DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is responsive to the communication received on 07/23/2024. The claims 1-4 are pending, of which the claim(s) 1 & 3 is/are in independent form.
This application is a Divisional of U.S. Application No. 17/286,826, filed April 20, 2021, which is a National Stage application of PCT/JP2019/041726, filed on October 24, 2019 and claims priority to International Application PCT/JP2018/039906, filed on October 26, 2018.
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.
The factual inquiries 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.
Claim(s) 1- 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shamoto et al. (US 20180133807 A1) in view of Oosawa (US 20190155245 A1).
Regarding claim 1, Shamoto teaches a numerical control device [“the processing apparatus 1”] comprising: (figs.1 ,8, 9 [024, 0124]);
control computation circuitry [“control unit 28 includes a processor 70 and a memory 72”] to control a spindle [e.g., “workpiece spindle 8”] that is a rotation axis of a machining target [“a workpiece W”] ([025, 030, 046]),
a first drive axis to drive a first tool [a first tool 61 of “vibration cutting tool 61 is mounted at the distal end 60” of each vibration portions 50”, wherein “plurality of cutting tools 12 and a plurality of vibration portions 50 may be arranged”] to perform vibration cutting machining on the machining target ([047, 078, 0119, 0124]), and
a second drive axis to drive a second tool [second vibration cutting unit 20 and its tool 61 when “at least two of the cutting tool 12, the quenching unit 16, and the vibration cutting unit 20 may be placed” or “plurality of cutting tools 12 and a plurality of vibration portions 50 may be arranged”] to perform vibration cutting machining on the machining target ([047, 098, 0119, 0124]);
wherein: the control computation circuitry comprises: a computing device [processor and memory of the control unit 28]) (Fig. 1).
Shamoto teaches its control circuitry to efficiently vibrating the vibration cutting tools 61 by changing amplitude and the frequency in order to reduce effect of reducing wear of the cutting tool ([053-055, 061, 078]). However, Shamoto’s control unit 28 fails to recognize failure condition(s) on the machining based on ongoing state variables and performing of appropriate changes. Put differently, while Shamoto teaches its computing device [“the control unit 28”] controlling the state variable including a vibration cutting condition [“amplitude and the frequency”] for the first drive axis and the second drive axis for the vibration cutting machining ([098, 051]), it still does not teach the control unit having machine learning capabilities to perform prediction of pass/fail and make appropriate adjustments. Thus, Shamoto does not teach:
the control computation circuitry to comprise a machine learning device to learn a pass/fail prediction in which whether the vibration cutting machining passes or fails is predicted, and the machine learning device comprises: observation circuitry to observe a state variable including a vibration cutting condition for the first drive axis and the second drive axis for the vibration cutting machining;
data acquisition circuitry to acquire pass/fail information indicating whether the vibration cutting machining has passed or failed; and
learning circuitry to learn the pass/fail prediction according to a data set created based on a combination of the state variable and the pass/fail information.
Oosawa relates to a control computation circuitry [“a controller” 1 shown in fig. 1, analogous to Shamoto’s “control unit 28”] including a machine learning device [item 100] provided in machining of a workpiece based on state variables [S(S1, S2…)] and machining accuracy determination data [D (D1, D2,…), analogous to Shamoto’s quality of the machining results] (Fig. 2, [008, 011, 025, 0301-032]). Specifically, Oosawa teaches a control computation circuitry comprises: a machine learning device to learn a pass/fail prediction in which whether the
observation circuitry [“controller 1 includes a state observation unit 106,”] to observe [Fig. 4, SA02] a state variable [“unit 108 observes state variables S representing the current state …machining condition data S3 indicating the machining conditions, and machining environment data S4 relating to the machining environment”, analogous to “amplitude and the frequency” of the both vibration cutting tools 61/cutting unit 20 of Shamoto in para. 098] including a ig. 6, “for each wire electrical discharge machine 2”] and the second drive axis
data acquisition circuitry [“determination data acquisition unit 108”] to acquire [Fig. 4, SA03] pass/fail information [“determination data D including machining accuracy determination data D1 indicating a machining accuracy realized”, analogous to quality of finished workpiece information of Shamoto. PHOSITA would understand the accuracy of the machining as pass/fail information since higher accuracy indicates passing and lower accuracy indicates failing] indicating whether the vibration cutting machining has passed or failed ([035, 041-042]); and
learning circuitry [“a learning unit 110”] to learn [“learning unit 110 uses the state variables S and the determination data D to learn the correction amount for the machining path in association with the partial machining path, machining conditions, and machining environment of the machining performed by the wire electrical discharge machine”. The needing of the correction can be understood as failing situation and the not needing of the correction can be understood as passing condition] the pass/fail prediction according to a data set created based on a combination [considering all the subsets (“state variables S input simultaneously into the learning unit 110”) of the “state variables S” including S1, S2, S3 by all of them proving into the learning model] of the state variable and the pass/fail information ([035, 043-045, 069]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Oosawa and Shamoto because they both related to a control computation circuitry controlling machining of a workpiece using multiple cutting tools and (2) have modified the control computation circuitry (i.e., “control unit 28” of Shamoto to include a machining learning device having observation, data acquisition, and learning circuitries as in Oosawa to determine correction required to the vibration cutting tools while performing vibration cutting machining) of Shamoto to include missing limitations using the known technique of Oosawa. Doing so would allow to automatically and accurately correct the machining paths of the vibration cutting tools only when needed while the vibration cutting is ongoing (Oosawa, [005, 047]). Accordingly, when the Shamoto’s control unit 28 is modified to use the machine learning device 100 of Oosawa to automatically determine whether there is need to correct machining path of its vibration cutting tool 61 of the vibration portion 50, the modified Shamoto will teach each element of the claim and renders invention of this claim obvious to PHOSITA.
Regarding claim 2, Shamoto in view of Oosawa teaches the numerical control device according to claim 1, wherein: the vibration cutting condition corresponds to the number of vibrations [frequency] information indicating the number of vibrations, vibration amplitude information indicating a vibration amplitude [“the amplitude”], and spindle rotation speed information indicating a rotation speed [“control unit 28 moves the main spindle head 4”] of the spindle (Shamoto [053-054, 069] & Oosawa [031, 034]).
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oosawa (US 20190155245 A1) in view of Shamoto et al. (US 20180133807 A1).
Regarding claim 3, Oosawa teaches a machine learning device [machine learning device 100 of fig. 6/3/1 implemented inside a controller 1] to learn a pass/fail prediction [determining need of “correction amount for the machining path” by the unit 122 and the learning unit 110] 1in which whether [controller 1 to perform machining using “a machining program”] being configured to control a spindle that is a rotation axis of a machining target [“discharge machine 2 include the workpiece material, the workpiece plate”], a first drive axis to drive a first tool [tool of first wire discharge machine 2 of fig. 6] to perform [tool of the rightmost wire electrical discharge machine 2] to perform (Figs. 1- 6, [027, 039]),
wherein the machine learning device comprises: (Fig. 2);
observation circuitry [Fig. 2, state observation unit 106] to observe a state variable [“unit 108 observes state variables S representing the current state …machining condition data S3 indicating the machining conditions, and machining environment data S4 relating to the machining environment”] including a
data acquisition circuitry [Fig. 2, determination data acquisition unit 108] to acquire pass/fail information [“determination data D including machining accuracy determination data D1 indicating a machining accuracy realized”] indicating whether the
learning circuitry [Fig. 2, learning unit 110] to learn [“learning unit 110 uses the state variables S and the determination data D to learn the correction amount for the machining path in association with the partial machining path, machining conditions, and machining environment of the machining performed by the wire electrical discharge machine”] the pass/fail prediction according to a data set created based on a combination of the state variable and the pass/fail information ([035, 043-045, 069]).
Oosawa teaches it’s a control computation circuitry [controller 1] including a machine learning model device 100 is to learn whether the ongoing machining will pass/fail and determine appropriate correction amount to make the machining passing (“machining accuracy of the machining performed”, para.055), it is silent about using to determine amount of correction for the vibration machining tool as claimed and shown above with strikethrough emphasis.
Thus, Oosawa may not teach its machine learning device being used to monitor and control vibration cutting type of the machining as claimed.
As fully discussed above in claim 1, Shamoto teaches a control computation circuitry [“control unit 28 includes a processor 70”, analogous to Oosawa’ controller 1] including a processor and memory. Shamoto teaches the control computation circuitry controlling vibration cutting machining using a numerical control device [using of the machining program], the numerical control device being configured to control a spindle that is a rotation axis of a machining target, a first drive axis to drive a first tool [vibration cutting tool 61 of one of the “vibration cutting unit 20” from at least two vibration cutting tool] to perform vibration cutting machining on the machining target, and a second drive axis to drive a second tool [second cutting tool 61 of the 2nd vibration cutting unit 20] to perform vibration cutting machining on the machining target [“for the workpiece W”] ([098, 0121], Fig. 1);
wherein the control computation circuitry [“control unit 28 includes a processor 70 and a memory 72”] to control a state variable including a vibration cutting condition [“increasing the amplitude of the distal end 60”] for the first drive axis and the second drive axis when the vibration cutting machining is being performed ([046-055, 0115]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have (1) combined Shamoto and Oosawa because they both related to using a control computation circuitry for controlling state variables including cutting condition for the pluralities of the machining tools during processing of a workpiece and (2) modified the machine learning device of Oosawa to include missing limitations as suggested in Shamoto.
Doing so would allow to expanded the usages of already known technique of Oosawa even for other (vibration cutting) types of the machining. Vibration cutting with first and second tools types of the machining of Shamoto can be understood by PHOSITA as an specific type of example of the general machining where whole machining path needs to be corrected is already envisioned by Shamoto at least in para. 048. Furthermore, one ordinary skill in the art would have recognized that applying the known technique (using of machine learning to determine whether the machining needs correction before machining is complete) of Oosawa would have yielded predictable results and resulted in an improved (technique can be expanded in other types of the machining) system.
Regarding claim 4, Oosawa in view of Shamoto teaches The machine learning device according to claim 3, wherein:
the vibration cutting condition corresponds to the number of vibrations information indicating the number of vibrations, vibration amplitude information indicating a vibration amplitude, and spindle rotation speed information indicating a rotation speed of the spindle (Shamoto [053-054, 069] & Oosawa [031, 034]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Hachmeister (US 20170220930 A1) teaches a machine learning problem assessment system that identifies potential machine learning problems in a machine learning system in which learning code evaluates data to correlate estimated learned data with data patterns (Abstract).
2) Xu (US 20180341244 A1) teaches controller and a machine learning device used in machining ([001]).
3) MATSUMOTO Hitoshi (JP 2014-172110 A) teaches to execute low frequency vibration cutting with optimal vibrations capable of minutely parting chips (Abstract).
Contacts
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
1 Please note that unlike the claim 1, in claim 3, the limitations shown with italic emphasis cover material or article being worked upon by the actually claimed “machine learning device” and hence do not limit the scope of the machine learning device and cannot receive patentable weight. “ The inclusion of the material or article worked upon by a structure being claimed does not impart patentability to the claims." MPEP §2115. However, solely in the interest of the compact prosecution, these limitations without patentable weights are also mapped with the prior art.
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