plurality of flight maneuvers including one or more of: go-around, In FIG. 7 and the detailed description of FIG. 7 (see at least Delisle et al., paragraph [0079]), it is shown that there are different training simulations and one of the simulations is a Go-Around 7400. See image directly below.
PNG
media_image1.png
491
753
media_image1.png
Greyscale
Regarding claim 6, and similarly claims 13 and 19, Kurtz in view of Wayne in further view of Delisle and Gracio teach the claimed matter as stated above and Delisle further teaches, for each of the plurality of maneuvers, the evaluating the identified one or more performance metrics associated with the maneuver using the corresponding segment of the session data comprising evaluating whether the flight variables indicated by the segment satisfy the evaluation criteria specified by abnormal detection data. Delisle et al., straining system computes the plurality of performance metric datasets to identify actual maneuvers of the virtual element during the training activity, identifies one or more failed actual maneuvers of the virtual element during the training activity against corresponding ones of the expected maneuvers and performs computational regression on the actual maneuvers of the virtual element compared to the expected maneuvers of the virtual element to identify one or more root causes of the failed actual maneuvers… (See at least Delisle et al., paragraph [0114]).
Regarding claim 7, and similarly claim 20, Kurtz in view of Wayne et al., and Delisle et al. teach the claimed invention as stated above, and Delisle et al. further teaches teaches the evaluation criteria based on one of: airline standard operating procedures (one or more standard operating procedures (SOP) from the detected actual maneuvers). However, Delisle et al., does not explicitly teach that the abnormal detection data having been generated based on a machine learning algorithm trained to determine. Wayne et al., teaches a machine learning model that is trained to predict outcomes based on the environment and training data. Environments can include flight simulation data and the agent (See at least: Wayne et al. [0027]). The neural network/machine learning model is used to predict observations based on the data inputted.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have added the ability to detect abnormal detection data/fail or pass on the maneuvers done by the pilot being tested. Without this feature it would be difficult to determine if a pilot completed the simulated test. Adding the machine learning model to detect/predict outcomes based on SOP during a flight simulation test can allow for instructors to provide real-time guidance to help the pilot be able to fix errors while testing.
Regarding claim 8, Kurtz discloses A non-transitory computer-readable storage medium storing one or more sequences of instructions (a non-transitory computer readable medium having instructions stored thereon can be executed by one or more processors and cause the processors to record flight data from a flight simulator during performance (see at least Kurtz paragraph [0005]) which when executed using one or more processors of a first computer cause the processors to execute the steps of: the method recited in claim 1 as recited above.
Regarding claim 14, A system comprising: one or more processors of a first computer and one or more computer-readable non-transitory storage media in communication with the one or more processors, the one or more computer-readable non-transitory storage media comprising instructions that when executed by the one or more processors, cause the system to perform operations comprising (a non-transitory computer readable medium having instructions stored thereon can be executed by one or more processors and cause the processors to record flight data from a flight simulator during performance (see at least Kurtz paragraph [0005]): the method recited in claim 1 as recited above.
Response to Arguments
Drawings
Objections made to the drawings are now moot because of the Amendments made by the Applicant.
35 U.S.C. § 112(b)
The rejections made under 35 U.S.C. § 112(b) for the limitation “recovery from unusual attitude” are now moot. The Applicant submitted an NPL that recited the following on page 22:
“The term "upset" was formally introduced by an industry work group in 2004 in the "Pilot Guide to Airplane Upset Recovery," which is a part of the "Airplane Upset Recovery Training Aid." The work group was primarily focused on large transport airplanes and sought to come up with one term to describe an "unusual attitude" or "loss of control," for example, and to generally describe specific parameters as part of its definition. Consistent with the Guide, the FAA considers an upset to be an event that unintentionally exceeds the parameters normally experienced in flight or training.”
The definition is accepted and the rejections previously applied are withdrawn.
35 U.S.C. § 101
Applicant amended the independent claims that necessitated further considerations under 35 U.S.C. § 101 which is found above.
However, Applicant on page 4 of the Remarks stated “the claims recite or describe, and therefore are "directed to," a technical method of identifying and evaluating conditional maneuvers in real time in a specific manner, and not an abstract idea or any other judicial exception” and “Each of these features is significantly more, reciting elements that address emergency, unanticipated, or abnormal condition during the flight session. These technical features are significantly more than the asserted abstract idea, specifically, because each element recites a step in a computer implemented process to identify, partition, and evaluate conditional flight maneuvers in response to the emergency, unanticipated, or abnormal conditions.” The information/data processed by the machine learning algorithms are considered as extra-solution data gathering and the analysis/feedback is considered as abstract. Additionally, merely “[u]sing a computer to accelerate an ineligible mental process does not make that process patent-eligible.” Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Canada (U.S.), 687 F.3d 1266, 1279 (Fed. Cir. 2012); see also CLS Bank Int’l v. Alice Corp. Pty. Ltd., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) (“simply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.”), aff’d, 573 U.S. 208 (2014). Accordingly, the additional element of a processor/machine learning does not transform the abstract idea into a practical application of the abstract idea.
35 U.S.C. § 103
Applicant stated on page 6 and 7 of the remarks that the combination of Kurtz, Wayne and Delisle do not disclose the features claimed in the independent claims. Examiner reconsidered the rejection in light of the amendments and included further prior art to clarify the rejection. Examiner agrees that Kurtz does not teach the use artificial learning; however, Wayne teaches that you can use machine learning to predict outcomes in flight simulation. Marco was introduced to show that time-series classifiers can predict outcomes on the elements within the time series. Gracio also teaches that the flight simulation can be implemented to teach artificial intelligence (see at least: Gracio paragraph [0112]).
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SELWA A ALSOMAIRY whose telephone number is (703)756-5323. The examiner can normally be reached M-F 7:30AM to 5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Vasat can be reached at (571) 270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SELWA A ALSOMAIRY/Examiner, Art Unit 3715
/PETER S VASAT/Supervisory Patent Examiner, Art Unit 3715