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 .
Status of Claims
2. Claims 1-13 are pending in this application.
Claims 1-13 are original claims.
Response to Arguments
Applicant’s arguments, see Remarks, filed 08/18/2026, with respect to the 35 U.S.C. 102(a)(1) rejection(s) of claim(s) 1-13 under Nishikawa et al. (US PG. PUB. No. 2020/0101717 A1) have been fully considered but they are not persuasive.
Applicant’s first arguments states that Claim 1 is not anticipated by Nishikawa.
Applicant states “Claim 1 recites processors configured to "determine a configuration of the printer for the operation in response to predicting the occurrence of the operational issue,' to "configure the printer with the configuration," and to "cause the printer to perform the operation according to the configuration." Read together, these features require that the printer arrive at a run-time configuration and then carry out the pending printing operation in accordance with that configuration.
The Office Action cites paragraph [0128] of Nishikawa as allegedly disclosing the "configure" feature of claim 1. Specifically, the Office Action interprets the cited paragraph as meaning that "[t]he maintenance controller 118 determines the maintenance execution timing of for each nozzle or for each nozzle group based on the occurrence interval of ink discharge failures, which is predicted by the prediction section 143." Nishikawa's output is therefore a maintenance- timing determination - a decision about when to service printhead nozzles - not a configuration of the printer that governs how the pending printing operation is performed. One of ordinary skill in the art would understand that a schedule for servicing nozzles is not a printer configuration that is applied to a print job as required by claim 1.
The examiner acknowledges applicant’s first argument against Claim 1, however, the Examiner respectfully disagrees.
Sect. [0128] of Nishikawa teaches printer 1 which includes a print head 2 having a plurality of nozzles, wherein, each of the plurality of nozzles are assigned a specific color. A configuration of the printer 1 is determined based on the prediction of the failure of the ink discharged at a specific nozzle. Hence, the printer 1 configuration is changed due to a failure of the one or more nozzles and a maintenance controller 118 performs maintenance execution timing of for each nozzle or for each nozzle group based on the occurrence interval of ink discharge failures, which is predicted by the prediction section 143. Therefore, Nishikawa does in fact teach the limitations of claim 1 as follows: "determine a configuration of the printer for the operation in response to predicting the occurrence of the operational issue,' to "configure the printer with the configuration," and to "cause the printer to perform the operation according to the configuration."
Additionally, applicant’s further argues with regards to Claim 1 that “Read together, these features require that the printer arrive at a run-time configuration and then carry out the pending printing operation in accordance with that configuration.” However, neither Applicant’s Specification nor Claims explicitly teaches or states, that the features of claim 1: require that the printer arrive at a run-time configuration and then carry out the pending printing operation in accordance with that configuration. The Examiner recommends including such language into the claims in order to help clarify claim meaning.
Secondly, Applicant’s argues that Nishikawa nowhere ties its predicted discharge-failure interval to a run-time configuration that governs execution of the pending print job.
However, Sect. [0127] of Nishikawa teaches the printer performs the print operation based on the parameter S1 being the color ink of each print head 2. The parameter S1 may also include the operation time information regarding the operation time of the printer 1. The parameter S1 may also include the ink information on the ink used by the print head 2. Sect. [0127] of Nishikawa further teaches it is possible to use the information based on the actual operation of the printer 1 for the learning performed by the learning section 150. Sect. [0127] of Nishikawa also teaches it is possible to perform more effective learning and to predict the occurrence interval of discharge failures with high precision by reflecting the use environment and the use state of the printer 1. Thus, Nishikawa does in fact tie its predicted discharge-failure interval to a run-time configuration that governs execution of the pending print job.
Applicant’s third argument, with respect to claim 7, states that: “Claim 7 requires "a plurality of printers; one or more sensors; and a printer management system operatively coupled to the plurality of printers and the one or more sensors via a network," where the printer management system receives sensor measurements associated with the plurality of printers or their shared environment; monitors each of the plurality of printers; predicts an operational issue of a first one of the plurality of printers; determines a configuration of that first printer; configures that first printer; and causes that first printer to perform the operation. Claim 7
therefore requires a networked management-system architecture in which one actor selects and configures a chosen printer among many.
Nishikawa discloses no such architecture. The Office Action cites Nishikawa's "Printer Communication Section 160" as allegedly disclosing the recited "printer management system." In particular, the Office Action cites paragraph [0132] of Nishikawa as allegedly disclosing the "printer management system" where the cited paragraph states that "[t]he printer communication section 160 and the server communication section 201 are communication devices that perform data communication via the communication network N." However, paragraph [0132] of Nishikawa describes only communication interfaces ======================= not a management-system actor that receives sensor measurements from a plurality of printers, selects a first printer, and configures that first printer for a pending printing operation.
The Examiner respectfully, disagrees. Although Sect. [0132] of Nishikawa teaches the printer communication section 160 and the server communication section 201 are communication devices that perform data communication via the communication network N. The server communication section 201 is included within the server 200 as tatught in Sect. [0141] which is configured to communicate with a plurality of printers 1A, and the learning device 140 may learn the parameter S1 and the event data S2 that are obtained by the operations of a plurality of printers 1A. In this case, the learning by the learning device 140 progresses at a high level, and thus it is possible to predict the occurrence interval of discharge failure with higher precision.
Therefore, Nishikawa explicitly teaches the limitations of Claim 7 which requires "a plurality of printers; one or more sensors; and a printer management system operatively coupled to the plurality of printers and the one or more sensors via a network.
Lastly, dependent claims 4 and 10 are explicitly taught to be unpatentable over Nishikawa et al. (US PG. PUB. No. 2020/0101717 A1) in view of Kanada (US PG. Pub. 2022/0108501 A1) as described in the office action below.
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 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) 1-3, 5-9 and 11-13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nishikawa et al. (US PG. PUB. No. 2020/0101717 A1).
Referring to Claim 1, Nishikawa teaches a printer (See Nishikawa, Figs. 1 and 3, Printer 1, Sect. [0030], The printer 1 is an ink-jet recording device), comprising:
a printhead (See Nishikawa, Fig. 1, Printhead 2);
a platen (See Nishikawa, Fig. 1, Platen 5);
a sensor (See Nishikawa, Fig. 3, Sect. [0047], temperature Sensor 47.);
one or more memories (See Nishikawa, Fig. 3, Memory 105 and Fig. 4, ink information memory 123); and
one or more processors operatively coupled to the one or more memories (See
Nishikawa, Fig. 3, Processor 101 of Controller 100, Sect. [0037] lines 9-10, The controller 100 includes a processor 101 that executes a program, and a memory 105), the sensor, and the printhead (See Nishikawa, Sect. [0037], The processor 101 is an operation processing device that includes a CPU (central processing unit), a DSP (digital signal processor), a microcomputer, or the like. Also, the processor 101 may include a plurality of pieces of hardware including programs that have the functions of individual sections such as the sensor and printhead.), the one or more processors (See Nishikawa, Fig. 3, Processor 101) configured to:
predict (See Nishikawa, Sect. [0061], The learning device 140 includes
a prediction section 143 that predicts the occurrence interval of ink discharge failures based on the event data obtained by the event data obtaining section 142 and the prediction condition learned by the learning section 150.), using a trained machine learning model, an occurrence of an operational issue associated with the printer in response to a printing operation to be performed (See Nishikawa, Sect. [0107] lines 7-10, the learning device 140 continuously performs machine learning and learns the prediction of the occurrence interval of discharge failures of the print head 2. ) and an output of the sensor (See Nishikawa, Sect. [0107] lines 10-12, When the power to the printer 1 is not to be turned off (step ST53; YES), the learning device 140 terminates this processing.);
determine a configuration of the printer for the operation in response to predicting
the occurrence of the operational issue (See Nishikawa, Sect. [0119] lines 8-16, The learning method also includes a learning step (step ST3) for performing machine learning on the prediction condition of the occurrence interval of ink discharge failures by using a learning data set created based on a combination of the parameter S1 obtained by the information obtaining step and the event data S2 obtained by the event data obtaining step. It is possible for the learning device 140 to configure as a program executable by a computer for performing the processing on the printer 1.);
configure the printer with the configuration (See Nishikawa, Sect. [0128],
the print head 2 includes a plurality of nozzles, each of which is assigned to a corresponding one of the ink colors. The prediction section 143 predicts the occurrence interval of ink discharge failures for each nozzle or for each nozzle group including a plurality of nozzles. The maintenance controller 118 determines the maintenance execution timing of for each nozzle or for each nozzle group based on the occurrence interval of ink discharge failures, which is predicted by the prediction section 143. With this configuration, when the occurrence interval of discharge failures is predicted for each ink color, it is possible to perform maintenance of a part of nozzles of the print head 2 in accordance with the prediction value of each color.); and
cause the printer to perform the operation according to the configuration (See
Nishikawa, Sect. [0127] lines 10-21, The parameter S1 may also include the operation time information regarding the operation time of the printer 1. The parameter S1 may also include the ink information on the ink used by the print head 2. Since the information obtaining section 141 obtains at least one of the pieces of the information described above as the parameter S1, it is possible to use the information based on the actual operation of the printer 1 for the learning performed by the learning section 150. Accordingly, it is possible to perform more effective learning and to predict the occurrence interval of discharge failures with high precision by reflecting the use environment and the use state of the printer 1.).
Referring to Claim 2, Nishikawa teaches the printer of claim 1 (See Nishikawa,
Figs. 1 and 3, Printer 1, Sect. [0030], The printer 1 is an ink-jet recording
device), wherein the trained machine learning model is trained to predict the occurrence of the operational issue based on reference data associated with historical operations associated with like printing devices to the printer (See Nishikawa, Sect. [0073], The maintenance controller 118 determines a maintenance execution timing based on the occurrence interval of discharge failures predicted by the prediction section 143. The maintenance execution timing determined by the maintenance controller 118 has the effects of preventing, suppressing, resolving, or relieving ink discharge failures of the print head 2.) , the reference data includes reference configurations associated with the historical operations and corresponding conditions of the like printing devices during the historical operations (See Nishikawa, Sect. [0076] lines 3-11, The maintenance controller 118 stores the information indicating the type of the performed maintenance and the time or the timing of the performed maintenance in the maintenance information storage section 124 as maintenance information that indicates history of the maintenance performed by the maintenance controller 118.).
Referring to Claim 3, Nishikawa teaches the printer of claim 1 (See Nishikawa,
Figs. 1 and 3, Printer 1, Sect. [0030], The printer 1 is an ink-jet recording device), wherein the sensor is a temperature sensor (See Nishikawa, Fig. 3, Temperature Sensor 47) and the condition is a temperature within the printer or of the environment (See Nishikawa, Sect. [0047], The temperature sensor 47 is a sensor disposed to detect the temperature of the environment in which the printer 1 is installed and, for example, a thermistor is used. The temperature sensor 47 is coupled to the controller 100 and detects the temperature under the control of the controller 100 to obtain a detection value. The controller 100 is capable of detecting the temperature of the installation environment of the printer 1. The temperature sensor 47 may be located at any position and, for example, the temperature sensor may be mounted on the substrate on which the processor 101 is implemented. Alternatively, the temperature sensor may be disposed at the home position HP or at the position of the platen 5. The temperature sensor 47 may also be implemented on the substrate that is mounted on the print head 2.).
Referring to Claim 5, Nishikawa teaches the printer of claim 1 (See Nishikawa,
Figs. 1 and 3, Printer 1, Sect. [0030], The printer 1 is an ink-jet recording device), wherein the operational issue includes at least one of damage or wear on a printing element of the printhead, damage or wear on the platen, pixel failures on the printhead, traction degradation, sensor errors, user-related intervention events, service-related intervention events, registration related issues, or media tracking issues (See Nishikawa, Sect. [0068], The discharge failure determination section 119 determines whether or not a discharge failure has occurred in the print head 2. The discharge failure determination section 119 performs at least either of an autonomous determination function that automatically detects and determines a discharge failure using the drive circuit 45 and the detection circuit 46, or a manual determination function that detects and determines a discharge failure based on input by a user of the printer 1.).
Referring to Claim 6, Nishikawa teaches the printer of claim 1 (See Nishikawa,
Figs. 1 and 3, Printer 1, Sect. [0030], The printer 1 is an ink-jet recording device), wherein the configuration provides one or more settings for the printer, the one or more settings include at least one of a setting for the printhead, a setting for certain printing elements of the printhead, a setting for the platen, a resistance of one or more printing elements of the printhead, a pressure applied toward the platen, or an alignment of a feeder component of the printer (See Nishikawa, Sect. [0060] lines 12-15, The information obtaining section 141 obtains, as the parameter S1, at least any one of the color of ink used by the print head 2, the ink consumption amount of each color ink in the occurrence interval of ink discharge failures, the temperature of the use environment of the printer 1, the maintenance information indicating the execution state of the maintenance operation of the print head 2, the operation time information regarding the operation time of the printer 1, and the ink information of the ink used by the print head 2.).
Referring to Claim 7, Nishikawa teaches a system (See Nishikawa, Fig. 9, Sect. [0129], Information Processing System 1000), comprising:
a plurality of printers (See Nishikawa, Figs. 1 and 9, Printer 1 and 1A);
one or more sensors (See Nishikawa, Fig. 3, Sect. [0047], temperature Sensor 47.); and
a printer management system (See Nishikawa, Fig. 9, Printer Communication
Section 160) operatively coupled to the plurality of printers and the one or more sensors via a network, the printer management system including one or more processors configured to (See Nishikawa, Sect. [0132], The printer communication section 160 and the server communication section 201 are communication devices that perform data communication via the communication network N. The printer communication section 160 and the server communication section 201 may include respective wired communication interfaces that perform data communication via a cable or wireless communication interfaces. For example, well-known various standards may be used for the specific specification and the supporting protocol of the printer communication section 160 and the server communication section 201. Also, the communication network N may include a dedicated line, a public line network, and a mobile communication network, or may be a network installed in a specific area or building.):
receive, from the one or more sensors, one or more sensor measurements
associated with conditions of plurality of printers or an environment within which the plurality of printers reside (See Nishikawa, Sect. [0047] lines 15-27, The temperature sensor 47 is a sensor disposed to detect the temperature of the environment in which the printer 1 is installed and, for example, a thermistor is used. The temperature sensor 47 is coupled to the controller 100 and detects the temperature under the control of the controller 100 to obtain a detection value. The controller 100 is capable of detecting the temperature of the installation environment of the printer 1. The temperature sensor 47 may be located at any position and, for example, the temperature sensor may be mounted on the substrate on which the processor 101 is implemented. Alternatively, the temperature sensor may be disposed at the home position HP or at the position of the platen 5. The temperature sensor 47 may also be implemented on the substrate that is mounted on the print head 2.);
monitor each of the plurality of printers based on at least the one or more sensor
measurements (See Nishikawa, Sect. [0065], The operation time management section 113 monitors the operation state of the printer 1 for each set time and counts the operation time when the printer 1 is in operation. The operation time management section 113 stores the operation time of the printer 1 in the operation time information storage section 122, and when the operation time information storage section 122 stores the operation time, the operation time management section 113 adds the stored operation time. The operation time management section 113 may have a configuration capable of performing an RTC (real time clock) function that keeps the current time or may store the time when the operation state of the printer 1 changes in the operation time information storage section 122.);
predict (See Nishikawa, Sect. [0061], The learning device 140 includes
a prediction section 143 that predicts the occurrence interval of ink discharge failures based on the event data obtained by the event data obtaining section 142 and the prediction condition learned by the learning section 150.), using a trained machine learning model, an occurrence of an operational issue of a first one of the plurality of printers in response to receiving an input that includes an operation to be performed (See Nishikawa, Sect. [0107] lines 7-10, the learning device 140 continuously performs machine learning and learns the prediction of the occurrence interval of discharge failures of the print head 2. ) and the one or more sensor measurements (See Nishikawa, Sect. [0107] lines 10-12, When the power to the printer 1 is not to be turned off (step ST53; YES), the learning device 140 terminates this processing.);
determine a configuration of the first one of the plurality of printers for the
operation in response to predicting the occurrence of the operational issue (See Nishikawa, Sect. [0119] lines 8-16, The learning method also includes a learning step (step ST3) for performing machine learning on the prediction condition of the occurrence interval of ink discharge failures by using a learning data set created based on a combination of the parameter S1 obtained by the information obtaining step and the event data S2 obtained by the event data obtaining step. It is possible for the learning device 140 to configure as a program executable by a computer for performing the processing on the printer 1.);
configure the first one of the plurality of printers with the configuration (See
Nishikawa, Sect. [0128], the print head 2 includes a plurality of nozzles, each of which is assigned to a corresponding one of the ink colors. The prediction section 143 predicts the occurrence interval of ink discharge failures for each nozzle or for each nozzle group including a plurality of nozzles. The maintenance controller 118 determines the maintenance execution timing of for each nozzle or for each nozzle group based on the occurrence interval of ink discharge failures, which is predicted by the prediction section 143. With this configuration, when the occurrence interval of discharge failures is predicted for each ink color, it is possible to perform maintenance of a part of nozzles of the print head 2 in accordance with the prediction value of each color.); and
cause the first one of the printers to perform the operation according to the
configuration (See Nishikawa, Sect. [0127] lines 10-21, The parameter S1 may also include the operation time information regarding the operation time of the printer 1. The parameter S1 may also include the ink information on the ink used by the print head 2. Since the information obtaining section 141 obtains at least one of the pieces of the information described above as the parameter S1, it is possible to use the information based on the actual operation of the printer 1 for the learning performed by the learning section 150. Accordingly, it is possible to perform more effective learning and to predict the occurrence interval of discharge failures with high precision by reflecting the use environment and the use state of the printer 1.).
Referring to Claim 8, Kothari teaches the system of claim 7 (See Nishikawa,
Fig. 9, Sect. [0129], Information Processing System 1000), wherein the trained machine learning model is trained to predict the occurrence of the operational issue based on reference data associated with historical operations associated with like devices to the network-connected device See Nishikawa, Sect. [0073], The maintenance controller 118 determines a maintenance execution timing based on the occurrence interval of discharge failures predicted by the prediction section 143. The maintenance execution timing determined by the maintenance controller 118 has the effects of preventing, suppressing, resolving, or relieving ink discharge failures of the print head 2.), the reference data includes reference configurations associated with the historical operations and corresponding conditions of the like device during the historical operations (See Nishikawa, Sect. [0076] lines 3-11, The maintenance controller 118 stores the information indicating the type of the performed maintenance and the time or the timing of the performed maintenance in the maintenance information storage section 124 as maintenance information that indicates history of the maintenance performed by the maintenance controller 118.).
Referring to Claim 9, Nishikawa teaches the system of claim 7 (See Nishikawa,
Fig. 9, Sect. [0129], Information Processing System 1000), wherein the one or more sensors include a temperature sensor (See Nishikawa, Fig. 3, Temperature Sensor 47) and the condition is a temperature (See Nishikawa, Sect. [0047], The temperature sensor 47 is a sensor disposed to detect the temperature of the environment in which the printer 1 is installed and, for example, a thermistor is used. The temperature sensor 47 is coupled to the controller 100 and detects the temperature under the control of the controller 100 to obtain a detection value. The controller 100 is capable of detecting the temperature of the installation environment of the printer 1. The temperature sensor 47 may be located at any position and, for example, the temperature sensor may be mounted on the substrate on which the processor 101 is implemented. Alternatively, the temperature sensor may be disposed at the home position HP or at the position of the platen 5. The temperature sensor 47 may also be implemented on the substrate that is mounted on the print head 2.).
Referring to Claim 11, Nishikawa teaches the system of claim 7 (See
Nishikawa, Fig. 9, Sect. [0029], Information Processing System 1000), wherein the operational issue includes at least one of damage or wear on a printing element of a printhead, damage or wear on a platen, printhead pixel failures, traction degradation, sensor errors, user-related intervention events, service-related intervention events, registration related issues, or media tracking issues (See Nishikawa, Sect. [0091], The discharge failure determination section 119 determines whether or not a discharge failure of the print head 2 has occurred based on the determination result of each nozzle in step ST18 (step ST19). When a discharge failure of the print head 2 has occurred, the discharge failure determination section 119 determines affirmatively. In step ST19, if the number of nozzles, which is higher than a threshold value set in advance, is determined to have a discharge failure in step ST18, the discharge failure determination section 119 determines affirmatively. For example, if the threshold value is set to “0”, when one or more nozzles are determined to have a discharge failure in step ST18, the discharge failure determination section 119 determines affirmatively in step ST19. The threshold value may be set to any number and ought to be suitably determined in accordance with the specification of the printer 1.).
Referring to Claim 12, Nishikawa teaches the system of claim 7 (See
Nishikawa, Fig. 9, Sect. [0029], Information Processing System 1000), wherein the configuration provides one or more settings for the network-connected device (See Nishikawa, Fig. 9, Network N, Sect. [0132], The printer communication section 160 and the server communication section 201 are communication devices that perform data communication via the communication network N. The printer communication section 160 and the server communication section 201 may include respective wired communication interfaces that perform data communication via a cable or wireless communication interfaces. For example, well-known various standards may be used for the specific specification and the supporting protocol of the printer communication section 160 and the server communication section 201. Also, the communication network N may include a dedicated line, a public line network, and a mobile communication network, or may be a network installed in a specific area or building.), the one or more settings include at least one of printhead settings, a setting for certain printing elements of the printhead, or a platen setting (See Nishikawa, Sect. [0010], The operation in FIG. 8 is applicable when the discharge failure determination section 119 determines whether or not a discharge failure has occurred for each ink color. That is to say, the discharge failure determination section 119 determines a discharge failure of each nozzle of the print head 2 in step ST18 in FIG. 6, and determines whether or not a discharge failure has occurred for each ink color of the print head 2 in step ST19 based on the determination result. The event data S2 output by the discharge failure determination section 119 includes data indicating whether or not a discharge failure has occurred and the time when the discharge failure has occurred for each ink color used in the print head 2.).
Referring to Claim 13, Nishikawa teaches the system of claim 7 (See
Nishikawa, Fig. 9, Sect. [0029], Information Processing System 1000), wherein the configuration provides one or more settings for the network-connected device (See Nishikawa, Fig. 9, Network N, Sect. [0132], The printer communication section 160 and the server communication section 201 are communication devices that perform data communication via the communication network N. The printer communication section 160 and the server communication section 201 may include respective wired communication interfaces that perform data communication via a cable or wireless communication interfaces. For example, well-known various standards may be used for the specific specification and the supporting protocol of the printer communication section 160 and the server communication section 201. Also, the communication network N may include a dedicated line, a public line network, and a mobile communication network, or may be a network installed in a specific area or building.) the one or more settings include a resistance of one or more printing elements of a printhead, a pressure applied toward a platen of the printer, an alignment of a feeder component of the printer (See Nishikawa, Sect. [0060] lines 12-15, The information obtaining section 141 obtains, as the parameter S1, at least any one of the color of ink used by the print head 2, the ink consumption amount of each color ink in the occurrence interval of ink discharge failures, the temperature of the use environment of the printer 1, the maintenance information indicating the execution state of the maintenance operation of the print head 2, the operation time information regarding the operation time of the printer 1, and the ink information of the ink used by the print head 2.).
Claim Rejections - 35 USC § 103
18. 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 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.
19. 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.
20. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
21. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
22. Claims 4 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nishikawa et al. (US PG. PUB. No. 2020/0101717 A1) in view of Kanada (US PG. Pub. 2022/0108501 A1).
Referring to Claim 4, Nishikawa teaches the printer of claim 1 (See Nishikawa, Fig. 9, Sect. [0029], Information Processing System 1000).
Nishikawa fails to explicitly teach
wherein the sensor is a humidity sensor and the condition is a humidity within the printer or the environment.
However, Kanada teaches
wherein the sensor is a humidity sensor and the condition is a humidity within the printer or the environment (See Kanada, Sect. [0044], The machine apparatus 10 has various sensors 11 disposed for measuring physical quantities related to the state of the machine apparatus 10. Examples of the sensors 11 may include humidity sensors, Note that although FIG. 1 illustrates a single sensor 11 for convenience of illustration, a plurality of sensors is commonly disposed so as to be able to communicate with the time-series-data display apparatus 100.).
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 Nishikawa to incorporate the teachings of Kanada to provide wherein the sensor is a humidity sensor and the condition is a humidity within the printer or the environment. Doing so would increase the accuracy of prediction, it is important to create a learned model that is suitable for predicting the failure. For this reason, it is important to prepare learning data (training data) for a failure prediction model of the machine apparatus, which is created through the machine learning. For determining whether extracted data is suitable for the learning data, it is necessary to perform detailed data analysis, such as check and comparison of waveforms., as recognized by Kanada.
Referring to Claim 10, Nishikawa teaches the system of claim 7 (See
Nishikawa, Fig. 9, Sect. [0029], Information Processing System 1000),
Nishikawa fails to explicitly teach
wherein the sensor is a humidity sensor and the condition is a humidity within the printer or the environment.
However, Kanada teaches
wherein the one or more sensors include a humidity sensor and the condition is a humidity (See Kanada, Sect. [0044], The machine apparatus 10 has various sensors 11 disposed for measuring physical quantities related to the state of the machine apparatus 10. Examples of the sensors 11 may include humidity sensors, Note that although FIG. 1 illustrates a single sensor 11 for convenience of illustration, a plurality of sensors is commonly disposed so as to be able to communicate with the time-series-data display apparatus 100.).
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 Nishikawa to incorporate the teachings of Kanada to provide wherein the one or more sensors include a humidity sensor and the condition is a humidity. Doing so would increase the accuracy of prediction, it is important to create a learned model that is suitable for predicting the failure. For this reason, it is important to prepare learning data (training data) for a failure prediction model of the machine apparatus, which is created through the machine learning. For determining whether extracted data is suitable for the learning data, it is necessary to perform detailed data analysis, such as check and comparison of waveforms., as recognized by Kanada.
Cited Art
23. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure Schweid (US PG. PUB. 2022/0201129 A1) discloses a method executed by a processor of a multi-function device (MFD) includes tracking a machine state of the MFD, predicting a potential defect based on a determination that the machine state is associated with a defect class of a plurality of different defect classes, determining a maintenance routine associated with the defect class, and executing the maintenance routine to prevent the potential defect.
Conclusion
THIS ACTION IS MADE FINAL. 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 DARRYL V DOTTIN whose telephone number is (571)270-5471. The examiner can normally be reached M-F 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abderrahim Merouan can be reached on 571-270-5254. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DARRYL V DOTTIN/Primary Examiner, Art Unit 2683 /DARRYL V DOTTIN/
Primary Examiner, Art Unit 2683
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