Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application and Claims
This action is in reply to the application filed on 5/19/2025.
This communication is the first action on the merits.
Claims 1-20 is/are currently pending and have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-16 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-7,9-14 and 16-22 of U.S. Patent Application No. 18364380. Although the claims at issue are not identical, they are not patentably distinct from each other because the Claims reciter the same inventive concept with the same features being used in the same field of endeavor.
Table 1:
Instant Application: 19212409
Claim 1, 11
Application: 18364380
Claim 1, 9, 16
receiving a simulation state update from a computing system of the training environment;
receiving a simulation state update indicating a change in a simulation state of the computer simulated training scenario from a training computing system of the training environment;
querying a behavioral elicitation event (BEE) database to determine, from a plurality of BEEs stored in the BEE database, a set of active BEEs based upon the simulation state update, each BEE of the plurality of BEEs comprising one or more elicited actions and one or more observable behaviors;
querying a behavioral elicitation event (BEE) database to determine, from a plurality of BEEs stored in the BEE database, a set of active BEEs based upon the simulation state update, each BEE of the plurality of BEEs comprising one or more elicited actions and one or more corresponding observable behaviors;
receiving a plurality of simulation action notifications from the computing system of the training environment;
receiving a plurality of simulation action notifications indicating a corresponding plurality of user inputs entered into the training interface;
for each simulation action notification, determining whether an action represented by the simulation action notification matches an elicited action for at least one active BEE in the set of active BEEs and storing information regarding the elicited action as a response to the at least one active BEE in the set of active BEEs;
for each simulation action notification, determining whether an action represented by the simulation action notification matches an elicited action for at least one active BEE in the set of active BEEs and storing, in a learning record, information regarding the elicited action as a response to the at least one active BEE in the set of active BEEs;
determining a set of completed BEEs, the set of completed BEEs comprising each active BEE that has all corresponding elicited actions met;
for each active BEE, determine whether the one or more elicited actions of the active BEE has been met, and when all corresponding elicited actions have been met, determine that the active BEE is a completed BEE; and
for each completed BEE of the set of completed BEEs, storing information regarding the training performance, the information comprising the one or more observable behaviors of the completed BEE
query the BEE database for the corresponding observable behaviors for each elicited action of the one or more elicited actions of the completed BEE, and store information regarding the one or more corresponding observable behaviors of the completed BEE.
Claims 1-16 of the Instant Application are substantially similar to Claims 1-7,9-14 and 16-22 of U.S. Patent Application 18364380.
The respective corresponding Dependent Claims recite substantially similar limitations and are therefore also obvious between the US Application and the Instant Application.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11-16, 20 are rejected under is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant(s) regard as their invention.
Claim 11 recites “…A computing system, comprising …”, “…receive a simulation state update from a computing system of the training environment …”, it is not clear if these elements refer to the same computer system. Appropriate correction is required.
Claims 12-16 depend on claim 11 and do not cure the aforementioned deficiencies of claim 11, and thus, claims 12-16 is rejected for the reasons set forth above regarding claim 11 as a result.
Claim 20 recites “…displaying on a user interface a graphical representation…”, it is not clear if this elements refer to the same user interface introduced in claim 17. Appropriate correction is required.
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 11) recites, A method of evaluating a training performance of a training subject against a competency framework using a simulation, the training performance being performed in a training environment that simulates a physical environment, the method comprising:
receiving a simulation state update from a … of the training environment;
querying a behavioral elicitation event (BEE) database to determine, from a plurality of BEEs stored in the BEE database, a set of active BEEs based upon the simulation state update, each BEE of the plurality of BEEs comprising one or more elicited actions and one or more observable behaviors;
receiving a plurality of simulation action notifications from the … of the training environment;
for each simulation action notification, determining whether an action represented by the simulation action notification matches an elicited action for at least one active BEE in the set of active BEEs and storing information regarding the elicited action as a response to the at least one active BEE in the set of active BEEs;
determining a set of completed BEEs, the set of completed BEEs comprising each active BEE that has all corresponding elicited actions met;
for each completed BEE of the set of completed BEEs, storing information regarding the training performance, the information comprising the one or more observable behaviors of the completed BEE; and
displaying at least the information regarding the training performance on a ….
Claim 17 recite: A method of displaying information related to a flight simulation, the method comprising:
displaying on a … an observable behavior display comprising information regarding one or more observable behaviors of at least one phase of the flight simulation, the one or more observable behaviors corresponding to a behavioral elicitation event (BEE) that occurs during the flight simulation; and
displaying on the … information regarding one or more elicited actions performed by a trainee for each observable behavior of the one or more observable behaviors.
Analyzing under Step 2A, Prong 1:
The limitations regarding, …evaluating a training performance of a training subject against a competency framework using a simulation, the training performance being performed in a training environment that simulates a physical environment…receiving a simulation state update from a … of the training environment; querying a behavioral elicitation event (BEE) database to determine, from a plurality of BEEs stored in the BEE database, a set of active BEEs based upon the simulation state update, each BEE of the plurality of BEEs comprising one or more elicited actions and one or more observable behaviors; receiving a plurality of simulation action notifications from the … of the training environment; for each simulation action notification, determining whether an action represented by the simulation action notification matches an elicited action for at least one active BEE in the set of active BEEs and storing information regarding the elicited action as a response to the at least one active BEE in the set of active BEEs; determining a set of completed BEEs, the set of completed BEEs comprising each active BEE that has all corresponding elicited actions met; for each completed BEE of the set of completed BEEs, storing information regarding the training performance, the information comprising the one or more observable behaviors of the completed BEE; and displaying at least the information regarding the training performance on a…displaying information related to a flight simulation… displaying on a … an observable behavior display comprising information regarding one or more observable behaviors of at least one phase of the flight simulation, the one or more observable behaviors corresponding to a behavioral elicitation event (BEE) that occurs during the flight simulation; and displaying on the … information regarding one or more elicited actions performed by a trainee for each observable behavior of the one or more observable behaviors.…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the above identified limitations, therefore, the claims recite a mental process.
Further, …evaluating a training performance of a training subject against a competency framework using a simulation, the training performance being performed in a training environment that simulates a physical environment…receiving a simulation state update from a … of the training environment; querying a behavioral elicitation event (BEE) database to determine, from a plurality of BEEs stored in the BEE database, a set of active BEEs based upon the simulation state update, each BEE of the plurality of BEEs comprising one or more elicited actions and one or more observable behaviors; receiving a plurality of simulation action notifications from the … of the training environment; for each simulation action notification, determining whether an action represented by the simulation action notification matches an elicited action for at least one active BEE in the set of active BEEs and storing information regarding the elicited action as a response to the at least one active BEE in the set of active BEEs; determining a set of completed BEEs, the set of completed BEEs comprising each active BEE that has all corresponding elicited actions met; for each completed BEE of the set of completed BEEs, storing information regarding the training performance, the information comprising the one or more observable behaviors of the completed BEE; and displaying at least the information regarding the training performance on a…displaying information related to a flight simulation… displaying on a … an observable behavior display comprising information regarding one or more observable behaviors of at least one phase of the flight simulation, the one or more observable behaviors corresponding to a behavioral elicitation event (BEE) that occurs during the flight simulation; and displaying on the … information regarding one or more elicited actions performed by a trainee for each observable behavior of the one or more observable behaviors…, are human instructor observing and training human pilot actions and behaviors with training scenarios and learning simulations, which are, managing personal behavior or relationships or interactions between people, therefore the claims recite certain methods of organizing human activities.
Accordingly, the claims recite a mental process, certain methods of organizing human activities, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 11, 17: computing system, user interface, logic system, computer-readable storage system
Claim 2, 12, 18: menu
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…receiving…”, “…storing…”, “…displaying…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…receiving…”, “…storing…”, data output – “…displaying…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0022] Computing system 100 is configured to evaluate the training performance of training subject 104 against a competency framework, such as the ICAO pilot competencies, for example. Computing system 100 comprises a processor 110 operably coupled to aircraft training environment 102, and a storage system 112. Storage system 112 comprises a BEE database 114 comprising a plurality of BEEs as discussed with reference to FIG. 2. Storage system 112 further comprises a BEE monitor 116 configured to be executable by processor 110. Storage system 112 further comprises a learning record store (LRS) 118 configured to store information relating to the evaluation of the performance of training subject 104. In some examples, LRS 118 can be configured to also store information from aircraft training environment 102. In other examples, the information from a training environment and/or information relating to the performance evaluation can be stored in another location or across multiple locations. Further aspects of processor 110 and storage system 112 are discussed with reference to FIG. 9. In other examples, computing system 100 can be configured to receive flight operations quality assurance (FOQA) data recorded during a live flight and to detect pilot behavior against the ICAO competency framework. Such a configuration can help to determine training recommendations for an airline fleet and/or to determine effectiveness of training programs of the airline fleet. While discussed herein with reference to aircraft training environment 102, a BEE database can be used to evaluate another training performance against any suitable competency framework in other examples.
[0043] FIG. 8 illustrates a flowchart of an example method 800 for evaluating a training performance of a training subject against a competency framework. The training performance is performed in a training environment that simulates a physical environment, such as aircraft training environment 102, for example. In such an example the competency framework can comprise the ICAO competency framework, for example. In other examples, another training environment and/or another competency framework can be used. Method 800 can be performed by any suitable computing system comprising a BEE database, such as computing system 100, for example.
[0064] FIG. 14 schematically shows a simplified representation of a computing system 1400 configured to provide any to all of the compute functionality described herein. Computing system 1400 can take the form of one or more personal computers, server computers, and computers integrated with aircraft, as examples. Computing system 100, training computing system 108, and computing system 402 are examples of computing system 1400.
[0066] Logic subsystem 1402 includes one or more physical devices configured to execute instructions. For example, the logic subsystem 1402 can be configured to execute instructions that are part of one or more applications, services, programs, routines, libraries, objects, components, data structures, or other logical constructs. Such instructions can be implemented to perform a task, implement a data type, transform the state of one or more components, achieve a technical effect, or otherwise arrive at a desired result.
[0067] The logic subsystem 1402 can include one or more hardware processors configured to execute software instructions. Additionally, or alternatively, the logic subsystem 1402 can include one or more hardware or firmware devices configured to execute hardware or firmware instructions. Processors of the logic subsystem 1402 can be single-core or multi-core, and the instructions executed thereon can be configured for sequential, parallel, and/or distributed processing. Individual components of the logic subsystem 1402 optionally can be distributed among two or more separate devices, which can be remotely located and/or configured for coordinated processing. Aspects of the logic subsystem 1402 can be virtualized and executed by remotely accessible, networked computing devices configured in a cloud-computing configuration.
[0071] The logic subsystem 1402 and the storage subsystem can cooperate to instantiate one or more logic machines. As used herein, the term “machine” is used to collectively refer to the combination of hardware, firmware, software, instructions, and/or any other components cooperating to provide computer functionality. In other words, “machines” are never abstract ideas and always have a tangible form. A machine can be instantiated by a single computing device, or a machine can include two or more sub-components instantiated by two or more different computing devices. In some implementations a machine includes a local component (e.g., software application executed by a computer processor) cooperating with a remote component (e.g., cloud computing service provided by a network of server computers). The software and/or other instructions that give a particular machine its functionality can optionally be saved as one or more unexecuted modules on one or more suitable storage devices.
[0072] When included, display subsystem 1406 can be used to present a visual representation of data held by storage subsystem 1404. This visual representation can take the form of a graphical user interface (GUI). As the herein described methods and processes change the data held by the storage subsystem, and thus transform the state of the storage subsystem, the state of display subsystem 1406 can likewise be transformed to visually represent changes in the underlying data. Display subsystem 1406 can include one or more display devices utilizing virtually any type of technology. Such display devices can be combined with the logic subsystem and the storage subsystem in a shared enclosure, or such display devices can be peripheral display devices.
[0116] It will be understood that the configurations and/or approaches described herein are exemplary in nature, and that these specific embodiments or examples are not to be considered in a limiting sense, because numerous variations are possible. The specific routines or methods described herein may represent one or more of any number of processing strategies. As such, various acts illustrated and/or described may be performed in the sequence illustrated and/or described, in other sequences, in parallel, or omitted. Likewise, the order of the above-described processes may be changed.
[0117] The subject matter of the present disclosure includes all novel and non-obvious combinations and sub-combinations of the various processes, systems and configurations, and other features, functions, acts, and/or properties disclosed herein, as well as any and all equivalents thereof.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 17-20 is/are rejected under 35 U.S.C. 102 as being unpatentable by US Patent Publication to US20230343236A1 to Gagnon et al., (hereinafter referred to as “Gagnon”).
As per Claim 17, Gagnon teaches: A method of displaying information related to a flight simulation, the method comprising:
displaying on a user interface an observable behavior display comprising information regarding one or more observable behaviors of at least one phase of the flight simulation, the one or more observable behaviors corresponding to a behavioral elicitation event (BEE) that occurs during the flight simulation; and (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
displaying on the user interface information regarding one or more elicited actions performed by a trainee for each observable behavior of the one or more observable behaviors. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
As per Claim 18 Gagnon teaches: The method of claim 17, wherein the method further comprises
displaying on the user interface a menu, the menu comprising selectable menu items each corresponding to a flight phase of the flight simulation. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
As per Claim 19, Gagnon teaches: The method of claim 17,
wherein the observable behavior display for each observable behavior of the one or more observable behaviors is expandable to display an expanded observable behavior display, and wherein the expanded observable behavior display comprises a progress bar for each elicited action displayed. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
As per Claim 20, Gagnon teaches: The method of claim 17, further comprising
displaying on a user interface a graphical representation of a flight path during at least one phase of the flight simulation. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
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, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable by US Patent Publication to US20210272469A1 to Kennedy et al., (hereinafter referred to as “Kennedy”) in view of US Patent Publication to US20240105076A1 to Peyronnet et al., (hereinafter referred to as “Peyronnet”)
As per Claim 1, Kennedy teaches: A method of evaluating a training performance of a training subject against a competency framework using a simulation, the training performance being performed in a training environment that simulates a physical environment, the method comprising: (in at least [0052] Referring to FIG. 6, a conceptual diagram 600 depicting an embodiment of embedded training for commercial aviation is depicted. As shown in the diagram 600, a learning management system 610 and mid-fidelity simulation (MFS) training scenarios 630 may be used to provide embedded training 650 via mid-fidelity simulation. The learning management system 610 may receive a training browsing history 612, completed training records 614, and data from a crew roster 616. The training browsing history 612 may be associated with multiple trainees and can help the learning management system 610 identify common training topics and scenarios where training might be beneficial. The completed training records 614 may function as a checklist to indicate what training trainees have completed and what training still needs to be completed. The data from the crew roster 616 may enable the learning management system 610 to identify time periods when trainees may be available for training and to identify training scenarios that may be relevant to the trainee's scheduled flights. The learning management system 610 may correspond to the learning management system 110 of FIG. 1 and may rely on artificial intelligence to identify training needs, at 620.)
receiving a simulation state update from a computing system of the training environment; (in at least [0025] training modules that could be available, and may be adapted to long-haul flights, include: a simulated Himalaya crossing during a Dubai to Beijing flight, or a drift-down procedures module during the same flight; a North Atlantic Tracks (NAT) refresher training for flights across the Atlantic Ocean; a refresher training on volcanic ash and related procedures for routes over areas with risk for volcanic activity; and polar operations for polar routes. [0026] The modules could be part of an overall learning management system that tracks pilots' training and examinations for regulatory and audit purposes. When linked to a rostering system, suggestions to pilots could be sent out before a flight and reminders provided for the flight. As an example, this system could remind a pilot that he has not done an Atlantic crossing in the last few months and offer the module as a refresher before or in flight. The system could then provide different levels of recommendations, based on regulatory requirements. For example, the system could generate a notification with various recommendations, such as “You need to complete this module by [a particular date]—do you want to do it now?” or “This flight will pass over active volcanic areas—do you want more information on this?” or “You have not passed over the Himalayas recently—do you want to have an update on this?” or “Many pilots have recently looked at the module on new approach procedures for Hong Kong—would you like to take this module?” [0036] In order to determine which training exercises may be applicable to an employee, the learning management system 110 may retrieve regulatory or audit data 142 from the regulatory or audit database 140. The regulatory or audit data 142 may indicate training exercises or concepts that are required for the employee and have not yet been performed. [0053] Once training needs have been identified, at 620, the mid-fidelity simulation training scenarios 630 may be selected and calibrated. For example, the mid-fidelity simulation training scenarios 630 may upload actual data relevant to a trainee. To illustrate, during a flight associated with the trainee, the mid-fidelity simulation training scenario 630 may upload an aircraft position, at 632, and/or upload aircraft systems data, at 634. (i.e. change) An example of using uploaded flight data in mid-fidelity simulation is further described in U.S. patent application Ser. No. 16/275,723, filed on Feb. 14, 2019 and entitled “Mid-Fidelity Simulation Approach and Method for Flight Crew Training and Evaluation,” which has been incorporated by reference herein. [0056] During the embedded training 650, data logs 656 may be generated. Further, after the training, feedback 652 via in-situ analytics may be performed to evaluate the trainee's performance and the relevance of the mid-fidelity simulation training scenarios 630. Also, the trainee may engage in a debriefing 654 with an instructor, with an automated feedback application, or with another type of feedback collection method. Data and results associated with the data logs 656, the feedback 652, and the debriefing 654 may be provided to the learning management system 610 for future use in determine what training to provide and when to provide it. For example, training data from the use of MFS, which may be included in the data logs 656, can be used to determines a next exercise to be performed. If a trainee does well, they may not need further training in a particular area. Otherwise, the data logs 656 can be used by the learning management system 610 to determine whether to offer another training, or additional training, within a certain time frame.)
querying a behavioral elicitation event (BEE) database to determine, from a plurality of BEEs stored in the BEE database, a set of active BEEs based upon the simulation state update, each BEE of the plurality of BEEs comprising one or more elicited actions and one or more observable behaviors; (in at least [0026] The modules could be part of an overall learning management system that tracks pilots' training and examinations for regulatory and audit purposes. When linked to a rostering system, suggestions to pilots could be sent out before a flight and reminders provided for the flight. As an example, this system could remind a pilot that he has not done an Atlantic crossing in the last few months and offer the module as a refresher before or in flight (i.e. elicited actions and one or more observable behaviors). The system could then provide different levels of recommendations, based on regulatory requirements. For example, the system could generate a notification with various recommendations, such as “You need to complete this module by [a particular date]—do you want to do it now?” or “This flight will pass over active volcanic areas—do you want more information on this?” or “You have not passed over the Himalayas recently—do you want to have an update on this?” or “Many pilots have recently looked at the module on new approach procedures for Hong Kong—would you like to take this module?” (i.e. elicited actions and one or more observable behaviors) [0035] The learning management system 110 may select a training exercise 160 that is applicable to the employee. As an illustrative example, the learning management system 110 may retrieve flight data 132 from the flight database 130 for a flight associated with the employee and determine the training concept 168 associated with the flight. The learning management system 110 may select a training exercise 160 that is associated with the training concept 168 from the multiple training exercises 164. (i.e. BEE) [0036] In order to determine which training exercises may be applicable to an employee, the learning management system 110 may retrieve regulatory or audit data 142 from the regulatory or audit database 140. The regulatory or audit data 142 may indicate training exercises or concepts that are required for the employee and have not yet been performed. Based on the regulatory data or audit data 142, the training exercise 160 may be selected. [0037] Selecting the training exercise applicable to the employee may alternatively or additionally include retrieving sample data 152 associated with multiple employees and determining a common training exercise 172 associated with the multiple employees. The training exercise 160 may be the common training exercise 172. Determining the common training exercise 172 associated with multiple employees may include performing an artificial intelligence analysis of a training history associated with the multiple employees using the AI learning model 170. [0052] The learning management system 610 may receive a training browsing history 612, completed training records 614, and data from a crew roster 616. The training browsing history 612 may be associated with multiple trainees and can help the learning management system 610 identify common training topics and scenarios where training might be beneficial. The completed training records 614 may function as a checklist to indicate what training trainees have completed and what training still needs to be completed. (i.e. active BEE) The data from the crew roster 616 may enable the learning management system 610 to identify time periods when trainees may be available for training and to identify training scenarios that may be relevant to the trainee's scheduled flights. The learning management system 610 may correspond to the learning management system 110 of FIG. 1 and may rely on artificial intelligence to identify training needs, at 620. [0056] During the embedded training 650, data logs 656 may be generated. Further, after the training, feedback 652 via in-situ analytics may be performed to evaluate the trainee's performance and the relevance of the mid-fidelity simulation training scenarios 630. Also, the trainee may engage in a debriefing 654 with an instructor, with an automated feedback application, or with another type of feedback collection method. Data and results associated with the data logs 656, the feedback 652, and the debriefing 654 may be provided to the learning management system 610 for future use in determine what training to provide and when to provide it. For example, training data from the use of MFS, which may be included in the data logs 656, can be used to determines a next exercise to be performed. If a trainee does well, they may not need further training in a particular area. Otherwise, the data logs 656 can be used by the learning management system 610 to determine whether to offer another training, or additional training, within a certain time frame.)
receiving a plurality of simulation action notifications from the computing system of the training environment; (in at least [0026] The modules could be part of an overall learning management system that tracks pilots' training and examinations for regulatory and audit purposes. When linked to a rostering system, suggestions to pilots could be sent out before a flight and reminders provided for the flight. As an example, this system could remind a pilot that he has not done an Atlantic crossing in the last few months and offer the module as a refresher before or in flight. The system could then provide different levels of recommendations, based on regulatory requirements. For example, the system could generate a notification with various recommendations, such as “You need to complete this module by [a particular date]—do you want to do it now?” or “This flight will pass over active volcanic areas—do you want more information on this?” or “You have not passed over the Himalayas recently—do you want to have an update on this?” or “Many pilots have recently looked at the module on new approach procedures for Hong Kong—would you like to take this module?” [0038] Once the training exercise 160 has been selected, the learning management system 110 may send a notification 182 to an electronic device 180 associated with the employee. The notification 182 may include an offer 184 to perform the training exercise. The employee may then have an opportunity to accept or reject the training exercise 160. If the employee rejects the training exercise 160, it may be scheduled for another time. [0039] The system 100 may be used in conjunction with a mid-fidelity simulator. For example, the training exercise 160 may include a mid-fidelity simulation 162. The mid-fidelity simulation 162 may provide simulations and scenarios that are common among multiple aircraft. This may enable the employee to receive conceptual scenario training as opposed to aircraft-specific training. [0040] the learning management system 110 may receive feedback 186. This may be provided as user response data retrieved during performance of the training exercise 160, as survey data after performance of the training exercise 160, as instructor review data, or another form of evaluation data. The feedback 186 may be used to modify the training exercise 160 and/or the AI learning model 170. [0040] the learning management system 110 may receive feedback 186. This may be provided as user response data retrieved during performance of the training exercise 160, as survey data after performance of the training exercise 160, as instructor review data, or another form of evaluation data. The feedback 186 may be used to modify the training exercise 160 and/or the AI learning model 170. [0056] During the embedded training 650, data logs 656 may be generated. Further, after the training, feedback 652 via in-situ analytics may be performed to evaluate the trainee's performance and the relevance of the mid-fidelity simulation training scenarios 630. Also, the trainee may engage in a debriefing 654 with an instructor, with an automated feedback application, or with another type of feedback collection method. Data and results associated with the data logs 656, the feedback 652, and the debriefing 654 may be provided to the learning management system 610 for future use in determine what training to provide and when to provide it. For example, training data from the use of MFS, which may be included in the data logs 656, can be used to determines a next exercise to be performed. If a trainee does well, they may not need further training in a particular area. Otherwise, the data logs 656 can be used by the learning management system 610 to determine whether to offer another training, or additional training, within a certain time frame.)
for each simulation action notification, determining whether an action represented by the simulation action notification … an elicited action for at least one active BEE in the set of active BEEs and storing information regarding the elicited action as a response to the at least one active BEE in the set of active BEEs; (in at least [0026] The modules could be part of an overall learning management system that tracks pilots' training and examinations for regulatory and audit purposes. When linked to a rostering system, suggestions to pilots could be sent out before a flight and reminders provided for the flight. As an example, this system could remind a pilot that he has not done an Atlantic crossing in the last few months and offer the module as a refresher before or in flight. The system could then provide different levels of recommendations, based on regulatory requirements. For example, the system could generate a notification with various recommendations, (i.e. simulation action notification) such as “You need to complete this module by [a particular date]—do you want to do it now?” or “This flight will pass over active volcanic areas—do you want more information on this?” or “You have not passed over the Himalayas recently—do you want to have an update on this?” or “Many pilots have recently looked at the module on new approach procedures for Hong Kong—would you like to take this module?” (i.e. elicited action) [0028] provide engaging, useful, and operationally relevant interactions on the flight deck. Given the sensitivities around providing training (and potential distraction), one, or a few modules, with clear operational relevance to a flight could be offered initially. These could be deployed on a voluntary basis. Tracking of when the modules are taken can be used to identify patterns of usage and adapt the modules and system to fit the needs of the pilots. Over time, an increasing number of modules can be introduced. [0038] Once the training exercise 160 has been selected, the learning management system 110 may send a notification 182 to an electronic device 180 associated with the employee. The notification 182 may include an offer 184 to perform the training exercise. The employee may then have an opportunity to accept or reject the training exercise 160. If the employee rejects the training exercise 160, it may be scheduled for another time. (i.e. action, and elicited action) [0039] The system 100 may be used in conjunction with a mid-fidelity simulator. For example, the training exercise 160 may include a mid-fidelity simulation 162. The mid-fidelity simulation 162 may provide simulations and scenarios that are common among multiple aircraft. This may enable the employee to receive conceptual scenario training as opposed to aircraft-specific training. (i.e. simulation action notification) [0050] As the employees 410-412 complete the training, it may be removed from their list of required or preferred training exercises. The learning management system 110 of FIG. 1 may select the training exercise 160 to correspond to the regulatory or audit data 142 for the employees 410-412. (i.e. active) [0056] During the embedded training 650, data logs 656 may be generated. Further, after the training, feedback 652 via in-situ analytics may be performed to evaluate the trainee's performance and the relevance of the mid-fidelity simulation training scenarios 630. Also, the trainee may engage in a debriefing 654 with an instructor, with an automated feedback application, or with another type of feedback collection method. Data and results associated with the data logs 656, the feedback 652, and the debriefing 654 may be provided to the learning management system 610 for future use in determine what training to provide and when to provide it. For example, training data from the use of MFS, which may be included in the data logs 656, can be used to determines a next exercise to be performed. If a trainee does well, they may not need further training in a particular area. Otherwise, the data logs 656 can be used by the learning management system 610 to determine whether to offer another training, or additional training, within a certain time frame.)
determining a set of completed BEEs, the set of completed BEEs comprising each active BEE that has all corresponding elicited actions met; (in at least [0038] Once the training exercise 160 has been selected, the learning management system 110 may send a notification 182 to an electronic device 180 associated with the employee. The notification 182 may include an offer 184 to perform the training exercise. The employee may then have an opportunity to accept or reject the training exercise 160. If the employee rejects the training exercise 160, it may be scheduled for another time. [0050] As the employees 410-412 complete the training, it may be removed from their list of required or preferred training exercises. The learning management system 110 of FIG. 1 may select the training exercise 160 to correspond to the regulatory or audit data 142 for the employees 410-412. [0051] FIG. 5, an embodiment of the sample data 152 is depicted. The sample data 152 may map employees 510-512 to training histories 520, 530, 540. For example, a first employee 510 may be mapped to a first training history 520, a second employee 511 may be mapped to a second training history 530, and a third employee 512 may be mapped to a third training history 540. The learning management system 110 of FIG. 1 may use the sample data 152 to determining a common training exercise 172 associated with the employees 510-512. The training exercise 160 may correspond to and/or may be the common training exercise 172. Determining the common training exercise 172 associated with employees 510-512 may include performing an artificial intelligence analysis of the training histories 520, 530, 540. [0056] During the embedded training 650, data logs 656 may be generated. Further, after the training, feedback 652 via in-situ analytics may be performed to evaluate the trainee's performance and the relevance of the mid-fidelity simulation training scenarios 630. Also, the trainee may engage in a debriefing 654 with an instructor, with an automated feedback application, or with another type of feedback collection method. Data and results associated with the data logs 656, the feedback 652, and the debriefing 654 may be provided to the learning management system 610 for future use in determine what training to provide and when to provide it. For example, training data from the use of MFS, which may be included in the data logs 656, can be used to determines a next exercise to be performed. If a trainee does well, they may not need further training in a particular area. Otherwise, the data logs 656 can be used by the learning management system 610 to determine whether to offer another training, or additional training, within a certain time frame.)
for each completed BEE of the set of completed BEEs, storing information regarding the training performance, the information comprising the one or more observable behaviors of the completed BEE; and (in at least [0052] The completed training records 614 may function as a checklist to indicate what training trainees have completed and what training still needs to be completed. The data from the crew roster 616 may enable the learning management system 610 to identify time periods when trainees may be available for training and to identify training scenarios that may be relevant to the trainee's scheduled flights. The learning management system 610 may correspond to the learning management system 110 of FIG. 1 and may rely on artificial intelligence to identify training needs, at 620. [0056] During the embedded training 650, data logs 656 may be generated. Further, after the training, feedback 652 via in-situ analytics may be performed to evaluate the trainee's performance and the relevance of the mid-fidelity simulation training scenarios 630. Also, the trainee may engage in a debriefing 654 with an instructor, with an automated feedback application, or with another type of feedback collection method. Data and results associated with the data logs 656, the feedback 652, and the debriefing 654 may be provided to the learning management system 610 for future use in determine what training to provide and when to provide it (i.e. query). For example, training data from the use of MFS, which may be included in the data logs 656, can be used to determines a next exercise to be performed. If a trainee does well, they may not need further training in a particular area. Otherwise, the data logs 656 can be used by the learning management system 610 to determine whether to offer another training, or additional training, within a certain time frame.)
….
Although implied, Kennedy does not expressly disclose the following limitations, which however, are taught by Peyronnet,
…action … matches an elicited action for at least one active BEE in the set of active BEEs… (in at least [0067] FIG. 2 , the data correlation step 104 comprises a step 116 consisting in determining a trigger event originating from an operator, the trigger event originating from an operator is the occurrence of an event at the origin of an action of the at least one operator in response to this trigger event originating from an operator. The trigger parameter originating from an operator may thus be an endogenous datum, such as for example the initialization of technical dialog between the team of operators. However, it may also be contemplated, in a more generalized scenario, for the trigger event parameter to be an exogenous datum. [0068] The data correlation step 104 then comprises a step 118 consisting in determining a trigger event originating from the platform. The trigger event originating from the platform represents the occurrence of an event at the origin of a state of the platform, and may be interpreted as an exogenous datum. This is then followed by a step 120 of detecting trigger events originating from said at least one operator or from said platform, and a step 122 of selecting at least one trigger event. [0069] The detection 120 of a trigger event originating from an operator is based on the detection of an action from the at least one operator. The detection 120 of a trigger event originating from a platform is based on the detection of a state of the platform, such as for example a change of piloting mode, the extension or retraction of the landing gear, a fault, and on the departure from an envelope of dynamic parameters, such as for example speed, incline, attitude. [0070] In response to the trigger event, whether it is a trigger from the at least one operator or from the platform, the assessment method 100 then captures at least one action parameter represented by an endogenous datum and presenting the physical manifestation of a reaction of the at least one operator to the trigger parameter. Grouping together a trigger parameter, represented by an exogenous datum or an endogenous datum, and at least one action parameter, represented by endogenous data, thus makes it possible, during the correlation step 104, to generate at least one observable behavior datum reflecting, according to tangible parameters, the behavior of the at least one operator upon triggering of an event. [0079] In step 106, the assessment method 100 comprises a step consisting in analyzing the observable behavior data in predefined analysis sequences, each predefined analysis sequence being specific to a technical and non-technical skill to be assessed, and comprising at least one trigger event parameter and one action parameter for characterizing an expected observable behavior according to a predefined situation. The analysis 106 also makes it possible to generate a measurement indicator for each observed behavior. More specifically, step 106 analyzes observable behavior data under the prism of trigger event and action parameters by comparing (step 132) the detected observable behavior data with a predefined sequence defining the expected observable behavior, each predefined sequence representing at least one physical manifestation allocated to the expected behavior. The predefined sequences are contained in a correspondence database. This correspondence database thus comprises the predefined analysis sequences presenting observable behavior data known to those skilled in the art, as well as their assigned measurable and detectable physical manifestations. Each predefined analysis sequence thus comprises at least one trigger event parameter and at least one action parameter and other endogenous and exogenous data for characterizing a flight situation and a context for at least one operator, and also their expected reaction according to the predefined situation. This analysis provides the nature of the induced action, its temporal location and also its duration or its frequency. The correspondence database also comprises a reference table containing trigger event parameters associated with each behavior to be observed. [0080] in order to be able to analyze the behavior of at least one operator during a flight situation, the step 106 of analyzing the observable behavior data compares the detected endogenous and exogenous data, and more specifically the trigger event and action parameters, with the trigger event and action parameters and also the predefined endogenous and exogenous data. The predefined analysis sequences are specific to each technical and non-technical skill to be assessed. [0092] This generation of observed behavior indicators then makes it possible to initiate a step 108, shown in FIG. 1 , consisting in assessing a behavior of at least one operator. The assessment of the behavior of the at least one operator consists in comparing the observed behavior, which is based on a set of detected behavior elements, with predefined expected reference behaviors. The conformity of an observed behavior is assessed by comparison with known prior art of defined procedures or established protocols, contained in the correspondence database. [0093] The objectivity of the assessment of the technical and non-technical skills of an operator is thus based on the prior creation of the correspondence database between various observable behaviors and measurable physical variables in relation to these observable behaviors. [0094] The matching consists, for each observable behavior, in determining various ways of measuring same and then developing the tools necessary for each measurement. [0095] By way of indicative and non-exhaustive examples, various cases below are given to illustrate the matching of observable behaviors with measurable physical variables to allow behaviors to be assessed.)
displaying at least the information regarding the training performance on a user interface (in at least [0112] the assessment method 100 comprises, after the skill assessment step 110, a step 112 of displaying the assessments of the technical and non-technical skills. Thus, in order to facilitate the reading of the assessment of the technical and non-technical skills of the at least one operator, it is possible to display one or more assessed skills according to their nature or according to a time or mission scale that makes it possible to contextualize the assessment for the instructor. This display step 112 also makes it possible to display all of the detected endogenous data correlated with the exogenous data, making it possible to precisely present the reactions of the at least one operator on the basis of the state of the real or simulated platform.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Kennedy, as taught by Peyronnet above, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Kennedy with the motivation of, …analyzing the behavior of an operator in a simulation or training situation, allowing an observer to obtain statistical data that provide real-time information about the state and the behavior of the operator. Using these statistical data, the observer is able to carry out their own analysis of the technical and non-technical skills of the operator… to assist instructors in assessing these non-technical skills, the European Union Aviation Safety Agency (EASA) has published a list of Observable Behavior Indicators (OBI). These behavior indicators make it possible to objectify these various skills and provide a shared assessment framework between instructors, enabling a reduction in subjectivity in the assessment… allow the instructor to be easily directed to observable behavior data needed to assess skills…assessing and training commercial aviation pilots developed by players in the aeronautical world, based on an objective skills assessment (competency-based training)….to improve the assessment capabilities of the assessment method 100 by enriching the correspondence database for subsequent use of the assessment method…, as recited in Peyronnet.
As per Claim 11 for A computer system (see at least Kennedy [0013][0052]), respectively, substantially recite the subject matter of Claim 1 and are rejected based on the same reasoning and rationale.
Claims 2-10, 12-16 is/are rejected under 35 U.S.C. 103 as being unpatentable by US Patent Publication to US20210272469A1 to Kennedy et al., (hereinafter referred to as “Kennedy”) in view of US Patent Publication to US20240105076A1 to Peyronnet et al., (hereinafter referred to as “Peyronnet”) in view of US Patent Publication to US20230343236A1 to Gagnon et al., (hereinafter referred to as “Gagnon”).
As per Claim 2, Although implied, Kennedy does not expressly disclose the following limitations, which however, are taught by Peyronnet: The method of claim 1,
wherein the user interface comprises … to allow selection of a phase of the simulation. (in at least [0035] the step of detecting observable behavior data comprises a step consisting in determining a trigger event originating from the real or simulated platform, said trigger event being the occurrence of an event at the origin of a change in state of the platform. [0036] a step of detecting trigger events originating from said at least one operator or from said platform, and a step of selecting at least one trigger event. [0068] The trigger event originating from the platform represents the occurrence of an event at the origin of a state of the platform, and may be interpreted as an exogenous datum. This is then followed by a step 120 of detecting trigger events originating from said at least one operator or from said platform, and a step 122 of selecting at least one trigger event. [0069] The detection 120 of a trigger event originating from an operator is based on the detection of an action from the at least one operator. The detection 120 of a trigger event originating from a platform is based on the detection of a state of the platform, such as for example a change of piloting mode, the extension or retraction of the landing gear, a fault, and on the departure from an envelope of dynamic parameters, such as for example speed, incline, attitude.)
Although implied, Kennedy in view of Peyronnet does not expressly disclose the following limitations, which however, are taught by Gagnon:
wherein the user interface comprises a menu to allow selection of a phase of the simulation. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Kennedy in view of Peyronnet, as taught by Gagnon above, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Kennedy in view of Peyronnet with the motivation of, …providing adaptive training cursus to individuals…providing adaptive training cursus of aircraft pilots inflight and in simulator…allows individualization of training to the specific needs of each trainee. Skills from a training cursus that are required to perform a given task are mapped with a scenario or mission timeline, which is itself mapped to a “level of ease” curve (or an “ease in flight” curve for flight training) of the trainee, and therefore to a level of mastery of these skills…providing recommendations to both a trainee and an instructor. Suggestions are sent to an instructor to indicate which key skills (and associated abilities) are to prioritize for an identified trainee and to indicate which scenario fits best for practicing those key skills. A trainee receives as well information on his/her cursus progression and features of his/her individual profile…allows that real mission performance data may be associated with predefined skills by having a correspondence integrated between a cursus of skills to the real mission of the trainee while having an instructor being actively involved in the loop of the training cursus of a trainee.…to provide recommendations on various aspects of the training to improve…by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver…advantages gained by the invention are better training outcomes, economic benefits because of reduced time and reduced attrition which are associated to decreased costs. This is especially true in domains where training is mandatory but costly, such as in aeronautics…., as recited in Gagnon.
As per Claim 3, Although implied, Kennedy does not expressly disclose the following limitations, which however, are taught by Peyronnet: The method of claim 2,
wherein the user interface further displays, for a selected … item, an observable behavior display for the selected … item, the observed behavior display including information on elicited actions observed during the simulation. (in at least [0037] the analysis step comprises a step of comparing detected observable behavior data with a predefined sequence defining the expected behavior, each predefined sequence representing at least one physical manifestation allocated to the expected behavior, the predefined sequences being contained in a correspondence database. [0070] the assessment method 100 then captures at least one action parameter represented by an endogenous datum and presenting the physical manifestation of a reaction of the at least one operator to the trigger parameter. Grouping together a trigger parameter, represented by an exogenous datum or an endogenous datum, and at least one action parameter, represented by endogenous data, thus makes it possible, during the correlation step 104, to generate at least one observable behavior datum reflecting, according to tangible parameters, the behavior of the at least one operator upon triggering of an event.)
Although implied, Kennedy in view of Peyronnet does not expressly disclose the following limitations, which however, are taught by Gagnon:
wherein the user interface further displays, for a selected menu item, an observable behavior display for the selected menu item, the observed behavior display including information on elicited actions observed during the simulation. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
The reason and rationale to combine Kennedy, Peyronnet, Gagnon is the same as recited above.
As per Claim 4, Although implied, Kennedy in view of Peyronnet does not expressly disclose the following limitations, which however, are taught by Gagnon: The method of claim 3,
wherein the observable behaviors display for the selected menu item is expandable. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
The reason and rationale to combine Kennedy, Peyronnet, Gagnon is the same as recited above.
As per Claim 5, Although implied, Kennedy in view of Peyronnet does not expressly disclose the following limitations, which however, are taught by Gagnon: The method of claim 4, further comprising
displaying information regarding each elicited action for the selected menu item when the observable behaviors display is expanded for the selected menu item. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
The reason and rationale to combine Kennedy, Peyronnet, Gagnon is the same as recited above.
As per Claim 6, Although implied, Kennedy in view of Peyronnet does not expressly disclose the following limitations, which however, are taught by Gagnon: The method of claim 5,
wherein the information regarding each elicited action comprises a progress bar for each elicited action. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
The reason and rationale to combine Kennedy, Peyronnet, Gagnon is the same as recited above.
As per Claim 7, Although implied, Kennedy in view of Peyronnet does not expressly disclose the following limitations, which however, are taught by Gagnon: The method of claim 1, further comprising
displaying, on the user interface, summarized information regarding at least the training performance. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
The reason and rationale to combine Kennedy, Peyronnet, Gagnon is the same as recited above.
As per Claim 8, Although implied, Kennedy in view of Peyronnet does not expressly disclose the following limitations, which however, are taught by Gagnon: The method of claim 7,
wherein the summarized information regarding at least the training performance is displayed upon selection of the selected menu item displayed on the user interface. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
The reason and rationale to combine Kennedy, Peyronnet, Gagnon is the same as recited above.
As per Claim 9, Kennedy teaches: The method of claim 1,
wherein the simulation is a flight simulation, and (in at least [0003] recurrent flight crew training and evaluation may include computer-based training (CBT) (usually in the form of digital slide presentations), aircraft part-task simulators, aircraft fixed-base simulators, and highly sophisticated and realistic full-motion flight simulators. [0004] CBT is effectively a medium for presenting manuals, checklists, and other paper-based information in a digital slide pack. It typically includes no simulation that would allow someone to interact with the aircraft systems. Training may be enhanced with part-task trainers that enable subjects to practice skills in the operation of specific flight deck systems, however, these part-task trainers fail to provide a full picture of how the aircraft functions in given scenarios and cannot realistically simulate interactions between highly coupled aircraft systems. Fixed-base and full-motion simulators are a good medium for practicing physical maneuvers. However, they cannot evaluate whether the flight crew possess the requisite competencies to manage unusual/non-normal situations. They also cannot predict whether the flight crew understands the underlying mechanics of how the aircraft functions. Such understanding is crucial for correct decision making to occur.)
Although implied, Kennedy in view of Peyronnet does not expressly disclose the following limitations, which however, are taught by Gagnon:
wherein the user interface further comprises a graph showing information on at least one phase of the flight simulation. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
The reason and rationale to combine Kennedy, Peyronnet, Gagnon is the same as recited above.
As per Claim 10, Although implied, Kennedy in view of Peyronnet does not expressly disclose the following limitations, which however, are taught by Gagnon: The method of claim 9,
wherein the graph illustrates positional information for a simulated aircraft as a function of time. (in at least [0060]-[0067][0073] The output of the ease in flight processing unit 1024 provides a trainee ease in flight level also defined as ease in flight assessment or estimation of the trainee throughout a whole flight. In an embodiment, the processing unit allows computing at 1 Hz, providing a very detailed assessment of the trainees’ ease in flight state during the flight. This information is represented notably on area ‘A’ of a screenshot mockup on FIG. 6 . In an embodiment, the path followed during a flight is associated with colored points (from green to red) indicating the level of ease during the training mission at each moment, based on the physiological measures. [0074] Data used by the ease in flight processing unit 1024 also includes mission or flight data, such as geolocation data (latitude, longitude, altitude), accelerations (x, y, z in g), and velocity (inferred from geolocation data). These mission data are later used by the cursus recommender of the present invention as further described with reference to FIG. 2 , for a decomposition of the flight into learning phases or mission segments, and a classification of the phases into maneuvers and procedures, as represented notably on area ‘B’ (“Décollage”, “Atterrissage”) of the screenshot mockup on the FIG. 6. [0079] On the visualization side, the trainee interface unit 1060 is configured to display after a session, information that is stored in the secured database 1040. A trainee may thus visualize his/her cursus progression, and is provided with recommendations for his/her individual training, as represented notably on area ‘C’ (“Recommandations pour le vol BFMS”) of the screenshot mockup on FIG. 6. [0080] the interface displays a time scaled graph, where both ease in flight (%) and altitude (meter) are plotted throughout the flight. The interface allows the user to select a specific point on the graph by clicking on it to get information about altitude, time since beginning of the mission, distance covered, heart rate and ease in flight at this specific moment. The interface further allows focusing on a specific flight segment, using a selection tool, and selecting the segment of interest on the map to highlight the corresponding points on the ease in flight /altitude graph underneath the map. Since the interface is conceived as a tool for trainees and for their instructors, it also provides general statistics concerning the missions performed by a trainee (distance covered, time, average ease in flight), with comparison to those of the cohort. [0081] Recommendations are based on the quantification of “ease in flight” for each of the phase and maneuver recognized by the cursus recommendation system 104. In an embodiment as represented, the maneuvers are listed from the ones with the lowest ease in flight (i.e. maneuver 9) to the highest (i.e. maneuver 10). Then, by selecting a maneuver, the interface offers an area for the trainee (and similarly for the instructor on the instructor interface) to better understand the underlying skills that need to be improved in priority in order to increase ease in flight during later flights for this maneuver. The recommendation area is represented notably by area ‘D’ of the screenshot mockup on FIG. 6. [0082] in FIG. 6 , in a variant the system can represent multiple flights and provide recommendations based on a series of flights instead of only one. A trainee may represent the progression of his/her ease in flight throughout multiple flights on a single maneuver, such as take-off [0104] FIG. 3 is an example of a timeline for a flight A showing progression curves as shown to instructors and trainees in accordance with an embodiment of the present invention. The flight is decomposed into 5 learning phases labeled takeoff -looping - clover leaf - loose eight - landing. Each phase is defined as requiring one or more specific skills (and abilities): Skill A; Skill B; Skill C. The user (instructor or trainee) visualizes the cursus progression and profile elements, as well as detailed flight information (e.g., hearth rate and ease in flight) every N seconds.)
The reason and rationale to combine Kennedy, Peyronnet, Gagnon is the same as recited above.
As per Claim 12-16 for A computer system (see at least Kennedy [0013][0052]), respectively, substantially recite the subject matter of Claim 2-10 and are rejected based on the same reasoning and rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PO HAN (Max) LEE whose telephone number is (571) 272-3821. The examiner can normally be reached on Monday - Thursday, 9 AM-6:30 PM.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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