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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/29/2026 has been entered.
Response to Amendment
The Amendment filed Jan. 29, 2026 has been entered. Claims 1-3, 6, 11-13 remain pending in the application.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6 are rejected under 35 U.S.C. 103 as being unpatentable over Ohara et al. (WO 2021002119, English version based on US 2022/0242031), further in view of Nakamura et al. (US 2022/0152900 (Priority date is 11/06/2020)).
Regarding claim 1, Ohara discloses that, as illustrated in Figs. 1, 3, 5 (A and B), 6, 7, an abnormality detection system including an abnormality detection apparatus detecting an abnormality of a manufacturing device (ABSTRACT (e.g., determining an abnormality occurring in the twin-screw extrusion molding machine (lines 1-4 from bottom))), comprising:
a control device (item 13, Fig. 3 ([0067])) that performs operation control of the manufacturing device and transmits operating data (e.g., through the bus line 15 ([0067])) of the operation control; and
a sensor (item 20, Figs. 1, 6 ([0032], [0033], [0034])) that detects a physical quantity related to operation of the manufacturing device or a product manufactured by the manufacturing device and outputs time-series sensor value data (e.g., ‘wear of screw’ as shown in Fig. 5B) indicating the physical quantity detected,
wherein the abnormality detection apparatus includes
a communication unit (item 16, Fig. 6 ([0070])) that receives the operating data transmitted from the control device,
an acquisition unit (e.g., the A/D converter 17 ([0071], lines 1-3)) that acquires sensor value data output from the sensor,
a processing unit (item 13a, Fig. 6 ([0066])) that calculates statistics of the sensor value data acquired by the acquisition unit and determines a presence or an absence of an abnormality of the manufacturing device based on the statistics calculated and a threshold depending on the operating data received (e.g., in steps S30 and S31 as shown in Fig. 15, the average values of M1(t) and M2(t) are calculated; e.g., as shown in Fig. 5B, the thresholds of Th1 and Th2 are provided for controlling ‘wear of the screw 44’ ([0060])), and
wherein the communication unit transmits a determination result and the statistics to the control device (e.g., steps S32, S33, S34 and S36 in Fig. 15).
Ohara discloses that, as illustrated in Figs. 3, 5, 7, the manufacturing device is a molding machine (item 30, Fig. 3 ([0043])) having a gear reducer (item 40, Fig. 3 ([0044], lines 1-2)), the sensor detects vibrations of the gear reducer ([0032], lines 1-7 from bottom), and the processing unit determines a presence or an absence of abnormal vibrations in the gear reducer (e.g., as shown in Fig. 7). It is noticed that, at least AE sensor is highly sensitive to detect the high-frequency vibrations of the gear reducer.
However, Ohara does not explicitly disclose that, the diagnostic apparatus diagnosing an abnormality of the manufacturing device by using a learning model. In the same field of endeavor, injection molding machine management, Nakamura discloses that, as illustrated in Figs. 1, 2, the on-premise server 100 coupled to the cloud server 110 via an IF 111. The cloud server 110 includes a virtual server 112 that executes an analysis program, a control program, a management program ([0048], lines 1-7). Here, at least the analysis program in the virtual machine 112 in the cloud server 110 can be considered as to play a function of the diagnostic apparatus.
Nakamura discloses that, for example, the cloud server includes the first storing section configured to store information concerning at least one of the physical quantity of the injection molding machine and the physical quantity of the molded article detected by the first detecting section and the virtual machine (in the cloud server) configured to generate a control rule (for the control device) for the injection molding machine based on the information ([0013], lines 5-11). It is noticed that, the cloud server 110 can quickly input the detection result and quickly generate a control rule considering the detection result ([0065], lines 1-3 from bottom).
Here, at least the control rule generated by the virtual machine should include the diagnostic result based on the physical quantity of the molded article detected by the first detecting section through an analyzing program/process (e.g., as shown in Fig. 2) (i.e. involving the learning model).
It is noticed that, for example, the physical quantity of the molded article is detected by the sensors. These sensors are considered as the abnormality detection apparatus which has a lower hardware specification than the diagnostic apparatus provided in the virtual server in the cloud server.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ohara to incorporate the teachings of Nakamura to provide the abnormality detection apparatus is configured to communicate with a diagnostic apparatus on cloud and receive a diagnostic result (i.e. involving the learning model during the analyzing process) from the abnormality detection apparatus. Doing so would be possible to appropriately manage the injection molding machine according to the response speed requested of the injection molding machine, as recognized by Nakamura ([0004]).
Regarding claim 2, Ohara discloses that, as illustrated in Figs. 5 (A and B), 8, the control device receives the determination result (e.g., in steps S11, S12, and S13 in Fig. 8) transmitted from the abnormality detection apparatus and monitors a condition of the manufacturing device based on the determination result.
Regarding claim 3, Ohara discloses that, as illustrated in Figs. 5 (A and B), 6, the control device receives the statistics transmitted from the abnormality detection apparatus and displays a graph or numerical values based on the statistics received (item 18, Fig. 6 ([0072]); [0079]; [0108]).
Regarding claim 6, Ohara does not explicitly disclose the sensors detecting an axial torque of the screw shaft and measuring the dimensions of the molded product. Nakamura discloses that, as illustrated in Figs. 1, 2, 4, the physical quantity of the injection molding machine detected by the first detecting section is at least one of the temperatures, the pressure, the torque, and the vibration in the injection molding machine ([0027]). Nakamura discloses that, a physical quantity of the molded article detected by the sensor 61C (as shown in Fig. 2) functioning as the sensor 61 can be at least one of a dimension, luminance, and temperature of the molded article ([0056], lines 1-4).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ohara to incorporate the teachings of Nakamura to provide the sensors detecting an axial torque of the screw shaft and measuring the dimensions of the molded product. Doing so would be possible to appropriately manage the injection molding machine according to the response speed requested of the injection molding machine, as recognized by Nakamura ([0004]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Ohara et al. (WO 2021002119, English version based on US 2022/0242031), further in view of Nakamura et al. (US 2022/0152900 (Priority date is 11/06/2020)) and Isotani et al. (JPS6244417A, English translation provided).
Regarding claim 11, Ohara discloses that, an abnormality detection system including an abnormality detection apparatus detecting an abnormality of a manufacturing device, comprising:
a control device (item 13, Fig. 3 ([0067])) that performs operation control of the manufacturing device and transmits operating data (e.g., through the bus line 15 ([0067])) of the operation control; and
a sensor (item 20, Figs. 1, 6 ([0032], [0033], [0034])) that detects a physical quantity related to operation of the manufacturing device or a product manufactured by the manufacturing device and outputs time-series sensor value data (e.g., ‘wear of screw’ as shown in Fig. 5B) indicating the physical quantity detected,
wherein the abnormality detection apparatus includes
a communication unit (item 16, Fig. 6 ([0070])) that receives the operating data transmitted from the control device,
an acquisition unit (e.g., the A/D converter 17 ([0071], lines 1-3)) that acquires sensor value data output from the sensor,
a processing unit (item 13a, Fig. 6 ([0066])) that calculates statistics of the sensor value data acquired by the acquisition unit and determines a presence or an absence of an abnormality of the manufacturing device based on the statistics calculated and a threshold depending on the operating data received (e.g., in steps S30 and S31 as shown in Fig. 15, the average values of M1(t) and M2(t) are calculated; e.g., as shown in Fig. 5B, the thresholds of Th1 and Th2 are provided for controlling ‘wear of the screw 44’ ([0060])), and
wherein the communication unit transmits a determination result and the statistics to the control device (e.g., steps S32, S33, S34 and S36 in Fig. 15).
However, Ohara does not explicitly disclose that, the diagnostic apparatus diagnosing an abnormality of the manufacturing device by using a learning model. In the same field of endeavor, injection molding machine management, Nakamura discloses that, as illustrated in Figs. 1, 2, the on-premise server 100 coupled to the cloud server 110 via an IF 111. The cloud server 110 includes a virtual server 112 that executes an analysis program, a control program, a management program ([0048], lines 1-7). Here, at least the analysis program in the virtual machine 112 in the cloud server 110 can be considered as to play a function of the diagnostic apparatus.
Nakamura discloses that, for example, the cloud server includes the first storing section configured to store information concerning at least one of the physical quantity of the injection molding machine and the physical quantity of the molded article detected by the first detecting section and the virtual machine (in the cloud server) configured to generate a control rule (for the control device) for the injection molding machine based on the information ([0013], lines 5-11). It is noticed that, the cloud server 110 can quickly input the detection result and quickly generate a control rule considering the detection result ([0065], lines 1-3 from bottom).
Here, at least the control rule generated by the virtual machine should include the diagnostic result based on the physical quantity of the molded article detected by the first detecting section through an analyzing program/process (e.g., as shown in Fig. 2) (i.e. involving the learning model).
It is also noticed that, for example, the physical quantity of the molded article is detected by the sensors. These sensors are considered as the abnormality detection apparatus which has a lower hardware specification than the diagnostic apparatus provided in the virtual server in the cloud server.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ohara to incorporate the teachings of Nakamura to provide the abnormality detection apparatus is configured to communicate with a diagnostic apparatus on cloud and receive a diagnostic result (i.e. involving the learning model during the analyzing process) from the abnormality detection apparatus. Doing so would be possible to appropriately manage the injection molding machine according to the response speed requested of the injection molding machine, as recognized by Nakamura ([0004]).
However, either Ohara or Nakamura does not disclose a molding machine having a screw shaft in which the sensor detects an axial torque applied to the screw shaft.
In the same field of endeavor, injection molding machine, Isotani discloses that, as illustrated in Figs. 1, 2, at least the load sensor 2 is used to compare with the preset screw thrust set value (page 1, lines 52-53). Then, based on the measured thrust of the screw shaft (item 1, Fig. 1 or 2), its axial torque can be calculated accordingly.
It would have been obvious to use the apparatus of either Ohara or Nakamura to have the screw shaft of the injection molding machine as Isotani teaches that it is known to have the comparison of the thrust of the screw shaft then the corresponding axial torque of the screw shaft will be available. It has been held that the combination of known technique to improve similar device is likely to be obvious when it does not more than yield predictable results to one of ordinary skill in the art. KSR Int’l Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Ohara et al. (WO 2021002119, English version based on US 2022/0242031), further in view of Nakamura et al. (US 2022/0152900 (Priority date is 11/06/2020)) and Ohashi (US 2005/0184415).
Regarding claim 12, Ohara discloses that, an abnormality detection system including an abnormality detection apparatus detecting an abnormality of a manufacturing device, comprising:
a control device (item 13, Fig. 3 ([0067])) that performs operation control of the manufacturing device and transmits operating data (e.g., through the bus line 15 ([0067])) of the operation control; and
a sensor (item 20, Figs. 1, 6 ([0032], [0033], [0034])) that detects a physical quantity related to operation of the manufacturing device or a product manufactured by the manufacturing device and outputs time-series sensor value data (e.g., ‘wear of screw’ as shown in Fig. 5B) indicating the physical quantity detected,
wherein the abnormality detection apparatus includes
a communication unit (item 16, Fig. 6 ([0070])) that receives the operating data transmitted from the control device,
an acquisition unit (e.g., the A/D converter 17 ([0071], lines 1-3)) that acquires sensor value data output from the sensor,
a processing unit (item 13a, Fig. 6 ([0066])) that calculates statistics of the sensor value data acquired by the acquisition unit and determines a presence or an absence of an abnormality of the manufacturing device based on the statistics calculated and a threshold depending on the operating data received (e.g., in steps S30 and S31 as shown in Fig. 15, the average values of M1(t) and M2(t) are calculated; e.g., as shown in Fig. 5B, the thresholds of Th1 and Th2 are provided for controlling ‘wear of the screw 44’ ([0060])), and
wherein the communication unit transmits a determination result and the statistics to the control device (e.g., steps S32, S33, S34 and S36 in Fig. 15).
However, Ohara does not explicitly disclose that, the diagnostic apparatus diagnosing an abnormality of the manufacturing device by using a learning model. In the same field of endeavor, injection molding machine management, Nakamura discloses that, as illustrated in Figs. 1, 2, the on-premise server 100 coupled to the cloud server 110 via an IF 111. The cloud server 110 includes a virtual server 112 that executes an analysis program, a control program, a management program ([0048], lines 1-7). Here, at least the analysis program in the virtual machine 112 in the cloud server 110 can be considered as to play a function of the diagnostic apparatus.
Nakamura discloses that, for example, the cloud server includes the first storing section configured to store information concerning at least one of the physical quantity of the injection molding machine and the physical quantity of the molded article detected by the first detecting section and the virtual machine (in the cloud server) configured to generate a control rule (for the control device) for the injection molding machine based on the information ([0013], lines 5-11). It is noticed that, the cloud server 110 can quickly input the detection result and quickly generate a control rule considering the detection result ([0065], lines 1-3 from bottom).
Here, at least the control rule generated by the virtual machine should include the diagnostic result based on the physical quantity of the molded article detected by the first detecting section through an analyzing program/process (e.g., as shown in Fig. 2) (i.e. involving the learning model).
It is also noticed that, for example, the physical quantity of the molded article is detected by the sensors. These sensors are considered as the abnormality detection apparatus which has a lower hardware specification than the diagnostic apparatus provided in the virtual server in the cloud server.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ohara to incorporate the teachings of Nakamura to provide the abnormality detection apparatus is configured to communicate with a diagnostic apparatus on cloud and receive a diagnostic result (i.e. involving the learning model during the analyzing process) from the abnormality detection apparatus. Doing so would be possible to appropriately manage the injection molding machine according to the response speed requested of the injection molding machine, as recognized by Nakamura ([0004]).
However, either Ohara or Nakamura does not disclose a molding machine having the sensor images a molded product.
In the same field of endeavor, extruded products, Ohashi discloses that, as illustrated in Figs. 7, 8, the portion of the weather strip (i.e., the molded part) passes the image recognizing device 40 to recognize the success/failure mark 59b attached the weather strip and transmit a signal to the weather strip cutting device 30 ([0139], lines 1-4 from bottom).
It would have been obvious to use the apparatus of either Ohara or Nakamura to have the molded parts from the injection molding machine as Ohashi teaches that it is known to have the image recognizing device 40 to recognize the success/failure mark 59b attached the weather strip (i.e., the molded part). It has been held that the combination of known technique to improve similar device is likely to be obvious when it does not more than yield predictable results to one of ordinary skill in the art. KSR Int’l Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Ohara et al. (WO 2021002119, English version based on US 2022/0242031), further in view of Nakamura et al. (US 2022/0152900 (Priority date is 11/06/2020)).
Regarding claim 13, Ohara discloses that, an abnormality detection system including an abnormality detection apparatus detecting an abnormality of a manufacturing device, comprising:
a control device (item 13, Fig. 3 ([0067])) that performs operation control of the manufacturing device and transmits operating data (e.g., through the bus line 15 ([0067])) of the operation control; and
a sensor (item 20, Figs. 1, 6 ([0032], [0033], [0034])) that detects a physical quantity related to operation of the manufacturing device or a product manufactured by the manufacturing device and outputs time-series sensor value data (e.g., ‘wear of screw’ as shown in Fig. 5B) indicating the physical quantity detected,
wherein the abnormality detection apparatus includes
a communication unit (item 16, Fig. 6 ([0070])) that receives the operating data transmitted from the control device,
an acquisition unit (e.g., the A/D converter 17 ([0071], lines 1-3)) that acquires sensor value data output from the sensor,
a processing unit (item 13a, Fig. 6 ([0066])) that calculates statistics of the sensor value data acquired by the acquisition unit and determines a presence or an absence of an abnormality of the manufacturing device based on the statistics calculated and a threshold depending on the operating data received (e.g., in steps S30 and S31 as shown in Fig. 15, the average values of M1(t) and M2(t) are calculated; e.g., as shown in Fig. 5B, the thresholds of Th1 and Th2 are provided for controlling ‘wear of the screw 44’ ([0060])), and
wherein the communication unit transmits a determination result and the statistics to the control device (e.g., steps S32, S33, S34 and S36 in Fig. 15).
Ohara discloses that, as illustrated in Figs. 3, 5, 7, the manufacturing device is a molding machine (item 30, Fig. 3 ([0043])) having a gear reducer (item 40, Fig. 3 ([0044], lines 1-2)), the sensor detects vibrations of the gear reducer ([0032], lines 1-7 from bottom), and the processing unit determines a presence or an absence of abnormal vibrations in the gear reducer (e.g., as shown in Fig. 7). It is noticed that, at least AE sensor is highly sensitive to detect the high-frequency vibrations of the gear reducer.
As illustrated in Figs. 13, 17, 18 in the teachings of Ohara, the diagnostic apparatus (i.e., AE sensor) generates a time-series data image representing the frequency spectrum of the vibrations ([0021], [0025], [0026]). Ohara discloses that, the second anomaly is the occurrence of metal wear on the screw 44, the housing 32, or the kneading disk 46 ([0054], lines 1-3).
However, Ohara does not explicitly disclose that, the diagnostic apparatus diagnosing an abnormality of the manufacturing device by using a learning model. In the same field of endeavor, injection molding machine management, Nakamura discloses that, as illustrated in Figs. 1, 2, the on-premise server 100 coupled to the cloud server 110 via an IF 111. The cloud server 110 includes a virtual server 112 that executes an analysis program, a control program, a management program ([0048], lines 1-7). Here, at least the analysis program in the virtual machine 112 in the cloud server 110 can be considered as to play a function of the diagnostic apparatus.
Nakamura discloses that, for example, the cloud server includes the first storing section configured to store information concerning at least one of the physical quantity of the injection molding machine and the physical quantity of the molded article detected by the first detecting section and the virtual machine (in the cloud server) configured to generate a control rule (for the control device) for the injection molding machine based on the information ([0013], lines 5-11). It is noticed that, the cloud server 110 can quickly input the detection result and quickly generate a control rule considering the detection result ([0065], lines 1-3 from bottom).
Here, at least the control rule generated by the virtual machine should include the diagnostic result based on the physical quantity of the molded article detected by the first detecting section through an analyzing program/process (e.g., as shown in Fig. 2) (i.e. involving the learning model).
It is also noticed that, for example, the physical quantity of the molded article is detected by the sensors. These sensors are considered as the abnormality detection apparatus which has a lower hardware specification than the diagnostic apparatus provided in the virtual server in the cloud server.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ohara to incorporate the teachings of Nakamura to provide the abnormality detection apparatus is configured to communicate with a diagnostic apparatus on cloud and receive a diagnostic result (i.e. involving the learning model during the analyzing process) from the abnormality detection apparatus. Doing so would be possible to appropriately manage the injection molding machine according to the response speed requested of the injection molding machine, as recognized by Nakamura ([0004]).
Response to Arguments
Applicant's arguments filed 1/29/2026 have been fully considered. They are not persuasive.
Regarding arguments (as amended) in claim 1 that AE sensor is not vibration sensor, and Ohara does not disclose that ‘the injection molding machine having a gear box is detected by the AE sensors’, it is not persuasive.
Ohara discloses that, as illustrated in Figs. 3, 5, 7, the manufacturing device is a molding machine (item 30, Fig. 3 ([0043])) having a gear reducer (item 40, Fig. 3 ([0044], lines 1-2)), the sensor detects vibrations of the gear reducer ([0032], lines 1-7 from bottom), and the processing unit determines a presence or an absence of abnormal vibrations in the gear reducer (e.g., as shown in Fig. 7). It is noticed that, at least AE sensor is highly sensitive to detect the high-frequency vibrations of the gear reducer.
It is well settled that the intended use of a claimed apparatus is not germane to the issue of the patentability of the claimed structure. If the prior art structure is capable of performing the claimed use then it meets the claim. In re Casey, 152 USPQ 235, 238 (CCPA 1967); In re Otto, 136 USPQ 459 (CCPA 1963). The manner or method in which a machine is to be utilized is not germane to the issue of patentability of the machine itself, In re Casey 152 USPQ 235.
Intended use has been continuously held not to be germane to determining the patentability of the apparatus, In re Finsterwalder, 168 USPQ 530.
Note: In re Pearson 181 USPQ 641; In re Yanush 177 USPQ 705, 706 In re Otto et al 136 USPQ 458.
Conclusion
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/SHIBIN LIANG/Examiner, Art Unit 1741
/ALISON L HINDENLANG/Supervisory Patent Examiner, Art Unit 1741
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