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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/04/2024, 10/06/2024, 11/03/2024, 01/06/2025, 05/17/2025, 11/11/2025, 12/09/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
Claims 1-20 are pending in this application.
Oath/Declaration
The receipt of Oath/Declaration is acknowledged.
Drawings
The receipt of Drawings is acknowledged.
Allowable Subject Matter
6. Claims 5, 6, 8, 13 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 5:
The prior art(s) searched, cited and/or of record fails to explicitly teach, disclose or suggest the teaching(s) of the computer implemented method of claim 1, further comprising:
creating a new record comprising:
(i) at least one actual media parameter of actual media printed thereon by an actual printing system,
(ii) the indication of the actual quality of the actual media processed and printed thereon by the actual printing system setup with the at least one actual process parameter, and
(iii) the label indicating the at least one actual process parameter;
adding the new record to the dataset to create an updated dataset; and
using the updated dataset for performing the assigning for new media parameters.
Regarding Claim 6:
The prior art(s) searched, cited and/or of record fails to explicitly teach, disclose or suggest the teaching(s) of the computer implemented method of claim 6,
wherein the actual process parameters of the new record, used to set up the actual printing system, are obtained by using the printing dataset for mapping the combination of a certain quality and the actual media parameters.
Regarding Claim 8:
The prior art(s) searched, cited and/or of record fails to explicitly teach, disclose or suggest the teaching(s) of the computer implemented method of claim 1, further comprising: when the at least one target process parameter is associated with a predicted quality below a threshold, adapting the at least one target process parameter for predicting an increase in the target quality associated with the adapted at least one target process parameter to above the threshold.
Regarding Claim 13:
The prior art(s) searched, cited and/or of record fails to explicitly teach, disclose or suggest the teaching(s) of computer implemented method of claim 1, wherein assigning comprises at least one of:
(i) feeding the combination of the target quality and the at least one target media parameter into a machine learning model training on the dataset, wherein the label of the dataset comprises a ground truth label, and
(ii) computing a shortest Euclidean distance within a multidimensional space, between a point represented by the target quality and the at least one target media parameter and a nearest point denoting a certain record of the plurality of records, wherein the at least one target process parameters are of the nearest point.
Regarding Claim 20:
The prior art(s) searched, cited and/or of record fails to explicitly teach, disclose or suggest the teaching(s) of the computer implemented method of claim 19, further comprising, performing at least one iteration of:
feeding a certain combination of a certain quality and a certain plurality of media parameters of a certain media ();
obtaining an outcome of the plurality of process parameters ();
creating at least one variation of the outcome by adapting at least one of the plurality of process parameters ();
printing and processing a plurality of printed samples of the certain media, each printed sample is printed and processed by the printing system set up respectively with the at least one variation of the outcome, or by a plurality of printing systems set up with respective variations ();
assigning a respective indication of quality to each printed sample ();
creating a plurality of new records, each record including respectively the at least one variation, and respective quality (); and
using an updated version of the ML model updated with new records during a next iteration ().
Claim Rejections - 35 USC § 103
8. 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.
9. 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.
10. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
11. 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.
12. Claims 1-4, 7, 9-12 and 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mizusawa (US PG. Pub. 2021/0114370 A1) in view of Moreira (US PAT No. 11,645022 B1).
Referring to Claim 1, Mizusawa teaches a computer implemented method of setting up a target printing system for printing on a target media (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), comprising:
providing a dataset of a plurality of records (See Mizusawa, Fig. 6, Data Set 2123 having a plurality of records J1-J11, Sect. [0113]-[0114], The data set 2123 includes work gap information J1, scanning velocity information J2, print resolution information J3, waveform information J4, elapsed time information J5, slot number information J6, chip number information J7, nozzle row number information J8, temperature information J9, and manufacturing error information J10 and landing position information J11.), wherein a record comprises:
(i) at least one sample media parameter of a sample media for processing and/or printing thereon by a sample printing system (See Mizusawa, Fig. 6, Print Resolution Information, Sect. [0117], The print resolution information J3 included in the data set 2123 is information indicating the print resolution set in the printing condition of the pattern image PT described later. Since the size of the ink IK changes when the print resolution changes, the air resistance that the ink IK receives during flight changes when the print resolution changes. Therefore, when the print resolution changes, the landing position of the ink IK deviates. Therefore, the data set 2123 includes the print resolution information J3 as information having a correlation with the landing position deviation of the ink IK.),
(ii) an indication of a quality of a processing and/or a printing by the
sample printing system set up with at least one sample process parameter (See Mizusawa, Fig. 6, Scanning Velocity Information J2, Sect. [0118], The waveform information J4 included in the data set 2123 is information indicating an ink discharge waveform set in the printing condition of the pattern image PT described later. The ink discharge waveform is a waveform of a signal input to the print head 811 for discharging the ink IK, and is a waveform that determines the size of one dot of the ink IK. In the ink discharge waveform, ON signals for driving piezo elements are included in the number corresponding to the size of one dot of the ink IK. Since the size of the ink IK changes when the ink discharge waveform changes, the air resistance that the ink IK receives during flight also changes when the ink discharge waveform changes. Therefore, when the ink discharge waveform changes, the landing position of the ink IK deviates. Therefore, in the data set 2123, the waveform information J4 is included as information having a correlation with the landing position deviation of the ink IK.), and
Mizusawa fails to explicitly teach
(iii) a label indicating the at least one sample process parameter ();
assigning using the dataset, a combination of a target quality and at least one target media parameter, to at least one target process parameter (); and
providing the at least one target process parameter predicted to obtain the target quality, for generating instructions for processing and/or printing on the target media by the target printing system.
However, Moreira teaches
(iii) a label indicating the at least one sample process parameter (See Moreira, Fig. 2, Col. 11 lines 29-39, the printer prints an optimized label. For example, the printer may print the optimized label based on receiving the printing configuration from the user device and/or based on setting, configuring, and/or adjusting one or more configurable settings associated with one or more components of the printer. More specifically, according to the printing configuration, the printer may set or adjust a resistance of one or more printing elements of a printhead, the printer may set or adjust a pressure applied toward a platen of the printer, the printer may set or adjust an alignment of a feeder component of the printer);
assigning using the dataset, a combination of a target quality and at least one target media parameter, to at least one target process parameter (See Moreira, Col. 6 lines26-40, the printer management system may sort the reference data according to printer quality identified or determined from the image data associated with the printed content (e.g., the image depicting the printed content and/or a grade assigned to the printed content). The printer management system may sort the reference data into a set that indicates a first subset of the historical printing operations that resulted in relatively high-quality content and a second subset of the historical printing operations that resulted in relatively low-quality content. In this way, the printer management system may permit the feature extraction model to efficiently extract features associated with the media data and/or the environment data for relatively low-quality content and/or features associated with the media data and/or environment data for relatively high-quality content.); and
providing the at least one target process parameter predicted to obtain the target quality, for generating instructions for processing and/or printing on the target media by the target printing system (See Moreira, Col. 13 lines 56-67 and Col. 14 lines 1-6, the trained machine learning model 325 may predict a value of Config_N for the target variable of printing configuration for the new observation, as shown by reference number 335. Based on this prediction, the machine learning system may provide a first recommendation, may provide output for determination of a first recommendation, may perform a first automated action, and/or may cause a first automated action to be performed (e.g., by instructing another device to perform the automated action), among other examples. The first recommendation may include, for example, to adjust a condition of the environment (e.g., to be within a desirable or operable range associated with a design of the printer) and/or to adjust a setting of the printer. The first automated action may include, for example, automatically adjusting a setting of the printer to optimize a performance characteristic of the printer (e.g., to achieve optimal print quality, to extend a useful life of the printer and/or a component of the printer, and so on).).
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 Mizusawa to incorporate the teachings of Moreira to provide (iii) a label indicating the at least one sample process parameter; assigning using the dataset, a combination of a target quality and at least one target media parameter, to at least one target process parameter; and providing the at least one target process parameter predicted to obtain the target quality, for generating instructions for processing and/or printing on the target media by the target printing system. Doing so would determine, using a print optimization model, a printing configuration for the printing operation based on the media type and the ambient condition and cause the printer to perform the printing operation according to the printing configuration (See Moreira, Col. 1 lines 49-54), as recognized by Moreira.
Referring to Claim 2, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 1 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), wherein the printing system includes a combination of a printer (See Mizusawa, Fig. 1, Printer 100) and at least one of a loader mechanism (See Mizusawa, Fig.2, Loader Mechanism made up of: Feeding Device 2, Rotary Shaft 2A, Transport Belt 4 and Rollers 10A-10B) that loads media into the printer and/or an unloader mechanism that unloads media from the printer (See Mizusawa, Figs. 1-2, Sect. [0043], The printer 100 includes a feeding device 2, driven rollers 10A, 10B, and 10C, transport rollers 3A and 3B, a transport belt 4, and a winding device 5. Each of sections configures a transport mechanism 1011 that transports a printing medium W.) and/or a drying system and/or folding and/or packing system (See Mizusawa, Drying System 9, Sect. [0061], The printer 100 includes a drying unit 9. The drying unit 9 is provided upstream of the winding device 5 and downstream of the driven roller 10C in the transport direction H. Note that, the drying unit 9 need not be provided downstream of the driven roller 10C as long as it is upstream of the winding device 5 and downstream of the print head 811 in the transport direction H. The drying unit 9 has, for example, a chamber that accommodates the printing medium W and a heater that is disposed inside the chamber, and dries undried ink IK on the printing medium W by the heat of the heater), wherein the target process parameters include a combination of a plurality of printer parameters for setting up the printer, and at least one of loading parameters for setting up the loader mechanism and unloading parameters for unloading the unloading mechanism (See Mizusawa, Sect. [0044], The feeding device 2 is a device that feeds a long printing medium W wound in a roll shape onto the transport belt 4. The feeding device 2 is positioned on the most upstream side in a transport direction H of the printing medium W. The feeding device 2 rotates a rotary shaft 2A counterclockwise in FIG. 2 to feed the printing medium W set on the rotary shaft 2A onto the transport belt 4 via the driven rollers 10A and 10B.).
Referring to Claim 3, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 1 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), wherein the target media comprises textile, and the at least one target media parameter comprises at least one property of the textile (See Mizusawa, Sect. [0042] lines 1-5, The printing medium W is, for example, a cloth made of natural fibers or synthetic fibers. The printer 100 is a textile printing machine that prints on the printing medium W by adhering ink IK to the printing medium W that is a cloth.).
Referring to Claim 4, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 3 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), wherein the at least one property of the textile is selected from a group comprising: garment, fabric, t-shirt, hat, hoodie, shoe, upper part of shoe, and roll (See Mizusawa, Sect. [0042] lines 5-10 and Sect. [0297] lines 13-15, the printing medium W is a material to be printed. A cloth is used as an example of the printing medium W. However, as the printing medium W, in addition to a cloth, plain paper, high-quality paper, dedicated paper for ink jet recording such as glossy paper, and the like may be used…printer 100 not only prints on cloth but also knit fabric, paper, synthetic resin sheets can be selected.).
Referring to Claim 7, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 1 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), wherein the target quality is at least one of: provided by a user, automatically selected as a highest quality, a default fixed value, provided as metadata, and implied but not explicitly provided (See Mizusawa, Sect. [0273], the printing section 120 prints at the discharge timing based on the accurate landing position deviation amount, the printing can be performed while accurately correcting the landing position deviation of the ink IK. Therefore, the printer 100 can generate high-quality printed products.).
Referring to Claim 9, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 1 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), further comprising:
analyzing the dataset for computing correlations between media parameters and quality (See Mizusawa, Sect. [0103’, The print control section 1112 causes the printing section 120 to print the print job IJ and the pattern image PT based on the correction parameter set 1125 stored in the printer storage section 112 and the printing condition.);
identifying most significant media parameters that most impact target quality (See Mizusawa, Sect. [0106], The print control section 1112 calculates a value of the first component δ1 by substituting the discharge velocity Vm of the correction parameter set 1125, the set work gap WGprint in the most recent printing, and the scanning velocity Vcr set in the printing condition in the most recent printing into Equation (2).);
generating instructions for suggesting an adaptation to the at least one target media parameter corresponding to the identified most significant media parameters for improving the target quality (See Mizusawa, Sect. [0258] lines 25-31, the printer 100 corrects the landing position deviation of the ink IK with high accuracy by correcting the discharge timing by using the landing position deviation amount without depending on the usage environment of the printer 100, the usage status of the printer 100, the individual difference of the printer 100, and the like, so that it is possible to generate a high-quality printed product.).
Referring to Claim 10, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 1 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), further comprising:
analyzing the dataset for computing correlations between process parameters and quality (See Mizusawa, Sect. [0103’, The print control section 1112 causes the printing section 120 to print the print job IJ and the pattern image PT based on the correction parameter set 1125 stored in the printer storage section 112 and the printing condition.);
identifying most significant process parameters that most impact target quality (See Mizusawa, Sect. [0106], The print control section 1112 calculates a value of the first component δ1 by substituting the discharge velocity Vm of the correction parameter set 1125, the set work gap WGprint in the most recent printing, and the scanning velocity Vcr set in the printing condition in the most recent printing into Equation (2).);
generating instructions for suggesting an adaptation to the process parameters corresponding to the identified most significant process parameters for improving the target quality (See Mizusawa, Sect. [0258] lines 25-31, the printer 100 corrects the landing position deviation of the ink IK with high accuracy by correcting the discharge timing by using the landing position deviation amount without depending on the usage environment of the printer 100, the usage status of the printer 100, the individual difference of the printer 100, and the like, so that it is possible to generate a high-quality printed product.).
Referring to Claim 11, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 1 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), wherein the plurality of sample process parameters of records of the dataset comprise at least one hardware parameter of the sample printing system, and the at least one target process parameters comprise at least one hardware parameter of the target printing system, and further comprising: when the at least one hardware parameter of the target printing system is different from the at least one hardware parameter of the sample printing system, generating the at least one target process parameters from the plurality of sample process parameters according to at least one of a calibration function and/or a conversion function, between hardware of the target printing system and hardware of the sample printing system (See Mizusawa, Sect. [0071], The printer communication section 130 is configured by communication hardware such as a connector that complies with a predetermined communication standard and an interface circuit, and communicates with an external device of the printer 100 under the control of the printer control section 110. In the present embodiment, the external device of the printer 100 includes a server 200. When the printer communication section 130 receives print image data 1123 from the external device, the printer control section 110 stores the received print image data 1123 in the printer storage section 112. Further, when the printer communication section 130 receives job data 1124 instructing printing from the external device, the printer control section 110 stores the received job data 1124 in the printer storage section 112.).
Referring to Claim 12, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 1 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1).
Mizusawa fails to explicitly teach
wherein at least one of: (i) the media parameter, and (ii) the quality, obtained by implementing the target process parameters, are automatically measured by at least one sensor associated with the target printing system, wherein the at least one sensor measures at least one of: thickness of the media, flatness of media, number and/or height of wrinkles, false loading and/or unloading procedure, bleeding of the print on the media, and false drying and/or curing process.
However, Moreira teaches
wherein at least one of: (i) the media parameter, and (ii) the quality, obtained by implementing the target process parameters, are automatically measured by at least one sensor associated with the target printing system, wherein the at least one sensor measures at least one of: thickness of the media, flatness of media, number and/or height of wrinkles, false loading and/or unloading procedure, bleeding of the print on the media, and false drying and/or curing process (See Moreira, Col. 5 lines 31-44, The media data may include information associated with the media that was involved in the printing operations. For example, the media data may include or identify a type of media, a dimension of the media (e.g., a thickness, a width, a length, or the like), a shape of the media, an authentication technique used to identify the type of media, an identifier of the media, a configuration of the media (e.g., whether on a media roll, on individual sheets, in a cartridge, or the like) and/or whether the type of media was able to be identified or authenticated. Additionally, or alternatively, the media data may indicate whether the media matched a designated media type that was to be used in association with the historical printing operations.).
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 Mizusawa to incorporate the teachings of Moreira to provide wherein at least one of: (i) the media parameter, and (ii) the quality, obtained by implementing the target process parameters, are automatically measured by at least one sensor associated with the target printing system, wherein the at least one sensor measures at least one of: thickness of the media, flatness of media, number and/or height of wrinkles, false loading and/or unloading procedure, bleeding of the print on the media, and false drying and/or curing process. Doing so would determine, using a print optimization model, a printing configuration for the printing operation based on the media type and the ambient condition and cause the printer to perform the printing operation according to the printing configuration (See Moreira, Col. 1 lines 49-54), as recognized by Moreira.
Referring to Claim 14, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 1 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), wherein the at least one target media parameter and the at least one sample media parameter are selected from a group comprising: chemistry, physical properties, absorption of ink, pretreatment, post treatment, topography, woven or non-woven, weaving pattern, knitting pattern, type, width, material, physical dimensions, thickness, stretchability, manufacturer (See Mizusawa, Sect. [0225], The print length information NJ is information for designating a print length that is a length for printing the image designated by the image designationi information GJ. The print length designates a size of the printing medium W on which the image of the print job 131 is printed in the transport direction H in units of meters, for example. When the print length is larger than the image size of the print image data 1123, the print control section 1112 repeatedly prints the image of the print image data 1123 on the printing medium W. Therefore, the print image data 1123 may be data of an image having a size smaller than the print length. Further, the print image data 1123 may be data of an image having a size smaller than that of the printing medium W in the intersecting direction K, that is, an image having a size smaller than the width of the printing medium W. In this case, the print control section 1112 repeatedly prints the image of the print image data 1123 even in the width direction of the printing medium W.).
Referring to Claim 15, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 1 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), wherein the at least one target process parameter and the at least one sample process parameter are selected from a group comprising: physical printer setup, pallet automation for automatic selection and setting of a type of pallet, print height, logical printer setup, pre-treatment, print speed, print resolution, and white underbase, drier temperature, drying duration (See Mizusawa, Sect. [0237], the processing section 1114 inputs the work gap information J1, the scanning velocity information J2, the print resolution information J3, and the waveform information J4 included in the printing condition information JJ acquired in step SD3, the nozzle row number information J8 of the nozzle row NzR selected in step SD31, the chip number information J7 of the chip 812 to which the nozzle row NzR selected in step SD31 belongs, the slot number information J6 of the reservoir that supplies the ink IK to the nozzle row NzR selected in step SD31, the temperature information J9 acquired in step SD32, the elapsed time information J5 acquired in step SD33, and the manufacturing error information J10 of the chip 812 to which the nozzle row NzR selected in step SD31 belongs, to the learned model 1127 (step SD34).).
Referring to Claim 16, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 1 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), wherein the at least one target media parameter comprises a unique identifier, assigning comprises matching the unique identifier of the at least one target media parameter with a unique identifier of the at least one sample media parameter, and when no match is found between the unique identifier of the at least one target media parameter and the unique identifier of the at least one sample media parameter, assigning comprises identifying at least one sample media parameter that is statistically similar to the at least one target media (See Mizusawa, Sect. [0036], the printing system 1000 includes a plurality of printers 100, the server 200 identifies the plurality of printers 100 included in the printing system 1000 by identification information and communicates with a target printer 100. As the identification information, a unique ID assigned to each individual printer 100).
Referring to Claim 17, the combination of Mizusawa in view of Moreira teaches the computer implemented method of claim 1 (See Mizusawa, Figs. 12-16, Flowchart methods illustrating operation setup of print media printing from printer 100 of printing system 1000 shown in Fig. 1), wherein the label of the record of the dataset is for a specific media type, wherein assigning comprises assigning the combination of the target quality and the at least one target media parameter indicating a requested print job to the specific media type, and providing further comprises providing the specific media type for printing the requested print job (See Mizusawa, Sect. [0042], The printing medium W is, for example, a cloth made of natural fibers or synthetic fibers. The printer 100 is a textile printing machine that prints on the printing medium W by adhering ink IK to the printing medium W that is a cloth. Therefore, the printing medium W is a material to be printed. Here, a cloth is used as an example of the printing medium W. However, as the printing medium W, in addition to a cloth, plain paper, high-quality paper, dedicated paper for ink jet recording such as glossy paper, and the like may be used.).
Referring to Claim 18, Mizusawa teaches a system for setting up a target printing system for printing on a target media (See Mizusawa, Fig. 1, Sect. [0035], The printing system 1000 includes a printer 100 and a server 200. In the present embodiment, the printer 100 corresponds to an example of an information processing apparatus. The printer 100 and the server 200 are communicably coupled to each other via a communication network N.), comprising:
a server (See Mizusawa, Fig. 1, Server 200) in network connection (See Mizusawa, Fig. 1, Sect. [0037] Communication Network N) with a plurality of printers (See Mizusawa, Fig. 1, Sect. [0036] lines 1-5, FIG. 1 illustrates a case where one printer 100 is coupled to the server 200, the number of printers 100 included in the printing system 1000, that is, the number of printers 100 coupled to the server 200 may be plural.), the server comprising at least one hardware processor executing a code for (See Moreira, Sect. [0183] lines 1-2, The server control section 210 includes a processor 211 that executes programs of a CPU or an MPU):
accessing at least one target media parameter associated with a target printer of the plurality of printers (See Mizusawa, Fig. 1, Sect. [0036] lines 5-11, When the printing system 1000 includes a plurality of printers 100, the server 200 identifies the plurality of printers 100 included in the printing system 1000 by identification information and communicates with a target printer 100.);
assigning a combination of a target quality and at least one target media parameter, to a plurality of target process parameters, using a dataset comprising a plurality of records obtained from a plurality of sample printers, wherein a record comprises (See Mizusawa, Sect. [0141], the data set generation section 1113 photographs each of the discharge velocity pattern images PT-Vm printed by the print control section 1112 with the camera 7, and acquires a photographed image for each discharge velocity pattern image PT-Vm. Next, the data set generation section 1113 calculates the separation distance Diff-Vm in the intersecting direction K from the photographed image for each nozzle row NzR. Then, the data set generation section 1113 stores the calculated separation distance Diff-Vm in the data set 2123 as the landing position deviation amount caused by the discharge velocity Vm in association with an appropriate nozzle row number.):
(i) at least one sample media parameter of a sample media for processing and printing thereon by a sample printing system (See Mizusawa, Fig. 6, Print Resolution Information, Sect. [0117], The print resolution information J3 included in the data set 2123 is information indicating the print resolution set in the printing condition of the pattern image PT described later. Since the size of the ink IK changes when the print resolution changes, the air resistance that the ink IK receives during flight changes when the print resolution changes. Therefore, when the print resolution changes, the landing position of the ink IK deviates. Therefore, the data set 2123 includes the print resolution information J3 as information having a correlation with the landing position deviation of the ink IK.),
(ii) an indication of a quality of a processing and a printing by the sample printing system set up with a plurality of sample process parameters (See Mizusawa, Fig. 6, Scanning Velocity Information J2, Sect. [0118], The waveform information J4 included in the data set 2123 is information indicating an ink discharge waveform set in the printing condition of the pattern image PT described later. The ink discharge waveform is a waveform of a signal input to the print head 811 for discharging the ink IK, and is a waveform that determines the size of one dot of the ink IK. In the ink discharge waveform, ON signals for driving piezo elements are included in the number corresponding to the size of one dot of the ink IK. Since the size of the ink IK changes when the ink discharge waveform changes, the air resistance that the ink IK receives during flight also changes when the ink discharge waveform changes. Therefore, when the ink discharge waveform changes, the landing position of the ink IK deviates. Therefore, in the data set 2123, the waveform information J4 is included as information having a correlation with the landing position deviation of the ink IK.),
Mizusawa fails to explicitly teach
(iii) a label indicating the plurality of sample process parameters ();
providing the plurality of target process parameters predicted to obtain the target quality, for generating instructions for processing and printing on the target media by the target printing system.
However, Moreira teaches
(iii) a label indicating the plurality of sample process parameters
(See Moreira, Fig. 2, Col. 11 lines 29-39, the printer prints an optimized label. For example, the printer may print the optimized label based on receiving the printing configuration from the user device and/or based on setting, configuring, and/or adjusting one or more configurable settings associated with one or more components of the printer. More specifically, according to the printing configuration, the printer may set or adjust a resistance of one or more printing elements of a printhead, the printer may set or adjust a pressure applied toward a platen of the printer, the printer may set or adjust an alignment of a feeder component of the printer);
providing the plurality of target process parameters predicted to obtain the target quality, for generating instructions for processing and printing on the target media by the target printing system (See Moreira, Col. 13 lines 56-67 and Col. 14 lines 1-6, the trained machine learning model 325 may predict a value of Config_N for the target variable of printing configuration for the new observation, as shown by reference number 335. Based on this prediction, the machine learning system may provide a first recommendation, may provide output for determination of a first recommendation, may perform a first automated action, and/or may cause a first automated action to be performed (e.g., by instructing another device to perform the automated action), among other examples. The first recommendation may include, for example, to adjust a condition of the environment (e.g., to be within a desirable or operable range associated with a design of the printer) and/or to adjust a setting of the printer. The first automated action may include, for example, automatically adjusting a setting of the printer to optimize a performance characteristic of the printer (e.g., to achieve optimal print quality, to extend a useful life of the printer and/or a component of the printer, and so on).).
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 Mizusawa to incorporate the teachings of Moreira to provide (iii) a label indicating the plurality of sample process parameters; providing the plurality of target process parameters predicted to obtain the target quality, for generating instructions for processing and printing on the target media by the target printing system. Doing so would determine, using a print optimization model, a printing configuration for the printing operation based on the media type and the ambient condition and cause the printer to perform the printing operation according to the printing configuration (See Moreira, Col. 1 lines 49-54), as recognized by Moreira.
Referring to Claim 19, Mizusawa teaches a computer implemented method of training a machine learning model for generating a plurality of target process parameters for setting up a target printing system for printing on a target media, comprising (See Mizusawa, Sect. [0261], In the control method of the printer 100, the learned model 1127 that has been trained by machine learning based on the data set 2123 in which the work gap information J1 and the landing position information J11 related to the landing position deviation of the ink IK discharged from the print head 811 are associated is stored. In addition, in the control method of the printer 100, the printing condition is acquired, and the work gap information J1 included in the acquired printing condition is input to the stored learned model 1127, and the landing position deviation amount is output from the learned model 1127. The control method of the printer 100 corresponds to an example of the control method of the information processing apparatus.);
creating a dataset of a plurality of records (See Mizusawa, Fig. 6, Data Set 2123 having a plurality of records J1-J11, Sect. [0113]-[0114], The data set 2123 includes work gap information J1, scanning velocity information J2, print resolution information J3, waveform information J4, elapsed time information J5, slot number information J6, chip number information J7, nozzle row number information J8, temperature information J9, and manufacturing error information J10 and landing position information J11.), wherein a record comprises:
(i) at least one sample media parameter of a sample media for processing and printing thereon by a sample printing system (See Mizusawa, Fig. 6, Print Resolution Information, Sect. [0117], The print resolution information J3 included in the data set 2123 is information indicating the print resolution set in the printing condition of the pattern image PT described later. Since the size of the ink IK changes when the print resolution changes, the air resistance that the ink IK receives during flight changes when the print resolution changes. Therefore, when the print resolution changes, the landing position of the ink IK deviates. Therefore, the data set 2123 includes the print resolution information J3 as information having a correlation with the landing position deviation of the ink IK.),
(ii) an indication of a quality of a processing and a printing by the sample printing system set up with a plurality of sample process parameters (See Mizusawa, Fig. 6, Scanning Velocity Information J2, Sect. [0118], The waveform information J4 included in the data set 2123 is information indicating an ink discharge waveform set in the printing condition of the pattern image PT described later. The ink discharge waveform is a waveform of a signal input to the print head 811 for discharging the ink IK, and is a waveform that determines the size of one dot of the ink IK. In the ink discharge waveform, ON signals for driving piezo elements are included in the number corresponding to the size of one dot of the ink IK. Since the size of the ink IK changes when the ink discharge waveform changes, the air resistance that the ink IK receives during flight also changes when the ink discharge waveform changes. Therefore, when the ink discharge waveform changes, the landing position of the ink IK deviates. Therefore, in the data set 2123, the waveform information J4 is included as information having a correlation with the landing position deviation of the ink IK.).
Mizusawa fails to explicitly teach
(iii) a ground label indicating the plurality of sample process parameters;
training the machine learning model on the dataset for receiving an input of a combination of a target quality and at least one target media parameter, and generating an outcome of the plurality of target process parameters predicted to obtain the target quality,
wherein instructions are generated for processing and printing on the target media by the target printing system set up using the plurality of target process parameters.
However, Moreira teaches
(iii) a ground label indicating the plurality of sample process parameters (See Moreira, Fig. 2, Col. 11 lines 29-39, the printer prints an optimized label. For example, the printer may print the optimized label based on receiving the printing configuration from the user device and/or based on setting, configuring, and/or adjusting one or more configurable settings associated with one or more components of the printer. More specifically, according to the printing configuration, the printer may set or adjust a resistance of one or more printing elements of a printhead, the printer may set or adjust a pressure applied toward a platen of the printer, the printer may set or adjust an alignment of a feeder component of the printer);
training the machine learning model on the dataset for receiving an input of a combination of a target quality and at least one target media parameter, and generating an outcome of the plurality of target process parameters predicted to obtain the target quality (See Moreira, Col. 13 lines 8-17, The target variable may represent a value that a machine learning model is being trained to predict, and the feature set may represent the variables that are input to a trained machine learning model to predict a value for the target variable. The set of observations may include target variable values so that the machine learning model can be trained to recognize patterns in the feature set that lead to a target variable value. A machine learning model that is trained to predict a target variable value may be referred to as a supervised learning model.),
wherein instructions are generated for processing and printing on the target media by the target printing system set up using the plurality of target process parameters (See Moreira, Col. 13 lines 1-7, A target variable may be associated with a target variable value, and a target variable value may be specific to an observation. In example 300, the target variable is printing configuration, which has a value of Config_1 for the first observation (e.g., corresponding to settings of the printer used to perform a printing operation of the first observation).).
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 Mizusawa to incorporate the teachings of Moreira to provide (iii) a ground label indicating the plurality of sample process parameters; training the machine learning model on the dataset for receiving an input of a combination of a target quality and at least one target media parameter, and generating an outcome of the plurality of target process parameters predicted to obtain the target quality, wherein instructions are generated for processing and printing on the target media by the target printing system set up using the plurality of target process parameters. Doing so would determine, using a print optimization model, a printing configuration for the printing operation based on the media type and the ambient condition and cause the printer to perform the printing operation according to the printing configuration (See Moreira, Col. 1 lines 49-54), as recognized by Moreira.
Cited Art
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure Stephenson et al. (US PG. PUB. No. 2010/0082120 A1) discloses A technique is disclosed for optimizing a quality parameter in a process that is not directly measurable online using conventional measurement devices. The technique includes the use of a first inferential model to predict a value for the parameter based upon other process variables. A second inferential model predicts a residual component of the process parameter based off non-controllable residual variables of the process. The inferential model outputs are combined to produce a composite predicted value which may be further adjusted by an actual prediction error determined via comparison with an offline measurement. The adjusted predicted value is provided to a dynamic predictive model which may be adapted to implement control actions to drive or maintain the quality parameter at a target set point. The technique may further consider cost optimization factors and production reliability factors in order to produce a product meeting the target quality set point or range while considering production requirements and minimizing overall costs.
Conclusion
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/DARRYL V DOTTIN/Primary Examiner, Art Unit 2683
/DARRYL V DOTTIN/Primary Examiner, Art Unit 2683
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