7DETAILED 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.
Claims13 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Comez et al (US 10,962,955) and further in view of Chang et al (US 11,263,172).
For claim 13, Comez et al teach a method for monitoring an apparatus, the method comprising:
providing a mounting interface (e.g. figures 2A shows housing for the sensors) between a vibration sensor (e.g. figure 1A: sensor subsystem 110, column 6, line60- column 7, line 10: sensor subsystem 110 can include vibration sensors couple to one or more motors) coupled to a signal processing subsystem, and the apparatus, without direct contact between the vibration sensor and a motor of the apparatus (e.g. figure 1, column 3, lines 35-57: an interface 120 between the sensor subsystem 110 and a hydraulic pump 5 of a hydraulic apparatus 10; a monitor 130 coupled to the sensor subsystem 110 and configured to receive outputs of the sensor subsystem; and a processing subsystem 140 operatively coupled to the monitor 130);
sampling a vibration signal stream generated from the vibration sensor during operation of the apparatus (e.g. 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);
performing a set of transformation operations upon the vibration signal stream (e.g. figure 4: stem S320: performing a set of transformation operation upon the set of data stream), wherein the set of transformation operations comprises operations applied by self-attention (e.g. column 10, lines 48-50: time series sensor data) transformer architecture (e.g. column 17, lines 1-14: the set of transformation operations can take, as inputs, data derived from the signal streams and process them with trained AI/NN models for returning unique signatures in Block S330);
identifying a set of unique signatures corresponding to faults of a set of subcomponents of the apparatus from the set of transformation operations (e.g. figure 4, step S330: identify a set of unique signatures corresponding to sates and events of the hydraulic apparatus and subcomponents of the hydraulic apparatus, from the set of transformation operations); and
returning an analysis comprising a recommended action for improving or maintaining proper performance of the apparatus, based upon the set of unique signatures (e.g. figure 4, Step S340: returning an analysis including a recommended action for improving or maintaining proper performance of the hydraulic apparatus, based upon the set of unique signatures).
Comez et al do not further disclose a time-series transformer architecture. Chang et al teach a 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 Comez 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).
For claim 19, Comez et al teach the apparatus comprises an electric utility apparatus (e.g. figure 1A: Hydralic Apparatus).
For claim 20, Comez et al teach the recommended action comprises shutting down the electric utility apparatus (e.g. column 25, lines 40-65: In an example related to fleet management, Block S350 can execute instructions for remotely deactivating a heavy mobile vehicle having a hydraulic apparatus (e.g., a Bobcat, an excavator, a crane, etc.) based upon an analysis that the hydraulic apparatus is near catastrophic failure).
Claims 1, 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Comez et al (US 10,962,955) and further in view of Giordano et al (US 2020/0252500).
For claim 1, Comez et al teach a system for monitoring an apparatus comprising a motor, the system comprising:
a vibration sensor (e.g. figure 1A: sensor subsystem 110, column 6, line60- column 7, line 10: sensor subsystem 110 can include vibration sensors couple to one or more motors);
a housing surrounding the vibration sensor (e.g. figures 2A shows housing for the sensors);
a mounting interface coupled to the housing and configured to couple the system to the apparatus away from the motor (e.g. figure 1, column 3, lines 35-57: an interface 120 between the sensor subsystem 110 and a hydraulic pump 5 of a hydraulic apparatus 10; a monitor 130 coupled to the sensor subsystem 110 and configured to receive outputs of the sensor subsystem; and a processing subsystem 140 operatively coupled to the monitor 130);
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 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); and
a processing subsystem coupled to the signal conditioning and communications subsystem and comprising a processing unit (e.g. figure 1A, column 13, lines 21-40: the system includes a processing subsystem 140 operatively coupled to the monitor 130 and/or sensor subsystem 110 and including non-transitory computer-readable media storing instructions that, when executed by the processing subsystem 140; column 17, lines 1-14: the set of transformation operations can take, as inputs, data derived from the signal streams and process them with trained AI/NN models for returning unique signatures in Block S330), the processing subsystem contained within the housing and comprising non-transitory media storing instructions that, when executed, perform operations for:
receiving vibration data derived from the vibration signal stream (e.g. figure 4, step S310; 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);
performing a set of transformation operations upon said vibration data (e.g. figure 4, step S320);
identifying a set of unique signatures corresponding to states of a set of subcomponents of the apparatus, 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 apparatus, based upon the set of unique signatures (e.g. figure 4, step S340).
Comez et al do not further specify a NPU. Giordano et al each a NPU (e.g. paragraph 60). 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 Comez et al to utilize the deep learning, by reducing the amount of processing resources required, enable the context analysis to be more frequently used by various different context-aware applications, to provide a greater variety of context-specific user interactions (e.g. Giordano et al, paragraph 60).
For claim 9, Comez et al teach the set of subcomponents comprises a bearing, a shaft, a belt, and a gear of the apparatus (e.g. figure 1A, the Hydraulic should have subcomponent comprises a bearing, a shaft, a belt, and a gear).
For claim 10, Comez et al teach a system for monitoring an apparatus comprising a motor, the system comprising:
a vibration sensor (e.g. figure 1A: sensor subsystem 110, column 6, line60- column 7, line 10: sensor subsystem 110 can include vibration sensors couple to one or more motors);
a housing surrounding the vibration sensor (e.g. figures 2A shows housing for the sensors);
a mounting interface coupled to the housing and configured to couple the system to the apparatus away from the motor (e.g. figure 1, column 3, lines 35-57: an interface 120 between the sensor subsystem 110 and a hydraulic pump 5 of a hydraulic apparatus 10; a monitor 130 coupled to the sensor subsystem 110 and configured to receive outputs of the sensor subsystem; and a processing subsystem 140 operatively coupled to the monitor 130);
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 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); and
a processing subsystem coupled to the signal conditioning and communications subsystem and comprising a processing unit (e.g. figure 1A, column 13, lines 21-40: the system includes a processing subsystem 140 operatively coupled to the monitor 130 and/or sensor subsystem 110 and including non-transitory computer-readable media storing instructions that, when executed by the processing subsystem 140, perform operations for identifying, from outputs of the monitor 130, a set of unique signatures corresponding to states and events…the processing subsystem 140 can also function to generate and process training and test datasets for development and refinement of machine learning (ML) models, where the ML models return outputs associated with hydraulic apparatus and subcomponent statuses and events from received sensor data.), the processing subsystem contained within the housing and comprising on-chip self-attention time-series transformer architecture for processing a vibration signal stream (e.g. column 17, lines 1-14: the set of transformation operations can take, as inputs, data derived from the signal streams and process them with trained AI/NN models for returning unique signatures in Block S330).
Comez et al do not further specify a NPU. Giordano et al each a NPU (e.g. paragraph 60). 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 Comez et al to utilize the deep learning, by reducing the amount of processing resources required, enable the context analysis to be more frequently used by various different context-aware applications, to provide a greater variety of context-specific user interactions (e.g. Giordano et al, paragraph 60).
For claim 11, Comez et al teach receiving vibration data derived from the vibration signal stream (e.g. figure 1A: sensor subsystem 110, column 6, line60- column 7, line 10: sensor subsystem 110 can include vibration sensors couple to one or more motors); performing a set of transformation operations upon said vibration data; identifying a set of unique signatures corresponding to states of a set of subcomponents of the apparatus, from the set of transformation operations; and returning an analysis comprising a recommended action for improving or maintaining proper performance of the apparatus, based upon the set of unique signatures (e.g. figure 4: steps S320, S330, S340 and S350).
For claim 8, Comez et al teach the apparatus comprises an electric utility apparatus (e.g. figure 1A: Hydralic Apparatus).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Comez et al and Giordano et al, as applied to claims 1, 8-11 above, and further in view of Drews et al (US 2022/0019200).
For claim 3, 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).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Comez et al and Giordano et al, as applied to claims 1,8-11 above, and further in view of Weng et al (US 2023/0394823).
For claim 4, Comez et al and Giordano et al do not further disclose an encoder block comprising multi-head attention subarchitecture. Weng et al teach an encoder block comprising multi-head attention subarchitecture (e.g. paragraph 115: The encoder applies a series of self-attention blocks to the first embedded data, where each block contains a multi head attention). 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 Weng 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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Comez et al, Giordano et al, and Weng et al as applied to claims 1, 5, 8- 11 above, and further in view of Chang et al (US 11,263,172).
For claim 5, Comez et al, Giordano et al and Weng et al do not further disclose self-attention time-series transformer architecture omits a decoder block. Chang et al teach self-attention time-series transformer architecture omits a decoder block (e.g. figure 1 shows no decoder, abstract: 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). 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 Comez et al, Giordano et al and Weng 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).
Claims 15 -16 are rejected under 35 U.S.C. 103 as being unpatentable over Comez et al and Chang et al, as applied to claims 13 and 19-20 above, and further in view of Weng et al (US 2023/0394823).
For claim 15, Comez et al and Chang et al do not further disclose an encoder block comprising multi-head attention subarchitecture. Weng et al teach an encoder block comprising multi-head attention subarchitecture (e.g. paragraph 115: The encoder applies a series of self-attention blocks to the first embedded data, where each block contains a multi head attention). 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 Weng et al into the teaching of Comez et al and Chang 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.
For claim 16, Comez et al do not further disclose self-attention time-series transformer architecture omits a decoder block. Chang et al teach self-attention time-series transformer architecture omits a decoder block (e.g. figure 1 shows no decoder, abstract: 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). 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 Comez 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).
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Comez et al and Giordano et al, as applied to claims 1,8-11 above, and further in view of Zheng et al (US 2018/0145377).
For claim 6, Comez et al and Giordano et al do not further teach an unmanned aerial vehicle. Zheng et al teach an unmanned aerial vehicle (e.g. paragraph 61: flight control system of unmanned aerial vehicle). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to apply the monitor method of Comez et al and Giordano et al to the unmanned aerial vehicle to efficiently identify data that is useful while improving the efficiency of that specific system.
For claim 7, Comez et al and Giordano et al do not further disclose ubcomponents of an engine of the unmanned aerial vehicle and flight control surfaces of the unmanned aerial vehicle. Zheng et al teach ubcomponents of an engine of the unmanned aerial vehicle and flight control surfaces of the unmanned aerial vehicle. (e.g. paragraph 61: flight control system of unmanned aerial vehicle). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to apply the monitor method of Comez et al and Giordano et al to the unmanned aerial vehicle to efficiently identify data that is useful while improving the efficiency of that specific system.
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Comez et al and Chang et al, as applied to claims 13 and 19-20 above, and further in view of Zheng et al (US 2018/0145377).
For claim 17, Comez et al and Chang et al do not further disclose an unmanned aerial vehicle and wherein the set of subcomponents comprises subcomponents of an engine and flight control surfaces of the unmanned aerial vehicle. Zheng et al teach an unmanned aerial vehicle and wherein the set of subcomponents comprises subcomponents of an engine and flight control surfaces of the unmanned aerial vehicle. (e.g. paragraph 61: flight control system of unmanned aerial vehicle). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to apply the monitor method of Comez et al and Chang et al to the unmanned aerial vehicle to efficiently identify data that is useful while improving the efficiency of that specific system.
For claim 18, Comez et al and Chang et al do not further disclose the recommended action, wherein the recommended action comprises controlling flight operation of the unmanned aerial vehicle in response to a fault of at least one of the set of subcomponents. Zheng et al teach the recommended action, wherein the recommended action comprises controlling flight operation of the unmanned aerial vehicle in response to a fault of at least one of the set of subcomponents (e.g. paragraph 61: detect whether the battery fails and perform a fault handling process). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to apply the monitor method of Comez et al and Chang et al to the unmanned aerial vehicle to efficiently identify data that is useful while improving the efficiency of that specific system.
Allowable Subject Matter
Claims 2, 12 and 14 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