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 § 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, 2, 4, 5, 7, 8 and 11 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Iijima et al (US PUB. 20190101897, herein Iijima).
Regarding claim 1, Iijima teaches A state determination device for determining a state of an injection molding machine (abstract “generates a characteristic amount that characterizes the state of the injection operation from the state amount, and infers an evaluation value of the state of the injection operation from the characteristic amount. The numerical control system detects an abnormal state on the basis of the evaluation value”), the state determination device comprising:
a data acquisition unit configured to acquire data related to a predetermined physical quantity as data indicating a state related to the injection molding machine (0038 “characteristic amount generation unit 210 included in the inference processing unit 200 generates a characteristic amount indicating the characteristics of the state of the injecting operation of the numerical control unit 100 on the basis of the state amount detected by the state amount detection unit”, 0036 “state amount detection unit 140 included in the numerical control unit 100 detects the state of an injection operation by the numerical control unit 100 (and the injection molding machine controlled by the numerical control unit 100) as a state amount”);
a feature amount calculation unit configured to calculate a feature amount indicating a feature of a state of the injection molding machine based on the data related to the physical quantity (0017 “detecting a state amount indicating a state of the injection operation of the injection molding machine; inferring an evaluation value of the state of the injection operation from the state amount”, 0012 “a state amount detection unit that detects a state amount indicating a state of the injection operation of the injection molding machine; an inference computing unit that infers an evaluation value of the state of the injection operation from the state amount;”);
a feature amount storage unit configured to store the feature amount (0038 “state of the injection operation generated by the characteristic amount generation unit 210 serves as input data when an inference computing unit 220 “)
a statistical condition storage unit configured to store a statistical condition including at least a statistical function for calculating a predetermined statistic from a predetermined feature amount (0039 “from a plurality of learning models stored in the learning model storage unit 300”);
a statistical data calculation unit configured to calculate a statistic as statistical data with reference to a statistical condition stored in the statistical condition storage unit based on the feature amount stored in the feature amount storage unit (0039 “inference computing unit 220 included in the inference processing unit 200 infers an evaluation value of the state of the injection operation controlled by the numerical control unit 100 (and the injection molding machine controlled by the numerical control unit 100) on the basis of one learning model selected, on the basis of the conditions of the present injecting operation, from a plurality of learning models stored in the learning model storage unit 300 and the characteristic amount generated by the characteristic amount generation unit. The inference computing unit 220 is realized by applying a learning model stored in the learning model storage unit 300 to a platform capable of executing an inference process based on machine learning. The inference computing unit 220 may be one for performing an inference process using a multilayer neural network, for example, and may be one for performing an inference process using a known learning algorithm as machine learning such as a Bayesian network, a support vector machine, or a mixture Gaussian model”, 0049 “learning model generation unit 500 generates or updates (performs machine learning on) the learning model stored in the learning model storage unit 300 on the basis of the conditions of the injection operation designated by the condition designating unit 110 and the characteristic amount indicating the characteristics of the state of the injection operation generated by the characteristic amount generation unit 210”)
a statistical data storage unit configured to store the statistical data (0043 “learning model stored in the learning model storage unit 300 is stored as information that can form one learning model suitable for the inference process of the inference computing unit 220. When the learning model stored in the learning model storage unit 300 is a learning model”);;
and a state determination unit configured to determine a state of the injection molding machine based on fluctuation of a plurality of pieces of consecutive statistical data in the statistical data stored in the statistical data storage unit (0047 “abnormality detection unit 400 detects an abnormality occurring in the numerical control unit 100 (and the injection molding machine controlled by the numerical control unit 100) on the basis of the evaluation value of the state of the injection operation inferred by the inference processing unit…abnormality detection unit 400 may detect that the injecting operation state is abnormal when a wear amount of the check valve, for example, exceeds a predetermined threshold and may detect that the injecting operation state is normal in other cases”, 0060 “In this modification, since a large-volume learning model is stored in the external storage 4, it is possible to use a large number of learning models and to read learning models without via a network or the like. Therefore, this numerical control system 1 of this modification is useful when a real-time inference process is required”, real-time data is inferenced).
Regarding claim 2, the cited prior art teaches the state determination device according to claim 1.
wherein: the state determination unit includes: a determination condition storage unit configured to store a determination condition for determining a state of the injection molding machine (0038 “A characteristic amount generation unit 210 included in the inference processing unit 200 generates a characteristic amount indicating the characteristics of the state of the injecting operation of the numerical control unit 100 on the basis of the state amount detected by the state amount detection unit 140.”);
and a statistical analysis unit configured to statistically analyze whether or not a plurality of pieces of consecutive statistical data stored in the statistical data storage unit satisfies a determination condition stored in the determination condition storage unit, and a state of the injection molding machine is determined based on an analysis result of the statistical analysis unit (0060 “In this modification, since a large-volume learning model is stored in the external storage 4, it is possible to use a large number of learning models and to read learning models without via a network or the like. Therefore, this numerical control system 1 of this modification is useful when a real-time inference process is required”, 0047 “The abnormality detection unit 400 may detect that the injecting operation state is abnormal when a wear amount of the check valve, for example, exceeds a predetermined threshold and may detect that the injecting operation state is normal in other cases”).
Regarding claim 4, the cited prior art teaches the state determination device according to claim 1.
Iijima teaches wherein the state determination unit includes: a learning model storage unit configured to store a learning model learning a correlation between a plurality of pieces of consecutive statistical data in the statistical data calculated by the statistical data calculation unit and a state of the injection molding machine when the statistical data is calculated (0015 “In the inferring step, a learning model to be used is selected on the basis of the condition of the injection operation designated in the step of designating the condition among at least one learning model correlated in advance with a combination of conditions of the injection operation of the injection molding machine, and the evaluation value of the state of the injection operation is computed using the selected learning model”, 0044 “learning model storage unit 300 may store one learning model in correlation with combinations of conditions of one injection operation and may store learning models which use two or more different learning algorithms in correlation with combinations of conditions of one injection operation”);
and an estimation unit configured to estimate a state of the injection molding machine using the learning model based on a plurality of pieces of consecutive statistical data stored in the statistical data storage unit (0033 “numerical control system 1 further includes an abnormality detection unit 400 that detects an abnormality (wear of a check valve) in an injecting operation of an injection molding machine 120 on the basis of the result of inference by the inference processing unit 200 about the state of the edge device and a learning model generation unit 500 that generates or updates the learning model to be stored in the learning model storage unit”).
Regarding claim 5, the cited prior art teaches the state determination device according to claim 4.
Iijima teaches wherein the learning model performs learning using at least one learning method among supervised learning, unsupervised learning, and reinforcement learning (0039 “The inference computing unit 220 may be one for performing an inference process using a learning algorithm such as, for example, supervised learning, unsupervised learning, or reinforcement learning. Moreover, the inference computing unit 220 may be able to execute inference processes based on a plurality of types of learning algorithms”).
Regarding claim 7, the cited prior art teaches the state determination device according to claim 1.
Iijima teaches wherein a result of determination by the state determination unit is displayed on and output to a display device (0048 “Upon detecting that the injecting operation state is abnormal, the abnormality detection unit 400 may notify an operator of an abnormality in the injecting operation state with the aid of a display device, a lamp, an audio output device”).
Regarding claim 8, the cited prior art teaches the state determination device according to claim 1.
Iijima teaches wherein, when the state determination unit determines that a state of the injection molding machine is abnormal, at least one of signals for suspending or decelerating an operation of the injection molding machine or limiting driving torque of a prime mover driving the injection molding machine is output (0048 “upon detecting that the injecting operation state abnormal, the abnormality detection unit 400 may instruct the numerical control unit 100 to stop machining”).
Claim 11 is rejected using similar reasoning as the rejection of claims 1 due to reciting similar limitations but directed towards a method.
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.
Claim(s) 3, 9, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iijima et al (US PUB. 20190101897, herein Iijima) in view of Asaoka et al (US PUB. 20180281256, herein Asaoka).
Regarding claim 3, the cited prior art teaches the state determination device according to claim 2.
The cited prior art do not teach wherein the determination condition defines a condition related to any one of the monotonically increasing number of times, the monotonically decreasing number of times, an increase rate, and a decrease rate of a plurality of pieces of consecutive statistical data.
Asaoka teaches wherein the determination condition defines a condition related to any one of the monotonically increasing number of times, the monotonically decreasing number of times, an increase rate, and a decrease rate of a plurality of pieces of consecutive statistical data (0007 “in the case where the sampling data acquired in each molding cycle is examined in order to determine the state of the injection molding machine, when the sampling data is used without alteration, it is not possible to correctly determine the state of the injection molding machine. Such a problem becomes conspicuous, e.g., in the comparison of the sampling data between the molding cycles. For example, when the component is worn, as shown in FIGS. 9A and 9C, the shape of the curve changes even under the same operation condition. When FIG. 9A is compared with FIG. 9C (the operation conditions both being the operation condition A), it is possible to easily determine the change of the shape of the curve, but it is not possible to easily determine the change of the shape of the curve when FIG. 9B is compared with FIG. 9C (the operation condition being different between the condition B and the condition A)”, 0004 “Data acquired from the injection molding machine is recorded as two types of data pieces that include sampling data (discrete time-series data) that is acquired at specific sampling intervals for each molding cycle, and data that is acquired once for each molding cycle”).
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 Iijima with the teachings of Asaoka since Asaoka teaches a means for ensuring that the injection molding machine is maintained (0002).
Regarding claim 9, the cited prior art teaches the state determination device according to claim 1.
The cited prior art do not teach wherein the data acquisition unit acquires data from a plurality of injection molding machines connected via a wired or wireless network.
Asaoka teaches wherein the data acquisition unit acquires data from a plurality of injection molding machines connected via a wired or wireless network (0040 “A state determination apparatus 10 can be implemented as, e.g., a controller for controlling an injection molding machine, or a PC that is connected to the injection molding machine using a wired/wireless communication line so as to be capable of data communication”).
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 Iijima with the teachings of Asaoka since Asaoka teaches a means for ensuring that the injection molding machine is maintained (0002).
Regarding claim 10, the cited prior art teaches the state determination device according to claim 1.
The cited prior art do not teach wherein the state determination device is mounted on a host device connected to the injection molding machine via a wired or wireless network.
Asaoka teaches wherein the state determination device is mounted on a host device connected to the injection molding machine via a wired or wireless network (0040 “A state determination apparatus 10 can be implemented as, e.g., a controller for controlling an injection molding machine, or a PC that is connected to the injection molding machine using a wired/wireless communication line so as to be capable of data communication”).
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 Iijima with the teachings of Asaoka since Asaoka teaches a means for ensuring that the injection molding machine is maintained (0002).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iijima et al (US PUB. 20190101897, herein Iijima) in view of Maruyama et al (US PUB. 20130156875, herein Maruyama).
Regarding claim 6, the cited prior art teaches the state determination device according to claim 1.
Maruyama teaches wherein the statistical function is any one of a variance, a standard deviation, an average deviation, a coefficient of fluctuation, a weighted mean, a weighted harmonic mean, a trimmed mean, a root mean square, a minimum value, a maximum value, a mode value, and a weighted median value (0023 “variation index calculating unit may calculate as a deviation variation index any one of a standard deviation and variance of the physical quantity, an average value of absolute deviations, and maximum/minimum values”).
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 Iijima with the teachings of Maruyama since Maruyama teaches a means for “reduce the burden on the operator by automatically setting threshold values for abnormality detection” (0024).
Conclusion
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/TAMEEM D SIDDIQUEE/
Primary Examiner
Art Unit 2116