DETAILED ACTION
This is the First Office Action on the Merits and is directed towards claims 1-20 as originally presented and filed on 10/24/2024.
This application is subject to Double Patent rejections with the parent applications.
Notice of Pre-AIA or AIA Status
Priority is claimed as set forth below, accordingly the earliest effective filing date is August 29, 2017 (20170829).
The present application, effectively filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
This application is a continuation of U.S. Patent Application No. 18/083,325, entitled ACTUATOR MONITORING SYSTEM USING INERTIAL SENSORS filed December 16, 2022, now U.S. Patent No. 12,151,827, which is a continuation of U.S. 17/028,773, now U.S. Patent No. 11,628,950 (“Parent Application”), entitled ACTUATOR MONITORING SYSTEM USING INERTIAL SENSORS filed September 22, 2020, which is a continuation of U.S. Patent Application No.16/126,950 (“Parent Application”), now U.S. Patent No. 10,822,113, entitled ACTUATOR MONITORING SYSTEM USING INERTIAL SENSORS filed September 10, 2018, which is a continuation of U.S. Patent Application No. 15/689,892 (“Parent Application”), now U.S. Patent No. 10,112,727, entitled ACTUATOR MONITORING SYSTEM USING INERTIAL SENSORS filed August 29, 2017. See MPEP §201.07[R-08.2017]. In accordance with MPEP §609.02 [R-07.2015] Section A. 2 and MPEP §2001.06(b)[R-08.2017] (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) [R-08.2017] (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 [R-07.2015] Section A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
Information Disclosure Statement
As required by M.P.E.P. 609 [R-07.2022], Applicant's 10/24/2024 submission(s) of Information Disclosure Statement (IDS)(s) is/are acknowledged by the Examiner and the reference(s) cited therein has/have been considered in the examination of the claim(s) now pending. A copy of the submitted IDS(s) initialed and dated by the Examiner is/are attached to the instant Office action.
Specification
The disclosure is objected to because of the following informalities: para [0001] must be updated to reflect the issuance of the parent application 18/083,325 filed 12/16/2022 now U.S. PAT# 12,151,827.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20160159471 A1 TO CHAN; ALISTAIR K. et al. (hereinafter Chan, cited in the 10/24/2024 IDS) in view of US 20160107751 A1 to D'ANDREA; Raffaello et al. (hereinafter D'ANDREA cited in the 10/24/2024 IDS).
Regarding claim 1 Chan teaches in for example the Figure(s) reproduced immediately below:
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and associated descriptive texts a system, comprising:
a processor (in the Figures above, see for example Fig. 24, “”PROCESSOR” as explained in for example para:
“[0109] Referring to FIG. 24, a computing system is shown schematically according to an exemplary embodiment, to comprise a processor and memory/storage for data/programs as well as network/communication interfaces and input/output (I/O) system (e.g. allowing interaction through a user interface, etc.).”;
and a memory coupled with the processor (see Fig. 24 and para [0109] above “and memory/storage for data/programs”),
wherein the memory is configured to provide the processor with instructions which, when executed, cause the processor to (see Fig. 24 and para [0109] above “and memory/storage for data/programs”):
receive vehicle state information and at least one actuator command from a vehicle (as shown in for example Fig. 25 ”monitoring/communication system” and explained in for example para:
“[0110] As shown schematically according to an exemplary embodiment in FIG. 25, the UAV/craft system comprises multiple functional subsystems (which may be independent or combined in implementation) including a master control system, monitoring/communication system, flight/operation control system, configuration control system, energy/power control system (and other associated subsystems).”);
determine a plurality of models associated with vehicle failure (as explained in for example para:
“[0113] As shown schematically according to an exemplary embodiment in FIG. 27, UAV/craft status monitoring comprises management of the configuration and mission (e.g. plan/route) for the UAV/craft as well as monitoring of configuration options, conditions (e.g. operating conditions), capability/mode of operation, state/status of systems, etc.; monitoring may comprise tracking of operation history (e.g. data available to assess status/state of health/operating condition such as to facilitate predictive/advance identification of potential system issues, e.g. rotor failures/malfunctions, etc.).”);
determine a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude (as explained in for example paras:
“[0158] According to an exemplary embodiment of a method of operation/management of a reconfigurable UAV/craft, if a malfunctioning rotor is detected by the monitoring system in advance of the malfunction it may be possible for responsive corrective action to be determined/commanded and taken prior to a larger or more complete failure (e.g. prior to the malfunctioning rotor becoming totally inoperable) or in any event so that the control system is better able to manage the situation and maintain flight operation/stability of the reconfigurable UAV/craft. According to the method of operation/management as indicated, the reconfiguration of position of at least one operational rotor can be performed in response to predicted or anticipated (future possible/probable) malfunction of a rotor (e.g. see FIG. 30); the reconfiguration of rotor position may be commanded or directed by the control system at or in the early stages of a malfunction (e.g. if a malfunction is in process of occurring gradually the control system will have time gradually to implement corrective action such as a modification of rotor/boom configuration as will permit more stable control/maintenance of flight characteristics/stability in the situation).
[0159] According to an exemplary embodiment a method of reconfiguring selectively reconfigurable aircraft with a rotor system with at least one rotor that is at least partially malfunctioning may comprise the steps of identifying the rotor that is malfunctioning; identifying a rotor that is able to function (and is in an initial position); repositioning at least one functional rotor to a reconfigured position. According to the exemplary embodiment, the rotor system with at least one functional rotor after reconfiguration (e.g. in the reconfigured position) is able to compensate for the loss of function of the malfunctioning rotor. According to a preferred embodiment, the aircraft is able to remain in flight/operation without thrust/lift otherwise contributed/available to be provided by the malfunctioning/inoperable rotor; at least one functional rotor when repositioned to the reconfigured position is able to compensate for the loss of contribution of thrust/lift resulting from the malfunctioning/inoperable rotor.”);
determine an observed flight characteristic (as explained in for example para:
“[0157] According to an exemplary embodiment, the reconfigurable UAV/craft system will comprise a monitoring system to detect potential rotor/rotor system issues or other problems before (or immediately upon) a complete malfunction (e.g. so that a nearly immediate response may be initiated). See FIGS. 25 and 26. The monitoring system of the UAV/craft may evaluate (e.g. in real time from sensors/devices) data representative of such parameters as (a) rotational speed of rotor; (b) force at rotor bearings; (c) force applied at rotor mount; (d) vibration at rotor; (e) temperature of rotor motor system. Other operational parameters may be monitored to determine the status/condition and health of components of the UAV/craft system including the rotor system using any of a wide variety of sensors/detectors/devices (e.g. load cells, stress/strain sensors, accelerometers, vibration sensors, electronic detectors, video/visual monitoring, etc.) and methodologies presently in use, including with aircraft systems. See e.g. U.S. Pat. No. 8,775,013 titled “System and Method for Acoustic Signature Health Monitoring of Unmanned Autonomous Vehicles (UAVS)” (acoustic monitoring of UAV/aircraft systems). The detector/sensor D of the monitoring system is shown representationally and schematically according to an exemplary embodiment in FIGS. 1 and 2 (e.g. at or adjacent to a rotor, boom, joint, etc.)”);
determine an expected flight characteristic ( as explained in for example para:
“[0155] According to an exemplary embodiment of a method of operation/management of a reconfigurable UAV/craft, the determination to reconfigure the rotor system may result from a malfunction of an aircraft system or subsystem. For example, a malfunction may comprise of (a) the rotor unable to provide commanded rotation speed; (b) the rotor unable to provide expected thrust; (c) the rotor unable to be given intended pitch; (d) the rotor unable to be positioned to the intended rotor position. A malfunctioning/inoperable rotor may be caused by a failing power plant (e.g. motor or engine) or energy storage system (e.g. fuel or battery problem) or other causes such as impact with an object, improper maintenance/service, defective component, etc.”);
identify, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, ( as explained in for example para:
“[0156] According to an exemplary embodiment of the method of operation/management of the reconfigurable UAV/craft the rotor that is malfunctioning or becomes inoperable may be shut down (and repositioned relative to the base and/or each other rotor) and at least one operational rotor may be repositioned to reestablish a balanced configuration for the UAV/craft; according to any preferred embodiment, after reconfiguration the UAV/craft is able to operate in the reconfigured position to compensate for the loss of function of the non-operational/malfunctioning rotor. See FIGS. 30-34B.”);
and in response to identifying the malfunctioning rotor, perform a responsive action ( as explained in for example para:
“[0143] Referring to FIG. 30, according to an exemplary embodiment of a method of operation/management the reconfigurable UAV/craft is configured in an initial configuration and operated to perform a duty/mission while operating conditions are being monitored; if monitoring of operating conditions indicates that reconfiguration is advisable (e.g. monitoring of acoustic data, data from stress/strain/force gauges/sensors at a rotor/rotor mount, etc. indicates a potential rotor malfunction may be imminent) the UAV/craft will determine whether to reconfigure. As shown, if the UAV/craft is reconfigured (e.g. configuration is modified to adjust rotor positioning to adjust capability to the conditions or to shutdown/retract the malfunctioning/about-to-fail rotor while repositioning the operable rotors) the UAV/craft can complete some or all of the remaining mission segments. As indicated, according to an exemplary embodiment the method comprises use of data from data sources (e.g. on aircraft systems, network data, control/commands, operating programs, data communications, etc.) by the UAV/craft system in operation, monitoring, configuration, etc. See FIGS. 24-28B (control system/program implementation).”).
Chan does not appear to expressly disclose; determine a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate;
determine an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle;
determine an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle;
determine an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models:
identify, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate (emphasis added).
In analogous art D'ANDREA teaches in for example, the figures below:
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And associated descriptive texts determining a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate (in for example para:
“[0174] Flight modules typically receive high level inputs in the form of goals or commands from a user, base station, command center, or high level control algorithm via an input unit 304 and passed on to a control unit 306, evaluation unit 308, and sensing unit 310. Control units 306 are typically used to generate control signals for a multicopter's effectors. Evaluation units 308 are typically used to evaluate data from input units 304, sensing units 310, and memory units 312. Such data may be representative of user commands or high level commands as well as both relative or absolute position, particularly that of GPS sensors, visual odometry/SLAM, retro-reflective positioning systems, laser range finders, WiFi positioning systems, barometric altimeters and variometers, or ultra-sound sensors (none shown). Sensor data may be gathered and preprocessed using a sensor unit 310 or stored in a memory unit 312. Typical examples of processed information are those received from sensors, such as accelerometers, gyroscopes, magnetometers, cameras, optical flow sensors, laser or sonar range finders, radar, barometers, thermometers, hygrometers, bumpers, chemical sensors, electromagnetic sensors, air flow sensors, or microphones (none shown). Memory units 312 are typically used to store data. For example, they may be used to store data on past sensor readings, operational states or user commands, as well as properties of the multicopter.”);
determine an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle (in Fig. 6 IMU as explained in for example para:
“[0218] An attitude controller 642 receives this target translational acceleration, and sends control signals to each of the propellers 660. This attitude controller 642 computes a target orientation of the multicopter's primary axis 150 and a total commanded force that results in the target acceleration; then using the disclosed method, to generate a control signal for each propeller. Sensor measurements are passed to a state estimator 646 which estimates the multicopter's rotation and angular velocity and sends these estimates to the attitude controller. The sensor measurements are obtained from inertial sensors 644, which may include accelerometers, rate gyroscopes. Further examples of onboard sensors may include visual sensors such as cameras, range sensors, height sensors and relative airspeed sensors.”);
determine an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models (see Figs. 5 and 7 and para:
“[0211] FIG. 5 shows examples of effectors disabled due to a failure 100 for a standard quadrocopter, with all propellers having parallel axes of rotation, and with propellers opposing one another having the same sense of rotation, and adjacent propellers having the opposite senses of rotation. Effectors disabled due to a failure are indicated by a large “X” above the concerned effector. Standard multicopter control methods can not control a multicopter having a failed propeller as they are then no longer able to independently produce angular accelerations in all axes.”):
identify, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate (see para:
“[0218] An attitude controller 642 receives this target translational acceleration, and sends control signals to each of the propellers 660. This attitude controller 642 computes a target orientation of the multicopter's primary axis 150 and a total commanded force that results in the target acceleration; then using the disclosed method, to generate a control signal for each propeller. Sensor measurements are passed to a state estimator 646 which estimates the multicopter's rotation and angular velocity and sends these estimates to the attitude controller. The sensor measurements are obtained from inertial sensors 644, which may include accelerometers, rate gyroscopes. Further examples of onboard sensors may include visual sensors such as cameras, range sensors, height sensors and relative airspeed sensors.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the failure determination method disclosed in D'ANDREA with the failure determination method taught in Chan with a reasonable expectation of success because it would have “mitigated the effects of failures, malfunction, operator errors,” as taught by D'ANDREA Para(s):
“[0004] Technical advantages of certain embodiments of the present invention may allow to increase the safety and reliability of existing multicopters. For example, the present invention may allow to minimize or eliminate risks inherent in such vehicles arising from collisions, mechanical or electrical failures, electronic malfunctions, operator errors, or adverse environmental conditions, such as wind or turbulence. The present invention may also mitigate the effects of failures, malfunction, operator errors, and the like by allowing for graceful degradation of performance rather than catastrophic failure with complete loss of control.”.
Regarding claim 2 and the limitation the system of claim 1, wherein: the observed flight characteristic includes an observed attitude;
the observed flight characteristic rate includes an observed attitude rate;
the expected flight characteristic includes an expected attitude;
and the expected flight characteristic rate includes an expected attitude rate (see the obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference wherein it is understood that D'ANDREA teaches “rates”.).
Regarding claim 3 and the limitation the system of claim 1, wherein: the vehicle state information includes historical vehicle state information; and the at least one actuator command includes at least one historical actuator command (see Chan para:
“[0113] As shown schematically according to an exemplary embodiment in FIG. 27, UAV/craft status monitoring comprises management of the configuration and mission (e.g. plan/route) for the UAV/craft as well as monitoring of configuration options, conditions (e.g. operating conditions), capability/mode of operation, state/status of systems, etc.; monitoring may comprise tracking of operation history (e.g. data available to assess status/state of health/operating condition such as to facilitate predictive/advance identification of potential system issues, e.g. rotor failures/malfunctions, etc.).”).
Regarding claim 4 and the limitation the system of claim 3, wherein the vehicle state information includes at least one of: an attitude, an attitude rate, a position, a velocity, a wind velocity, or a geometry of the vehicle (see the teachings of both Chan and D'ANDREA in the obviousness to combine and the rejection of corresponding parts of claims 3 and 1 above incorporated herein by reference wherein it is understood that the claim limitation “at least one of” connotes that only one is sufficient to anticipate the claim and that the actual art teaches more than one as explained elsewhere above).
Regarding claim 5 and the limitation the system of claim 3, wherein the vehicle state information identifies the vehicle failure associated with a set of data (given the Broadest reasonable Interpretation (BRI) of a set of data it is considered that the failure modes determined by Chan and D'ANDREA connote the claimed limitations in the obviousness to combine and the rejection of corresponding parts of claims 3 and 1 above incorporated herein by reference).
Although the claims are interpreted in light of the specification, limitations from the specification are NOT imported into the claims. The Examiner must give the claim language the broadest reasonable interpretation (BRI) the claims allow.
See MPEP 2111.01 Plain Meaning [R-10.2019], which states
II. IT IS IMPROPER TO IMPORT CLAIM LIMITATIONS FROM THE SPECIFICATION
"Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment." Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). See also Liebel-Flarsheim Co. v. Medrad Inc., 358 F.3d 898, 906, 69 USPQ2d 1801, 1807 (Fed. Cir. 2004) (discussing recent cases wherein the court expressly rejected the contention that if a patent describes only a single embodiment, the claims of the patent must be construed as being limited to that embodiment); E-Pass Techs., Inc. v. 3Com Corp., 343 F.3d 1364, 1369, 67 USPQ2d 1947, 1950 (Fed. Cir. 2003) ("Inter US-20100280751-A1 1pretation of descriptive statements in a patent’s written description is a difficult task, as an inherent tension exists as to whether a statement is a clear lexicographic definition or a description of a preferred embodiment. The problem is to interpret claims ‘in view of the specification’ without unnecessarily importing limitations from the specification into the claims."); Altiris Inc. v. Symantec Corp., 318 F.3d 1363, 1371, 65 USPQ2d 1865, 1869-70 (Fed. Cir. 2003) (Although the specification discussed only a single embodiment, the court held that it was improper to read a specific order of steps into method claims where, as a matter of logic or grammar, the language of the method claims did not impose a specific order on the performance of the method steps, and the specification did not directly or implicitly require a particular order). See also subsection IV., below. When an element is claimed using language falling under the scope of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, 6th paragraph (often broadly referred to as means- (or step-) plus- function language), the specification must be consulted to determine the structure, material, or acts corresponding to the function recited in the claim, and the claimed element is construed as limited to the corresponding structure, material, or acts described in the specification and equivalents thereof. In re Donaldson, 16 F.3d 1189, 29 USPQ2d 1845 (Fed. Cir. 1994) (see MPEP § 2181- MPEP § 2186).
In Zletz, supra, the examiner and the Board had interpreted claims reading "normally solid polypropylene" and "normally solid polypropylene having a crystalline polypropylene content" as being limited to "normally solid linear high homopolymers of propylene which have a crystalline polypropylene content." The court ruled that limitations, not present in the claims, were improperly imported from the specification. See also In re Marosi, 710 F.2d 799, 802, 218 USPQ 289, 292 (Fed. Cir. 1983) ("'[C]laims are not to be read in a vacuum, and limitations therein are to be interpreted in light of the specification in giving them their ‘broadest reasonable interpretation.'" (quoting In re Okuzawa, 537 F.2d 545, 548, 190 USPQ 464, 466 (CCPA 1976)). The court looked to the specification to construe "essentially free of alkali metal" as including unavoidable levels of impurities but no more.).”
Regarding claim 6 and the limitation the system of claim 5, wherein determining the subset of the plurality of models includes at least one of grouping or sorting data (see Chan para:
“[0116] The flight characteristics of the UAV/craft may comprise at least one of aerodynamic profile, maneuverability, available thrust (e.g. total available thrust), available lift (e.g. total available lift), energy consumption, energy efficiency, mass, center of gravity, mass properties, center of mass, balance, stability, controllability, maneuverability, control axes, maximum relative ground velocity, maximum relative air speed, ascent rate, descent rate, sink rate, flight altitude, aerodynamic drag, number of operational rotors, control system type, equipment status, etc. (or any other characteristic affecting the flight/performance of the UAV/craft).”).
Regarding claim 7 and the limitation the system of claim 1, wherein at least one model of the plurality of models includes an expected attitude associated with the vehicle failure (see for example Chan para:
“[0147] Referring to FIGS. 34A and 34B, a method of operation and management of a reconfigurable UAV/craft is shown representationally and schematically according to an exemplary embodiment. According to an exemplary embodiment, the reconfigurable UAV/craft is configured for adjustment of rotor speed and for modification of rotor position; operation and management of the UAV/craft comprises the capability to use both rotor speed and rotor position for flight control (e.g. for control of flight characteristics) of the UAV/craft. (As indicated, rotor position modification may comprise reconfiguration of position or pitch/attitude of one or more rotors of the rotor system of the UAV/craft.)”).
Regarding claim 8 and the limitation the system of claim 1, wherein at least one model of the plurality of models includes an expected attitude rate associated with the vehicle failure (see the teachings of D'ANDREA with regard to, inter alia “rates” in the obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference).
Regarding claim 9 and the limitation the system of claim 1, wherein: the vehicle includes at least one tilt wing; and determining at least one model of the plurality of models is based at least in part on at least one of a direction or a mode of the at least one tilt wing (given the BRI connotes Chan Figs. 8A-C).
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Regarding claim 10 and the limitation the system of claim 9, wherein determining the at least one model includes selecting a model to use during flight based at least in part on at least one of a direction or a mode of the at least one tilt wing (given the BRI connotes Chan Figs. 8A-C above).
Regarding claim 11 and the limitation the system of claim 1, wherein the processor is further configured to update at least one model of the plurality of models in real time as the vehicle is flying (see Chan para:
“[0144] Referring to FIG. 31, a method of planning/configuring a reconfigurable UAV/craft for a mission is shown representationally and schematically according to an exemplary embodiment. The mission (including mission segments, duty/route, etc.) is planned; forecasted/anticipated conditions (e.g. operating conditions expected to be encountered) for the mission are evaluated (including using data from data sources/analytics); the UAV/craft is configured for the mission (including by deployment and positioning of the rotor system) in consideration of the mission/payload and anticipated operating conditions, among other considerations; the UAV/craft is deployed to begin the mission in the configuration. The operation of the UAV/craft in the configuration (including situation/conditions of operation) is monitored; adjustment in the operation (e.g. control of rotor speed) and/or configuration (e.g. reconfiguration of rotor position) may be implemented for the UAV/craft as necessary or advisable in the situation (e.g. under operation of a control system/program); operation of the UAV/craft is monitored in real-time as the mission is executed to completion. (As indicated, data may be interchanged between the UAV/craft and a base station/data sources during operation.)”).
Regarding claim 12 and the limitation the system of claim 11, wherein updating the at least one model in real time as the vehicle is flying includes collecting flight data during flight to update the at least one model which the vehicle is airborne (see the teachings of Chan with regard to “real-time” such as in para:
“[0146] Referring to FIG. 33, the reconfigurable UAV/craft may be provided with certain set (e.g. pre-programmed) configurations (e.g. of the rotor system) for certain routine/regular/other functions or operations of the UAV/craft; as indicated, the UAV/craft has an “ascend” configuration for the rotor system (e.g. intended to optimize performance/stability as the UAV/craft ascends to take flight at a station/stop); the UAV/craft has a “descend” configuration of the rotor system (e.g. intended to optimize or enhance performance/stability as the UAV/craft lands at a station/stop to complete a flight). As indicated, during flight (after ascent/take-off) the UAV/craft may be monitored (e.g. with data from data sources) and reconfigured as needed/advisable for the situation (e.g. determined by the control system/program) until the UAV/craft is to descend/land at the end of the mission; as indicated, during the flight on the mission monitoring may be regular and/or a continuous (e.g. in real-time) during operation and the UAV/craft may be reconfigured at least once or multiple times (or not at all/only at ascent and descent). (According to an exemplary embodiment, the UAV/craft may be reconfigured while in flight without landing or may be landed or hovered in a location for reconfiguration to be executed.)”)).
Regarding claim 13 and the limitation the system recited in claim 1, wherein the responsive action includes updating a geometry matrix, which is used to generate a plurality of actuator commands for the plurality of rotors, including by updating a previous geometry matrix with a precomputed geometry matrix (given the BRI see the teachings of D'ANDREA with regard to, inter alia “geometry matrix” shown in for example Figures 5 and 7 and the obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference).
Regarding claim 14 and the limitation the system recited in claim 13, wherein: the malfunctioning rotor is associated with a first region of the vehicle; and updating the geometry matrix includes increasing an authority of a second region of the vehicle in response to detection of the malfunctioning rotor in the first region (given the BRI see the teachings of D'ANDREA with regard to, inter alia “geometry matrix” shown in for example Figures 5 and 7 and the obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference).
Regarding claim 15 and the limitation the system of claim 1, further comprising modeling the vehicle failure independently from at least another one of the plurality of models including by applying a respective function for the vehicle failure to determine a respective output value based on a same set of inputs provided to all functions (given the BRI see the teachings of D'ANDREA with regard to, inter alia “geometry matrix” shown in for example Figures 5 and 7 and the obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference).
Regarding claim 16 and the limitation A method, comprising:
receiving vehicle state information and at least one actuator command from a vehicle;
determining a plurality of models associated with vehicle failure;
determining a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate;
determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle;
determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models;
identifying, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate;
and in response to identifying the malfunctioning rotor, performing a responsive action (see the rejection of corresponding parts of claim 1 above incorporated herein by reference.).
Regarding claim 17 and the limitation the method of claim 16, wherein: the vehicle includes at least one tilt wing; and determining at least one model of the plurality of models is based at least in part on at least one of a direction or a mode of the at least one tilt wing (see the rejection of corresponding parts of claim 9 above incorporated herein by reference).
Regarding claim 18 and the limitation the method of claim 17, wherein determining at least one model of the plurality of models includes selecting a model to use during flight based at least in part on at least one of a direction or a mode of the at least one tilt wing (see the rejection of corresponding parts of claim 10 above incorporated herein by reference).
Regarding claim 19 and the limitation the method of claim 16, further comprising updating the at least one model in real time as the vehicle is flying (see the rejection of corresponding parts of claim 11 above incorporated herein by reference).
Regarding claim 20 and the limitation A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions which when executed by a processor cause the processor to be configured for:
receiving vehicle state information and at least one actuator command from a vehicle; determining a plurality of models associated with vehicle failure;
determining a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate;
determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle;
determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models;
identifying, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate;
and in response to identifying the malfunctioning rotor, performing a responsive action (see the rejection of corresponding parts of claim 1 above incorporated herein by reference and especially CHAN paras:
“[0108] According to an exemplary embodiment as shown representationally and schematically in FIGS. 24-28B, the system and method can be implemented using a computing system programmed or otherwise configured to manage the operations, functions and associated data/network communications. Referring to FIGS. 24-28B according to an exemplary embodiment shown representationally and schematically, a control system is provided to manage, configure and operate the UAV/craft.
[0109] Referring to FIG. 24, a computing system is shown schematically according to an exemplary embodiment, to comprise a processor and memory/storage for data/programs as well as network/communication interfaces and input/output (I/O) system (e.g. allowing interaction through a user interface, etc.).
[0110] As shown schematically according to an exemplary embodiment in FIG. 25, the UAV/craft system comprises multiple functional subsystems (which may be independent or combined in implementation) including a master control system, monitoring/communication system, flight/operation control system, configuration control system, energy/power control system (and other associated subsystems).
[0111] As shown schematically according to an exemplary embodiment in FIG. 26, functional modules may be associated with a computing system to manage and operate the UAV/craft, including for the power plant/energy storage systems (e.g. motors and/or engines, battery and/or fuel systems, etc.), administration, status/condition monitoring, mission control, configuration management, etc.“)).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1, 16 AND 20 of U.S. Patent No. US 12,151,827 B2
Although the claims at issue are not identical, they are not patentably distinct from each other as shown by the side by side comparison in the table immediately below. Those claims not cited are rejected for depending from a rejected base claim.
Claim of instant application.
Claim of U.S. Patent No. US 12,151,827 B2
A system, comprising:
a processor;
and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which, when executed, cause the processor to:
receive vehicle state information and at least one actuator command from a vehicle; determine a plurality of models associated with vehicle failure;
determine a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate;
determine an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle;
determine an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models;
identify, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate;
and in response to identifying the malfunctioning rotor, perform a responsive action.
1. A system, comprising:
a processor;
and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to:
receive vehicle state information and at least one actuator command from a vehicle; determine a plurality of models associated with vehicle failure, wherein at least one model of the plurality of models outputs a vehicle state in a respective failure mode and models a failure mode independently from at least another one of the plurality of models;
determine an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle;
determine an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models;
identify, from a plurality of rotors associated with the vehicle, a malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate;
and in response to identifying the malfunctioning rotor, perform a responsive action.
16. A method, comprising: receiving vehicle state information and at least one actuator command from a vehicle; determining a plurality of models associated with vehicle failure; determining a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate; determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models; identifying, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic is rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing a responsive action.
16. A method, comprising: receiving, by a processor, vehicle state information and at least one actuator command from a vehicle; determining, by a processor, a plurality of models associated with vehicle failure, wherein at least one model of the plurality of models outputs a vehicle state in a respective failure mode and models a failure mode independently from at least another one of the plurality of models; determining, by a processor, an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining, by a processor, an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models; identifying, by a processor, from a plurality of rotors associated with the vehicle, a malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing, by a processor, a responsive action.
20. A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions which when executed by a processor cause the processor to be configured for: receiving vehicle state information and at least one actuator command from a vehicle; determining a plurality of models associated with vehicle failure; determining a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate; determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models; identifying, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing a responsive action.
20. A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions which when executed by a processor cause the processor to be configured for: receiving vehicle state information and at least one actuator command from a vehicle; determining a plurality of models associated with vehicle failure, wherein at least one model of the plurality of models outputs a vehicle state in a respective failure mode and models a failure mode independently from at least another one of the plurality of models; determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models; identifying from a plurality of rotors associated with the vehicle, a malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing a responsive action.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 16 AND 20 -21 of U.S. Patent No. 11,628,950.
Although the claims at issue are not identical, they are not patentably distinct from each other as shown in the side by side comparison below.
Those claims not cited are rejected for depending from a rejected base claim.
Instant Application claim number
U.S. Patent No. 11,628,950
1. A system, comprising:
a processor;
and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which, when executed, cause the processor to:
receive vehicle state information and at least one actuator command from a vehicle; determine a plurality of models associated with vehicle failure;
determine a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate;
determine an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle;
determine an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models;
identify, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate;
and in response to identifying the malfunctioning rotor, perform a responsive action.
1. A system, comprising: a processor; and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to:
receive vehicle state information and at least one actuator command from a vehicle; determine a model associated with a vehicle failure mode, wherein the model includes an output state vector representing a vehicle state in the vehicle failure mode, the output state vector being determined based at least in part on at least one function that:
receives the vehicle state information and the at least one actuator command, models each failure mode independently, and outputs the output state vector;
determine an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle;
determine an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the model associated with a vehicle failure mode;
identify, from a plurality of rotors associated with the vehicle, a malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate;
and in response to identifying the malfunctioning rotor, perform a responsive action.
16. A method, comprising: receiving vehicle state information and at least one actuator command from a vehicle; determining a plurality of models associated with vehicle failure; determining a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate; determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models; identifying, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic is rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing a responsive action.
16. A method, comprising: receiving, by a processor, vehicle state information and at least one actuator command from a vehicle; determining, by a processor, a model associated with a vehicle failure mode, wherein the model includes an output state vector representing a vehicle state in the vehicle failure mode, the output state vector being determined based at least in part on at least one function that: receives the vehicle state information and the at least one actuator command, models each failure mode independently, and outputs the output state vector; determining, by a processor, an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining, by a processor, an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the model associated with a vehicle failure mode; identifying, by the processor, from a plurality of rotors associated with the vehicle, a malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing, by a processor, a responsive action.
20. A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions which when executed by a processor cause the processor to be configured for: receiving vehicle state information and at least one actuator command from a vehicle; determining a plurality of models associated with vehicle failure; determining a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate; determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models; identifying, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing a responsive action.
20. A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions which when executed by a processor cause the processor to be configured for: receiving vehicle state information and at least one actuator command from a vehicle; determining a model associated with a vehicle failure mode, wherein the model includes an output state vector representing a vehicle state in the vehicle failure mode, the output state vector being determined based at least in part on at least one function that: receives the vehicle state information and the at least one actuator command, models each failure mode independently, and outputs the output state vector; determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the model associated with a vehicle failure mode; identifying, from a plurality of rotors associated with the vehicle, a malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing a responsive action.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8, 9, 10, 11, 13, 17 and 18 of U.S. Patent No. 10,112,727.
Although the claims at issue are not identical, they are not patentably distinct from each other as shown in the side by side comparison below.
Those claims not cited are rejected for depending from a rejected base claim.
Instant Application
U.S. Patent No. 10,112,727
1. A system, comprising:
a processor;
and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which, when executed, cause the processor to:
receive vehicle state information and at least one actuator command from a vehicle; determine a plurality of models associated with vehicle failure;
determine a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate;
determine an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle;
determine an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models;
identify, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate;
and in response to identifying the malfunctioning rotor, perform a responsive action.
1. A system, comprising: an interface configured to receive sensor data from an inertial measurement unit on a vehicle, wherein the vehicle includes a first rotor and a second rotor, both of which rotate about a vertical axis of rotation; and a processor configured to: determine an observed attitude and an observed rate of change in attitude of the vehicle based on the sensor data; obtain a set of models of vehicle failure modes, wherein the set of models include: (1) a first model associated with a failure of the first rotor and (2) a second model associated with a failure of the second rotor;
determine a first expected attitude and a first expected rate of change in attitude of the vehicle based at least in part on the first model associated with a failure of the first rotor; determine a second expected attitude and a second expected rate of change in attitude of the vehicle based at least in part on the second model associated with a failure of the second rotor; identify a set of one or more malfunctioning rotors based on the observed attitude, the observed rate of change in attitude, the first expected attitude, the first expected rate of change in attitude, the second expected attitude, and the second expected rate of change in attitude;
and perform a responsive action based on the set of one or more malfunctioning rotors.
16. A method, comprising: receiving vehicle state information and at least one actuator command from a vehicle; determining a plurality of models associated with vehicle failure; determining a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate; determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models; identifying, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic is rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing a responsive action.
17. A method, comprising: receiving sensor data from an inertial measurement unit on a vehicle, wherein the vehicle includes a first rotor and a second rotor, both of which rotate about a vertical axis of rotation; determining an observed attitude and an observed rate of change in attitude of the vehicle based on the sensor data; obtaining a set of models of vehicle failure modes, wherein the set of models include: (1) a first model associated with a failure of the first rotor and (2) a second model associated with a failure of the second rotor; determining a first expected attitude and a first expected rate of change in attitude of the vehicle based at least in part on the first model associated with a failure of the first rotor; determining a second expected attitude and a second expected rate of change in attitude of the vehicle based at least in part on the second model associated with a failure of the second rotor; identifying a set of one or more malfunctioning rotors based on the observed attitude, the observed rate of change in attitude, the first expected attitude, the first expected rate of change in attitude, the second expected attitude, and the second expected rate of change in attitude; and performing a responsive action based on the set of one or more malfunctioning rotors.
20. A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions which when executed by a processor cause the processor to be configured for: receiving vehicle state information and at least one actuator command from a vehicle; determining a plurality of models associated with vehicle failure; determining a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate; determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models; identifying, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing a responsive action.
18. A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for: receiving sensor data from an inertial measurement unit on a vehicle, wherein the vehicle includes a first rotor and a second rotor, both of which rotate about a vertical axis of rotation; determining an observed attitude and an observed rate of change in attitude of the vehicle based on the sensor data; obtaining a set of models of vehicle failure modes, wherein the set of models include: (1) a first model associated with a failure of the first rotor and (2) a second model associated with a failure of the second rotor; determining a first expected attitude and a first expected rate of change in attitude of the vehicle based at least in part on the first model associated with a failure of the first rotor; determining a second expected attitude and a second expected rate of change in attitude of the vehicle based at least in part on the second model associated with a failure of the second rotor; identifying a set of one or more malfunctioning rotors based on the observed attitude, the observed rate of change in attitude, the first expected attitude, the first expected rate of change in attitude, the second expected attitude, and the second expected rate of change in attitude; and performing a responsive action based on the set of one or more malfunctioning rotors.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 7 AND 13 of U.S. Patent No. 10,822,113.
Although the claims at issue are not identical, they are not patentably distinct from each other as shown in the side by side comparison below.
Those claims not cited are rejected for depending from a rejected base claim.
Instant Application claim number
U.S. Patent No. 10,822,113
1.A system, comprising:
a processor;
and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which, when executed, cause the processor to:
receive vehicle state information and at least one actuator command from a vehicle; determine a plurality of models associated with vehicle failure;
determine a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate;
determine an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle;
determine an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models;
identify, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate;
and in response to identifying the malfunctioning rotor, perform a responsive action.
1. A system, comprising: a processor; and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to:
receive sensor data from an inertial measurement unit on a vehicle; determine an observed attitude and an observed attitude rate of the vehicle based at least in part on the sensor data;
determine an expected attitude and an expected attitude rate of the vehicle based at least in part on a model associated with a vehicle failure mode;
identify, from a plurality of rotors associated with the vehicle, a malfunctioning rotor based at least in part on the observed attitude, the observed attitude rate, the expected attitude, and the expected attitude rate; and in response to identifying the malfunctioning rotor,
perform a responsive action, including by updating a geometry matrix, which is used to generate a plurality of actuator commands for the plurality of rotors, so that at least one non-malfunctioning rotor in the plurality of rotors compensates for the malfunctioning rotor.
16. A method, comprising: receiving vehicle state information and at least one actuator command from a vehicle; determining a plurality of models associated with vehicle failure; determining a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate; determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models; identifying, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic is rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing a responsive action.
7. A method, comprising: receiving sensor data from an inertial measurement unit on a vehicle; determining an observed attitude and an observed attitude rate of the vehicle based at least in part on the sensor data; determining an expected attitude and an expected attitude rate of the vehicle based at least in part on a model associated with a vehicle failure mode; identifying, from a plurality of rotors associated with the vehicle, a malfunctioning rotor based at least in part on the observed attitude, the observed attitude rate, the expected attitude, and the expected attitude rate; and in response to identifying the malfunctioning rotor, performing a responsive action, including by updating a geometry matrix, which is used to generate a plurality of actuator commands for the plurality of rotors, so that at least one non-malfunctioning rotor in the plurality of rotors compensates for the malfunctioning rotor.
20. A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions which when executed by a processor cause the processor to be configured for: receiving vehicle state information and at least one actuator command from a vehicle; determining a plurality of models associated with vehicle failure; determining a subset of the plurality of models associated with vehicle failure to identify a malfunctioning rotor including by sorting the plurality of models based on similarity to at least one of an attitude and an attitude rate; determining an observed flight characteristic and an observed flight characteristic rate of the vehicle based at least in part on sensor data from an inertial measurement unit on the vehicle; determining an expected flight characteristic and an expected flight characteristic rate of the vehicle based at least in part on the determined plurality of models; identifying, from a plurality of rotors associated with the vehicle, the malfunctioning rotor based at least in part on the observed flight characteristic, the observed flight characteristic rate, the expected flight characteristic, and the expected flight characteristic rate; and in response to identifying the malfunctioning rotor, performing a responsive action.
13. A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for: receiving sensor data from an inertial measurement unit on a vehicle; determining an observed attitude and an observed attitude rate of the vehicle based at least in part on the sensor data; determining an expected attitude and an expected attitude rate of the vehicle based at least in part on a model associated with a vehicle failure mode; identifying, from a plurality of rotors associated with the vehicle, a malfunctioning rotor based at least in part on the observed attitude, the observed attitude rate, the expected attitude, and the expected attitude rate; and in response to identifying the malfunctioning rotor, performing a responsive action, including by updating a geometry matrix, which is used to generate a plurality of actuator commands for the plurality of rotors, so that at least one non-malfunctioning rotor in the plurality of rotors compensates for the malfunctioning rotor.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure as teaching, inter alia, the state of the art at the time of the invention. For example:
US 20160048132 A1 to Cherepinsky; Igor et al. teaches, inter alia a TAIL-SITTER FLIGHT MANAGEMENT SYSTEM in for example the ABSTRACT, Figures and/or Paragraphs below:
“A system and method for controlling flight of an aircraft having a propeller, memory and a processor includes receiving one or more signals indicative of a flight plan comprising a plurality of waypoints; determining information indicative of a trajectory between the plurality of waypoints; determining information indicative of vehicle attitude commands; determining information indicative of flight control command signals; and determining an error between sensed vehicle states and the vehicle attitude commands.”.
US 10053236 B1 to Buchmueller; Daniel et al. teaches, inter alia Automated aerial vehicle inspections in for example the ABSTRACT, Figures and/or Paragraphs below:
“Automated inspections of aerial vehicles may be performed using imaging devices, microphones or other sensors. Between phases of operation, the aerial vehicle may be instructed to perform a plurality of testing evolutions, e.g., in a sequence, at a testing facility, and data may be captured during the evolutions by sensors provided on the aerial vehicle and by ground-based sensors at the testing facility. The imaging and acoustic data may be processed to determine whether any vibrations or radiated noises during the evolutions are consistent with faults or discrepancies of the aerial vehicle such as microfractures, corrosions or fatigue. If no faults or discrepancies are detected, the aerial vehicle may be returned to service without delay. If any faults or discrepancies are detected, however, then the aerial vehicle may be subjected to maintenance, repairs or further manual or visual inspections.”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL LAWSON GREENE JR whose telephone number is (571)272-6876. The examiner can normally be reached on MON-THUR 7-5:30PM (EST).
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/DANIEL L GREENE/Primary Examiner, Art Unit 3665 20260123