Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Application Status
Present office action is in response to application filed 03/13/2025. Claims 1-20 are currently pending in the application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. In particular, claim 13 recites “… wherein at least one of the pump panel training devices includes: … a camera device capturing a plurality of images of a user operating the pump panel training device” (lines 2-6 – emphasis added). The originally filed specification, as published recites similar language. It is unclear whether “the pump panel training device” was intended to recite “the at least one of the pump panel training devices” or was intended to refer to any other “pump panel training device”. Subsequently, the metes and bounds of claim 13 cannot be discerned. For purpose of examination, the recitations of “the pump panel training device” in claim 13 and dependents thereof will be interpreted to recite “the at least one of the pump panel training devices”.
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 (i.e., changing from AIA to pre-AIA ) 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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-7 and 20 are rejected under 35 U.S.C. 103 as obvious over Cerrano (US 20160078733 A1) in view of Correia Gracio et al. (US 20180165979 A1), hereinafter Correia.
Re claims 1-7 and 20:
[Claims 1-4] Cerrano teaches or at least suggests a pump panel training device, comprising: a plurality of gages operable to imitate gages upon a firetruck pump panel; a plurality of controls operable to imitate controls upon the firetruck pump panel; a camera device capturing a plurality of images of a user operating the pump panel training device; a display device; and one or more computerized processors: receiving the plurality of images from the camera device (at least ¶ 2: the pumper truck controls used to regulate the valves and the fire pump, as well as the pressure and flow rate gauges, are commonly positioned in a control panel on the side of the pumper truck; ¶ 4: simulate the operation of the controls of the pumper truck or to the fighting of a fire to aid in training of fire-fighting personnel; ¶ 16: a base component 110 that is coupled by a communication link 112 to a pump 120. A tank 130 and a liquid source 140 are also coupled to pump 120; ¶ 17: base component 110, ladder 170, tank 130, and pump 120 are coupled to a fire-fighting device (not shown), such as a fire truck, used in system 100. In other embodiments, any of base component 110, ladder 170, tank 130, and/or pump 120 may not be coupled to the fire-fighting device; ¶ 18: Tank 130 is coupled via a tank supply line 138 to pump 120 to enable liquid to be selectively supplied to pump 120; ¶ 20: A first nozzle 156 is coupled to pump 120 via a first hose line 150. A first hose valve 154 coupled to line 150 is used to selectively control a flow of liquid from pump 120 to first nozzle 156, and a first pressure gauge 152 coupled to line 150 is used to measure an operating pressure of liquid in first hose line 150. A second nozzle 166 is coupled to pump 120 via a second hose line 160. A second hose valve 154 coupled to line 160 is used to control a flow of liquid from pump 120 to second nozzle 166, and a second pressure gauge 162 is coupled to line 160 to measure the operating pressure of liquid in second hose line 160; ¶ 21: a camera (not shown) may be coupled to the end of ladder 170 and/or in the alternative, to the mounting structure. Such a camera may be wirelessly coupled to base component 110 and/or to remote component 180 such that images captured by the camera may be wirelessly communicated to base component 110 and/or remote component 180 for viewing by a user remote from the camera; ¶ 22: Simulation system 300 enables remote component 180 to be used in training a user (i.e., a trainee) in a simulation environment using emulation component 210 and computing system 220 … during a simulation exercise, computing system 220 displays a graphical representation to trainee depicting a fire-fighting scenario; ¶ 28: images captured by the camera positioned near the end of ladder 170, and position details of first nozzle 156 and/or second nozzle 166 are wirelessly communicated to remote component 182; ¶ 37: FIG. 5 is a schematic view of an exemplary fire-fighting simulation system 300. In the exemplary embodiment, simulation system 300 includes remote component 180, a data logging component 190, an emulation component 210 (broadly referred to here as, an “interface component”), and a computing system 220. Remote component 180 is wirelessly coupled to data logging component 190 and emulation component 210, and emulation component 210 is wirelessly coupled to computing system 220; ¶ 38: during a simulation exercise, computing system 220 displays a graphical representation to trainee depicting a fire-fighting scenario. Emulation component 210 and computing system 220 enable the graphical representation to be easily changed through manipulation of the controls of remote component 180 by the trainee … a trainee presented with a graphical representation of a fire by emulation component 210 and computing system 220, is able to use remote component 180 to manipulate the position valves and/or ladder 170 of system 100 within the simulation environment set forth by emulation component 210 and computing system 220; ¶ 39: using emulation component 210, the trainer is able to present various scenarios to the trainee that replicate a scenario that the trainee may encounter when fighting an actual fire … emulation component 210 presents a graphical display on computing system 220 that resembles a typical control panel on a fire-fighting device. The trainee is thus able to use emulation component 210 to practice and train on the operation of the control panel included on the fire-fighting device).
Cerrano appears to be silent on but Correia which relates to “system … for evaluating the performance of trainees during a training session, and more particularly for evidence-based training” (¶ 2), teaches or at least suggests providing a training output to the user through the display device based upon the plurality of images (at least ¶ 8: acquire real-time training session data during a training session of a trainee, wherein the real-time training session data includes training device data obtained from a training device, and video data. The real-time data acquisition module is configured to generate time-stamped training session data. The input unit is configured to acquire instructor rating data input by an instructor during the training session. The real-time instructor rating module is configured to generate time-stamped instructor rating data; ¶ 32: FIG. 1 schematically depicts … a training tool framework, in which a rating and debriefing electronic device 100 acquires data from a video camera 110 and a training device 120 (such as a flight simulator, a desktop simulation, a videogame or any other interactive training platform) during a training session executed by one or more students or trainees 130 …), ([Claim 2]) wherein the one or more computerized processors further: provides the plurality of images to an instructor; and receives an instructor review input of the user operating the pump panel training device; and wherein providing the training output to the user includes providing the instructor review input to the user (at least ¶ 39: the training device 120 can be a flight simulator physically reproducing the flight deck of an aircraft, including flight deck panels with manipulators, buttons, and displays; ¶ 40: when the training device 120 is a flight simulator, the training device data 208 may include simulator data comprising input data … generated by the trainee 130 and output data computed by the … simulator; ¶ 41: the real-time instructor rating module 210 receives the instructor rating data 150 and generates time-stamped instructor rating data 232), ([Claim 3]) wherein the one or more computerized processors further: monitors the user operating the pump panel training device, wherein the monitoring includes: monitoring user inputs to the plurality of controls; monitoring resulting outputs generated upon the plurality of gages; and scoring a performance of the user based upon the monitored user inputs or the monitored resulting outputs (at least ¶ 74: … Instructor rating data 1008 comprises at least one of comments, scoring data, or other suitable information used to rate or score the trainee 1016; ¶ 81: … the instructor rating data takes the form of a comment. The instructor rating data also can include scoring data or other types of information used to grade or rate actions by the trainee …; ¶ 90: The training data may include information about positions of controls such as levers, switches, when particular controls were activated during the event, and other suitable data that may be obtained from a training device; ¶ 91: A determination is made as to whether the sequence of performed actions match the sequence of required actions (operation 1306). If a match is present, a pass is indicated for the scenario event (operation 1308) with the process terminating thereafter. On the other hand, if a match is absent in operation 1306, a fail is indicated for the scenario event (operation 1310)), ([Claim 4]) wherein the providing the training output includes combining the instructor review input to the user and the scored performance (at least ¶ 33: The instructor rating data 150 may include event ratings, comments, and/or competency performance; ¶ 46: … instructor's comments or ratings may be integrated, for instance, with the video data 206 of the training session 202, in a similar way as subtitles are integrated into a video stream to form integrated data. Alternatively, or additionally, instructor's comments or ratings may also be integrated with the output simulator data of the training device 120). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention that, when faced with the issue of utilizing a plurality of images received from a camera device, one would have looked to other fields in which utilizing a plurality of images received from a camera device generally occurs, particularly to the art of evaluating the performance of trainees during a training session, and more particularly for evidence-based training. Hence, before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to apply known usage of collecting evidence during training sessions in form of comments, observations and instructor ratings and then debriefing evidence-based training sessions as taught by Correia and to modify Cerrano as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claims 5-6] Cerrano in view of Correia teaches or at least suggests wherein the one or more computerized processors further automatically evaluates the user operating the pump panel training device based upon the plurality of images (at least Correia: ¶ 8: acquire real-time training session data during a training session of a trainee, wherein the real-time training session data includes training device data obtained from a training device, and video data. The real-time data acquisition module is configured to generate time-stamped training session data. The input unit is configured to acquire instructor rating data input by an instructor during the training session. The real-time instructor rating module is configured to generate time-stamped instructor rating data), wherein automatically evaluating the user operating the pump panel training device includes: initiating a simulation including identifying a set of spatial definitions defining a desired sequence of operations to be performed by a trainee during the simulation; operating the simulation upon the pump panel training device including monitoring movements of the user through a camera device; rendering the monitored movements of the user into spatial definitions defining performance of the user during the simulation; scoring the performance of the user based upon comparing the spatial definitions defining the performance of the user to the spatial definitions defining the desired sequence of operations; and generating an output based upon the scoring (at least Correia: ¶ 90: The process begins by identifying a scenario event from a training session (operation 1300). The process identifies a sequence of required actions for the scenario event (operation 1302). The process identifies a sequence of performed actions performed during the training session for the scenario event (operation 1304). The sequence of actions performed may be identified any number of different ways. For example, comments, checklists for desired flight crew performance, video, audio, and other training data may be used. These actions may be automatically identified using an analysis process. This process may be implemented in an artificial intelligence system or some other suitable manner. The training data may include information about positions of controls such as levers, switches, when particular controls were activated during the event, and other suitable data that may be obtained from a training device; ¶ 91: A determination is made as to whether the sequence of performed actions match the sequence of required actions (operation 1306). If a match is present, a pass is indicated for the scenario event (operation 1308) with the process terminating thereafter. On the other hand, if a match is absent in operation 1306, a fail is indicated for the scenario event (operation 1310; ¶ 95: The process begins by identifying a scenario event (operation 1400). The process identifies a sequence of required actions for the scenario event (1402). When the event begins, the process monitors for a required action (operation 1404). The process determines whether the required action is performed (operation 1406). In this illustrative example, the determination in operation 1406 can be made using an artificial intelligence process such that this process runs in real-time during the occurrence of the scenario event in the training session. The determination in operation 1406 can be formed by an artificial intelligence process using training session data. In this illustrative example, the training session data includes at least one of training device data obtained from a training device, video data, audio data, or other suitable data for the training session).
In the event the above interpretation of Cerrano in view of Correia “automatically” performing the claim steps is viewed as not being reasonable, it was known, at the time before of the effective filing date of the invention, that merely providing automatic means to replace manual activities which accomplish the same result is not sufficient to distinguish over the prior art, In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958). Hence, it would have been obvious to a person of ordinary still in the art, at the time before the effective filing date of the invention, to have modified Cerrano in view of Correia to automate the evaluation of the user as claimed because this would speed up the process of evaluating the performance of trainees during a training session, which is purely known, and an expected result from automation of what is known in the art.
[Claim 7] Cerrano in view of Correia teaches or at least suggests wherein evaluating the user operating the pump panel training device includes: monitoring movements of the user through the camera device; scoring a performance of the user based upon comparing the monitored movements of the user to initial judgment criteria; evaluating an outcome of the scoring and updating judgment criteria with an artificial intelligence subroutine; and utilizing the updated judgment criteria during subsequent iterations of scoring the performance of the user (at least Correia: ¶ 28: FIG. 17 is an illustration of a flow diagram for analyzing comments made by a trainer during a training session utilizing an artificial intelligence system in accordance with an illustrative embodiment; ¶ 75: the computer system 1004 with the artificial intelligence system 1002, the rating and briefing device 1012, the training device 1050, and the video camera form a training system 1054. This data can be analyzed in real-time to provide feedback during the training session 1010. This feedback can be provided to at least one of the trainer 1014 or the trainee 101; ¶ 76: more efficient training can be performed utilizing the artificial intelligence system 1002. With feedback provided during the training session 1010, a maneuver, a procedure, or other scenario event can be performed again in the same training session depending on the feedback received; ¶ 93: using an artificial intelligence system trained to analyze performed actions for a scenario event from a training session; ¶ 95: The process begins by identifying a scenario event (operation 1400). The process identifies a sequence of required actions for the scenario event (1402). When the event begins, the process monitors for a required action (operation 1404). The process determines whether the required action is performed (operation 1406). In this illustrative example, the determination in operation 1406 can be made using an artificial intelligence process such that this process runs in real-time during the occurrence of the scenario event in the training session. The determination in operation 1406 can be formed by an artificial intelligence process using training session data. In this illustrative example, the training session data includes at least one of training device data obtained from a training device, video data, audio data, or other suitable data for the training session; ¶ 119: The process then returns operation 1700 as described above. In analyzing the training session data in real-time, the artificial intelligence system can suggest keywords for use in comments, thus reducing the need for the trainer to enter comments; ¶ 120: the artificial intelligence system can also be trained to identify discrepancies between comments made by the trainer for actions performed by a trainee and the actions as analyzed by an artificial intelligence system. In other words, the artificial intelligence system can identify when the comments made by the trainer do not correctly characterize the actions performed by the training based on a standard or other policy defining actions that are to be performed for an event).
[Claim 20] Cerrano teaches or at least suggests a system comprising a pump panel training device and a driver training device, comprising: the pump panel training device, including: a plurality of gages operable to imitate gages upon a firetruck pump panel; a plurality of controls operable to imitate controls upon the firetruck pump panel; a camera device capturing a plurality of images of a user operating the pump panel training device; a display device; the driver training device operable to simulate driving operation of a fire truck; and one or more computerized processors: coordinating data between the pump panel training device and the driver training device; receiving the plurality of images from the camera device (at least ¶ 2: the pumper truck controls used to regulate the valves and the fire pump, as well as the pressure and flow rate gauges, are commonly positioned in a control panel on the side of the pumper truck; ¶ 4: simulate the operation of the controls of the pumper truck or to the fighting of a fire to aid in training of fire-fighting personnel; ¶ 16: a base component 110 that is coupled by a communication link 112 to a pump 120. A tank 130 and a liquid source 140 are also coupled to pump 120; ¶ 17: base component 110, ladder 170, tank 130, and pump 120 are coupled to a fire-fighting device (not shown), such as a fire truck, used in system 100. In other embodiments, any of base component 110, ladder 170, tank 130, and/or pump 120 may not be coupled to the fire-fighting device; ¶ 18: Tank 130 is coupled via a tank supply line 138 to pump 120 to enable liquid to be selectively supplied to pump 120; ¶ 20: A first nozzle 156 is coupled to pump 120 via a first hose line 150. A first hose valve 154 coupled to line 150 is used to selectively control a flow of liquid from pump 120 to first nozzle 156, and a first pressure gauge 152 coupled to line 150 is used to measure an operating pressure of liquid in first hose line 150. A second nozzle 166 is coupled to pump 120 via a second hose line 160. A second hose valve 154 coupled to line 160 is used to control a flow of liquid from pump 120 to second nozzle 166, and a second pressure gauge 162 is coupled to line 160 to measure the operating pressure of liquid in second hose line 160; ¶ 21: a camera (not shown) may be coupled to the end of ladder 170 and/or in the alternative, to the mounting structure. Such a camera may be wirelessly coupled to base component 110 and/or to remote component 180 such that images captured by the camera may be wirelessly communicated to base component 110 and/or remote component 180 for viewing by a user remote from the camera; ¶ 22: Simulation system 300 enables remote component 180 to be used in training a user (i.e., a trainee) in a simulation environment using emulation component 210 and computing system 220 … during a simulation exercise, computing system 220 displays a graphical representation to trainee depicting a fire-fighting scenario; ¶ 28: images captured by the camera positioned near the end of ladder 170, and position details of first nozzle 156 and/or second nozzle 166 are wirelessly communicated to remote component 182; ¶ 37: FIG. 5 is a schematic view of an exemplary fire-fighting simulation system 300. In the exemplary embodiment, simulation system 300 includes remote component 180, a data logging component 190, an emulation component 210 (broadly referred to here as, an “interface component”), and a computing system 220. Remote component 180 is wirelessly coupled to data logging component 190 and emulation component 210, and emulation component 210 is wirelessly coupled to computing system 220; ¶ 38: during a simulation exercise, computing system 220 displays a graphical representation to trainee depicting a fire-fighting scenario. Emulation component 210 and computing system 220 enable the graphical representation to be easily changed through manipulation of the controls of remote component 180 by the trainee … a trainee presented with a graphical representation of a fire by emulation component 210 and computing system 220, is able to use remote component 180 to manipulate the position valves and/or ladder 170 of system 100 within the simulation environment set forth by emulation component 210 and computing system 220; ¶ 39: using emulation component 210, the trainer is able to present various scenarios to the trainee that replicate a scenario that the trainee may encounter when fighting an actual fire … emulation component 210 presents a graphical display on computing system 220 that resembles a typical control panel on a fire-fighting device. The trainee is thus able to use emulation component 210 to practice and train on the operation of the control panel included on the fire-fighting device).
Cerrano appears to be silent on but Correia which relates to “system … for evaluating the performance of trainees during a training session, and more particularly for evidence-based training” (¶ 2), teaches or at least suggests providing a training output to the user through the display device based upon the plurality of images (at least ¶ 8: acquire real-time training session data during a training session of a trainee, wherein the real-time training session data includes training device data obtained from a training device, and video data. The real-time data acquisition module is configured to generate time-stamped training session data. The input unit is configured to acquire instructor rating data input by an instructor during the training session. The real-time instructor rating module is configured to generate time-stamped instructor rating data; ¶ 32: FIG. 1 schematically depicts … a training tool framework, in which a rating and debriefing electronic device 100 acquires data from a video camera 110 and a training device 120 (such as a flight simulator, a desktop simulation, a videogame or any other interactive training platform) during a training session executed by one or more students or trainees 130 …). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention that, when faced with the issue of utilizing a plurality of images received from a camera device, one would have looked to other fields in which utilizing a plurality of images received from a camera device generally occurs, particularly to the art of evaluating the performance of trainees during a training session, and more particularly for evidence-based training. Hence, before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to apply known usage of collecting evidence during training sessions in form of comments, observations and instructor ratings and then debriefing evidence-based training sessions as taught by Correia and to modify Cerrano as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claims 8-12 are rejected under 35 U.S.C. 103 as obvious over Cerrano in view of Correia as applied to claim 5, and further in view of Ottnad et al. (US 20190244309 A1) (Ottnad).
Re claims 8-12:
[Claims 8-11] Cerrano in view of Correia appears to be silent on but Ottnad teaches or at least suggests wherein the one or more computerized processors evaluating the user operating the pump panel training device includes the one or more computerized processors tracking movement of at least one hand of the user (at least ¶ 212: A camera allows, for example, to analyze the movement of the hand as an additional localization system, for example, to detect gestures or to evaluate motion trajectories in general. By tracking the operator's hand, it can be determined whether workpieces have been placed in the correct crates. For example, motion trajectories 32B of the hand when removing a workpiece 23 are shown in FIG. 6), ([Claim 9]) further comprising a marker device disposed and configured to provide a location of the at least one hand of the user to the computerized processor through the plurality of images (at least ¶ 133: a dynamic association of a mobile unit can be carried out by scanning, for example, order papers and/or a code (e. g., barcode, QR code, magnetic code etc.) of the mobile unit. Furthermore, a common or two separate photos can be evaluated with the code of the order papers/other persons and the code of the mobile unit …), ([Claim 10]) wherein the computerized processor evaluating the user operating the pump panel training device includes scoring movement of a hand of the user in comparison to desired hand movement (at least ¶ 36: determining a motion trajectory of the mobile unit by evaluating the localization of the mobile unit over time, in particular by evaluating positions of the mobile unit determined with the interior localization system, detecting a gesture in a section of motion trajectory, and performing a process associated with the gesture; ¶ 44: evaluating the motion analysis with regard to a load on the person and/or an ergonomics in the handling of a workpiece by the person and/or a qualification of the performance capability of the person, in particular for an operational planning of the person in the manufacturing), ([Claim 11]) wherein the computerized processor evaluating the user operating the pump panel training device includes scoring the performance of the user in comparison to desired operation of the pump panel training device (at least ¶ 43: evaluating the motion information and/or position information of the person for the person-specific setting of operating parameters of a machine tool such as the setting of rights to operate the machine tool and displayed information as well as the setting of safety-relevant operating parameters of robot units; ¶ 46: evaluating the motion information and/or position information of the person for controlling operating parameters of the production plant; ¶ 53: evaluating by the manufacturing control and influencing the manufacturing control. For example, it is possible to detect when a certain work step has taken place and the manufacturing control can adjust the status of the production accordingly. The manufacturing control can detect a suspected erroneous processing and inform the operator about the suspected error (e.g.: only drilled twice, although three holes are provided?) …). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention that, when faced with the issue of utilizing collected behavior/interaction data, one would have looked to other fields in which utilization of collected behavior/interaction data generally occurs, particularly to the art of determining position/motion of person and/or body part for performance evaluation. Hence, before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to apply known usage of position/motion of person and/or body part for performance evaluation as taught by Ottnad and to modify Cerrano in view of Correia as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 12] Cerrano in view of Correia and Ottnad teaches or at least suggests wherein the computerized processor evaluating the user operating the pump panel training device includes comparing movement of a hand of the user in comparison to correctly operating the pump panel training device (at least Ottnad: ¶ 68: a detailed analysis of the motion profiles of persons in manufacturing allows the performance of employees to be qualified for specific processes in order to derive an optimum staff deployment plan; ¶ 212: By tracking the operator's hand, it can be determined whether workpieces have been placed in the correct crates. For example, motion trajectories 32B of the hand when removing a workpiece 23 are shown in FIG. 6).
Cerrano in view of Correia and Ottnad appears to be silent on comparison to correctly operating the pump panel training device being comparison to recorded movement of an instructor. The Examiner takes official notice that the concept and advantages of comparing movement of a hand of the user in comparison to recorded movement of an instructor were old and well known to one of ordinary skill in the art before the effective filing date of the invention. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Cerrano in view of Correia and Ottnad as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claims 13-19 are rejected under 35 U.S.C. 103 as obvious over Cerrano in view of AlDarwish (US 20250006075 A1), and further in view of Correia.
Re claims 13-19:
[Claims 13-14] Cerrano teaches or at least suggests a system comprising a pump panel training device, comprising: the pump panel training device, wherein the pump panel training device includes: a plurality of gages operable to imitate gages upon a fire truck pump panel; a plurality of controls operable to imitate controls upon the fire truck pump panel; a camera device capturing a plurality of images of a user operating the pump panel training device; and a display device; at least one computerized processor: operating to coordinate data between the plurality of pump panel training devices; receiving the plurality of images from the camera device (at least ¶ 2: the pumper truck controls used to regulate the valves and the fire pump, as well as the pressure and flow rate gauges, are commonly positioned in a control panel on the side of the pumper truck; ¶ 4: simulate the operation of the controls of the pumper truck or to the fighting of a fire to aid in training of fire-fighting personnel; ¶ 16: a base component 110 that is coupled by a communication link 112 to a pump 120. A tank 130 and a liquid source 140 are also coupled to pump 120; ¶ 17: base component 110, ladder 170, tank 130, and pump 120 are coupled to a fire-fighting device (not shown), such as a fire truck, used in system 100. In other embodiments, any of base component 110, ladder 170, tank 130, and/or pump 120 may not be coupled to the fire-fighting device; ¶ 18: Tank 130 is coupled via a tank supply line 138 to pump 120 to enable liquid to be selectively supplied to pump 120; ¶ 20: A first nozzle 156 is coupled to pump 120 via a first hose line 150. A first hose valve 154 coupled to line 150 is used to selectively control a flow of liquid from pump 120 to first nozzle 156, and a first pressure gauge 152 coupled to line 150 is used to measure an operating pressure of liquid in first hose line 150. A second nozzle 166 is coupled to pump 120 via a second hose line 160. A second hose valve 154 coupled to line 160 is used to control a flow of liquid from pump 120 to second nozzle 166, and a second pressure gauge 162 is coupled to line 160 to measure the operating pressure of liquid in second hose line 160; ¶ 21: a camera (not shown) may be coupled to the end of ladder 170 and/or in the alternative, to the mounting structure. Such a camera may be wirelessly coupled to base component 110 and/or to remote component 180 such that images captured by the camera may be wirelessly communicated to base component 110 and/or remote component 180 for viewing by a user remote from the camera; ¶ 22: Simulation system 300 enables remote component 180 to be used in training a user (i.e., a trainee) in a simulation environment using emulation component 210 and computing system 220 … during a simulation exercise, computing system 220 displays a graphical representation to trainee depicting a fire-fighting scenario; ¶ 28: images captured by the camera positioned near the end of ladder 170, and position details of first nozzle 156 and/or second nozzle 166 are wirelessly communicated to remote component 182; ¶ 37: FIG. 5 is a schematic view of an exemplary fire-fighting simulation system 300. In the exemplary embodiment, simulation system 300 includes remote component 180, a data logging component 190, an emulation component 210 (broadly referred to here as, an “interface component”), and a computing system 220. Remote component 180 is wirelessly coupled to data logging component 190 and emulation component 210, and emulation component 210 is wirelessly coupled to computing system 220; ¶ 38: during a simulation exercise, computing system 220 displays a graphical representation to trainee depicting a fire-fighting scenario. Emulation component 210 and computing system 220 enable the graphical representation to be easily changed through manipulation of the controls of remote component 180 by the trainee … a trainee presented with a graphical representation of a fire by emulation component 210 and computing system 220, is able to use remote component 180 to manipulate the position valves and/or ladder 170 of system 100 within the simulation environment set forth by emulation component 210 and computing system 220; ¶ 39: using emulation component 210, the trainer is able to present various scenarios to the trainee that replicate a scenario that the trainee may encounter when fighting an actual fire … emulation component 210 presents a graphical display on computing system 220 that resembles a typical control panel on a fire-fighting device. The trainee is thus able to use emulation component 210 to practice and train on the operation of the control panel included on the fire-fighting device).
Cerrano appears to be silent on a plurality of pump panel training devices, comprising: the plurality of pump panel training devices, wherein at least one of the pump panel training devices includes the claim elements noted above. However, the plurality of pump panel training devices with all their elements recite a duplication of parts. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention to modify Cerrano to provide a system comprising a plurality of pump panel training devices since doing so would be no more than a duplication of elements, which has no patentable significance where, as here, no new and unexpected result is produced. See In re Harza, 274 F.2d 669, 671 (CCPA 1960); see also In re Marcum, 47 F.2d 377, 378 (CCPA 1931) (citing Topliff v. Topliff, 145 U.S. 156, 163 (1892)) (A "mere duplication of parts" is not invention.). Further, it is no more than a "combination of familiar elements according to known methods [that] does no more than yield predictable results." KSR Int'! Co. v. Teleflex Inc., 550 U.S. 398,416 (2007).
Cerrano appears to be silent on but AlDarwish which relates to simulating the operation of a vehicle, and associated components (¶ 2), teaches or at least suggests operating to coordinate data between the plurality of pump panel training devices (at least ¶ 3: Operating a complex vehicle, such as an aircraft, a ship, etc., may include coordinating the efforts of multiple members of a crew or team; ¶ 19: Operating a complex vehicle, such as an aircraft, a drone, a ship, etc., may include coordinating the efforts of multiple operators, such as a pilot, co-pilot, crew chief, engineer, first officer, second officer, third officer; ¶ 20: A first nozzle 156 is coupled to pump 120 via a first hose line 150. A first hose valve 154 coupled to line 150 is used to selectively control a flow of liquid from pump 120 to first nozzle 156, and a first pressure gauge 152 coupled to line 150 is used to measure an operating pressure of liquid in first hose line 150. A second nozzle 166 is coupled to pump 120 via a second hose line 160. A second hose valve 154 coupled to line 160 is used to control a flow of liquid from pump 120 to second nozzle 166, and a second pressure gauge 162 is coupled to line 160 to measure the operating pressure of liquid in second hose line 160; ¶ 21: Each of the operator stations 102 may include a seat 110, operator inputs 112, and a display 114. The seat 110 may provide an anchor point for one or more of the operator inputs 112. The anchor points in the seat 110 may be adjustable, such as to adjust the location of the operator inputs 112 for operators of different sizes. In some embodiments, the seat 110 may be configured to provide multiple different anchor points, such that the seat 110 may be configured to represent different vehicles and/or different positions within the different vehicle …; ¶ 25: As illustrated in FIG. 1, the simulation system 100 may include multiple different operator stations 102 … The multiple different operator stations 102 may allow multiple crew members to interact with the simulation system 100 simultaneously, such that the crew members may act as a crew in the simulation). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention that, when faced with the issue of simulating operation of a physical structure and associated components, one would have looked to other fields in which utilizing simulation generally occurs, particularly to the art of running a simulation including environmental data, operational data, and physical structure data. Hence, before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to apply known usage of environmental data, operational data, and physical structure data as taught by AlDarwish and to modify Cerrano as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Cerrano in view of AlDarwish appears to be silent on but Correia which relates to “system … for evaluating the performance of trainees during a training session, and more particularly for evidence-based training” (¶ 2), teaches or at least suggests providing a training output to the user through the display device based upon the plurality of images (at least ¶ 8: acquire real-time training session data during a training session of a trainee, wherein the real-time training session data includes training device data obtained from a training device, and video data. The real-time data acquisition module is configured to generate time-stamped training session data. The input unit is configured to acquire instructor rating data input by an instructor during the training session. The real-time instructor rating module is configured to generate time-stamped instructor rating data; ¶ 32: FIG. 1 schematically depicts … a training tool framework, in which a rating and debriefing electronic device 100 acquires data from a video camera 110 and a training device 120 (such as a flight simulator, a desktop simulation, a videogame or any other interactive training platform) during a training session executed by one or more students or trainees 130 …), ([Claim 14]) wherein the at least one computerized processor further: provides the plurality of images to an instructor; and receives an instructor review input of the user operating the pump panel training device including the camera device; and wherein providing the training output to the user includes providing the instructor review input to the user (at least ¶ 39: the training device 120 can be a flight simulator physically reproducing the flight deck of an aircraft, including flight deck panels with manipulators, buttons, and displays; ¶ 40: when the training device 120 is a flight simulator, the training device data 208 may include simulator data comprising input data … generated by the trainee 130 and output data computed by the … simulator; ¶ 41: the real-time instructor rating module 210 receives the instructor rating data 150 and generates time-stamped instructor rating data 232),. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention that, when faced with the issue of utilizing a plurality of images received from a camera device, one would have looked to other fields in which utilizing a plurality of images received from a camera device generally occurs, particularly to the art of evaluating the performance of trainees during a training session, and more particularly for evidence-based training. Hence, before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to apply known usage of collecting evidence during training sessions in form of comments, observations and instructor ratings and then debriefing evidence-based training sessions as taught by Correia and to modify Cerrano in view of AlDarwish as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).:
[Claims 15-16] Cerrano in view of AlDarwish and Correia teaches or at least suggests wherein each of the plurality of pump panel training devices includes: a respective plurality of gages operable to imitate gages upon a fire truck pump panel; a respective plurality of simulated controls operable to imitate controls upon the fire truck pump panel; a respective camera device capturing a plurality of images of a respective user operating the pump panel training device; and a respective display device, ([Claim 16]) wherein the at least one computerized processor: evaluates performances of each of the respective users; compares the performances of the respective users; and provides a training output based upon the comparing (at least Cerrano: ¶ 2: the pumper truck controls used to regulate the valves and the fire pump, as well as the pressure and flow rate gauges, are commonly positioned in a control panel on the side of the pumper truck; ¶ 4: simulate the operation of the controls of the pumper truck or to the fighting of a fire to aid in training of fire-fighting personnel; ¶ 16: a base component 110 that is coupled by a communication link 112 to a pump 120. A tank 130 and a liquid source 140 are also coupled to pump 120; ¶ 17: base component 110, ladder 170, tank 130, and pump 120 are coupled to a fire-fighting device (not shown), such as a fire truck, used in system 100. In other embodiments, any of base component 110, ladder 170, tank 130, and/or pump 120 may not be coupled to the fire-fighting device; ¶ 18: Tank 130 is coupled via a tank supply line 138 to pump 120 to enable liquid to be selectively supplied to pump 120; ¶ 20: A first nozzle 156 is coupled to pump 120 via a first hose line 150. A first hose valve 154 coupled to line 150 is used to selectively control a flow of liquid from pump 120 to first nozzle 156, and a first pressure gauge 152 coupled to line 150 is used to measure an operating pressure of liquid in first hose line 150. A second nozzle 166 is coupled to pump 120 via a second hose line 160. A second hose valve 154 coupled to line 160 is used to control a flow of liquid from pump 120 to second nozzle 166, and a second pressure gauge 162 is coupled to line 160 to measure the operating pressure of liquid in second hose line 160; ¶ 28: images captured by the camera positioned near the end of ladder 170, and position details of first nozzle 156 and/or second nozzle 166 are wirelessly communicated to remote component 182; AlDarwish: ¶ 20: FIG. 1 …The simulation system 100 may include one or more operator stations 102, at least one administrative station 104, as well as optional observation stations 106. Each of the operator stations 102, administrative station 104, and observation stations 106 may be communicatively coupled through a network …; ¶ 20: A first nozzle 156 is coupled to pump 120 via a first hose line 150. A first hose valve 154 coupled to line 150 is used to selectively control a flow of liquid from pump 120 to first nozzle 156, and a first pressure gauge 152 coupled to line 150 is used to measure an operating pressure of liquid in first hose line 150. A second nozzle 166 is coupled to pump 120 via a second hose line 160. A second hose valve 154 coupled to line 160 is used to control a flow of liquid from pump 120 to second nozzle 166, and a second pressure gauge 162 is coupled to line 160 to measure the operating pressure of liquid in second hose line 160; ¶ 21: Each of the operator stations 102 may include a seat 110, operator inputs 112, and a display 114. The seat 110 may provide an anchor point for one or more of the operator inputs 112. The anchor points in the seat 110 may be adjustable, such as to adjust the location of the operator inputs 112 for operators of different sizes. In some embodiments, the seat 110 may be configured to provide multiple different anchor points, such that the seat 110 may be configured to represent different vehicles and/or different positions within the different vehicle …; ¶ 25: As illustrated in FIG. 1, the simulation system 100 may include multiple different operator stations 102 … The multiple different operator stations 102 may allow multiple crew members to interact with the simulation system 100 simultaneously, such that the crew members may act as a crew in the simulation), ([Claim 16]) wherein the at least one computerized processor: evaluates performances of each of the respective users; compares the performances of the respective users; and provides a training output based upon the comparing (at least Correia: ¶ 90: The process begins by identifying a scenario event from a training session (operation 1300). The process identifies a sequence of required actions for the scenario event (operation 1302). The process identifies a sequence of performed actions performed during the training session for the scenario event (operation 1304). The sequence of actions performed may be identified any number of different ways. For example, comments, checklists for desired flight crew performance, video, audio, and other training data may be used. These actions may be automatically identified using an analysis process. This process may be implemented in an artificial intelligence system or some other suitable manner. The training data may include information about positions of controls such as levers, switches, when particular controls were activated during the event, and other suitable data that may be obtained from a training device; ¶ 91: A determination is made as to whether the sequence of performed actions match the sequence of required actions (operation 1306). If a match is present, a pass is indicated for the scenario event (operation 1308) with the process terminating thereafter. On the other hand, if a match is absent in operation 1306, a fail is indicated for the scenario event (operation 1310; ¶ 95: The process begins by identifying a scenario event (operation 1400). The process identifies a sequence of required actions for the scenario event (1402). When the event begins, the process monitors for a required action (operation 1404). The process determines whether the required action is performed (operation 1406). In this illustrative example, the determination in operation 1406 can be made using an artificial intelligence process such that this process runs in real-time during the occurrence of the scenario event in the training session. The determination in operation 1406 can be formed by an artificial intelligence process using training session data. In this illustrative example, the training session data includes at least one of training device data obtained from a training device, video data, audio data, or other suitable data for the training session; AlDarwish: ¶ 25: As illustrated in FIG. 1, the simulation system 100 may include multiple different operator stations 102 … The multiple different operator stations 102 may allow multiple crew members to interact with the simulation system 100 simultaneously, such that the crew members may act as a crew in the simulation). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Cerrano in view of AlDarwish and Correia as claimed, because a person of ordinary skill has good reasons to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 17] Cerrano in view of AlDarwish and Correia teaches or at least suggests wherein evaluating the user operating the pump panel training device includes: monitoring movements of the user through the camera device; scoring a performance of the user based upon comparing the monitored movements of the user to initial judgment criteria; evaluating an outcome of the scoring and updating judgment criteria with an artificial intelligence subroutine; and utilizing the updated judgment criteria during subsequent iterations of scoring the performance of the user (at least Correia: ¶ 28: FIG. 17 is an illustration of a flow diagram for analyzing comments made by a trainer during a training session utilizing an artificial intelligence system in accordance with an illustrative embodiment; ¶ 75: the computer system 1004 with the artificial intelligence system 1002, the rating and briefing device 1012, the training device 1050, and the video camera form a training system 1054. This data can be analyzed in real-time to provide feedback during the training session 1010. This feedback can be provided to at least one of the trainer 1014 or the trainee 101; ¶ 76: more efficient training can be performed utilizing the artificial intelligence system 1002. With feedback provided during the training session 1010, a maneuver, a procedure, or other scenario event can be performed again in the same training session depending on the feedback received; ¶ 93: using an artificial intelligence system trained to analyze performed actions for a scenario event from a training session; ¶ 95: The process begins by identifying a scenario event (operation 1400). The process identifies a sequence of required actions for the scenario event (1402). When the event begins, the process monitors for a required action (operation 1404). The process determines whether the required action is performed (operation 1406). In this illustrative example, the determination in operation 1406 can be made using an artificial intelligence process such that this process runs in real-time during the occurrence of the scenario event in the training session. The determination in operation 1406 can be formed by an artificial intelligence process using training session data. In this illustrative example, the training session data includes at least one of training device data obtained from a training device, video data, audio data, or other suitable data for the training session; ¶ 119: The process then returns operation 1700 as described above. In analyzing the training session data in real-time, the artificial intelligence system can suggest keywords for use in comments, thus reducing the need for the trainer to enter comments; ¶ 120: the artificial intelligence system can also be trained to identify discrepancies between comments made by the trainer for actions performed by a trainee and the actions as analyzed by an artificial intelligence system. In other words, the artificial intelligence system can identify when the comments made by the trainer do not correctly characterize the actions performed by the training based on a standard or other policy defining actions that are to be performed for an event).
[Claim 18] Cerrano in view of AlDarwish and Correia teaches or at least suggests
wherein the at least one computerized processor providing the training output based upon the comparing includes providing a side-by-side illustration of movements of the respective users or providing an illustration of the movements of a first of the respective users overlaid upon the movements of a second of the respective users (at least Correia: ¶ 33: The instructor rating data 150 may include event ratings, comments, and/or competency performance; ¶ 51: debriefing module 250 can also provide feedback in real-time after the performance of one or more actions. For example, the debriefing module 250 can notify the instructor during the training session that the students did not perform a certain action required by a checklist (e.g., turn-on seat belt sign); ¶ 59: Graphical user interface 700 may be used to review segments of video associated with comments made by trainer during a training session. In this illustrative example, comments 908 are made by a trainer during a training session regarding a trainee. These comments may be selected by the trainer to see corresponding video of actions performed when comment 908 was made; ¶ 70: On the timeline 918 below the video, the instructor 140 also has access to the comments taken during the session (black squares on the timeline). The comments would show in a top-right box, so the instructor 140 could read what he/she noted while watching the video 916 like subtitles in a movie. The instructor could also add new comments 920 while watching the video as well as marking additional DFCPs that were missed during the training session 202, which would cause the update of the general statistics 902 in the debriefing screen 900). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Cerrano in view of AlDarwish and Correia as claimed, because a person of ordinary skill has good reasons to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 19] Cerrano in view of AlDarwish and Correia teaches or at least suggests wherein the at least one computerized processor further provides real-time instruction to the user (at least Correia: ¶ 8: acquire real-time training session data during a training session of a trainee, wherein the real-time training session data includes training device data obtained from a training device, and video data. The real-time data acquisition module is configured to generate time-stamped training session data. The input unit is configured to acquire instructor rating data input by an instructor during the training session. The real-time instructor rating module is configured to generate time-stamped instructor rating data; ¶ 10: Real-time feedback is provided to the instructor regarding the execution of said at least one action; ¶ 41: the real-time instructor rating module 210 receives the instructor rating data 150 and generates time-stamped instructor rating data 232; ¶ 51: debriefing module 250 can also provide feedback in real-time after the performance of one or more actions). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Cerrano in view of AlDarwish and Correia as claimed, because a person of ordinary skill has good reasons to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense).
Conclusion
The prior art made of record and not relied upon is listed in the attached PTO Form 892 and is considered pertinent to applicant's disclosure.
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715
