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 .
Information Disclosure Statement
The information disclosure statement filed 11/19/2024 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the internation search report is not translated. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Masuda et al (US PUB. 20200030939 , herein Masuda) in view of Ma et al (CN 103886373, herein Ma).
Regarding claim 1, Masuda teaches An abnormal-vibration-predicting method for a roll grinder that grinds an outer peripheral surface of a rolling roll with a grinding wheel while the rolling roll rotates (0061 “the first learning model is used to estimate the following conditions as the surface condition of the grinding wheel”, claim 2 “estimation model creating device for grinding wheel surface condition estimation according to claim 1, wherein each measurement data piece includes both the first measurement data and the second measurement data, the first measurement data includes at least one of vibration of the structural member of the grinding machine and an amount of deformation of the structural member of the grinding machine”), the abnormal-vibration-predicting method comprising:
an acquisition step of acquiring (0008 “Each measurement data piece includes at least one of first measurement data and second measurement data. The first measurement data indicates a condition of a structural member of the grinding machine. The second measurement data relates to a ground portion of the corresponding workpiece. The first learning model creating unit performs machine learning using the measurement data relating to the workpieces as first-learning input data so as to create a first learning model for estimating a surface condition of the grinding wheel”) [a rigidity parameter related to a rigidity of the roll grinder] and a wheel rotation parameter related to a rotational speed of the grinding wheel (0052 “a command rotation speed for the grinding wheel”)
and a prediction step of predicting an occurrence of an abnormal vibration in a process of grinding the rolling roll by using the [rigidity parameter] and the wheel rotation parameter (claim 2 “estimation model creating device for grinding wheel surface condition estimation according to claim 1, wherein each measurement data piece includes both the first measurement data and the second measurement data, the first measurement data includes at least one of vibration of the structural member of the grinding machine and an amount of deformation of the structural member of the grinding machine”).
The cited prior art do not teach a rigidity parameter related to a rigidity of the roll grinder and rigidity parameter.
Ma teaches a rigidity parameter related to a rigidity of the roll grinder; rigidity parameter (page 4 (1) “collection of data: 1. roll-force factor: along with the increase of roll-force, working roll displacement will change, the vibration of working roll will make the contact arc length of milling train and rolled piece change, the variation of contact arc length can cause the variation of entrance tension force, causes the variation of rigidity and the stability of system. Roll-force is larger, and the entrance tension force of rolled piece is just larger, and its corresponding undulate quantity is larger, and the negative damping causing is larger, makes system more unstable, more easily causes vibration.2. research shows, mill speed has impact to two aspects of system generation autovibration, and mill speed is more high more easily to be vibrated. In addition, the mill speed of milling train also has larger impact to the lubrication circumstances in mill roll-gap.3. belt steel thickness is also a key factor that causes milling train vertical vibration, and belt steel thickness changes excessive, can cause tension variation excessive, also can become greatly the disturbance quantity of system stiffness, more easily causes rolling mill vibration. The vibration of milling train is multifactor being coupled to form, and also comprises drafts, tension force etc. The present invention chooses the data that record in actual production process as training sample, comprises roll-force, mill speed, drafts, belt steel thickness, tension force and the vibration data of the rolling-mill housing of same period of obtaining by remote monitor device, and by its normalization”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Masuda with the teachings of Ma since Ma teaches a means for improved neural network prediction model for determining flutter prediction (page 3).
Regarding claim 2, the cited prior art teach The abnormal-vibration-predicting method according to Claim 1.
Masuda teaches wherein the rigidity parameter includes a distance between a rotation center of the rolling roll and a rotation center of the grinding wheel or a sum of a diameter of the rolling roll and a diameter of the grinding wheel (0081 “grinding-wheel-shape-information obtaining unit 220 obtains shape information about the shape of the grinding wheel 16. Specifically, the grinding-wheel-shape-information obtaining unit 220 obtains, as the shape information, the size (e.g., the diameter) of the grinding wheel 16 measured by the grinding wheel correction device 18. That is, the grinding-wheel-shape-information obtaining unit 220 obtains the shape information when the grinding wheel correction device 18 performs truing or dressing of the grinding wheel 16. The grinding-wheel-shape-information obtaining unit 220 may further obtain, as the shape information, a change in the size of the grinding wheel 16 and deformation of the grinding wheel 16”).
Regarding claim 3, the cited prior art teach The abnormal-vibration-predicting method according to Claim 1.
Masuda teaches wherein, in the acquisition step, a load parameter related to a load applied to the grinding wheel is further acquired (0052 “As shown in Table 1, the actual operation data of the operation-related data includes the following: drive currents through the driving devices such as the motor 12a; and actual positions of the driving devices such as the motor 12a”), and wherein, in the prediction step, the occurrence of the abnormal vibration in the process of grinding the rolling roll is predicted by using the rigidity parameter, the wheel rotation parameter, and the load parameter (0079).
Regarding claim 4, the cited prior art teach The abnormal-vibration-predicting method for a roll grinder according to Claim 3.
Masuda teaches wherein the load parameter includes a load current value of an electric motor that rotates the grinding wheel (0052).
Claims 5-8 are rejected using similar reasoning as the rejection of claims 1-5 due to reciting similar limitations but directed towards a method.
Regarding claim 9, the cited prior art teach A rolling-roll-grinding method using the abnormal- vibration-predicting method according to claim 1.
Masuda teaches wherein, when the abnormal vibration is predicted to occur in the prediction step, grinding conditions of the roll grinder are changed to grinding conditions for which the abnormal vibration is not predicted to occur (0012).
Regarding claim 10, the cited prior art teach A metal-strip-rolling method.
Ma teaches comprising rolling a metal strip in a rolling mill in which a rolling roll is installed, the rolling roll being ground by the rolling-roll-grinding method according to Claim 9 (page 5).
Claims 11-13 are rejected using similar reasoning as the rejection of claims 1-5, 9-10 due to reciting similar limitations but directed towards a device.
Regarding claim 14, the cited prior art teach A roll-grinding apparatus.
Masuda teaches comprising: a roll grinder that grinds an outer peripheral surface of a rolling roll with a grinding wheel while the rolling roll rotates (0030);
and the abnormal-vibration-predicting device according to claim 11 (see rejection of claim 11).
Claims 15 and 20 are rejected using similar reasoning as the rejection of claims 1-5, 9-10, 14 due to reciting similar limitations but directed towards an apparatus.
Claims 16, 17, 18 and 19 are rejected using similar reasoning as the rejection of claims 1-5, 9-10 due to reciting similar limitations but directed towards a method.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAMEEM SIDDIQUEE whose telephone number is (571)272-1627. The examiner can normally be reached M-F 8:00-4:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TAMEEM D SIDDIQUEE/
Primary Examiner
Art Unit 2116
Prosecution Insights