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 .
Claims 1-6 are pending.
Claims 1-6 are rejected below.
Drawings
Examiner notes that fig. 8 and 9 had specific shading but nothing that describes this as in fig. 19. Nothing is incorrect, Examiner only points this out in case Applicant is missing a legend. If not, this can be disregarded.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ueyama (U.S. PG Pub. 2021/0049506).
As to claim 1, Ueyama teaches a servo adjustment system for adjusting control parameter setting information of a servo motor controlled by a control device of an industrial machine, the servo adjustment system comprising: a servo motor model in which operation of the servo motor is virtualized [0056 The processing part 211 includes a learning device 214 which has learned to take a torque value of the servo motor 1 as an input and output a position command value to the servo motor according to the input. 0073 in the case where the control has a predetermined physical model, i.e., a model capable of simulating a response to a command, the following may be performed. FIG. 8 is an example of a graph showing the relationship between the input and the output. As shown in FIG. 8, the relationship between the input and the output in the current learning device is based on a predetermined control model formula (physical model), and in the case where the input and the output are learned with the control model formula as a reference, for example, when there is an input falling outside the input range which has been learned in the current learning device, the learning device cannot correspond. Therefore, using the control model formula, feedforward is performed from the input response of the anomalous input to generate an ideal position command value.]; a virtual control device which virtually controls the servo motor model by executing an evaluation program based on the control parameter setting information[0073]; and a servo adjustment device which determines the control parameter setting information, based on virtual feedback information obtained by executing the evaluation program a plurality of times based on different pieces of the control parameter setting information in the virtual control device[0011 In the learning assistance device, the relearning part may be configured to generate an ideal output with respect to the anomalous input by performing feedback control on an output outputted by the processing part from the anomalous input, and perform the relearning by taking the anomalous input and the ideal output as the additional learning data].
As to claim 2, Ueyama teaches further comprising a control target model in which the industrial machine is virtualized, wherein the servo adjustment device acquires the virtual feedback information by causing the servo motor model virtually controlled by the virtual control device to run the control target model[0011, 0064].
As to claims 3 and 5, Ueyama teaches further comprising a machine learning device which performs machine learning on the control parameter setting information using the virtual feedback information, wherein the servo adjustment device determines the control parameter setting information based on a learning result from the machine learning device (fig. 9, claim 5).
As to claims 4 and 6, Ueyama teaches comprising a plurality of virtual environments each having the virtual control device, and at least one of the servo motor models, wherein the servo adjustment device includes a multiple environment management unit which manages the control parameter setting information to be applied to the plurality of the virtual environments, and wherein the machine learning device performs machine learning on the control parameter setting information using the virtual feedback information obtained from the plurality of the virtual environments( [0059] having multiple motors of same system as shown.).
A reference to specific paragraphs, columns, pages, or figures in a cited prior artreference is not limited to preferred embodiments or any specific examples. It iswell settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in theart. Stated differently, a prior art disclosure reading on a limitation of Applicant'sclaim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a singleprior art reference is not intended to exclusively dictate, but rather, todemonstrate an exemplary disclosure commensurate with the specificlimitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038,1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275,277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319,1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. BiocraftLabs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d792,794 n.1,215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747,750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969).
Other Art of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Aizawa (U.S. PG Pub. 2021/0200179) teaches machining surface simulation unit that uses the machining position data that is stored to perform a simulation of a machining surface.
Ozeki (U.S. PG Pub. 2020/0290169) teaches a learning model for a machine tool.
Itou (U.S. PG Pub. 2017/0131702) teaches an automatic position adjustment system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN L LAUGHLIN whose telephone number is (571)270-1042. The examiner can normally be reached Monday-Friday 8AM-4PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATHAN L LAUGHLIN/Primary Examiner, Art Unit 2119
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