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
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A 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.
Claim(s) 1-9 and 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (2020/0201292A1) in view of Okita et al. (EP 2012206A2).
With respect to claim 1, Cella et al. teaches a diagnostic apparatus (118; [0154]) comprising; a memory (i.e. storage medium; [0284]) having computer readable instructions [0284]; and processing circuitry (processor; [0284]) configured to execute the computer readable instructions [0284] to, receive context information defining an operation of a tool attached to a spindle of a machine (for example wattage, air flow, head pressure, horse power etc.; [0218]), rotation information of the spindle running speed; [0218]), tool information identifying the tool (i.e. machinery type; [0219]), and a detection result of a time-varying physical quantity (i.e. vibrational data; [0219]), the time-varying physical quantity being generated by the tool during at least one machining operation performed by the machine on a workpiece (like screw compressors and hammer mills, can be shown to run much noisier and can be expected to vibrate significantly more than other machines; [0219]), determine a frequency analysis result (i.e. a result of a signal evaluation circuit 8538) by performing frequency analysis (i.e. FFT on detected signals; [0497]) on the detection result (from sensors; [0497]), set a frequency range (as Cella et al. teaches setting frequency ranges based on accessed signal data; [0291]), set a bandwidth of a frequency band to be note in the frequency range (as Cella et al. teaches setting a bandwidth based on the observed signals; [0830]); extract feature information from the detection result using the band pass filter (i.e. filtering techniques; [0247]) and the frequency analysis result (from the FFT or other transforms; [0247]) and determine a machine state of the machine using the feature information (as Cella et al. teaches using filtering techniques and the results of the FFT as imported into intelligence and machine learning aspects to determine a machine state; [0245]).
Cella et al. remains silent regarding setting a band pass filter using a plurality of center frequencies and the bandwidth, the plurality of center frequencies being set using the rotation information, the tool information and the frequency range.
Okita et al. teaches a similar apparatus system having processing structure for setting a band pass filter using a plurality of center frequencies and a bandwidth [0047], the plurality of center frequencies being set using the rotation information, the tool information and the frequency range (i.e. as the settings for the filter are based on center frequencies of the signals, servo motor speeds and bandwidth [0047 [0050]]).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the apparatus of Cella et al. to include the control and filter setting logic and structure of Okita et al. because Okita et al. teaches such a modification improves frequency detection [0017-0019], thereby improving the overall detection capabilities of Cella et al.
The method steps of claim 7 are performed during the operation of rejected structure of claim 1.
With respect to claim 14, Cella et al. as modified by Okita et al. teaches a non-transitory computer readable recording medium [0284] of Cella et al. and as modified by Okita including computer-readable code [0284], which when executed by processing circuitry (processor; [0284], causes the processing circuitry execute the rejected method according to claim 7 during the operation of rejected claim 1.
With respect to claim 2, Cella et al. teaches the diagnostic apparatus (118; [0154]) wherein the processing circuitry is further configured to generate a model by learning of the feature information (as Cella et al. teaches training an AI model based on the feedback from the industrial environment, which includes that tool/machine; [0017]) and determines the machine state using the model (and using the disclosed model to predict faults; [0243].
The method steps of claim 8 are performed during the operation of rejected structure of claim 2.
With respect to claim 3, Cella et al. as modified teaches the diagnostic apparatus (118; [0154]) wherein the processing circuitry (as modified by Okita et al.) is further configured to calculate a plurality of band pass filters using the plurality of center frequencies and the bandwidth (as the combination as a whole teaches applying via a selection of a wide range of filter techniques [0247] of Cella et al., based on the plurality of center frequencies and bandwidth, a taught in Okita et al.); select, from the plurality of band pass filters (i.e. the wide range of filters, as taught in Cella et al., as specific filter), the band pass filter to be used for extracting information (to be used in the analysis); and extract the feature information using the selected band pass filter [0247].
With respect to claim 4, Cella et al. as modified teaches the diagnostic apparatus (118; [0154]) wherein the processing circuitry (as modified by Okita et al.) is further configured to: calculate a plurality of ban pass filtering use the plurality of center frequencies and the bandwidth (as the combination as a whole teaches applying, via a selection any wide range of filter techniques as taught in Cella et al. in [0247], based on the plurality of center frequencies and bandwidth, a taught in Okita et al.); and exclude, from the plurality of band pass filters (taught in Cella et al.), a band pass filter including a natural frequency of the machine and natural frequency of the tool (as Okita teaches using selected band pass filter to strip away the natural frequency of the machine and its tool; Abstract).
With respect to claim 5, Cella et al. as modified teaches the diagnostic apparatus (118; [0154]) wherein the plurality of center frequencies (is capable of including): a fundamental rotation frequency calculated using the rotation information (as the rotational information does not further define the apparatus itself and the taught structure of Cella et al. is capable of calculating fundamental frequencies using the taught control logic and mathematical approach of the combination as a whole), and a frequency that is an integral multiple of the fundamental rotation frequency (as the frequency of operation of the machine and tool does not further define the diagnostic apparatus over the prior art), and the processing circuity (as modified) is further configured to correct the fundamental rotation frequency using the frequency analysis result and the rotation information (as Cella et al. discloses using FFT analysis to identify peaks and then uses a measured or known rotational speed as a reference to adjust the results to filter out frequencies not of interest; [0494]).
With respect to claim 6, Cella et al. teaches a machining system (100) comprising: a machine (i.e. for example, Cella discloses a machine to be a large industrial machine like a drill, thereby teaching a rotating spindle [0184]) configured to perform at least one machining operation (i.e. a drilling operation) on a workpiece (i.e. object being drilled) using a tool (drill) attached to a spindle of the machine (as Cella et al. teaches the drilling machine, thereby indirectly teaching a spindle used to support the drill), the machine (industrial drill) including a transmitter (as part of the network data transport system 108) configured to transmit context information defining an operation of the tool attached to the spindle of the machine (for example wattage, air flow, head pressure, horse power etc.; [0218]), rotating information of the spindle (running speed; [0218]), tool information identifying the tool (i.e. machinery type; [0219]), and a detection result (i.e. vibrational data; [0219]) of a time-varying physical quantity, the time-varying physical quantity being generated by the tool during the at least one machining operating (like screw compressors and hammer mills, can be shown to run much noisier and can be expected to vibrate significantly more than other machines; [0219]); and a diagnostic apparatus (100), the diagnostic apparatus (100) configured to, receive context information, the rotation information, the tool information, and the detection results (as sent by data transport system 108, which includes data like operational data, running speed, vibration data, and tool identification data), determine a frequency analysis result (i.e. a result of a signal evaluation circuit 8538) by performing frequency analysis (i.e. FFT on detected signals; [0497]) on the detection result (from sensors; [0497]), set a frequency range (as Cella et al. teaches setting frequency ranges based on accessed signal data; [0291]), set a bandwidth of a frequency band to be noted in the frequency range (as Cella et al. teaches setting a bandwidth based on the observed signals; [0830]); extract feature information from the detection result using the band pass filter (i.e. filtering techniques; [0247]) and the frequency analysis result (from the FFT or other transforms; [0247]) and determine a machine state of the machine using the feature information (as Cella et al. teaches using filtering techniques and the results of the FFT as imported into intelligence and machine learning aspects to determine a machine state; [0245]).
Cella et al. remains silent regarding setting a band pass filter using a plurality of center frequencies and the bandwidth, the plurality of center frequencies being set using the rotation information, the tool information and the frequency range.
Okita et al. teaches a similar apparatus system having processing structure for setting a band pass filter using a plurality of center frequencies and a bandwidth [0047], the plurality of center frequencies being set using the rotation information, the tool information and the frequency range (i.e. as the settings for the filter are based on center frequencies of the signals, servo motor speeds and bandwidth [0047 [0050]]).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the apparatus of Cella et al. to include the control and filter setting logic and structure of Okita et al. because Okita et al. teaches such a modification improves frequency detection [0017-0019], thereby improving the overall detection capabilities of Cella et al.
With respect to claim 9, Cella et al. as modified teaches the method wherein the setting the band pass filter (as taught by the combination as a whole) includes; setting the plurality of center frequencies by calculating a fundamental rotation frequency using the rotation information (as the combination as a whole teaches setting a band pass frequency such that fundamental rotation frequencies are moved, see the Abstract of Okita), and setting a frequency that is an integral multiple of the fundamental rotation frequency (as Cella et al. discloses using FFT analysis to identify peaks and then uses a measured or known rotational speed as a reference to adjust the results to filter out frequencies not of interest, which is capable of being an integral multiple of the fundamental rotation frequency; [0494], as the frequency of operation of the machine and tool does not further define the diagnostic apparatus over the prior art).
With respect to claim 11, Cella et al. as modified teaches the method wherein the setting the band pass filter includes setting a plurality of band pass filters using the plurality of center frequencies and the bandwidth (as the combination as a whole teaches applying to band pass filters via a selection of a wide range of filter techniques [0247] of Cella et al., based on the plurality of center frequencies and bandwidth, a taught in Okita et al.); selecting, from the plurality of band pass filters (i.e. the wide range of filters, as taught in Cella et al., as specific filter), the band pass filter to be used for extracting information (to be used in the analysis); and the extracting the feature information from the detection result using the selected band pass filter [0247].
With respect to claim 12, Cella et al. as modified teaches the method further comprising; calculating an autocorrelation function of the frequency analysis result; obtaining a delay value of the autocorrelation function, the delay value at which the autocorrelation function returns a maximum value, the delay value being greater than the fundamental rotation frequency; and estimating a number of cutting edges of the tool using the delay value; and the setting the band pass filter further includes setting the plurality of center frequencies using the estimated number of cutting edges as tool information.
With respect to claim 13, Cella et al. as modified teaches the method wherein the determining the machining state includes: calculating a likelihood that the feature information is normal using the model (as Cella et al. teaches calculating the likelihood of feature information indicating failure, thereby indirectly calculating if the machine is normal if the calculated likelihood indicates no failure); and determining the machining state by comparing a value calculated using the likelihood with a desired threshold [0501].
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (2020/0201292A1) in view of Okita et al. (EP 2012206A2), as applied to claim 7, further in view of Kamiya et al. (Pub. No. US2019/0033169).
With respect to claim 10, Cella et al. as modified teaches all that is modified by remains silent regarding the method wherein the setting the band pass filter includes; setting the plurality of center frequencies by calculating a tool passing frequency using a fundamental rotation frequency and a number edges in the tool information, the fundamental rotation frequency being calculated using the rotational information and setting a sideband wave of an integral multiple of the tool passing frequency.
Kamiya et al. teaches a similar method that includes setting the band pass filter includes; setting the plurality of center frequencies by calculating a tool passing frequency (i.e. meshing frequency) using a fundamental rotation frequency (as sensed) and a number cutting edges in the tool information (i.e. a number of structural features of the tool, [0009]), the fundamental rotation frequency being calculated using the rotational information (i.e. rotation speed) and setting a sideband wave of an integral multiple of the tool passing frequency (as setting a side band aids in reducing an influence of unwanted frequency ranges; [0087] [0116]).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the method of Cella et al. to include the information regarding a tool passing frequency, a number of structural features that impact the frequency response and setting a side band as taught by Kamiya et al. because Kamiya et al. teaches such a modification provides a more accurate wear detection [0137], thereby improving the overall machine state determination of Cella et al.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (2020/0201292A1) in view of Okita et al. (EP 2012206A2), as applied to claim 9, further in view of ‘839 (JP 4942839B2).
With respect to claim 12, Cella et al. as modified teaches all that is claimed in the above rejection of claim 9, but remains silent regarding the method further comprising; calculating an autocorrelation function of the frequency analysis result; obtaining a delay value of the autocorrelation function, the delay value at which the autocorrelation function returns a maximum value, the delay value being greater than the fundamental rotation frequency; and estimating a number of cutting edges of the tool using the delay value; and the setting the band pass filter further includes setting the plurality of center frequencies using the estimated number of cutting edges as tool information.
The reference ‘839 teaches a similar method that includes calculating an autocorrelation function (Rxx; [0020]) of a frequency analysis result (from collected vibration data; [0020]); obtaining a delay value (time t; [0020]) of the autocorrelation function (Rxx), the delay value (t) at which the autocorrelation function returns a maximum value [0022], the delay value (t) being greater than the fundamental rotation frequency (as the delay value t increase and decreases, thereby being capable of being greater than a fundamental rotation frequency of the tool); and estimating a number of cutting edges (C) of the tool using the delay value z (t).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the control structure of Cella et al. to include the autocorrelation function and estimation of cutting edges of the tool, as taught by ‘839 such that the setting of the band pass filter, as taught by the combination of Cella in view of Okita, further includes setting the plurality of center frequencies (already taught in Okita) using the estimated number of cutting edges as tool information, as taught by ‘839. Such a modification ensures frequencies related to the tool edges are removed from the signals, thereby improving the overall detection of the state of the machine, as taught in [0026] of ‘839.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kar (2010/0256953) which teaches using a family of frequencies to determine the health of a mechanical element.
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/MATTHEW G MARINI/ Primary Examiner, Art Unit 2853