DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 21 January 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Claims 1, 12-14, and 18 have been amended. Claims 21 and 22 have been newly added. No claims have been newly canceled. The previous objections to claims 1, 12-13, and 18, as well as the previous nonstatutory double patenting rejection of claims 1-20 have been withdrawn as a result of amendment.
Response to Arguments
Applicant’s arguments with respect to claims 1 and 14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-7, 12-18, 20, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Gracia Berna (US 20210375141 A1, having a filing date of 12 February 2021), hereafter Gracia Berna, in view of Bertram (US 10935938 B1), hereafter Bertram, and further in view of Sampath (US 20190346992 A1), hereafter Sampath.
Regarding claim 1, Gracia Berna teaches an aircraft system comprising:
An autonomous computing system communicatively coupled to a sensor suite onboard an aircraft, the autonomous computing system comprising a set of pre-trained neural networks (0020, memory 122 is configured to store an aircraft performance model 181, the aircraft performance model 181 includes at least a recurrent neural network layer 183, which enables the aircraft performance model 181 to exhibit temporal dynamic behavior, for example, the recurrent neural network layer 183 enables the aircraft performance model 181 to process real-time time-series data, e.g., flight data 105, and detect temporal trends, 0021, the sensors 142 are configured to provide flight data 105 to the data bus 140), wherein the autonomous computing system is configured to determine a flight command with the set of pre-trained neural networks based on sensor data from the sensor suite (0054-0056, the method 600 includes generating one or more aircraft performance parameters based on the real-time time-series flight data and an aircraft performance model, for example, the parameter generator 174 generates the aircraft performance parameters 141 based on the flight data 105 and the aircraft performance model 181, which includes recurrent neural network layer 183, method 600 further includes providing the aircraft performance parameters to a display device, predicted values of the aircraft performance parameters 141 are displayed in real-time within seconds of detection of the corresponding flight data 105, using the aircraft performance model 181, e.g., a machine-learning model, enables the aircraft performance parameters 141 to be estimated in real-time rather than using more time-intensive calculations after landing, in a particular aspect, the aircraft performance parameters 141 are used to generate recommended settings, automatically update a setting or configuration of the aircraft 108, one or more of the aircraft performance parameters 141, the recommended settings, or a combination thereof, can be displayed to improve pilot situational awareness, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information);
An aircraft computing system communicatively coupled to the autonomous computing system (0018, one or more functions performed by the on-board computing device 102 are divided amongst multiple devices, including on-board computing device 102, and AID, a flight management system, see also Fig. 1), the aircraft computing system comprising:
A pilot validation interface communicatively coupled to a flight management system (FMS) (0026, GUI generator 176 generates the GUI 163, GUI generator 176 generates one or more recommended settings, e.g., a recommended trim setting, based on the aircraft performance parameters 141), the pilot validation interface configured to:
Facilitate pilot validation of the flight command in a human-interpretable format (0026, GUI generator 176 provides the GUI 163 to the display device 144, one or more of the aircraft performance parameters 141, recommended settings, or a combination thereof, can be displayed to enable the pilot to make informed flight decisions based on real-time data); and
In response to pilot validation of the flight command in the human-interpretable format, automatically facilitate execution of the flight command via the FMS (0026, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information, in some examples, flight control settings of the aircraft 108 can be automatically adjusted based on the aircraft performance parameters 141).
Gracia Berna fails to explicitly teach, however, wherein the aircraft computing system is partitioned from the autonomous computing system.
Bertram, however, in an analogous field of endeavor, does teach wherein the aircraft computing system is partitioned from the autonomous computing system (Col. 9, Lines 43-53, different components of the system 100a may be separated into one or more individual partitions, for example, the observation sub-system 102, the ML module 104, the failsafe controller 106, and the autopilot 128 may be implemented on multiple, individual partitions).
Gracia Berna and Bertram are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the partitioning of Bertram in order to provide a means of separating the aircraft’s computing system. The motivation to combine is to ensure that the failure of one computing system does not directly affect another computing system.
The combination of Gracia Berna and Bertram fails to explicitly teach, however, wherein the flight command comprises a set of waypoints; and
Wherein the pilot validation interface is configured to:
Deterministically transform the flight command into a human interpretable format.
Sampath, however, in an analogous field of endeavor, does teach wherein the flight command comprises a set of waypoints (0053, Referring now to FIG. 7, in exemplary embodiments, the data entry system 10 may be used to reduce pilot workload in other flight scenarios. For example, the ATC instruction, which may be a text instruction sent via CPDLC or a voice instruction sent via VF or VHF, may relate to a flight instruction directing for a change in the flight plan of the aircraft in order to route the aircraft to a particular waypoint. An example of such an ATC instruction may be “DELTA TWENTY FOUR ZERO FIVE CLEARED DIRECT PUMPS DESCEND AND MAINTAIN FLIGHT LEVEL THREE ZERO ZERO”); and
Wherein the pilot validation interface is configured to:
Deterministically transform the flight command into a human interpretable format (0053-0054, Referring now to FIG. 7, in exemplary embodiments, the data entry system 10 may be used to reduce pilot workload in other flight scenarios. For example, the ATC instruction, which may be a text instruction sent via CPDLC or a voice instruction sent via VF or VHF, may relate to a flight instruction directing for a change in the flight plan of the aircraft in order to route the aircraft to a particular waypoint. An example of such an ATC instruction may be “DELTA TWENTY FOUR ZERO FIVE CLEARED DIRECT PUMPS DESCEND AND MAINTAIN FLIGHT LEVEL THREE ZERO ZERO” … In this example ATC instruction, the parsing module 17 identifies the keyword “DIRECT” and the associated text “PUMPS” as a constituent data element contained within this textual data string. The contextual tagging module 19 determines an association between this constituent data element and the data entry field location “ATC instruction to DIRECT-TO”. The processor 21 is configured, upon selection of the relevant waypoint “PUMPS” in the data entry field location, which in this case is the waypoint list, the portion of the constituent data element “DIRECT-TO”. The pilot may then confirm this previewed portion using the pilot user-control device in order to enter the previewed portion “DIRECT-TO” into the selected data field entry location, thereby confirming that a “direct-to” operation should be performed with respect to the waypoint “PUMPS”. In exemplary embodiments, after pilot confirmation of the previewed portion of the constituent data element corresponding to the instructed waypoint, the processor 21 instructs for a “DIRECT-TO” operation to the waypoint “PUMPS” in the pending flight plan. 0056, At step S200, the command is parsed into constituent data elements. In exemplary embodiments, the parsing is performed through the identification of pre-determined key words).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the present invention, with a reasonable expectation of success, to have included the flight command transformation of Sampath in order to provide a means of automatically parsing relevant sensor information. The motivation to combine is to further reduce the workload on the pilot (see at least 0002-0005 of Sampath).
Regarding claim 2, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 1, and Gracia Berna further teaches wherein the sensor data comprises unstructured information, wherein the set of pre-trained neural networks are configured to autonomously determine the flight command in a predetermined, machine-readable format from the unstructured information (0054-0056, the method 600 includes generating one or more aircraft performance parameters based on the real-time time-series flight data and an aircraft performance model, for example, the parameter generator 174 generates the aircraft performance parameters 141 based on the flight data 105 and the aircraft performance model 181, which includes recurrent neural network layer 183, method 600 further includes providing the aircraft performance parameters to a display device, predicted values of the aircraft performance parameters 141 are displayed in real-time within seconds of detection of the corresponding flight data 105, using the aircraft performance model 181, e.g., a machine-learning model, enables the aircraft performance parameters 141 to be estimated in real-time rather than using more time-intensive calculations after landing, in a particular aspect, the aircraft performance parameters 141 are used to generate recommended settings, automatically update a setting or configuration of the aircraft 108, one or more of the aircraft performance parameters 141, the recommended settings, or a combination thereof, can be displayed to improve pilot situational awareness, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information).
Regarding claim 3, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 2, and Gracia Berna further teaches wherein the flight command is provided to the pilot validation interface (0026, GUI generator 176 provides the GUI 163 to the display device 144, one or more of the aircraft performance parameters 141, recommended settings, or a combination thereof, can be displayed to enable the pilot to make informed flight decisions based on real-time data).
Additionally, Bertram teaches it further comprising: an application programming interface (API) communicatively coupled to the autonomous computing system and the pilot validation interface (Col. 10, Lines 44-55, application program interface API may be used to further ensure security, a system integrator may host all or a portion of the hypervisor-based OS 132 with definitions and structure for accessing individual partitions, which may have their own program definitions, allowable forms of input, and protocols such that data that is not in a proper format, input configuration, or does not use proper definitions or protocols will not be validated or authenticated for use by the hypervisor based OS 132), the API configured to:
Receive the flight command from the autonomous computing system (Col. 14, Lines 27-41, step 210 may include generating control output for the estimated maneuver based on an observable parameter input into the ML module);
Automatically validate flight command adherence to the predetermined, machine-readable data format (Col. 10, Line 44 - Col. 11, Line 3, application program interface API may be used to further ensure security, a system integrator may host all or a portion of the hypervisor-based OS 132 with definitions and structure for accessing individual partitions, which may have their own program definitions, allowable forms of input, and protocols such that data that is not in a proper format, input configuration, or does not use proper definitions or protocols will not be validated or authenticated for use by the hypervisor based OS 132, data guard 133 may be programmed, attached, and/or associated with the API to ensure compliance with a ruleset, for example, the ruleset may include a protocol, definition, or data configuration, e.g., output from a control stick requires at least two parameters including a direction in which and a position to which the control stick has moved, and the data guard 133 may be tasked with enforcing the ruleset, Col. 14, Lines 58-61, step 212 may include performing a check of a control output against every corresponding and relevant/applicable baseline, threshold, limit, or combination thereof, within the safety envelope); and
Provide the flight command based on validation of flight command adherence to the predetermined, machine-readable data format (Col. 14, Lines 42-58, step 212 may include performing the estimated maneuver if it is within the safety envelope).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the API and validation of Bertram in order to provide a means of validating the commands or data sent between partitions. The motivation to combine is to ensure that the commands or data are in a valid format before transmitting them between partitions.
Regarding claim 4, the combination of Gracia Berna and Bertram Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 3, and Bertram further teaches wherein the API is further configured to automatically validate the flight command with a hash function or a checksum (Col. 10, Lines 23-37, each trusted partition may have a cryptographic signature securing entry into and out of it such that a respective trusted partition may be validated by the hypervisor-based OS 132, cryptographic signatures may include a cryptographic hash value, a checksum value, or a digital signature including mathematical analysis, for validating authenticity and/or integrity).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the checksum or hash validation of Bertram in order to provide a means of validating data. The motivation to combine is to ensure that the validity of data sent to a partition can be maintained.
Regarding claim 5, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 3, and Bertram further teaches wherein the API is further configured to automatically validate the flight command based on a flight envelope (Col. 5, Line 48 - Col. 6, Line 16, failsafe controller 106 may receive the control commands and monitor them using a control-theory based algorithm, the monitoring may enable a determination as to whether the control commands result output that is within a safety envelope or baseline for safe operation of the platform).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the validation based on flight envelope in order to provide a means of ensuring that a maneuver is safe to perform. The motivation to combine is to ensure that only safe-to-perform maneuvers are performed.
Regarding claim 6, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 1, and Gracia Berna further teaches wherein the human-interpretable format comprises a human-readable format, wherein the pilot validation interface comprises a display, wherein the pilot validation interface is configured to provide the flight command at the display in the human-readable format (0026, GUI generator 176 provides the GUI 163 to the display device 144, one or more of the aircraft performance parameters 141, recommended settings, or a combination thereof, can be displayed to enable the pilot to make informed flight decisions based on real-time data, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information).
Regarding claim 7, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 6, and Bertram further teaches wherein pilot validation comprises:
Automatic acceptance of the flight command based on satisfaction of a validation condition (Col. 14, Lines 42-57, step 212 may include analyzing the maneuver estimated in step 210 against the safety envelope, step 212 may include performing the estimated maneuver if it is within the safety envelope).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the automatic flight command of Bertram in order to provide a means of automatically performing a valid flight command. The motivation to combine is to ensure that a required maneuver is performed.
Regarding claim 12, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 1, and Sampath further teaches wherein the sensor data comprises an aircraft traffic control (ATC) audio signal (0041, receiving module may receive input corresponding to audible data, such as audio or voice instructions received from a local ATC tower), wherein the autonomous computing system comprises:
A speech-to-text module configured to determine an utterance hypothesis from the ATC audio signal with a first pre-trained neural network of the set (0043, voice recognition module 23 includes a speech processing and feature extraction module 25 operably connected to the receiving module 12, voice and/or audio data received at the receiving module 12 is transmitted to the speech processing and feature extraction module 25, 0045, since the terminology used in ATC may be more limited than the terminology used in everyday language, training of the speech processing and feature extraction module 25 on test voice data using, for example, an artificial neural network may result in the speech recognition algorithms used in the speech processing and feature extraction module 25 having a relatively high accuracy as compared to general-purpose speech recognition algorithms, 0046, speech processing and feature extraction module 25 generates the textual data string via the recognition of certain key words); and
A question-and-answer (Q/A) module configured to determine the flight command based on the utterance hypothesis using a plurality of natural language queries, wherein the Q/A module is configured to determine aircraft commands by querying the utterance hypothesis with a second pre-trained neural network of the set according to a structured sequence of the natural language queries (0025-0026, data-entry system 10 includes a parsing module 17, the parsing module 17 operably connected to the receiving module 12, and is configured to parse the input data received by the receiving module 12 into constituent data elements, parsing module 17 uses a natural language processing algorithm to identify key words in the textual data string and associated information related to those key words, 0028-0030, in order to perform parsing of the textual data string, the parting module 17 uses an artificial neural network that is pre-trained using machine learning techniques to identify key words and recognized clauses in the textual data string, data entry system 10 also includes contextual tagging module 19 operably connected to the parsing module 17, which is configured to determine associations between the constituent data elements identified and separated out by the parsing module 17 and the multiple data field entry locations 51-54 on the display, contextual tagging module 19 is configured to use correlations between pre-defined key words identified in the constituent data elements and pre-defined "known" terms and clauses to determine an association between a constituent data element and a particular data entry field location on the display 14).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the present invention, with a reasonable expectation of success, to have included the air traffic control parsing of Sampath in order to provide a means of automatically determining air traffic control clearances. The motivation to combine is to further reduce the workload on the pilot (see at least 0002-0005 of Sampath).
Claim 20 is similar in scope to claim 12, and is similarly rejected.
Regarding claim 13, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 1, and Bertram further teaches wherein the sensor suite comprises a set of time-of-flight sensors and the sensor data comprises time-of-flight data from the set of time-of-flight sensors (Col. 5, Line 48 - Col. 6, Line 16, observation subsystem 102 may observe pulsed laser returns from a light detection and ranging, LIDAR, sensor and generate output that is utilized for determining the relative position of the platform 108), wherein the autonomous computing system is configured to:
Identify features based on the time-of-flight data using one pre-trained neural network of the set of pre-trained neural networks (Col. 5, Line 48 - Col. 6, Line 16, observation subsystem 102 may observe pulsed laser returns from a light detection and ranging, LIDAR, sensor and generate output that is utilized for determining the relative position of the platform 108, Col. 6, Line 54 - Line 8, ML module 104 may include memory and one or more processors to receive the relative position from the observation sub-system 102 and estimate a state of the platform 108); and
Autonomously determine the flight command based on the identified features (Col. 14, Lines 27-41, step 210 may include determining a second maneuver for the state of the platform, step 210 may further include generating control output for the estimated maneuver, wherein the control output for the estimated maneuver may be based on an observable parameter input into the ML module).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the time-of-flight sensing and command generation of Bertram in order to provide further means of determining a flight command. The motivation to combine is to ensure that the aircraft is able to effectively respond to its environment.
Regarding claim 14, Gracia Berna teaches a method for control of an aircraft, the method comprising:
Determining sensor information with a sensor suite of the aircraft (0021, the sensors 142 are configured to provide flight data 105 to the data bus 140, flight data 105 indicate measurements performed by the sensors 142);
Based on the sensor information, determining a flight command with an autonomous computing system of the aircraft using a set of pre-trained models of the autonomous computing system (0054-0056, the method 600 includes generating one or more aircraft performance parameters based on the real-time time-series flight data and an aircraft performance model, for example, the parameter generator 174 generates the aircraft performance parameters 141 based on the flight data 105 and the aircraft performance model 181, which includes recurrent neural network layer 183, method 600 further includes providing the aircraft performance parameters to a display device, predicted values of the aircraft performance parameters 141 are displayed in real-time within seconds of detection of the corresponding flight data 105, using the aircraft performance model 181, e.g., a machine-learning model, enables the aircraft performance parameters 141 to be estimated in real-time rather than using more time-intensive calculations after landing, in a particular aspect, the aircraft performance parameters 141 are used to generate recommended settings, automatically update a setting or configuration of the aircraft 108, one or more of the aircraft performance parameters 141, the recommended settings, or a combination thereof, can be displayed to improve pilot situational awareness, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information);
Providing the flight command in a human-interpretable format at a pilot validation interface (0026, GUI generator 176 provides the GUI 163 to the display device 144, one or more of the aircraft performance parameters 141, recommended settings, or a combination thereof, can be displayed to enable the pilot to make informed flight decisions based on real-time data);
Determining a pilot validation of the flight command using the pilot validation interface (0026, GUI generator 176 provides the GUI 163 to the display device 144, one or more of the aircraft performance parameters 141, recommended settings, or a combination thereof, can be displayed to enable the pilot to make informed flight decisions based on real-time data, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information, in some examples, flight control settings of the aircraft 108 can be automatically adjusted based on the aircraft performance parameters 141); and
In response to determining the pilot validation, automatically facilitating execution of the flight command (0026, GUI generator 176 provides the GUI 163 to the display device 144, one or more of the aircraft performance parameters 141, recommended settings, or a combination thereof, can be displayed to enable the pilot to make informed flight decisions based on real-time data, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information, in some examples, flight control settings of the aircraft 108 can be automatically adjusted based on the aircraft performance parameters 141).
Gracia Berna fails to teach, however, receiving the flight command at an application program interface (API) which is partitioned from the autonomous computing system;
Validating the flight command, comprising:
In response to receiving the flight command at the API, validating a format compliance of the flight command; and
In response to validating the format compliance, providing the flight command.
Bertram, however, in an analogous field of endeavor, does teach receiving the flight command at an application program interface (API) which is partitioned from the autonomous computing system (Col. 14, Lines 27-41, step 210 may include generating control output for the estimated maneuver based on an observable parameter input into the ML module Col. 10, Lines 44-55, application program interface API may be used to further ensure security, a system integrator may host all or a portion of the hypervisor-based OS 132 with definitions and structure for accessing individual partitions, which may have their own program definitions, allowable forms of input, and protocols such that data that is not in a proper format, input configuration, or does not use proper definitions or protocols will not be validated or authenticated for use by the hypervisor based OS 132); and
Validating the flight command, comprising:
In response to receiving the flight command at the API, validating a format compliance of the flight command (Col. 10, Line 44 - Col. 11, Line 3, application program interface API may be used to further ensure security, a system integrator may host all or a portion of the hypervisor-based OS 132 with definitions and structure for accessing individual partitions, which may have their own program definitions, allowable forms of input, and protocols such that data that is not in a proper format, input configuration, or does not use proper definitions or protocols will not be validated or authenticated for use by the hypervisor based OS 132, data guard 133 may be programmed, attached, and/or associated with the API to ensure compliance with a ruleset, for example, the ruleset may include a protocol, definition, or data configuration, e.g., output from a control stick requires at least two parameters including a direction in which and a position to which the control stick has moved, and the data guard 133 may be tasked with enforcing the ruleset, Col. 14, Lines 58-61, step 212 may include performing a check of a control output against every corresponding and relevant/applicable baseline, threshold, limit, or combination thereof, within the safety envelope);
In response to validating the format compliance, providing the flight command (Col. 14, Lines 42-58, step 212 may include performing the estimated maneuver if it is within the safety envelope).
Gracia Berna and Bertram are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the API and validation of Bertram in order to provide a means of validating the commands or data sent between partitions. The motivation to combine is to ensure that the commands or data are in a valid format before transmitting them between partitions.
The combination of Gracia Berna and Bertram fails to explicitly teach, however, wherein the flight command comprises a set of waypoints; and
Wherein validating the flight command comprises:
Deterministically transforming the flight command into a human interpretable format.
Sampath, however, in an analogous field of endeavor, does teach wherein the flight command comprises a set of waypoints (0053, Referring now to FIG. 7, in exemplary embodiments, the data entry system 10 may be used to reduce pilot workload in other flight scenarios. For example, the ATC instruction, which may be a text instruction sent via CPDLC or a voice instruction sent via VF or VHF, may relate to a flight instruction directing for a change in the flight plan of the aircraft in order to route the aircraft to a particular waypoint. An example of such an ATC instruction may be “DELTA TWENTY FOUR ZERO FIVE CLEARED DIRECT PUMPS DESCEND AND MAINTAIN FLIGHT LEVEL THREE ZERO ZERO”); and
Wherein validating the flight command comprises:
Deterministically transforming the flight command into a human interpretable format (0053-0054, Referring now to FIG. 7, in exemplary embodiments, the data entry system 10 may be used to reduce pilot workload in other flight scenarios. For example, the ATC instruction, which may be a text instruction sent via CPDLC or a voice instruction sent via VF or VHF, may relate to a flight instruction directing for a change in the flight plan of the aircraft in order to route the aircraft to a particular waypoint. An example of such an ATC instruction may be “DELTA TWENTY FOUR ZERO FIVE CLEARED DIRECT PUMPS DESCEND AND MAINTAIN FLIGHT LEVEL THREE ZERO ZERO” … In this example ATC instruction, the parsing module 17 identifies the keyword “DIRECT” and the associated text “PUMPS” as a constituent data element contained within this textual data string. The contextual tagging module 19 determines an association between this constituent data element and the data entry field location “ATC instruction to DIRECT-TO”. The processor 21 is configured, upon selection of the relevant waypoint “PUMPS” in the data entry field location, which in this case is the waypoint list, the portion of the constituent data element “DIRECT-TO”. The pilot may then confirm this previewed portion using the pilot user-control device in order to enter the previewed portion “DIRECT-TO” into the selected data field entry location, thereby confirming that a “direct-to” operation should be performed with respect to the waypoint “PUMPS”. In exemplary embodiments, after pilot confirmation of the previewed portion of the constituent data element corresponding to the instructed waypoint, the processor 21 instructs for a “DIRECT-TO” operation to the waypoint “PUMPS” in the pending flight plan. 0056, At step S200, the command is parsed into constituent data elements. In exemplary embodiments, the parsing is performed through the identification of pre-determined key words).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the present invention, with a reasonable expectation of success, to have included the flight command transformation of Sampath in order to provide a means of automatically parsing relevant sensor information. The motivation to combine is to further reduce the workload on the pilot (see at least 0002-0005 of Sampath).
Regarding claim 15, the combination of Gracia Berna, Bertram, and Sampath teaches the method of claim 14, and Gracia Berna further teaches wherein the sensor information is unstructured, and wherein the flight command comprises a predetermined machine-readable data structure (0054-0056, the method 600 includes generating one or more aircraft performance parameters based on the real-time time-series flight data and an aircraft performance model, for example, the parameter generator 174 generates the aircraft performance parameters 141 based on the flight data 105 and the aircraft performance model 181, which includes recurrent neural network layer 183, method 600 further includes providing the aircraft performance parameters to a display device, predicted values of the aircraft performance parameters 141 are displayed in real-time within seconds of detection of the corresponding flight data 105, using the aircraft performance model 181, e.g., a machine-learning model, enables the aircraft performance parameters 141 to be estimated in real-time rather than using more time-intensive calculations after landing, in a particular aspect, the aircraft performance parameters 141 are used to generate recommended settings, automatically update a setting or configuration of the aircraft 108, one or more of the aircraft performance parameters 141, the recommended settings, or a combination thereof, can be displayed to improve pilot situational awareness, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information).
Gracia Berna fails to explicitly teach, however, wherein validating format compliance comprises validating adherence to the predetermined machine-readable data structure.
Bertram, however, in an analogous field of endeavor, does teach wherein validating format compliance comprises validating adherence to the predetermined machine-readable data structure (Col. 10, Lines 44-55, application program interface API may be used to further ensure security, a system integrator may host all or a portion of the hypervisor-based OS 132 with definitions and structure for accessing individual partitions, which may have their own program definitions, allowable forms of input, and protocols such that data that is not in a proper format, input configuration, or does not use proper definitions or protocols will not be validated or authenticated for use by the hypervisor based OS 132).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the format validation of Bertram in order to further ensure the security of the control system. The motivation to combine is to ensure that inputs are provided in a proper format or configuration.
Regarding claim 16, the combination of Gracia Berna, Bertram, and Sampath teaches the method of claim 14, and Gracia Berna further teaches wherein determining the pilot validation comprises receiving a pilot confirmation at the pilot validation interface (0026, GUI generator 176 provides the GUI 163 to the display device 144, one or more of the aircraft performance parameters 141, recommended settings, or a combination thereof, can be displayed to enable the pilot to make informed flight decisions based on real-time data, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information, in some examples, flight control settings of the aircraft 108 can be automatically adjusted based on the aircraft performance parameters 141).
Regarding claim 17, the combination of Gracia Berna, Bertram, and Sampath teaches the method of claim 14, and Gracia Berna further teaches wherein automatically facilitating execution of the flight command comprises providing the flight command to a flight management system (FMS) (0017, on board computing device is a flight management system) and executing the command via a flight control system (FCS) (0026, flight control system of the aircraft 108 automatically updates a setting based on one or more of the aircraft performance parameters).
Regarding claim 18, the combination of Gracia Berna, Bertram, and Sampath teaches the method of claim 14, and Gracia Berna teaches it further comprising:
Determining a second flight command with the autonomous computing system of the aircraft using the set of pre-trained models of the autonomous computing system (0054-0056, the method 600 includes generating one or more aircraft performance parameters based on the real-time time-series flight data and an aircraft performance model, for example, the parameter generator 174 generates the aircraft performance parameters 141 based on the flight data 105 and the aircraft performance model 181, which includes recurrent neural network layer 183, method 600 further includes providing the aircraft performance parameters to a display device, predicted values of the aircraft performance parameters 141 are displayed in real-time within seconds of detection of the corresponding flight data 105, using the aircraft performance model 181, e.g., a machine-learning model, enables the aircraft performance parameters 141 to be estimated in real-time rather than using more time-intensive calculations after landing, in a particular aspect, the aircraft performance parameters 141 are used to generate recommended settings, automatically update a setting or configuration of the aircraft 108, one or more of the aircraft performance parameters 141, the recommended settings, or a combination thereof, can be displayed to improve pilot situational awareness, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information).
Providing the second flight command at the pilot validation interface (0026, GUI generator 176 generates the GUI 163 indicating the flight data 105, the aircraft performance parameters 141, or a combination thereof, the GUI generator 176 generates one or more recommended settings based on the aircraft performance parameters, one or more of the aircraft performance parameters 141, the recommended settings, or a combination thereof, can be displayed to improve pilot situational awareness);
Determining a pilot override of the second flight command with the pilot validation interface, the pilot override comprising a third flight command (0026, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information); and
In response to determining the pilot override, automatically facilitating execution of the third flight command (0026, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information, Examiner's note: the editing of the recommended setting causes the flight to adopt that edited setting).
Gracia Berna fails to teach, however, receiving the second flight command at the API.
Bertram, however, in an analogous field of endeavor, does teach receiving the second flight command at the API (Col. 14, Lines 27-41, step 210 may include generating control output for the estimated maneuver based on an observable parameter input into the ML module, Col. 10, Lines 44-55, application program interface API may be used to further ensure security, a system integrator may host all or a portion of the hypervisor-based OS 132 with definitions and structure for accessing individual partitions, which may have their own program definitions, allowable forms of input, and protocols such that data that is not in a proper format, input configuration, or does not use proper definitions or protocols will not be validated or authenticated for use by the hypervisor based OS 132).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the API of Bertram in order to provide a means of further validating the commands or data sent between partitions. The motivation to combine is to ensure that the commands or data are in a valid format before transmitting them between partitions.
Regarding claim 22, Gracia Berna teaches an aircraft system comprising:
An autonomous computing system communicatively coupled to a sensor suite onboard an aircraft, the autonomous computing system comprising a set of pre-trained neural networks (0020, memory 122 is configured to store an aircraft performance model 181, the aircraft performance model 181 includes at least a recurrent neural network layer 183, which enables the aircraft performance model 181 to exhibit temporal dynamic behavior, for example, the recurrent neural network layer 183 enables the aircraft performance model 181 to process real-time time-series data, e.g., flight data 105, and detect temporal trends, 0021, the sensors 142 are configured to provide flight data 105 to the data bus 140), wherein the autonomous computing system is configured to determine a flight command with the set of pre-trained neural networks based on sensor data from the sensor suite (0054-0056, the method 600 includes generating one or more aircraft performance parameters based on the real-time time-series flight data and an aircraft performance model, for example, the parameter generator 174 generates the aircraft performance parameters 141 based on the flight data 105 and the aircraft performance model 181, which includes recurrent neural network layer 183, method 600 further includes providing the aircraft performance parameters to a display device, predicted values of the aircraft performance parameters 141 are displayed in real-time within seconds of detection of the corresponding flight data 105, using the aircraft performance model 181, e.g., a machine-learning model, enables the aircraft performance parameters 141 to be estimated in real-time rather than using more time-intensive calculations after landing, in a particular aspect, the aircraft performance parameters 141 are used to generate recommended settings, automatically update a setting or configuration of the aircraft 108, one or more of the aircraft performance parameters 141, the recommended settings, or a combination thereof, can be displayed to improve pilot situational awareness, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information);
An aircraft computing system communicatively coupled to the autonomous computing system (0018, one or more functions performed by the on-board computing device 102 are divided amongst multiple devices, including on-board computing device 102, and AID, a flight management system, see also Fig. 1), the aircraft computing system comprising:
A pilot validation interface communicatively coupled to a flight management system (FMS) (0026, GUI generator 176 generates the GUI 163, GUI generator 176 generates one or more recommended settings, e.g., a recommended trim setting, based on the aircraft performance parameters 141), the pilot validation interface configured to:
Facilitate pilot validation of the flight command in a human interpretable format (0026, GUI generator 176 provides the GUI 163 to the display device 144, one or more of the aircraft performance parameters 141, recommended settings, or a combination thereof, can be displayed to enable the pilot to make informed flight decisions based on real-time data); and
In response to pilot validation of the flight command, automatically facilitate execution of the flight command via the flight management system (FMS) (0026, the pilot can update a flight setting, such as accept or edit a recommended setting, based on the displayed information, in some examples, flight control settings of the aircraft 108 can be automatically adjusted based on the aircraft performance parameters 141).
Gracia Berna fails to explicitly teach, however, wherein the aircraft computing system is partitioned from the autonomous computing system.
Bertram, however, in an analogous field of endeavor, does teach wherein the aircraft computing system is partitioned from the autonomous computing system (Col. 9, Lines 43-53, different components of the system 100a may be separated into one or more individual partitions, for example, the observation sub-system 102, the ML module 104, the failsafe controller 106, and the autopilot 128 may be implemented on multiple, individual partitions).
Gracia Berna and Bertram are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the partitioning of Bertram in order to provide a means of separating the aircraft’s computing system. The motivation to combine is to ensure that the failure of one computing system does not directly affect another computing system.
The combination of Gracia Berna and Bertram fails to explicitly teach, however, wherein the sensor data comprises an Air Traffic Control (ATC) utterance, and wherein determining the flight command comprises, using a pre-trained neural network of the set, determining a set of waypoints from the ATC utterance by querying the ATC utterance according to a set of natural language queries.
Sampath, however, in an analogous field of endeavor, does teach wherein the sensor data comprises an Air Traffic Control (ATC) utterance (0053, Referring now to FIG. 7, in exemplary embodiments, the data entry system 10 may be used to reduce pilot workload in other flight scenarios. For example, the ATC instruction, which may be a text instruction sent via CPDLC or a voice instruction sent via VF or VHF, may relate to a flight instruction directing for a change in the flight plan of the aircraft in order to route the aircraft to a particular waypoint. An example of such an ATC instruction may be “DELTA TWENTY FOUR ZERO FIVE CLEARED DIRECT PUMPS DESCEND AND MAINTAIN FLIGHT LEVEL THREE ZERO ZERO”), and wherein determining the flight command comprises, using a pre-trained neural network of the set, determining a set of waypoints from the ATC utterance by querying the ATC utterance according to a set of natural language queries (0025-0026, data-entry system 10 includes a parsing module 17, the parsing module 17 operably connected to the receiving module 12, and is configured to parse the input data received by the receiving module 12 into constituent data elements, parsing module 17 uses a natural language processing algorithm to identify key words in the textual data string and associated information related to those key words, 0028-0030, in order to perform parsing of the textual data string, the parting module 17 uses an artificial neural network that is pre-trained using machine learning techniques to identify key words and recognized clauses in the textual data string, data entry system 10 also includes contextual tagging module 19 operably connected to the parsing module 17, which is configured to determine associations between the constituent data elements identified and separated out by the parsing module 17 and the multiple data field entry locations 51-54 on the display, contextual tagging module 19 is configured to use correlations between pre-defined key words identified in the constituent data elements and pre-defined "known" terms and clauses to determine an association between a constituent data element and a particular data entry field location on the display 14, 0054, In this example ATC instruction, the parsing module 17 identifies the keyword “DIRECT” and the associated text “PUMPS” as a constituent data element contained within this textual data string. The contextual tagging module 19 determines an association between this constituent data element and the data entry field location “ATC instruction to DIRECT-TO”. The processor 21 is configured, upon selection of the relevant waypoint “PUMPS” in the data entry field location, which in this case is the waypoint list, the portion of the constituent data element “DIRECT-TO”. The pilot may then confirm this previewed portion using the pilot user-control device in order to enter the previewed portion “DIRECT-TO” into the selected data field entry location, thereby confirming that a “direct-to” operation should be performed with respect to the waypoint “PUMPS”. In exemplary embodiments, after pilot confirmation of the previewed portion of the constituent data element corresponding to the instructed waypoint, the processor 21 instructs for a “DIRECT-TO” operation to the waypoint “PUMPS” in the pending flight plan. 0056, At step S200, the command is parsed into constituent data elements. In exemplary embodiments, the parsing is performed through the identification of pre-determined key words.).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the present invention, with a reasonable expectation of success, to have included the flight command transformation of Sampath in order to provide a means of automatically parsing relevant sensor information. The motivation to combine is to further reduce the workload on the pilot (see at least 0002-0005 of Sampath).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Gracia Berna in view of Bertram and Sampath, and further in view of Groden (US 20190033862 A1), hereafter Groden.
Regarding claim 8, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 7, but fails to teach wherein the validation condition comprises a pilot response time threshold.
Groden, however, in an analogous field of endeavor, does teach wherein the validation condition comprises a pilot response time threshold (0066, in response to detecting an undesired aircraft condition, determining a set of corrective actions, presenting an alert to an operator, informing the operator of the undesired condition and/or the corrective actions, receiving confirmation, e.g., approval of corrective actions, absence of denial of the corrective actions for a threshold period of time, from the operator, and in response to receiving the confirmation, controlling the aircraft according to the corrective actions).
Gracia Berna, Bertram, Sampath and Groden are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the pilot response time threshold of Groden in order to provide a means of ensuring that necessary actions are taken even in an absence of pilot response. The motivation to combine is to ensure that corrective actions can be taken even when a pilot is unable to respond.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Gracia Berna in view of Bertram and Sampath, and further in view of Vestal (US 20100199280 A1), hereafter Vestal.
Regarding claim 9, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 1, and Gracia Berna further teaches wherein the autonomous computing system comprises a processor (0019, memory 122 includes one or more applications executable by processor 170 to initiate, control, or perform one or more operations described herein).
The combination of Gracia Berna, Bertram, and Sampath fails to teach, however, wherein the processor is a multi-core processor.
Vestal, however, in an analogous field of endeavor, does teach wherein the processor is a multi-core processor (0017, system 100 includes a multi-core processor unit 102, which comprises multiple processor cores 104, each processor core 104 can execute program instructions for a respective process or thread, multi core processor unit 102 is used to execute software 106, which comprises an operating system 108 that includes a partitioning kernel 110, which implements standard kernel functionality to manage access to resources).
Gracia Berna, Bertram, Sampath, and Vestal are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date, with a reasonable expectation of success, to have included the multi-core processor of Vestal in order to provide further processing power to the computing system. The motivation to combine is to ensure that the computing system has sufficient processing power to perform its functionality.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Gracia Berna in view of Bertram and Sampath, and further in view of Walker (US 20030093187 A1), hereafter Walker.
Regarding claim 10, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 1, but fails to explicitly teach wherein the flight command comprises a resolution advisory.
Walker, however, in an analogous field of endeavor, does teach wherein the flight command comprises a resolution advisory (2055, resolution advisory generated by TCAS, all pilot warning advisories given for collision are to be provided to the robotics program running in the TRAC/FACT process/processor for robotics flight augmentation to avoid a collision, 2085, resolution advisory is a display indication given to the pilot recommending a maneuver to increase vertical separation relative to an intruding aircraft, data provided to TRAC system running FACT robotics flight to alter the aircraft's flight path for the safest uninterrupted flight to the safe fly zone).
Gracia Berna, Bertram, Sampath, and Walker are analogous because they are in an analogous field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the resolution advisory determination of Walker in order to provide further means of collision avoidance. The motivation to combine is to ensure that the aircraft is flown without incident.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Gracia Berna in view of Bertram and Sampath, and further in view of Donahoe (US 20190250601 A1), hereafter Donahoe.
Regarding claim 11, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 1, and Bertram further teaches partitioning the aircraft computing system from the autonomous computing system (Col. 9, Lines 43-53, different components of the system 100a may be separated into one or more individual partitions, for example, the observation sub-system 102, the ML module 104, the failsafe controller 106, and the autopilot 128 may be implemented on multiple, individual partitions).
Gracia Berna, Bertram, and Sampath are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the partitioning of Bertram in order to provide a means of separating the aircraft’s computing systems. The motivation to combine is to ensure that a failure of one computing system does not directly affect another computing system.
The combination of Gracia Berna, Bertram, and Sampath fails to teach, however, wherein the computing systems are partitioned via firewall.
Donahoe, however, in an analogous field of endeavor, does teach wherein the computing systems are partitioned via firewall (0235, the network adapter 4212 can include a firewall which can govern and/or manage permission to access/proxy data in a computer network, and track varying levels of trust between different machines and/or applications, the firewall can be any number of modules having any combination of hardware and/or software components able to enforce a predetermined set of access rights between a particular set of machines and applications, machines and machines, and/or applications and applications to regulate the flow of traffic and resource sharing between these varying entities).
Gracia Berna, Bertram, Sampath, and Donahoe are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the firewall of Donahoe in order to provide further means of separating the aircraft’s computing system. The motivation to combine is to further ensure that the failure of one computing system does not directly affect another computing system.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Gracia Berna in view of Bertram and Sampath, and further in view of Auerbach (US 11435762 B1, having an effective filing date of 17 August 2021), hereafter Auerbach.
Regarding claim 19, combination of Gracia Berna, Bertram, and Sampath teaches the method of claim 18, but fails to teach it further comprising: updating at least one of the set of pre-trained models of the autonomous computing system based on the pilot override.
Auerbach, however, in an analogous field of endeavor, does teach updating at least one of the set of pre-trained models of the autonomous computing system based on the pilot override (Col. 19, Lines 32-56, autonomous machine learning model may be trained as a function of autonomous training data, flight controller 124 may receive autonomous training data by receiving correlations of flight element, pilot signal, and/or simulation data to an autonomous function that were previously received and/or determined during a previous iteration of generation of autonomous function, autonomous training data may be received by one or more remote devices that correlate a flight element, pilot override, and/or simulation data to an autonomous function).
Gracia Berna, Bertram, Sampath, and Auerbach are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the training based on pilot override of Auerbach in order to provide further means of training an autonomous flight system. The motivation to combine is to allow the autonomous flight system to be trained as thoroughly as possible.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Gracia Berna in view of Bertram and Sampath, and further in view of Bertram (US 10032111 B1), hereafter Bertram '111.
Regarding claim 21, the combination of Gracia Berna, Bertram, and Sampath teaches the aircraft system of claim 1, but fails to explicitly teach wherein deterministically transforming the set of waypoints of the flight command into a human-interpretable format comprises transforming the set of waypoints of the flight command from a non-semantic format to a semantic format.
Bertram ‘111, however, in an analogous field of endeavor, does teach wherein deterministically transforming the set of waypoints of the flight command into a human-interpretable format comprises transforming the set of waypoints of the flight command from a non-semantic format to a semantic format (Col. 13, Lines 27-38, The first semantic translator 220a can be configured to receive raw data (e.g., perception data, sensor data) from the perception system 216, and translate the raw data to semantic data for transmission to the control circuit 204. The second semantic translator 220b can be configured to receive raw data (e.g., communication data) from the communication electronics 224, and translate the raw data to semantic data for transmission to the control circuit 204. The third semantic translator 220c can be configured to receive raw data (e.g., sensor data) from the sensor devices 228, and translate the raw data to semantic data for transmission to the control circuit 204.).
Gracia Berna, Bertram, Sampath, and Bertram ‘111 are analogous because they are in a similar field of endeavor, e.g., flight control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the semantic transformation of Bertram ‘111 in order to provide further means of ensuring that a flight command is human-interpretable. The motivation to combine is to further ensure that a pilot or operator is able to properly ascertain the meaning of a flight command.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.A.W./Examiner, Art Unit 3658
/JASON HOLLOWAY/Primary Examiner, Art Unit 3658