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 .
Response to Amendment
The Amendment filed July 14, 2026 has been entered. Claims 1-3, 6, 11-13 remain pending in the application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11, 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 11, 13 recite the limitations "the rotation center" in lines 7-8 and line7, respectively. There is insufficient antecedent basis for these limitations in the claim. It is unclear which or where the items are pointed out. The claims 11, 13 are indefinite.
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)) and Christiano (US 2021/0402669).
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))), wherein the manufacturing device is a molding machine having a gear reducer (e.g., as shown in Fig. 3, including items 30 and 40 ([0044])), 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 vibration sensor (item 20 (AE sensor), 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]).
However, Ohara does not explicitly disclose to install the (vibration) sensors directly in or on the gear reducer to detect vibration of the gear reducer.
In the same field of endeavor, diagnostic system for extruder, Christiano discloses that, as illustrated in Figs. 4A, 8, the sensor systems 120A, 120B, 120C, 120D include vibration sensors ([0063], lines 11-13). Christiano discloses that, as illustrated in Figs. 4A, 8, the performance information about the speed control apparatus 150 includes …, gear box diagnostic data, …, gearbox vibration diagnostic data, … ([0069], lines 12-19).
Christiano discloses that, as illustrated in Fig. 4A, a data storage device 118 is also in communication with the computer processor controller 112. The data storage device 118 is configured to store a history of the aforementioned performance information and the algorithm includes a comparison module configured to compare the performance information of a current operating configuration to the history stored on the data storage device 118 ([0070], lines 1-9).
Thus, Christiano discloses a presence or an absence of abnormal vibrations in the gear reducer based on the statistics of vibrations and a threshold depending on the operating data received.
It would have been obvious to use the apparatus of Ohara to have the coextrusion system including a gearbox as Christiano teaches that it is known to install the (vibration) sensors directly in or on the gear reducer to detect vibration of the gear reducer and compare the performance information of a current operating configuration related to the vibration of the gearbox to the history stored on the data storage device. 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) (See MPEP 2143- exemplary rationales).
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 or 61B (i.e., as a second sensor) (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 by using multiple sensors. 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)), Christiano (US 2021/0402669), 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, wherein the manufacturing device is a molding machine having a screw shaft (as shown in Fig. 3), 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 is noticed that, the load sensor 2 in the teachings of Isotani is capable of measuring the overload of the screw shaft of the molding machine.
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).
However, Ohara does not disclose both the thermometer and displacement senor for the screw shaft of the molding machine.
In the same field of endeavor, diagnostic system for extruder, Christiano discloses that, as illustrated in Figs. 4A, the sensor systems 120A, 120B, 120C, 120D include temperature sensors, …, displacement sensors, … ([0063], lines 11-19).
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.
Therefore, it would have been obvious to one of ordinary skill in the art to include the thermometer and the displacement sensor in the system of Christiano thereby arriving at the claimed invention, with the reasonable expectation that the abnormality detection of the molding machine through the diagnostic system.
It would have been obvious to use the apparatus of Ohara to have the coextrusion system including a gearbox as Christiano teaches that it is known to install the temperature and displacement sensors to detect the temperature and displacement of the screw shaft of the molding machine. 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) (See MPEP 2143- exemplary rationales).
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).
Ohashi discloses that, a product identification signal based on the recognition of the image is transmitted to the process controller to carry out a predetermined process thereto. The process controller selects the specification data of the product and sends the specification data of the product to the processing step (ABSTRACT, lines 9-14). Here, the specification data of the product will include using statistics data to generate a threshold for the predetermined process.
Thus, Ohashi discloses that, a difference between the image obtained by imaging the molded product or the strand and the image in the normal condition (e.g., based on the (stored) specification data of the product) is determined (ABSTRACT).
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)) and Christiano (US 2021/0402669).
Regarding claim 13, Ohara discloses that, an abnormality detection system including an abnormality detection apparatus detecting an abnormality of a manufacturing device, wherein the manufacturing device is a molding machine having a hear box and a screw shaft (as shown in Fig. 3), 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]).
However, Ohara does not explicitly disclose to install the (vibration) sensors directly in or on the gear reducer to detect vibration of the gear reducer and a displacement sensor that detects displacement of the rotation center of the screw shaft.
In the same field of endeavor, diagnostic system for extruder, Christiano discloses that, as illustrated in Figs. 4A, 8, the sensor systems 120A, 120B, 120C, 120D include vibration sensors ([0063], lines 11-13). Christiano discloses that, as illustrated in Figs. 4A, 8, the performance information about the speed control apparatus 150 includes …, gear box diagnostic data, …, gearbox vibration diagnostic data, … ([0069], lines 12-19).
Christiano discloses that, as illustrated in Fig. 4A, a data storage device 118 is also in communication with the computer processor controller 112. The data storage device 118 is configured to store a history of the aforementioned performance information and the algorithm includes a comparison module configured to compare the performance information of a current operating configuration to the history stored on the data storage device 118 ([0070], lines 1-9).
Thus, Christiano discloses a presence or an absence of abnormal vibrations in the gear reducer based on the statistics of vibrations and a threshold depending on the operating data received.
Christiano discloses that, as illustrated in Figs. 4A, the sensor systems 120A, 120B, 120C, 120D include temperature sensors, …, displacement sensors, … ([0063], lines 11-19).
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.
Therefore, it would have been obvious to one of ordinary skill in the art to include the displacement sensor in the system of Christiano thereby arriving at the claimed invention, with the reasonable expectation that the abnormality detection of the molding machine through the diagnostic system.
It would have been obvious to use the apparatus of Ohara to have the extrusion system including a gearbox as Christiano teaches that it is known to install the displacement sensors to detect the temperature and displacement of the screw shaft of the molding machine and also install the (vibration) sensors directly in or on the gear reducer to detect vibration of the gear reducer and compare the performance information of a current operating configuration related to the vibration of the gearbox to the history stored on the data storage device. 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) (See MPEP 2143- exemplary rationales).
Response to Arguments
Applicant's arguments filed 7/14/2026 have been fully considered.
Regarding arguments (as amended) in claim 1 that Ohara does not disclose “a vibration sensor installed in or on the gear box to detect vibrations of the gear reducer and outputs time-series vibration data”, it is persuasive.
However, Applicant’s arguments with respect to claim(s) 1 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.
Regarding arguments (as amended) in claim 11 that Ohara does not disclose “presence or absence of an overload …”, it is not persuasive.
Basically, the load sensor 2 in the teachings of Isotani is capable of measuring the overload (for its presence or absence) of the screw shaft of the molding machine.
Regarding arguments (as amended) in claim 11 that Ohara does not disclose “a thermometer … a displacement sensor”, it is persuasive.
However, Applicant’s arguments with respect to claim(s) 11 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.
Regarding arguments in claim 12 that Ohashi does not disclose “a processing unit that calculates statistics of a difference between the image obtained by imaging the molded product and the image in the normal condition”, it is not persuasive.
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).
Ohashi discloses that, a product identification signal based on the recognition of the image is transmitted to the process controller to carry out a predetermined process thereto. The process controller selects the specification data of the product and sends the specification data of the product to the processing step (ABSTRACT, lines 9-14). Here, the specification data of the product will include using statistics data to generate a threshold for the predetermined process.
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Shibin Liang whose telephone number is (571)272-8811. The examiner can normally be reached on M-F 8:30 - 4:30.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alison L Hindenlang can be reached on (571)270 7001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHIBIN LIANG/Examiner, Art Unit 1741
/ALISON L HINDENLANG/Supervisory Patent Examiner, Art Unit 1741
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