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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Beckman et al (US 9964966), in view of Gomez et al (US 10,962,955) and further in view of Giordano et al (US 2020/0252500).
For claim 1, Beckman et al teach a system for monitoring an aerial vehicle (e.g. figure 1, abstract), the system comprising:
a sensor subsystem comprising a vibration sensor (e.g. figure 5, 506 vibration control system );
a housing surrounding the vibration sensor (e.g. figure 5, 506 vibration control system);
a mounting interface coupled to the housing and configured to couple the system to the aerial vehicle (e.g. column 1, lines 46-49: For example, Vibration sensors may be positioned at joints between structural members of a frame of an aerial vehicle Or figure 5, column 13, lines 38-44: The aerial vehicle 510 includes one or more memory or storage components 514 for storing information or data captured by vibration sensor 506-1. There’ must be interface connecting vibration control system 506 and memory of the Aerial Vehicle);
a signal conditioning and communications subsystem coupled to the vibration sensor within the housing and configured to receive a vibration signal stream from the vibration sensor (e.g. column 13, lines 38-44: The aerial vehicle 510 includes one or more memory or storage components 514 for storing information or data captured by vibration sensor 506-1); and
a processing subsystem coupled to the signal conditioning and communications subsystem within the housing (e.g. figure 5 processor 512) and comprising, wherein the processing subsystem (e.g. column 7, lines 44-50: anti-vibration may be determined based on a machine learned model) is structured for edge deployment (e.g. figure 5 processor 512 is in the aerial vehicle) and comprises on-chip architecture (e.g. figure 5, processor 512 is a chip).
Beckman et al do not further disclose:
a neural processing unit (NPU) processing the vibration signal stream and identifying a set of unique signatures corresponding to states of a set of subcomponents of the aerial vehicle.
Gomez et al teach processing the vibration signal stream and identifying a set of unique signatures corresponding to states of a set of subcomponents of the aerial vehicle (e.g. abstract: machine learning algorithms for event identification; figure 4, step S330: identifying a set of unique signature corresponds to states and events). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Gomez et al into the teaching of Beckman et al to provide an improved tools for monitoring, forecasting, and trouble- shooting events (e.g. column 2, lines 33-36, Comez et al).
Beckman et al and Gomez et al do not further specify a neural processing unit (NPU).
Giordano et al teach a neural processing unit (NPU) (e.g. paragraph 60: unprocessed vibration data in combination with deep learning…executed by a dedicated Neural processing unit (NPU).). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Giordano et al into the teaching of Beckman et al and Gomez et al to utilized NPU to process vibration data in combination with deep learning to significantly reduce complex data processing (e.g. paragraph 60, Giordano et al) to improve the processing efficiency of the system.
For claim 2, Beckman et al teach the aerial vehicle is an unmanned aerial vehicle (e.g. figure 1, column 1, line 45: UAV).
For claim 3, Beckman et al teach a strain gage sensor (e.g. column 14, lines 12-15, The thermometer 525, the barometer 526 and the hygrometer 527 may be any devices, components, systems, or instruments for determining local air temperatures, atmospheric pressures).
For claim 5, Beckman et al teach the mounting interface couples the system to the aerial vehicle away from a motor of the aerial vehicle (e.g. figure 1, Vibration control system 106).
For claim 4, Beckman et al teach a temperature sensor and a pressure sensor (e.g. column 14, lines 12-15, The thermometer 525, the barometer 526 and the hygrometer 527 may be any devices, components, systems, or instruments for determining local air temperatures, atmospheric pressures).
For claim 6, Beckman et al do not further disclose receiving vibration data derived from the vibration signal stream; performing a set of transformation operations upon said vibration data; identifying the set of unique signatures corresponding to states of the set of subcomponents of the aerial vehicle, from the set of transformation operations; and returning an analysis comprising a recommended action for improving or maintaining proper performance of the aerial vehicle, based upon the set of unique signatures. Gomez et al teach receiving vibration data derived from the vibration signal stream; performing a set of transformation operations upon said vibration data; identifying the set of unique signatures corresponding to states of the set of subcomponents of the aerial vehicle, from the set of transformation operations; and returning an analysis comprising a recommended action for improving or maintaining proper performance of the aerial vehicle, based upon the set of unique signatures (e.g. figure 4, steps S310, S320, S330 S340). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Gomez et al into the teaching of Beckman et al to provide an improved tools for monitoring, forecasting, and trouble shooting events (e.g. column 2, lines 33-36, Comez et al).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Beckman et al, Gomez et al and Giordano et al, as applied to claims 1-6 above, and further in view of Drews et al (US 2022/0019200).
For claim 13, Gomez et al and Giordano et al do not further disclose the NPU is an NPU with 1 trillions of operations per second (TOPS) capability with energy use performance of less than 1 picojoule per operation. Drews et al teach the NPU is an NPU with 1 trillions of operations per second (TOPS) capability with energy use performance of less than 1 picojoule per operation (e.g. paragraph 23: NPUs provides 5, 1 or 2 TOPS/Watt). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Drews et al into the teaching of Comez et al and Giordano et al to prove highly energy efficient processing units has great advantage of reducing the need of active power dissipation and reducing overall power consumption (e.g. paragraph 23, Drewes et al).
Claims 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Beckman et al (US 9964966), in view of Gomez et al (US 10,962,955), in view of Chang et al (US 11,263,172) and further in view of Giordano et al (US 2020/0252500).
For claim 15, Beckman et al teach a method for monitoring an aerial vehicle (e.g. figure 1), the method comprising:
providing a mounting interface (e.g. column 1, lines 46-49: For example, Vibration sensors may be positioned at joints between structural members of a frame of an aerial vehicle Or figure 5, column 13, lines 38-44: The aerial vehicle 510 includes one or more memory or storage components 514 for storing information or data captured by vibration sensor 506-1. There’ must be interface connecting vibration control system 506 and memory of the Aerial Vehicle) between a sensor subsystem coupled to a processing subsystem (e.g. figure 5; processor 512), and the aerial vehicle (e.g. figure 1: sensor 106-2), wherein the processing subsystem comprises and is structured for edge deployment (e.g. figure 5 processor 512 is in the aerial vehicle), and wherein the sensor subsystem comprises a vibration sensor (e.g. figure 1; vibration sensor 106-2, figure 5 Vibration sensor 506-1).
Beckman et al do not further disclose:
a neural processing unit (NPU):
sampling a vibration signal stream generated from the vibration sensor during operation of the aerial vehicle;
performing a set of transformation operations upon the vibration signal stream, wherein the set of transformation operations comprises operations applied by self-attention time-series transformer architecture;
identifying a set of unique signatures corresponding to faults of a set of subcomponents of the aerial vehicle from the set of transformation operations; and
returning an analysis comprising a recommended action for improving or maintaining proper performance of the aerial vehicle, based upon the set of unique signatures.
Gomez et al teach:
sampling (column 9, lines 9-21: the monitor 130 can include a control that samples data generated from the sensor subsystem, logs the data, and transmits the data to processing system 140) a vibration signal stream generated from the vibration sensor during operation of the aerial vehicle (e.g. Figure 4, Step S310);
performing a set of transformation operations upon the vibration signal stream, wherein the set of transformation operations (e.g. figure 4, step S320);
identifying a set of unique signatures corresponding to faults of a set of subcomponents of the aerial vehicle from the set of transformation operations (e.g. Figure 4: step S330); and
returning an analysis comprising a recommended action for improving or maintaining proper performance of the aerial vehicle, based upon the set of unique signatures (e.g. Figure 4: step S340).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Gomez et al into the teaching of Beckman et al to provide an improved tools for monitoring, forecasting, and trouble- shooting events (e.g. column 2, lines 33-36, Comez et al).
Beckman and Gomez et al do not further disclose:
a neural processing unit (NPU);
transformation operations comprise operations applied by self-attention time-series transformer architecture.
Chang et al teach:
transformation operations comprise operations applied by self-attention time-series transformer architecture (e.g. abstract, column 1, line 45-column 2, line 18: The processor(s) identify a time series pattern for the time series of data in the multiple historical snapshots and calculate their variability. The processor(s) then determine that the variability in a first sub-set of the time series pattern is larger than a predefined value, and determine that future values of the first set of the time series pattern are a set of non-forecastable future values. Also see column 10, lines 37-50 for artificial intelligence is used to determine variabilities in K-stem values for time series patterns of multivariate data). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Chang et al into the teaching of Beckman and Gomez et al to efficiently identify data that is useful while improving the efficiency of that specific system (e.g. column 2, lines 14-18, Chang et al).
Beckman, Gomez et al and Chang et al do not further disclose:
a neural processing unit (NPU);
Giordano et al teach a neural processing unit (e.g. paragraph 60: unprocessed vibration data in combination with deep learning…executed by a dedicated Neural processing unit (NPU).). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Giordano et al into the teaching of Beckman, Gomez et al and Chang et al to utilized NPU to process vibration data in combination with deep learning to significantly reduce complex data processing (e.g. paragraph 60, Giordano et al) to improve the processing efficiency of the system.
For claim 18, Beckman et al teach a strain gage sensor (e.g. column 14, lines 12-15, The thermometer 525, the barometer 526 and the hygrometer 527 may be any devices, components, systems, or instruments for determining local air temperatures, atmospheric pressures).
Claims 7- 8 are rejected under 35 U.S.C. 103 as being unpatentable over Beckman et al, Gomez et al and Giordano et al, as applied to claims 1-6 above, and further in view of Cella et al (US 2023/0058169).
For claim 8, Beckman et al, Gomez et al, and Giordano et al do not further disclose the analysis provides an indication of a bearing fault of the bearing of the aerial vehicle, wherein the bearing fault is associated with at least one of bearing fatigue, bearing lubrication, and bearing geometry of the bearing. Cella et al teach the analysis provides an indication of a bearing fault of the bearing of the aerial vehicle, wherein the bearing fault is associated with at least one of bearing fatigue, bearing lubrication, and bearing geometry of the bearing (e.g. paragraphs 600-601: “In this example, the causes of the irregular vibrational patterns could be a loose bearing, a lack of bearing lubrication, a bearing that is out of alignment…” paragraph 145: “For example, an expert driver’s interactions with a robotic system, such as…a UAV). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cella et al into the teaching of Beckman et al, Gomez et al and Giordano et al to provide an improved tools for monitoring, forecasting, and trouble- shooting events.
For claim 7, Beckman et al, Gomez et al, and Giordano et al do not further specify the set of subcomponents comprises a bearing of the aerial vehicle. Cella et al teach the set of subcomponents comprises a bearing of the aerial vehicle (e.g. paragraphs 600-601: “In this example, the causes of the irregular vibrational patterns could be a loose bearing, a lack of bearing lubrication, a bearing that is out of alignment…” paragraph 145: “For example, an expert driver’s interactions with a robotic system, such as…a UAV). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cella et al into the teaching of Beckman et al, Gomez et al and Giordano et al to provide an improved tools for monitoring, forecasting, and trouble- shooting events.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Beckman et al, Gomez et al, Chang et al and Giordano et al, as applied to claim 15 above, and further in view of Cella et al (US 2023/0058169).
For claim 17, Beckman et al, Gomez et al, Chang et al and Giordano et al do not further disclose the analysis provides an indication of a bearing fault of the bearing of the aerial vehicle, wherein the bearing fault is associated with at least one of bearing fatigue, bearing lubrication, and bearing geometry of the bearing. Cella et al teach the analysis provides an indication of a bearing fault of the bearing of the aerial vehicle, wherein the bearing fault is associated with at least one of bearing fatigue, bearing lubrication, and bearing geometry of the bearing (e.g. paragraphs 600-601: “In this example, the causes of the irregular vibrational patterns could be a loose bearing, a lack of bearing lubrication, a bearing that is out of alignment…” paragraph 145: “For example, an expert driver’s interactions with a robotic system, such as…a UAV). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cella et al into the teaching of Beckman et al, Gomez et al, Chang et al and Giordano et al to provide an improved tools for monitoring, forecasting, and trouble- shooting events.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Beckman et al, Gomez et al and Giordano et al, as applied to claims 1-6 above, and further in view of Ross et al (US 11,643,207).
For claim 9, Beckman et al, Gomez et al and Giordano et al do not further disclose the set of subcomponents comprises a flight control surface, an engine, an energy source, and a landing system of the aerial vehicle. Ross et al teach the set of subcomponents comprises a flight control surface, an engine, an energy source, and a landing system of the aerial vehicle (e.g. column 6, lines 26-41: UAVs 24, Batteries 32, flight control system 30, electric vertical takeoff and landing (EVTOL) aircraft, a hybrid power system that includes internal combustion engines). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Ross et al into the teaching of Beckman et al, Gomez et al and Giordano et al to provide an improved tools for monitoring, forecasting, and trouble- shooting events.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Beckman et al, Gomez et al, Giordano et al, and Ross et al as applied to claim 9 above, and further in view of Yardibi et al (US 2019/0017409).
For claim 10, Beckman et al, Gomez et al, Giordano et al, and Ross et al do not further disclose the analysis provides a prediction of a failure of the engine of the aerial vehicle in relation to a cycle time of the engine. Yardibi et al teach the analysis provides a prediction of a failure of the engine of the aerial vehicle in relation to a cycle time of the engine (e.g. paragraph 50: the total time to failure or number of cycles to failure for one or more components of the gas turbine engine). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Yardibi et al into the teaching of Beckman et al, Gomez et al, Giordano et al and Ross et al to provide an improved tools for monitoring, forecasting, and trouble- shooting events.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Beckman et al, Gomez et al and Giordano et al, as applied to claims 1-6 above, and further in view of Volpi (US 2024/0317396).
For claim 11, Beckman et al, Gomez et al and Giordano et al do not further disclose a weapons system and a surveillance system of the aerial vehicle. Volpi teaches a weapons system and a surveillance system of the aerial vehicle (e.g. figure 1, paragraph 12: weapons system, paragraph 74: camera system). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Volpi into the teaching of Beckman et al, Gomez et al and Giordano et al to provide an improved tools for monitoring, forecasting, and trouble- shooting events.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Beckman et al, Gomez et al, and Giordano et al as applied to claims 1-5 above, and further in view of Stollmeyer et al (US 2024/0210963).
For claim 20, Beckman et al, Gomez et al and Giordano et al do not further disclose executing the recommended action, wherein the aerial vehicle is an unmanned aerial vehicle, and wherein the recommended action comprises controlling flight operation of the aerial vehicle in response to a fault of at least one of the set of subcomponents. Stollmeyer et al teach executing the recommended action, wherein the aerial vehicle is an unmanned aerial vehicle, and wherein the recommended action comprises controlling flight operation of the aerial vehicle in response to a fault of at least one of the set of subcomponents (e.g. paragraph 132: “…in the case of the AI/ML models identifying more than one possible fault and associated recommendation, the flight control software 534 can provide ‘what if simulations’ and estimated likelihoods of each occurring, to aid the decision making while troubleshooting during runtime, in terms of which intervention could lead to which type of remedial states. ”). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Stollmeyer et al into the teaching of Beckman et al, Gomez et al and Giordano et al to provide an improved tools for monitoring, forecasting, and trouble- shooting events.
Allowable Subject Matter
Claims 12, 14, 16 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAQUAN ZHAO whose telephone number is (571)270-1119. The examiner can normally be reached M-Thur: 7:00 am-5:00 pm.
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Email: daquan.zhao1@uspto.gov.
Phone: (571)270-1119
/DAQUAN ZHAO/Primary Examiner, Art Unit 2484