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
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.
Claims 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kamiguchi et al. (5,344,301) in view of Horiuchi (2020/0254670).
Regarding claims 10 and 12, Kamiguchi et al. discloses a control device for an injection molding apparatus, comprising:
a controller – Fig. 1, 11-18, 26;
temperature sensors 35 to measure a temperature of a cylinder 29 that holds a molding material to be injected into a mold in between the mold platens 27-28,
wherein a control device comprising: a period setting unit – see Fig. 3 – configured to set a cold start prevention period – col. 5, lines 52-61, which is a period for restricting an operation of a plasticizing member provided in the cylinder so as to be movable and rotatable, until a temperature of the molding material or the temperature of the mold rises,
wherein the period setting unit stores temperature-time data in which the temperature measured by the temperature measurer is associated with time – normally 15-20 minutes – see col. 6, lines 27-38, see abstract, claims 2-5. A cold start prevention flag is set and is automatically reset when the passage of the predetermined time is detected by means of the cold start prevention timer. When the band heater 34 is connected again to the power supply, this flag will be set again – see col. 6, lines 27-38.
However, Kamiguchi fails to disclose one or more processor and a non-transitory storage medium storing instructions.
Horiuchi discloses an apparatus and a method for regulating an injection molding machine, comprising:
a state determination device or a computer 1 be mounted on a controller for controlling injection mold,
a management device 3 connected to the controller through a wired/wireless network, or a computer such as an edge computer, fog computer, or cloud server,
a CPU 11 of the state determination device 1 is a processor for controlling the state determination device 1. The CPU 11 reads out system programs stored in a ROM 12 via a bus 20 and controls the entire state determination device 1 according to these system programs. A RAM 13 is temporarily loaded with temporary calculation data, various data input by a worker through an input device 71;
a non-volatile memory 14 stores a setting area loaded with setting information on the operation of the state determination device 1, data input from the input device 71, and static data (machine type, mass and material of a mold, resin type, etc.) acquired from injection molding machines 2 through a network 7, time-series data on physical quantities (the temperature of a nozzle, the position, speed, acceleration, current, voltage, and torque of a motor for driving the nozzle, the temperature of the mold, the flow rate, flow velocity, and pressure of the resin, etc.) detected during molding operations of the injection molding machines 2, time-series data of information (information for identifying a mold closing process, mold clamping process, injection process, packing process, metering process, mold opening process, ejection process, cycle start, and cycle end, as molding processes of the injection molding machine 2, [0033];
a data acquisition unit 30 acquires time-series data on various physical quantities, such as the temperature of the nozzle, the temperature of the mold related to the molding operation of the injection molding machine 2, and the flow rate, flow velocity, and pressure of the resin, information indicative of machine states of the injection molding machine 2, such as an in-operation state, stop state, temperature rising state, and stores these data into the acquired data storage unit 50 [0041];
an extraction condition storage unit 52 stores at least one of the extraction conditions organized and managed by condition classification and for extracting data used for processing related to machine learning from the data acquired by the data acquisition unit;
a learning data extraction unit 32 configured to extract the data used for the processing related to the machine learning, out of the data acquired by the data acquisition unit, according to the extraction condition stored by the extraction condition storage unit 52; and a machine learning device 100 configured to execute the processing related to the machine learning using the data extracted by the learning data extraction unit
a learning unit 110 of the machine learning device 100 performs the machine learning using the learning data created by the preprocessing unit 34 based on the data for learning extracted by the learning data extraction unit 32;
an estimation unit 120 estimates the state of the injection molding machine using the learning models stored in the learning model storage unit 130;
The learning unit 110 generates a learning model by performing machine learning using the data acquired from the injection molding machine 2, based on a conventional machine learning method such as the unsupervised learning, supervised learning, or reinforcement learning, and stores the generated learning model in the learning model storage unit 130
In case where the learning model stored in the learning model storage unit 130 is a learning model generated by unsupervised learning, the estimation unit 120 of this embodiment inputs the state data S obtained by the preprocessing unit 34 to the learning model, and then estimates the extent of deviation of the state data S from the state data acquired during the normal-state operation, thereby calculating abnormality degree as the result of the estimation.
Horiuchi further discloses that states of various part of the injection molding machine 2 are detected by sensors and operations of various parts are controlled by the controller, [0035], and the management device 3 via network 7 in real time, [00343], [0041] – also see Fig. 2 and Fig. 7, where the network 7 connecting the injection molding machine and the data acquisition unit 30.
It would have been obvious to one of ordinary skill in the art to improve Kamiguchi’s control device by providing a new control system, including a network connecting sensors from the injection molding machine to a data acquisition unit, a learning data extraction unit, a pre-processing unit, a computer, and a machine learning device configured to execute the processing related to the machine learning using the data extracted by the learning data extraction unit as taught by Horiuchi in order to automatically and accurately control the injection molding machine in real time during the molding process.
Regarding claim 11, Kamiguchi further discloses that the cold start prevention flag is able to notify a user of information for changing the setting of the cold start prevention period via the period setting unit or information for limiting an input range, col. 6, lines 27-38.
Regarding claim 13, wherein the control device 14 is connected to a heater 34 via an output circuit 7 and a heater circuit 10, wherein the control device 14 determines whether or not the cold start prevention flag is set and whether or not the band heater 34 is in a heat-up stage for heating the injection cylinder 29. If it is detected in this step that the band heater 34 is in the heat-up stage for the present processing is added to a heat-up time storage register Ta, and the heater heat-up time is cumulatively stored whereupon the state detection processing for this cycle is finished – see col. 7, lines 15-46.
Response to Arguments
Applicant’s arguments with respect to claims 10-13 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Thu-Khanh T. Nguyen whose telephone number is (571)272-1136. The examiner can normally be reached 7:30-4:30.
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/Thu Khanh T. Nguyen/Primary Examiner, Art Unit 1743