Printing condition setting method and printing condition setting system

Abstract

To set a print condition suitable for an ink and a medium to be used in a printing device.SOLUTION: A print condition setting method for setting a print condition in a printing device, includes: an ink type learning step of executing machine learning of an ink type discriminator using physical property information of ink and an ink type identifier; a medium type learning step of executing machine learning of a medium type discriminator using characteristic information of a medium and medium type identification information; and a print condition setting step of setting the print condition according to an ink type discriminated by the ink type discriminator and a medium type discriminated by the medium type discriminator.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a printing condition setting method and a printing condition setting system. [Background technology]

[0002] Conventionally, Patent Document 1 describes an ink discrimination method that includes the steps of irradiating light onto filled ink and measuring the amount of light that is transmitted through or reflected by the ink, wherein a plurality of amounts of light with different wavelengths are measured for ink of one color, and discriminating whether the filled ink is a specified ink based on the plurality of measured amounts of light. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2005-231356 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the ink discrimination method described in Patent Document 1 aims to prevent deterioration of image quality and failures by discriminating whether or not a specific ink is used, and it is not possible to select printing conditions according to the ink. In addition, the combination of ink and recording paper is not taken into consideration. [Means for solving the problem]

[0005] The printing condition setting method is a printing condition setting method for setting printing conditions in a printing device, and includes an ink type learning process for performing machine learning on an ink type discriminator using ink physical property information and an ink type identifier, a medium type learning process for performing machine learning on a media type discriminator using medium characteristic information and medium type identification information, and a printing condition setting process for setting the printing conditions according to the ink type discriminated by the ink type discriminator and the media type discriminated by the media type discriminator.

[0006] The printing condition setting system is a printing condition setting system that sets printing conditions in a printing device, and has an ink type learning unit that performs machine learning on an ink type discriminator using ink physical property information and an ink type identifier, a medium type learning unit that performs machine learning on a media type discriminator using medium characteristic information and medium type identification information, and a printing condition setting unit that sets the printing conditions according to the ink type discriminated by the ink type discriminator and the media type discriminated by the media type discriminator. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a schematic diagram showing an example of the configuration of a printing apparatus. [Diagram 2] FIG. 1 is a block diagram showing the configuration of a printing condition setting system. [Diagram 3] 11 is a flowchart showing a processing method of an ink type learning process. [Figure 4] FIG. 4 is a schematic diagram showing an example of a training model used in the ink type learning process. [Diagram 5] FIG. 4 is an explanatory diagram showing an example of printing conditions (control parameters). [Figure 6] FIG. 4 is an explanatory diagram showing an example of printing conditions (maintenance mode). [Figure 7] FIG. 4 is an explanatory diagram showing an example of printing conditions (ICC profile). [Figure 8] 10 is a flowchart showing a processing method of an ink type determination process. [Figure 9] An explanatory diagram showing the configuration of a first machine learning model. [Figure 10] An explanatory diagram showing the configuration of a second machine learning model. [Figure 11] 11 is a flowchart showing the processing procedure of a medium type learning step. [Figure 12] FIG. 13 is an explanatory diagram showing a medium identifier list. [Figure 13] FIG. 4 is an explanatory diagram showing a medium and printing setting table. [Figure 14] FIG. 11 is an explanatory diagram showing spectroscopic data that has been subjected to clustering processing. [Figure 15] FIG. 4 is an explanatory diagram showing a group management table. [Figure 16] FIG. [Figure 17] FIG. 4 is an explanatory diagram showing the configuration of a group of known characteristic spectra. [Figure 18] 10 is a flowchart showing the processing procedure of a medium type determination process. [Figure 19] FIG. 11 is a flowchart showing the procedure of a medium addition process. [Figure 20] FIG. 4 is an explanatory diagram showing a management state of a group of spectral data. [Figure 21] FIG. 11 is an explanatory diagram showing a medium identifier list updated in response to the addition of a print medium. [Figure 22] FIG. 11 is an explanatory diagram showing a group management table updated in response to the addition of a print medium. [Figure 23] FIG. 13 is an explanatory diagram showing a group management table updated in response to the addition of a machine learning model. [Figure 24] FIG. 11 is a flowchart showing the procedure of a machine learning model update process. [Diagram 25] 11 is a flowchart showing a processing method for setting printing conditions. [Figure 26] FIG. 4 is an explanatory diagram showing an example of a printing condition table. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] The printing condition setting method of this embodiment is a printing condition setting method for setting printing conditions in a printing device 2001 (FIG. 1), and includes an ink type learning step (step S10 [not shown]) for performing machine learning of an ink type discriminator using ink physical property information and an ink type identifier, a medium type learning step (step S30 [not shown]) for performing machine learning of a medium type discriminator using medium characteristic information and medium type identification information, and a printing condition setting step (step S50 [not shown]) for setting printing conditions according to the ink type discriminated by the ink type discriminator and the medium type discriminated by the medium type discriminator. The printing condition setting method further includes an ink type discrimination step (step S20 [not shown]) and a medium type discrimination step (step S40 [not shown]). Furthermore, the machine learning method of the ink type discriminator and the machine learning method of the medium type discriminator are executed by different methods. In detail, the ink type learning process (step S10) has one machine learning model (training model 105), whereas the medium type learning process (step S30) has multiple (two in this embodiment) machine learning models 201, 202. By employing a learning method suitable for the target of machine learning, it is possible to execute the process efficiently. The ink type learning process (step S10), the ink type discrimination process (step S20), the medium type learning process (step S30) and the medium type discrimination process (step S40) do not necessarily have to be performed in parallel, and the number of times and timing of each process can be adjusted appropriately.

[0009] 1 is a schematic diagram showing an example of the configuration of a printing device 2001. The printing device 2001 is an inkjet printer capable of printing on a print medium PM (for example, paper) as a medium.

[0010] The printing apparatus 2001 includes a carriage 2020. The carriage 2020 includes a mounting portion 2030 and a head 2040. The mounting portion 2030 is configured to be able to detachably mount the cartridge 2010 capable of containing ink as liquid. The number of cartridges 2010 mounted in the mounting portion 2030 may be one or more.

[0011] The cartridge 2010 is attached to the mounting portion 2030 in a state where it is inserted into a liquid introduction needle (not shown) provided in the mounting portion 2030. The ink contained in the cartridge 2010 is supplied to the head 2040 via the liquid introduction needle.

[0012] The head 2040 has multiple nozzles (not shown) and ejects ink as droplets from each nozzle. The head 2040 has, for example, a piezoelectric element as an ink ejection mechanism, and ink is ejected from each nozzle by driving the piezoelectric element. By ejecting ink from the head 2040 onto the printing medium PM supported by the platen 2045, characters, figures, images, and the like are printed on the printing medium PM.

[0013] The printing device 2001 includes a main scanning feed mechanism and a sub-scanning feed mechanism for relatively moving the carriage 2020 and the printing medium PM. The main scanning feed mechanism includes a carriage motor 2052 and a drive belt 2054. The carriage 2020 is fixed to the drive belt 2054. The carriage 2020 reciprocates in a direction along the X-axis, guided by a guide rod 2055 installed by the power of the carriage motor 2052. The sub-scanning feed mechanism includes a transport motor 2056 and a transport roller 2058, and transports the printing medium PM in the +Y direction by transmitting the power of the transport motor 2056 to the transport roller 2058. The direction in which the carriage 2020 reciprocates is the main scanning direction, and the direction in which the printing medium PM is transported is the sub-scanning direction. The printing apparatus 2001 may be configured such that heating units for heating the transported print medium PM are disposed on the transport path upstream and downstream of the platen 2045, including the platen 2045.

[0014] The printing device 2001 includes a maintenance unit 2060. The maintenance unit 2060 performs various maintenance operations on the head 2040, for example. For example, the maintenance unit 2060 includes a capping unit 2061. The capping unit 2061 includes a cap 2062 having a recess. The capping unit 2061 is provided with a lifting mechanism including a drive motor (not shown) and is configured to be able to move the cap 2062 in a direction along the Z axis. When the printing device 2001 is not in operation, the maintenance unit 2060 brings the cap 2062 into close contact with the head 2040 to cap an area in which nozzles are formed, thereby preventing problems such as ink drying and clogging the nozzles.

[0015] The maintenance unit 2060 also has various functions for cleaning the nozzles. For example, if ink is not ejected from the nozzles for a long time or if foreign matter such as paper dust adheres to the nozzles, the nozzles may become clogged. If the nozzles become clogged, ink is not ejected from the nozzles when it should be, and ink dots are not formed where they should be, that is, nozzle clogging occurs. If nozzle clogging occurs, image quality deteriorates. Therefore, the maintenance unit 2060 forcibly ejects ink from the nozzles toward the recesses of the cap 2062. In other words, cleaning of the nozzles is performed by performing flushing. This allows the nozzle ejection state to be restored to a good state. In addition to the above, the maintenance unit 2060 also includes a wiping unit 2063 that wipes the nozzle surface, a nozzle inspection unit that inspects the state of the nozzles, and the like.

[0016] The printing apparatus 2001 includes a control unit 2002. A carriage motor 2052, a transport motor 2056, a head 2040, a maintenance unit 2060, and the like are controlled based on control signals from the control unit 2002. The printing device 2001 also includes a general-purpose interface, such as a LAN interface or a USB interface, and is capable of communicating with various external devices.

[0017] In the printing condition setting method of the present embodiment, machine learning is used to set printing conditions suitable for the ink used in the printing device 2001 and the printing medium PM to be used. The specific details will be explained below.

[0018] First, the ink type learning step (step S10) will be described. In the ink type learning process (step S10), an ink type discriminator 102 is generated as a trained model (trained learning ink) by machine learning using teacher data 104 that associates ink characteristic data, which is information on the physical properties of the ink, with ink attribute information including an ink type identifier.

[0019] Next, the generation process of the ink type discriminator 102 will be specifically described. The ink is the ink used in the printing device 2001. The device using the ink is not limited to the printing device 2001, but may be, for example, a drawing device, a painting device, a writing device, or the like.

[0020] Ink attribute information including an ink type identifier is information on the type of ink, i.e., the ink name (manufacturer name) and ink part number, as well as at least one of the following: ink color, ink type, ingredients contained in the ink, ink manufacturer, ink manufacturing region, and ink manufacturing date. The type of ink includes, for example, information such as water-based, oil-based, ultraviolet curing type, heat curing type, dye-based, pigment-based, and the like. The ink characteristic data is at least one of the absorbance A, transmittance T % and reflectance R % observed when light is irradiated onto the ink.

[0021] The ink type discriminator 102 is generated by training a training model 105 using teacher data 104 in the printing condition setting system 1 shown in Fig. 2. The ink type discriminator 102 is a discrimination program that uses a learned model obtained by training the training model 105 using teacher data 104 obtained up to a certain point in time. The printing condition setting system 1 is a printing condition setting system that sets printing conditions in a printing device 2001, and has an ink type learning unit that performs machine learning of an ink type discriminator using ink physical property information and an ink type identifier, a medium type learning unit that performs machine learning of a medium type discriminator using medium characteristic information and medium type identification information, and a printing condition setting unit that sets printing conditions according to the ink type discriminated by the ink type discriminator and the medium type discriminated by the medium type discriminator. The printing condition setting system 1 is also made up of an information processing device 20, a spectroscopic analysis device 30, and the like.

[0022] The information processing device 20 is a computer system, and includes a calculation unit 110, an input unit 140, a display unit 150, a storage unit 120, a communication unit 130, and the like. The ink type learning section, medium type learning section, and printing condition setting section are provided in the calculation section 110. The calculation section 110 also includes a print processing section 112 that executes printing processing using the printing device 2001. The information processing device 20 is preferably a notebook PC that is easy to carry. The print medium PM also includes a roll medium in which the print medium is wound around a roll-shaped core material. Note that, although printing is given as an example of recording in this embodiment, the present invention can be applied to a broader recording system, device, and method including fixing, in which the recording conditions need to be changed according to the physical information of the medium.

[0023] The calculation unit 110 includes a CPU, RAM, and ROM, and executes calculations necessary for machine learning according to the programs stored in the storage unit 120. The calculation unit 110 includes a GPU and various processors designed for machine learning in order to perform machine learning. CPU stands for Central Processing Unit, RAM stands for Random access memory, ROM stands for Read-Only Memory, and GPU stands for Graphics Processing Unit.

[0024] The input unit 140 is an information input means serving as a user interface, specifically, for example, a keyboard, a mouse pointer, or the like. The display unit 150 is an information display means as a user interface, and under the control of the calculation unit 110, for example, displays information input from the input unit 140, the results of calculations by the calculation unit 110, and the like.

[0025] The memory unit 120 is a rewritable storage medium such as a hard disk drive or a memory card, and stores the learning program operated by the calculation unit 110, teacher data 104 for machine learning, a training model 105, an ink type discriminator 102 as a learned model generated as a result of the machine learning, and various calculation programs operated by the calculation unit 110. The communication unit 130 includes a general-purpose interface such as a LAN interface or a USB interface, and is connected to external electronic devices such as the spectroscopic analysis device 30 and the printer 2001, as well as to a network NW, to transmit and receive information to and from these devices. The network NW is also connected to a cloud environment.

[0026] In the above, the printing device 2001, the information processing device 20, and the spectroscopic analysis device 30 have been described as being independent configurations, but the present invention is not limited to this configuration and may be configured in any manner as long as these functions are provided.

[0027] In this embodiment, the printing condition setting system 1 performs machine learning using a training model 105 with ink property data of various inks and information on the ink type corresponding to the ink property data as teacher data 104. The information on the ink type is ink type or ink attribute information including the ink type. The reason why data such as absorbance A, transmittance T%, and reflectance R% obtained by spectroscopic analysis of the ink are used as the ink characteristic data is that it is possible to utilize the fact that these characteristics differ depending on the type of ink.

[0028] The ink absorbance A, transmittance T%, and reflectance R% are obtained using a spectroscopic analysis device 30 by evaluating the intensity of light absorbed by the ink, the intensity transmitted through the ink, and the intensity reflected by the ink in response to the intensity of light irradiated onto the sample ink. When the intensity of the incident light is I0, the intensity of the transmitted light is I1, and the intensity of the reflected light is I2, they are calculated as follows. Absorbance A=log(I0 / I1) Transmittance T%=I1 / I0×100 Reflectance R%=I2 / I0×100 In spectroscopic analysis, the wavelength of the irradiated light is divided into a predetermined wavelength range, for example, from the ultraviolet region to the infrared region, in 10 nm intervals, and a set of data is obtained for the absorbance A, transmittance T%, and reflectance R% for each wavelength range.

[0029] 3 is a flowchart showing a detailed processing method in the ink type learning step (step S10). Specifically, this is a flowchart showing a process in which calculation unit 110 performs machine learning to generate ink type discriminator 102. Note that before this process starts, ink characteristic data for multiple types of ink has been collected as teacher data 104 associated with the ink type or ink attribute information including the ink type, and stored in storage unit 120.

[0030] First, in step S101, the training model 105 and the teacher data 104 are obtained from the storage unit 120. Next, in steps S102 and S103, machine learning processing is performed using the training model 105 until generalization is completed. In step S103, the determination as to whether generalization of the training model 105 is completed or not is made by threshold determination of the accuracy rate of the output obtained by inputting test data into the training model 105 up to that point. Teacher data 104 is provided to training model 105 for machine learning, and when generalization is complete, ink type discriminator 102 is generated. In step S104, ink type discriminator 102 is stored in storage unit 120 as a learned model.

[0031] The training model 105 for machine learning can be defined in various ways. FIG. 4 is a diagram showing a schematic example of the training model 105 used in this embodiment. In the figure, each of the total n layers of CNN is shown as L1 to Ln, and the nodes of a normal neural network are shown as white circles. In this embodiment, CNN is used, but other models such as various neural networks such as capsule network type and vector neural network type may be used. CNN stands for Convolutional Neural Network.

[0032] The first layer L1 is provided with a plurality of nodes that input ink characteristic data for each fixed wavelength. In this embodiment, for example, the reflectance R% for each fixed wavelength indicated by the spectral reflectance data is input as input data to each node of the first layer L1, which is the input layer, and final output data corresponding to the reflectance R% is output from the final output layer Ln. Instead of or in addition to the data on reflectance R%, transmittance T% or absorbance A for each certain wavelength may be used. For example, when three pieces of data, absorbance A, transmittance T%, and reflectance R%, are used, three training models 105 may be provided, and results corresponding to absorbance A, transmittance T%, and reflectance R% may be output from the final layer of each training model 105, and a final output layer Ln may be constructed that integrates and judges these results, and the final result may be output. Furthermore, among the ink attribute information, information that may affect the tendency of the ink characteristic data that is determined according to the type of ink may be input as input data from a newly provided node.

[0033] The output of each node in the first layer L1 is connected to a node in the next second layer L2 with a predetermined weighting applied. This is the same for the second layer L2 and onwards, up to the Ln-1th layer. By repeatedly correcting the weighting between each node in each layer using training data 104, learning progresses and an ink type discriminator 102 is generated using the trained model.

[0034] Furthermore, the storage unit 120 stores ink / printing condition data 106 . The ink / printing condition data 106 of this embodiment is table data including printing conditions that can be set for the printing device 2001, in which each learned learning ink (ink type) is associated with the printing conditions corresponding to each learned ink. The printing conditions of this embodiment include at least one of the control parameters for the printing device 2001, the maintenance mode, the ICC profile, and the printing mode.

[0035] As shown in FIG. 5, the control parameters include the temperature of the platen 2045, an after-heater temperature for heating the transport path downstream of the platen 2045, and a pre-heater temperature for heating the transport path upstream of the platen 2045, as well as at least one of the compression pressure (nip pressure of the transport roller 2058), the scanning speed of the head 2040, the driving voltage of the head 2040, the amount of heat (amount of heat of the head 2040), an LUT of the amount of ink ejected, and the number of passes.

[0036] As shown in FIG. 6, the maintenance mode includes at least one of the frequency of cleaning the head 2040, the frequency of inspection of the nozzles, the frequency of automatic cleaning of the nozzle surface, the frequency of inspection of the nozzle surface, the frequency of a warning, and the frequency of ink circulation.

[0037] As shown in Fig. 7, an ICC profile includes at least one of an input profile, an output profile, and a device link profile. An ICC profile is a series of data that characterizes input / output devices and color spaces related to color in accordance with standards published by the International Color Consortium in color management. An input profile is conversion data for an input device such as a camera or a display, an output profile is conversion data for an output device such as the printing device 2001, and a device link profile is conversion data that associates an input device with an output device. The print mode includes at least one of the print resolution, the number of passes, the type of halftone, the ink dot arrangement, and the ink dot size.

[0038] The spectroscopic analysis device 30 includes a spectroscopic analysis unit, a communication unit, and the like. The spectroscopic analysis device 30 includes a light source, a spectroscope, a detector, etc., and can obtain at least one ink observation data from among absorbance A, transmittance T%, and reflectance R% observed by irradiating light onto the ink.

[0039] Next, the ink type determination step (step S20) will be described. FIG. 8 is a flowchart showing a detailed processing method in the ink type determination step (step S20). To start the ink discrimination process, the information processing device 20 has an ink type discriminator 102 as a learned model stored in the storage unit 120. That is, in this embodiment, the ink type discriminator 102 is generated in advance by machine learning using teacher data 104 that associates at least one ink characteristic data item among absorbance A, transmittance T%, and reflectance R%, which are observed when light is irradiated onto the ink, with ink type or ink attribute information including ink type.

[0040] First, in step S201, a sample of the ink to be used is prepared for the printing device 2001. Specifically, the sample of the ink to be used is set in the spectroscopic analyzer 30 in a state where it can be analyzed.

[0041] Next, in step S202, spectroscopic analysis of the ink sample is performed by the spectroscopic analysis device 30 to obtain ink observation data. In the spectroscopic analysis, the wavelength of the irradiated light is divided into a predetermined wavelength range, for example, from the ultraviolet region to the infrared region, in 10 nm intervals, and the data is obtained as a set of data on the absorbance A, transmittance T%, and reflectance R% for that wavelength range. The spectroscopic analysis device 30 transmits the acquired ink observation data to the information processing device 20 via the communication unit.

[0042] Next, in step S203, the information processing device 20, which has received the ink observation data, inputs the ink observation data to the ink type discriminator 102 in the calculation unit 110, and discriminates the type of ink based on the output data of the ink type discriminator 102. The ink type may be discriminated by calculating a similarity and discriminating the ink type according to the similarity.

[0043] The similarity is calculated using the color difference (ΔE) between the learned learning ink types according to the following formula (1). Similarity=1.0-ΔE / Range...(1) However, if the similarity is <-1.0, the similarity is set to -1.0. Note that the Range is a value that can be adjusted as appropriate. Here, the similarity is calculated as a value between -1.0 and 1.0 inclusive. The closer the similarity is to 1.0, the higher the similarity of the used ink to the learning ink is determined to be. On the other hand, the closer the similarity is to -1.0, the lower the similarity of the used ink to the learning ink is determined to be. Here, for example, the ink type that shows the highest similarity value among the inks used is determined as the ink type.

[0044] Next, the processing methods of the medium type learning step (step S30) and the medium type discrimination step (step S40) will be described.

[0045] As shown in FIG. 2, the spectroscopic analysis device 31 can acquire the spectral reflectance as the characteristic information of the medium by performing a spectroscopic measurement on the print medium PM as the medium used in the printing device 2001 in an unprinted state. In this disclosure, the spectral reflectance as the characteristic information of the medium is also referred to as "spectroscopic data". The spectroscopic analysis device 31 includes, for example, a variable wavelength interference spectroscopic filter and a monochrome image sensor. The spectroscopic data obtained by the spectroscopic analysis device 31 is used as input data for a machine learning model described later. As described later, the information processing device 20 executes a class classification process of the spectroscopic data using the machine learning model, and classifies the print medium PM into which of a plurality of classes it belongs. The "class of the print medium PM" refers to the type of the print medium PM.

[0046] The calculation unit 110 functions as a class classification processing unit 114 that executes a class classification process for the spectral data of the print medium PM, and also as a print setting creation unit 116 that creates print settings suitable for the print medium PM. Furthermore, the calculation unit 110 also functions as a learning unit 117 that obtains a classifier through machine learning using physical information and type information of the print medium PM, and as a classifier management unit 118 that manages information related to the classifier. The classifier will be described later.

[0047] The class classification processing unit 114, the print setting creation unit 116, the learning unit 117, and the classifier management unit 118 are realized by the calculation unit 110 executing a program stored in the storage unit 120. In a preferred embodiment, the calculation unit 110 is configured to include one or more of these units. Each of these units may be realized by a hardware circuit. The calculation unit 110 in this embodiment is a term that includes such a hardware circuit. Furthermore, the calculation unit 110 that executes the class classification process may be a processor included in a remote computer that is connected to the information processing device 20 via a network NW including a cloud environment.

[0048] The storage unit 120 stores a plurality of machine learning models 201, 202 (medium type discriminators), a plurality of spectral data groups SD1, SD2, a medium identifier list IDL (medium type identification information), a plurality of group management tables GT1, GT2, a plurality of known characteristic spectrum groups KS1, KS2, and a medium / print setting table PST. The machine learning models 201, 202 are used for calculations by the class classification processing unit 114. An example of the configuration and operation of the machine learning models 201, 202 will be described later. The spectral data groups SD1, SD2 are a collection of labeled spectral data used for learning the machine learning models 201, 202. The medium identifier list IDL is a list in which a medium identifier and spectral data are registered for each printing medium. The plurality of group management tables GT1, GT2 are tables indicating the management state of the spectral data groups SD1, SD2. The known characteristic spectrum groups KS1, KS2 are a collection of characteristic spectra obtained when teacher data is input again to the learned machine learning models 201, 202. The characteristic spectrum will be described later. The medium / print setting table PST is a table in which print settings (print conditions) suitable for each print medium are registered.

[0049] 9 is an explanatory diagram showing the configuration of the first machine learning model 201. The machine learning model 201 includes, in order from the input data IM side, a convolution layer 211, a primary vector neuron layer 221, a first convolution vector neuron layer 231, a second convolution vector neuron layer 241, and a classification vector neuron layer 251. Of these five layers 211 to 251, the convolution layer 211 is the lowest layer, and the classification vector neuron layer 251 is the highest layer. In the following description, the layers 211 to 251 are also referred to as the "Conv layer 211", the "PrimeVN layer 221", the "ConvVN1 layer 231", the "ConvVN2 layer 241", and the "ClassVN layer 251", respectively.

[0050] In this embodiment, the input data IM is spectral data, and is therefore one-dimensional array data. For example, the input data IM is data in which 36 representative values ​​are extracted at intervals of 10 nm from spectral data in the range of 380 nm to 730 nm.

[0051] 9, two convolution vector neuron layers 231 and 241 are used, but the number of convolution vector neuron layers is arbitrary, and the convolution vector neuron layer may be omitted. However, it is preferable to use one or more convolution vector neuron layers.

[0052] 9 further includes a similarity calculation unit 261 that generates similarities. The similarity calculation unit 261 can calculate similarities S1_ConvVN1, S1_ConvVN2, and S1_ClassVN, which will be described later, from the outputs of the ConvVN1 layer 231, the ConvVN2 layer 241, and the ClassVN layer 251. However, the similarity calculation unit 261 may be omitted.

[0053] The configuration of each of the layers 211 to 251 can be described as follows. <Description of the configuration of the first machine learning model 201> ·Conv layer 211: Conv[32,6,2] ·PrimeVN layer 221: PrimeVN[26,1,1] ·ConvVN1 layer 231:ConvVN1[20,5,2] ·ConvVN2 layer 241:ConvVN2[16,4,1] ·ClassVN layer 251:ClassVN[n1,3,1] Vector dimension VD: VD=16 In the description of each of these layers 211 to 251, the character string before the parentheses is the layer name, and the numbers in the parentheses are, in order, the number of channels, the kernel size, and the stride. For example, the layer name of the Conv layer 211 is "Conv", the number of channels is 32, the kernel size is 1x6, and the stride is 2. In FIG. 9, these descriptions are shown under each layer. The hatched rectangles drawn in each layer represent the kernels used when calculating the output vector of the adjacent upper layer. In this embodiment, since the input data IM is data of a one-dimensional array, the kernel also has a one-dimensional array. Note that the parameter values ​​used in the description of each of the layers 211 to 251 are merely examples and can be changed arbitrarily.

[0054] The Conv layer 211 is a layer composed of scalar neurons. The other four layers 221 to 251 are layers composed of vector neurons. A vector neuron is a neuron that takes a vector as input and output. In the above description, the dimension of the output vector of each vector neuron is constant at 16. In the following, the term "node" is used as a superordinate concept of scalar neurons and vector neurons.

[0055] FIG. 9 shows the first axis x and the second axis y that define the planar coordinates of the node array, and the third axis z that represents the depth, for the Conv layer 211. It also shows that the sizes of the Conv layer 211 in the x, y, and z directions are 1, 16, and 32. The sizes in the x and y directions are called "resolutions." In this embodiment, the resolution in the x direction is always 1. The size in the z direction is the number of channels. These three axes x, y, and z are also used as coordinate axes that indicate the position of each node in other layers. However, in FIG. 9, the illustration of these axes x, y, and z is omitted in layers other than the Conv layer 211.

[0056] As is well known, the resolution W1 in the y direction after convolution is given by the following equation: W1=Ceil{(W0-Wk+1) / S} Here, W0 is the resolution before convolution, Wk is the kernel size, S is the stride, and Ceil{X} is a function that rounds up X. The resolution of each layer shown in FIG. 9 is an example in which the resolution in the y direction of the input data IM is set to 36, and the actual resolution of each layer is changed appropriately depending on the size of the input data IM.

[0057] The ClassVN layer 251 has n1 channels. The similarity calculation unit 261 has one channel. In the example of FIG. 9, (n1+1)=11. The channels of the ClassVN layer 251 output judgment values ​​Class1-1 to Class1-10 for a plurality of known classes, and the channel of the similarity calculation unit 261 outputs a judgment value Class1-UN indicating an unknown class. The class having the largest value among these judgment values ​​Class1-1 to Class1-10 and Class1-UN corresponds to the class to which the input data IM belongs. In general, n1 is an integer of 2 or more, and is the number of known classes that can be classified using the first machine learning model 201. In any one machine learning model, it is preferable that an upper limit nmax and a lower limit nmin are set in advance for the number of known classes that can be classified.

[0058] The judgment value Class1-UN indicating an unknown class may be omitted. In this case, if the largest value among the judgment values ​​Class1-1 to Class1-10 for the known classes is less than a predetermined threshold, the class of the input data IM is judged to be unknown.

[0059] 10 is an explanatory diagram showing the configuration of the second machine learning model 202. Like the first machine learning model 201, this machine learning model 202 has a Conv layer 212, a PrimeVN layer 222, a ConvVN1 layer 232, a ConvVN2 layer 242, a ClassVN layer 252, and a similarity calculation unit 262.

[0060] The configuration of each of the layers 212 to 252 can be described as follows. <Description of the configuration of the second machine learning model 202> ·Conv layer 212: Conv[32,6,2] ·PrimeVN layer 222: PrimeVN[26,1,1] ·ConvVN1 layer 232:ConvVN1[20,5,2] ·ConvVN2 layer 242:ConvVN2[16,4,1] ·ClassVN layer 252:ClassVN[n2,3,1] Vector dimension VD: VD=16

[0061] As can be understood by comparing FIG. 9 and FIG. 10, among the layers 212 to 252 of the second machine learning model 202, the lower four layers 212 to 242 have the same configuration as the layers 211 to 241 of the first machine learning model 201. On the other hand, the top layer 252 of the second machine learning model 202 differs from the top layer 251 of the first machine learning model 201 only in the number of channels. In the example of FIG. 10, the ClassVN layer 252 has n2 channels, the similarity calculation unit 262 has one channel, and (n2+1)=7. The channels of the ClassVN layer 252 output judgment values ​​Class2-1 to Class2-6 for a plurality of known classes, and the channel of the similarity calculation unit 262 outputs a judgment value Class2-UN indicating an unknown class. In the second machine learning model 202, it is preferable to set the same upper limit value nmax and lower limit value nmin as those of the first machine learning model 201 for the number of known classes.

[0062] The second machine learning model 202 is configured to have at least one known class different from the first machine learning model 201. In addition, since the first machine learning model 201 and the second machine learning model 202 have different classifiable classes, the values ​​of the kernel elements are also different from each other. In the present disclosure, when N is an integer of 2 or more, any one of the N machine learning models is configured to have at least one known class different from the other machine learning models. Note that, in the present embodiment, the number N of machine learning models is 2 or more, but the present disclosure is also applicable to the case where only one machine learning model is used.

[0063] 11 is a flowchart showing the procedure of a machine learning model preparation step in the medium type learning step (step S30). This preparation step is a step executed by the manufacturer of the printing device 2001, for example.

[0064] In step S310, spectral data of a plurality of initial print media is generated as initial spectral data. In this embodiment, the initial print media used for learning the machine learning model in the preparation process are all optional print media. In this disclosure, an "optional print medium" is a print medium that can be a target of a class classification process by a machine learning model and can be excluded from the target of the class classification process even without an exclusion instruction from the user. On the other hand, the print medium added in the medium addition process described later is a required print medium that cannot be excluded from the target of the class classification process without an exclusion instruction from the user. However, some or all of the initial print media may be required print media.

[0065] In step S310, initial spectral data is generated by performing spectral measurement on a plurality of initial print media in an unprinted state using the spectral analyzer 31. At this time, it is preferable to perform data extension in consideration of variations in spectral reflectance. In general, spectral reflectance varies depending on the date of color measurement and the measuring device. Data extension is a process for generating a plurality of spectral data by adding random variations to the measured spectral data in order to simulate such variations. Note that initial spectral data may be generated virtually without performing spectral measurement on the actual print media. In this case, the initial print media is also virtual.

[0066] In step S320, a medium identifier list IDL is created for a plurality of initial print media. FIG. 12 is an explanatory diagram showing the medium identifier list IDL. In the medium identifier list IDL, a medium identifier given to each print medium, a medium name, a data sub-number, and spectral data are registered. In this example, medium identifiers "A-1" to "A-16" are given to 16 print media. The medium name is the name of the print medium displayed in a window for the user to set print conditions. The data sub-number is a number for distinguishing between a plurality of spectral data relating to the same print medium. In this example, three pieces of spectral data are registered for each print medium. However, the number of pieces of spectral data for each print medium may be different. It is sufficient that one or more pieces of spectral data are registered for each print medium, but it is preferable that a plurality of pieces of spectral data are registered.

[0067] In step S330 in FIG. 11, print settings are created for each of a plurality of initial print media, and are registered in the medium and print setting table PST. FIG. 13 is an explanatory diagram showing the medium and print setting table PST. In each record of the medium and print setting table PST, a medium identifier and print settings (printing conditions) are registered for each print medium. In this example, printer profiles PR1 to PR16, medium feed speeds FS1 to FS16, and drying times DT1 to DT16 are registered as print settings. The medium feed speeds FS1 to FS16 and drying times DT1 to DT16 are part of the above-mentioned print conditions. The printer profiles PR1 to PR16 are output profiles of the printing device 2001, and are created for each ink type and printing medium. Specifically, a printer profile can be created by printing a test chart on a printing medium without color correction using the printing device 2001, subjecting the test chart to spectroscopic measurement by the spectroscopic analysis device 31, and processing the spectroscopic measurement results by the print setting creation unit 116. The medium feed speeds FS1 to FS16 and the drying times DT1 to DT16 can also be determined experimentally. The "drying time" refers to the time it takes to dry the print medium after printing in a dryer (not shown) inside the printing device 2001. In a printer that dries the print medium by blowing air onto it after printing, the "drying time" refers to the air blowing time. In a printer that does not have a dryer, the "drying time" refers to the waiting time for natural drying. Other initial items may also be set as print settings. For example, it is preferable to include any of the control parameters, maintenance mode, ICC profile, and print mode.

[0068] In step S340 of FIG. 11, grouping is performed by clustering multiple initial spectral data for multiple initial printing media. FIG. 14 is an explanatory diagram showing spectral data grouped by clustering. In this example, multiple spectral data are grouped into a first spectral data group SD1 and a second spectral data group SD2. For example, k-means can be used as the clustering process. The spectral data groups SD1 and SD2 have representative points G1 and G2 that represent the centers of the respective spectral data groups SD1 and SD2. These representative points G1 and G2 are, for example, centers of gravity. When the spectral data is composed of reflectances at m wavelengths, it is possible to calculate the distance between the spectral data and the center of gravity of multiple spectral data by regarding one spectral data as data representing one point in an m-dimensional space. In FIG. 14, multiple points of spectral data are drawn in a two-dimensional space for convenience of illustration, but in reality, the spectral data can be expressed as points in an m-dimensional space. As described below, when a new print medium is added as a target for classification processing, these representative points G1 and G2 are used to determine which of the multiple spectral data groups SD1 and SD2 the spectral data of the added print medium is closest to. Note that points other than the center of gravity may be used as the representative points G1 and G2. For example, the average values ​​of the maximum and minimum reflectance values ​​at each wavelength may be calculated for multiple spectral data belonging to one group, and the spectral data consisting of these average values ​​may be used as the representative point.

[0069] In this embodiment, the multiple spectroscopic data are grouped into two spectroscopic data groups SD1 and SD2, but only one spectroscopic data group may be used, or three or more spectroscopic data groups may be created. Furthermore, multiple spectroscopic data groups may be created using a method other than the clustering process. However, by grouping multiple spectroscopic data using the clustering process, it is possible to group similar spectroscopic data into the same group. If multiple machine learning models are trained using each of such multiple spectroscopic data groups, the accuracy of the class classification process using the machine learning model can be improved compared to the case where the clustering process is not performed. Even if spectral data of a new print medium is added after grouping by the clustering process, it is possible to maintain a state equivalent to the state of grouping by the clustering process.

[0070] In step S350 in FIG. 11, group management tables GT1 and GT2 are created. FIG. 15 is an explanatory diagram showing group management tables GT1 and GT2. In each record of group management tables GT1 and GT2, a group number, a medium identifier, a data sub number, a distance from a representative point, a model number, a class label, a current area, and coordinates of a representative point are registered for one spectral data. The group number is a number for distinguishing a plurality of group management tables GT1 and GT2. The medium identifier and the data sub number are used to distinguish each spectral data, similarly to the medium identifier list IDL described in FIG. 12. The model number is a number for identifying a machine learning model that performs learning using a group of spectral data of the group. Here, the symbols "201" and "202" of the two machine learning models 201 and 202 shown in FIG. 9 and FIG. 10 are used as model numbers. The "class label" is a value corresponding to the result of a class classification process by the machine learning model, and is also used as a label when the spectral data is used as training data. The model number and the class label are set for each medium identifier. "Current area" indicates whether the spectroscopic data belongs to the teacher area or the waiting area. "Teacher area" means that the spectroscopic data is actually being used for training the machine learning model. "Waiting area" means that the spectroscopic data is not being used for training the machine learning model and is in a waiting state from the teacher area. In the preparation process, all the spectroscopic data belongs to the teacher area because it is used for training the machine learning model.

[0071] In step S360 in FIG. 11, the user creates a machine learning model to be used in the class classification process and sets its parameters. In this embodiment, two machine learning models 201 and 202 shown in FIG. 9 and FIG. 10 are created and their parameters are set. However, in step S360, only one machine learning model may be created, or three or more machine learning models may be created. In step S370, the class classification processing unit 114 executes learning of the machine learning models 201 and 202 using the spectroscopic data groups SD1 and SD2. When learning is completed, the learned machine learning models 201 and 202 are stored in the storage unit 120.

[0072] In step S380, the class classification processing unit 114 inputs the spectral data groups SD1 and SD2 again to the trained machine learning models 201 and 202 to generate known feature spectrum groups KS1 and KS2. The known feature spectrum groups KS1 and KS2 are collections of feature spectra, which will be described below. Below, a method for generating the known feature spectrum group KS1 associated with the machine learning model 201 will be mainly described.

[0073] FIG. 16 is an explanatory diagram showing a feature spectrum Sp obtained by inputting arbitrary input data to the trained machine learning model 201. Here, the feature spectrum Sp obtained from the output of the ConvVN1 layer 231 will be described. The horizontal axis of FIG. 16 is a spectral position represented by a combination of an element number ND of an output vector of a node at one planar position (x, y) of the ConvVN1 layer 231 and a channel number NC. In this embodiment, since the vector dimension of the node is 16, the element number ND of the output vector is 16, ranging from 0 to 15. In addition, since the number of channels in the ConvVN1 layer 231 is 20, the channel number NC is 20, ranging from 0 to 19.

[0074] The vertical axis in FIG. 16 indicates the feature value CV at each spectrum position. In this example, the feature value CV is the value VND of each element of the output vector. Note that the feature value CV may be a value obtained by multiplying the value VND of each element of the output vector by an activation value, which will be described later, or the activation value may be used as is. In the latter case, the number of feature values ​​CV included in the feature spectrum Sp is equal to the number of channels, and is 20. Note that the activation value is a value corresponding to the vector length of the output vector of the node.

[0075] The number of feature spectra Sp obtained from the output of the ConvVN1 layer 231 for one piece of input data is equal to the number of planar positions (x, y) of the ConvVN1 layer 231, and is therefore 1×6=6. Similarly, for one input data, three feature spectra Sp are obtained from the output of the ConvVN2 layer 241, and one feature spectrum Sp is obtained from the output of the ClassVN layer 251.

[0076] When training data is input again to the trained machine learning model 201, the similarity calculation unit 261 calculates the feature spectrum Sp shown in FIG. 16 and registers it in the known feature spectrum group KS1.

[0077] 17 is an explanatory diagram showing the configuration of the known feature spectrum group KS1. In this example, the known feature spectrum group KS1 includes a known feature spectrum group KS1_ConvVN1 obtained from the output of the ConvVN1 layer 231, a known feature spectrum group KS1_ConvVN2 obtained from the output of the ConvVN2 layer 241, and a known feature spectrum group KS1_ConvVN obtained from the output of the ClassVN layer 251.

[0078] Each record of the known feature spectrum group KS1_ConvVN1 includes a record number, a layer name, a label Lb, and a known feature spectrum KSp. The known feature spectrum KSp is the same as the feature spectrum Sp in FIG. 16 obtained in response to the input of the training data. In the example of FIG. 17, the spectroscopic data group SD1 is input to the trained machine learning model 201, and the known feature spectrum KSp associated with each value of the label Lb is generated and registered from the output of the ConvVN1 layer 231. For example, N1_1max known feature spectrum KSp is registered in association with the label Lb=1, N1_2max known feature spectrum KSp is registered in association with the label Lb=2, and N1_n1max known feature spectrum KSp is registered in association with the label Lb=n1. N1_1max, N1_2max, and N1_n1max are each an integer equal to or greater than 2. As described above, each label Lb corresponds to a known class different from each other. Therefore, it can be understood that each known feature spectrum KSp in the known feature spectrum group KS1_ConvVN1 is registered in association with one of a plurality of known classes. The same is true for the other known feature spectrum groups KS1_ConvVN2, KS1_ConvVN.

[0079] The spectral data group used in step S380 does not need to be the same as the spectral data groups SD1 and SD2 used in step S370. However, if some or all of the spectral data groups SD1 and SD2 used in step S370 are used in step S380, there is an advantage in that there is no need to prepare new training data. Step S380 may be omitted.

[0080] 18 is a flowchart showing the procedure of a medium type discrimination process (step S40) using a trained machine learning model. The process here is executed by, for example, a user who uses the printing device 2001.

[0081] In step S410, it is determined whether or not a discrimination process is required for the target print medium, which is the print medium to be processed. If the discrimination process is not required, that is, if the type of the target print medium is known, the process ends. On the other hand, if the type of the target print medium is unknown and a discrimination process is required, the process proceeds to step S420.

[0082] In step S420, the class classification processing unit 114 acquires target spectral data by causing the spectroscopic analysis device 31 to perform spectroscopic measurement of the target print medium. This target spectral data is subjected to class classification processing using a machine learning model.

[0083] In step S430, the classification processing unit 114 inputs the target spectral data into the existing trained machine learning models 201, 202 to perform classification processing of the target spectral data. In this case, either a first processing method in which the multiple machine learning models 201, 202 are used one by one in sequence, or a second processing method in which the multiple machine learning models 201, 202 are used simultaneously, can be used. In the first processing method, the classification processing is first performed using one machine learning model 201, and when it is determined that the target spectral data belongs to an unknown class as a result, the classification processing is performed using the other machine learning model 202. In the second processing method, the two machine learning models 201, 202 are used simultaneously to perform classification processing on the same target spectral data in parallel, and the classification processing unit 114 integrates the processing results. According to an experiment by the inventor of the present disclosure, the second processing method is more preferable because it requires a shorter processing time than the first processing method.

[0084] In step S440, the classification processing unit 114 determines whether the target spectral data belongs to an unknown class or a known class based on the result of the classification processing in step S430. If the target spectral data belongs to an unknown class, the target print medium is a new print medium that does not correspond to any of the multiple initial print media used in the preparation step or the print medium added in the medium addition processing described below, so the process proceeds to step S500 described below, where the medium addition processing is executed. On the other hand, if the target spectral data belongs to a known class, the process proceeds to step S450.

[0085] In step S450, the similarity to the known feature spectrum group is calculated using one machine learning model that has been determined to belong to a known class among the multiple machine learning models 201 and 202. For example, when the processing of the first machine learning model 201 determines that the target spectral data belongs to a known class, the similarity calculation unit 261 calculates the similarities S1_ConvVN1, S1_ConvVN2, and S1_ClassVN to the known feature spectrum group KS1 from the outputs of the ConvVN1 layer 231, the ConvVN2 layer 241, and the ClassVN layer 251. On the other hand, when the processing of the second machine learning model 202 determines that the target spectral data belongs to a known class, the similarity calculation unit 262 calculates the similarities S2_ConvVN1, S2_ConvVN2, and S2_ClassVN to the known feature spectrum group KS2. A method for calculating the similarity S1_ConvVN1 from the output of the ConvVN1 layer 231 of the first machine learning model 201 will be described below.

[0086] The similarity S1_ConvVN1 can be calculated, for example, using the following formula. S1_ConvVN1(Class)=max[G{Sp(i,j),KSp(Class,k)}] Here, "Class" is an ordinal number for multiple classes, G{a,b} is a function for calculating the similarity between a and b, Sp(i,j) is a feature spectrum at all planar positions (i,j) obtained according to the target spectral data, KSp(Class,k) is all known feature spectra associated with the ConvVN1 layer 231 and a specific "Class", and max[X] is a logical operation for taking the maximum value of X. That is, the similarity S1_ConvVN1 is the maximum value of the similarities calculated between each of the feature spectra Sp(i,j) at all planar positions (i,j) of the ConvVN1 layer 231 and each of all known feature spectra KSp(k) corresponding to a specific class. Such a similarity S1_ConvVN1 is calculated for each of multiple classes corresponding to multiple labels Lb. The similarity S1_ConvVN1 indicates the degree to which the target spectral data is similar to the features of each class.

[0087] Similarities S1_ConvVN2 and S1_ClassVN related to the outputs of the ConvVN2 layer 241 and the ClassVN layer 251 are also generated in the same manner as the similarity S1_ConvVN1. It is not necessary to generate all three similarities S1_ConvVN1, S1_ConvVN2, and S1_ClassVN, but it is preferable to generate one or more of them. In the present disclosure, the layer used to generate the similarity is also referred to as a "specific layer."

[0088] In step S460, the classification processing unit 114 presents the similarity obtained in step S450 to the user, and the user checks whether the similarity is consistent with the result of the classification process. The similarities S1_ConvVN1, S1_ConvVN2, and S1_ClassVN represent the degree to which the target spectral data is similar to the characteristics of each class, so that the result of the classification process can be checked from at least one of these similarities S1_ConvVN1, S1_ConvVN2, and S1_ClassVN. For example, when at least one of the three similarities S1_ConvVN1, S1_ConvVN2, and S1_ClassVN does not match the result of the classification process, it can be determined that the two are not consistent. In another embodiment, when all of the three similarities S1_ConvVN1, S1_ConvVN2, and S1_ClassVN do not match the result of the classification process, it may be determined that the two are not consistent. In general, when a predetermined number of similarities among the multiple similarities generated from the outputs of multiple specific layers do not match the result of the classification process, it may be determined that the two are inconsistent. Note that the determination in step S460 may be performed by the class classification processing unit 114. Also, steps S450 and S460 may be omitted.

[0089] If the similarity is consistent with the result of the classification process, the process proceeds to step S470, where the classification processing unit 114 determines the medium identifier of the target print medium according to the result of the classification process. This process is performed, for example, by referring to the group management tables GT1 and GT2 shown in FIG.

[0090] If it is determined in step S460 that the similarity is not consistent with the result of the classification process, the target print medium is a new print medium that does not correspond to any of the initial print media used in the preparation process or the print medium added in the medium addition process described below, so the process proceeds to step S500 described below. In step S500, a medium addition process is executed to make the new print medium a target for the classification process. In the medium addition process, the machine learning model is updated and added, so the medium addition process can be considered as part of the process of preparing the machine learning model.

[0091] Fig. 19 is a flowchart showing the procedure of the medium addition process, and Fig. 20 is an explanatory diagram showing the management state of the spectral data group in the medium addition process. In the following description, a new print medium added as a target for the class classification process is called an "added print medium" or an "added medium".

[0092] In step S510, the class classification processing unit 114 searches for a machine learning model that is closest to the spectral data of the additional print medium from among the existing machine learning models 201, 202. The "machine learning model that is closest to the spectral data of the additional print medium" refers to the machine learning model in which the distance between the representative points G1, G2 of the teacher data group used in learning the machine learning models 201, 202 and the spectral data of the additional print medium is the smallest. The distance between each of the representative points G1, G2 and the spectral data of the additional print medium can be calculated, for example, as the Euclidean distance. The teacher data group that is the smallest distance from the spectral data of the additional print medium is also called the "proximal teacher data group."

[0093] In step S520, the class classification processing unit 114 determines whether the number of classes corresponding to the required print media has reached the upper limit for the machine learning model searched for in step S510. As described above, in this embodiment, all the initial print media used in the preparation process are optional print media, and all print media added after the preparation process are required print media. If the number of classes corresponding to the required print media has not reached the upper limit, the process proceeds to step S530, and the machine learning model is trained using the teacher data to which the spectral data of the additional print media has been added. The state S1 in FIG. 20 shows the state of the spectral data group SD2 used in the training of the machine learning model 202 in the above-mentioned preparation process, and the state S2 shows the state in which the spectral data of the additional print media has been added as the spectral data of the required print media in step S330. In FIG. 20, "optional medium" means the spectral data of the optional print media used in the preparation process, and "required medium" means the spectral data of the required print media added by the medium addition process in FIG. 19. The "teacher area" means that the spectral data is teacher data that is actually used in training the machine learning model. The "shelter area" means that the spectroscopic data is not used in learning the machine learning model and is in a state of being evacuated from the teacher area. Furthermore, a state in which the teacher area is empty means that the number of classes in the machine learning model 202 has not reached the upper limit. In state S1, since the number of classes corresponding to the required print medium in the machine learning model 202 has not reached the upper limit, the spectroscopic data of the additional print medium is added to the teacher area, resulting in state S2, and the machine learning model 202 is re-learned using the spectroscopic data belonging to the teacher area as teacher data. In this re-learning, only the added spectroscopic data may be used as teacher data.

[0094] FIG. 21 shows the medium identifier list IDL in state S2 of FIG. 20, and FIG. 22 shows the group management table GT2 for the second spectral data group SD2 in state S2. In the medium identifier list IDL, "B-1" is assigned as the medium identifier of the added print medium, and the medium name and spectral data are registered. It is preferable that multiple pieces of spectral data are generated for the spectral data of the added print medium by performing data extension that adds random variations to the measured spectral data. In the group management table GT2, multiple pieces of spectral data are also registered for the added print medium with the medium identifier B-1. The representative point G2 for the teacher data group in the second spectral data group SD2 is recalculated including the added spectral data.

[0095] When more printing media are added from state S2 in FIG. 20, the state transitions to state S3, state S4, and state S5. In states S2 to S4, as in state S1, the number of classes corresponding to the required printing media in the machine learning model 202 has not reached the upper limit, so step S530 is executed, and the spectral data of the additional printing media is added to the teacher area, and re-learning of the machine learning model 202 is executed. Note that in state S3, the sum of the number of classes corresponding to the required printing media and the number of classes corresponding to the optional printing media in the machine learning model 202 has reached the upper limit, and there is no space in the teacher area. Therefore, when transitioning from state S3 to state S4, in step S530, the spectral data of the additional printing medium, which is the required printing medium, is added to the teacher area, and the spectral data of the optional printing medium is deleted from the teacher area. The deleted spectral data is saved to a save area. The reason for saving the spectral data to the save area is to make it possible to reuse the spectral data. It is preferable that the spectral data of the arbitrary printing medium to be evacuated from the teacher area to the evacuation area is selected from the data that is the farthest from the representative point of the teacher data group. This reduces the distance between the teacher data, thereby improving the accuracy of the class classification process.

[0096] In state S5 of FIG. 20, the number of classes corresponding to the required print medium in the machine learning model 202 has reached the upper limit. In this case, the process proceeds from step S520 to step S540. In step S540, the class classification processing unit 114 searches for a machine learning model that belongs to the same group as the machine learning model searched for in step S510 and for which the number of classes corresponding to the required print medium has not reached the upper limit. If such a machine learning model exists, the process proceeds from step S550 to step S560, and the machine learning model is trained using the teacher data to which the spectral data of the additional print medium has been added. This process is the same as the process in step S530 described above.

[0097] If the search in step S540 does not find a machine learning model, the process proceeds from step S550 to step S570, where a new machine learning model is created and the new machine learning model is trained using training data including the spectral data of the additional print medium. This process corresponds to the process of changing from state S5 to state S6 in FIG. 20. In state S5, the number of classes corresponding to the required print medium in the machine learning model 202 reaches the upper limit value, and there are no other machine learning models belonging to the same group. Therefore, by the process of step S570, as shown in state S6, a new machine learning model 203 is created and the new machine learning model is trained using training data including the spectral data of the additional print medium, which is the new required print medium. At this time, since the spectral data of the additional print medium alone is insufficient as training data, the spectral data of one or more optional print media that have been sheltered in the shelter area are also used as training data. In this way, the accuracy of the class classification process by the new machine learning model 203 can be improved.

[0098] Note that the above-mentioned steps S540 to S560 may be omitted, and if the number of classes of the essential print medium is equal to the upper limit in step S520, the process may proceed directly to step S570.

[0099] FIG. 23 shows the group management table GT2 for the second group in state S6. The spectral data of the print media with the medium identifiers A-11 to A-16 is the spectral data of the optional print media used in the preparation process. The spectral data of the print media with the medium identifiers B-1 to B-11 is the spectral data of the mandatory print media added after the preparation process. In this group management table GT2, the states of the spectral data for the two machine learning models 202 and 203 belonging to the same group are registered. For the machine learning model 202, the spectral data for the added 10 mandatory print media is stored in the teacher area, and the spectral data for the six optional print media is stored in the evacuation area. For the machine learning model 203, the spectral data for one mandatory print medium and the spectral data for the six optional print media are stored in the teacher area, and the evacuation area is empty. The representative points G2a and G2b of the teacher data group of each machine learning model 202 and 203 are calculated using the spectral data stored in the respective teacher areas.

[0100] The medium addition process shown in FIG. 19 can also be executed when the number of existing machine learning models is one. When the number of existing machine learning models is one, for example, the second machine learning model 202 shown in FIG. 10 is not prepared, and the process of FIG. 18 is executed using only the first machine learning model 201 shown in FIG. 9. In this case, the process of step S370 in FIG. 11 is a process of adding the second machine learning model 202 as a new machine learning model. In this way, in the classification process performed using only the first machine learning model 201, the process of adding the second machine learning model 202 as a new machine learning model when it is determined that the input data belongs to an unknown class can also be understood as an example of a process of preparing two machine learning models 201 and 202.

[0101] When a machine learning model is updated or added in any of steps S530, S560, and S570, in step S580, the class classification processing unit 114 inputs the training data again into the updated or added machine learning model to generate a group of known feature spectra. This process is the same as the process in step S430 in Fig. 18, and therefore a description thereof will be omitted. In step S590, the print setting creation unit 116 creates print settings for the added target print medium. This process is the same as the process in step S330 in Fig. 11, and therefore a description thereof will be omitted.

[0102] When the process of Fig. 19 ends in this way, the process of Fig. 18 also ends. After this, the process of Fig. 18 is executed again at any timing.

[0103] In the process of FIG. 19 described above, the process of step S510 corresponds to a process of selecting a proximity teacher data group having a representative point closest to the spectral data of the additional print medium from among the N teacher data groups used to train the N machine learning models, and selecting a specific machine learning model that was trained using the proximity teacher data group. By performing such a process, even if the spectral data of the additional print medium is added to the proximity teacher data group, the teacher data group after the addition can be maintained in a state equivalent to the state grouped by the clustering process. As a result, the accuracy of the class classification process by the machine learning model can be improved.

[0104] 19, it is possible to add a new print medium to the class classification process targets. On the other hand, it is also possible to exclude a print medium from the class classification process targets in response to a user instruction.

[0105] FIG. 24 is a flowchart illustrating the procedure of the update process of the machine learning model. In step S610, it is determined whether or not there is a machine learning model with the number of classes less than the upper limit among the existing machine learning models. When N is an integer equal to or greater than 2, if there are N existing machine learning models, it is determined whether or not there is a machine learning model with the number of classes less than the upper limit among them. However, the number N of existing machine learning models may be 1. In this embodiment, there are two existing machine learning models 201 and 202 shown in FIG. 9 and FIG. 10, and the first machine learning model 201 has the number of classes equal to the upper limit, and the second machine learning model 202 has the number of classes less than the upper limit. If there is no machine learning model with the number of classes less than the upper limit among the existing machine learning models, the process proceeds to step S640 described later, where a new machine learning model is added. On the other hand, if there is a machine learning model with the number of classes less than the upper limit, the process proceeds to step S620, where the machine learning model is updated.

[0106] In step S620, the class classification processing unit 114 updates the machine learning model with the number of classes less than the upper limit value so as to increase the number of channels in the top layer by one. In this embodiment, the number of channels (n2+1) in the top layer of the second machine learning model 202 is changed from 3 to 4. In step S630, the class classification processing unit 114 executes learning of the machine learning model updated in step S620. In this learning, the target spectral data acquired in step S420 of FIG. 18 is used as new teacher data together with the teacher data group TD2 for the second machine learning model 202 that has been used until then. Note that, as the new teacher data, it is preferable to use, in addition to the target spectral data acquired in step S420, a plurality of other spectral data obtained from the spectroscopic measurement of the same printing medium PM. For this reason, it is preferable that the spectroscopic analysis device 31 is configured to acquire spectral data at a plurality of positions of one printing medium PM. When the learning is completed in this manner, the updated machine learning model 202 has a known class corresponding to the target spectral data. Therefore, it is possible to use the updated machine learning model 202 to recognize the type of this print medium PM.

[0107] In step S640, the class classification processing unit 114 adds a new machine learning model having a class corresponding to the target spectral data, and sets the parameters. This new machine learning model preferably has the same configuration as the first machine learning model 201 shown in FIG. 9, except for the number of channels in the top layer. This new machine learning model preferably has two or more known classes, for example, similar to the second machine learning model 202 shown in FIG. 10. One of the two or more known classes is a class corresponding to the target spectral data. In addition, it is preferable that at least one of the two or more known classes is the same as at least one known class of the existing machine learning model. Making one class of the new machine learning model the same as the known class of the existing machine learning model is realized by learning the new machine learning model using the same teacher data as the teacher data used in learning the existing machine learning model for that known class. The reason for providing two or more known classes in the new machine learning model is that if there is only one known class, the learning may not be performed with sufficient accuracy.

[0108] The class of existing machine learning model to be adopted for the new machine learning model is preferably selected from, for example, the following classes: (a) Among multiple known classes in an existing machine learning model, the class corresponding to the optical spectral data that is most similar to the target spectroscopic data. (b) Among multiple known classes in the existing machine learning model, the class corresponding to the optical spectral data that is least similar to the target spectroscopic data. (c) A class that is misclassified as the class to which the target spectroscopic data belongs in step S440 of FIG. 18, among a plurality of known classes in an existing machine learning model. Among these, by adopting the above class (a) or (c), it is possible to reduce misclassification in the new machine learning model, while by adopting the above class (b), it is possible to shorten the learning time of the new machine learning model.

[0109] In step S650, the class classification processing unit 114 executes learning of the added machine learning model. In this learning, the target spectral data acquired in step S420 in FIG. 18 is used as new training data. In addition to the target spectral data acquired in step S420, it is preferable to use other multiple spectral data obtained from spectroscopic measurements of the same print medium PM as the new training data. In addition, when one or more classes of the new machine learning model are made the same as known classes of the existing machine learning model, the training data used in the training of the existing machine learning model for the known classes is also used.

[0110] When the number of known classes of the second machine learning model 202 reaches the upper limit, a third machine learning model is added by steps S640 and S650 in Fig. 24. The same applies to the fourth and subsequent machine learning models. Thus, in this embodiment, when N is an integer equal to or greater than 2, the (N-1) machine learning models have a number of classes equal to the upper limit, and the remaining machine learning model has a number of classes equal to or less than the upper limit. Furthermore, when a classification process is performed on the target spectral data using the N machine learning models, and it is determined that the target spectral data belongs to an unknown class, one of the following processes is executed. (1) If the other machine learning model has a number of classes less than the upper limit, steps S620 and S630 are processed to add a new class for the target spectral data to the other machine learning model by training the other machine learning model using training data including the target spectral data. (2) If the other machine learning model has a number of classes equal to the upper limit, a new machine learning model having a class corresponding to the target spectral data is added by the processing of steps S640 and S650. According to these processes, even if the N machine learning models are unable to successfully classify the target spectral data, it is possible to classify the target spectral data into a class corresponding to the target spectral data.

[0111] The update process of the machine learning model shown in FIG. 24 can also be executed when the number of existing machine learning models is one. When the number of existing machine learning models is one, for example, the second machine learning model 202 shown in FIG. 10 is not prepared, and the process of FIG. 18 is executed using only the first machine learning model 201 shown in FIG. 9. In this case, steps S640 and S650 in FIG. 24 are processes of adding the second machine learning model 202 as a new machine learning model. In this way, in the classification process performed using only the first machine learning model 201, the process of adding the second machine learning model 202 as a new machine learning model when it is determined that the input data belongs to an unknown class can also be understood as an example of a process of preparing two machine learning models 201 and 202.

[0112] In step S660, the class classification processing unit 114 inputs the training data again into the updated or added machine learning model to generate a group of known feature spectra.

[0113] As described above, in this embodiment, when N is an integer equal to or greater than 2, class classification processing is performed using N machine learning models, so that processing can be performed faster than when classification processing into multiple classes is performed using one machine learning model. Furthermore, when an existing machine learning model is unable to classify data to be classified well, it becomes possible to perform classification into a class corresponding to the data to be classified by adding a class to the existing machine learning model or by adding a new machine learning model.

[0114] In the above, a vector neural network type machine learning model using vector neurons is used, but instead, a machine learning model using scalar neurons such as a normal convolutional neural network may be used. However, the vector neural network type machine learning model is preferable in that it has higher accuracy of class classification processing than the machine learning model using scalar neurons.

[0115] The method of calculating the output of each layer in the first machine learning model 201 shown in Fig. 9 is as follows. The same applies to the second machine learning model 202.

[0116] Each node of the PrimeVN layer 221 regards the scalar output of the 1×1×32 nodes of the Conv layer 211 as a 32-dimensional vector, and obtains the vector output of the node by multiplying this vector by a transformation matrix. This transformation matrix is ​​an element of a 1×1 kernel, and is updated by learning of the machine learning model 201. Note that it is also possible to integrate the processing of the Conv layer 211 and the PrimeVN layer 221 and configure them as one primary vector neuron layer.

[0117] When the PrimeVN layer 221 is referred to as the "lower layer L" and the ConvVN1 layer 231 adjacent to the upper side is referred to as the "upper layer L+1", the output of each node in the upper layer L+1 is determined using the following formula.

number

[0118] As the normalization function F(X), for example, the following formula (4a) or (4b) can be used.

number

[0119] In the above formula (4a), the activation value aj is obtained by normalizing the norm |uj| of the sum vector uj for all nodes in the upper layer L+1 with a softmax function. On the other hand, in formula (4b), the activation value aj is obtained by dividing the norm |uj| of the sum vector uj by the sum of the norms |uj| for all nodes in the upper layer L+1. Note that the normalization function F(X) may be a function other than formula (4a) or formula (4b).

[0120] The ordinal number i in the above equation (3) is assigned for convenience to the nodes in the lower layer L used to determine the output vector ML+1j of the jth node in the upper layer L+1, and takes a value from 1 to n. The integer n is the number of nodes in the lower layer L used to determine the output vector ML+1j of the jth node in the upper layer L+1. Therefore, the integer n is given by the following equation. n = Nk × Nc (6) Here, Nk is the number of elements of the kernel, and Nc is the number of channels in the lower layer, the PrimeVN layer 221. In the example of FIG.

[0121] One kernel used to calculate the output vector of the ConvVN1 layer 231 has 1×3×26=78 elements with the kernel size of 1×3 as the surface size and the number of channels of the lower layer of 26 as the depth, and each of these elements is a prediction matrix WLij. Also, 20 sets of this kernel are required to generate output vectors of 20 channels of the ConvVN1 layer 231. Therefore, the number of prediction matrices WLij of the kernel used to calculate the output vector of the ConvVN1 layer 231 is 78×20=1560. These prediction matrices WLij are updated by learning of the machine learning model 201.

[0122] As can be seen from the above equations (2) to (5), the output vector ML+1j of each node in the upper layer L+1 is calculated by the following calculation. (a) Multiply the output vector MLi of each node in the lower layer L by the prediction matrix WLij to obtain a prediction vector vij. (b) A sum vector uj, which is the sum, i.e., linear combination, of the prediction vectors vij obtained from each node in the lower layer L, is calculated. (c) Normalize the norm |uj| of the sum vector uj to obtain the activation value aj, which is the normalization coefficient. (d) The sum vector uj is divided by the norm |uj| and then multiplied by the activation value aj.

[0123] The activation value aj is a normalization coefficient obtained by normalizing the norm |uj| for all nodes in the upper layer L+1. Therefore, the activation value aj can be considered as an index indicating the relative output strength of each node among all nodes in the upper layer L+1. The norm used in equations (4), (4a), (4b), and (5) is typically the L2 norm, which represents the vector length. In this case, the activation value aj corresponds to the vector length of the output vector ML+1j. The activation value aj is only used in the above equations (4) and (5), and does not need to be output from the node. However, it is also possible to configure the upper layer L+1 to output the activation value aj to the outside.

[0124] The configuration of the vector neural network is almost the same as that of the capsule network, and the vector neurons of the vector neural network correspond to the capsules of the capsule network. However, the calculations according to the above formulas (2) to (5) used in the vector neural network are different from those used in the capsule network. The biggest difference between the two is that in the capsule network, the predicted vectors vij on the right side of the above formula (3) are multiplied by weights, and the weights are searched for by repeating dynamic routing multiple times. On the other hand, in the vector neural network of this embodiment, the output vector ML+1j is obtained by calculating the above formulas (2) to (5) in order once, so there is an advantage that there is no need to repeat dynamic routing and the calculation is faster. In addition, the vector neural network of this embodiment requires less memory for calculation than the capsule network, and according to an experiment by the inventor of the present disclosure, it also has the advantage that it requires about 1 / 2 to 1 / 3 of the memory amount.

[0125] A vector neural network is the same as a capsule network in that it uses nodes that use vectors as input and output. Therefore, the advantage of using vector neurons is also common to capsule networks. In addition, the multiple layers 211 to 251 are the same as normal convolutional neural networks in that the higher the layer, the larger the area of ​​the feature represented, and the lower the layer, the smaller the area of ​​the feature represented. Here, "feature" means a characteristic part included in the input data to the neural network. A vector neural network or capsule network is superior to a normal convolutional neural network in that the output vector of a certain node contains spatial information that represents the spatial information of the feature represented by the node. That is, the vector length of the output vector of a certain node represents the probability of the existence of the feature represented by the node, and the vector direction represents spatial information such as the direction and scale of the feature. Therefore, the vector direction of the output vectors of two nodes belonging to the same layer represents the positional relationship of each feature. Alternatively, it can be said that the vector direction of the output vectors of the two nodes represents the variation of the feature. For example, if a node corresponds to the feature of "eyes", the direction of the output vector can represent variations such as the thinness of the eyes and the way they are lifted. In a conventional convolutional neural network, it is said that spatial information of features is lost due to pooling processing. As a result, vector neural networks and capsule networks have the advantage of being superior in the performance of identifying input data compared to conventional convolutional neural networks.

[0126] The advantages of vector neural networks can also be considered as follows. That is, the advantage of vector neural networks is that the output vectors of nodes express the features of the input data as coordinates in a continuous space. Therefore, output vectors can be evaluated such that if the vector directions are close, the features are similar. Another advantage is that even if the features contained in the input data are not covered by the training data, the features can be determined by interpolation. On the other hand, normal convolutional neural networks have the disadvantage that the features of the input data cannot be expressed as coordinates in a continuous space because of the chaotic compression caused by the pooling process.

[0127] The output of each node of the ConvVN2 layer 241 and the ClassVN layer 251 is determined similarly using the above-mentioned formulas (2) to (5), so detailed explanations are omitted. The resolution of the top layer, the ClassVN layer 251, is 1×1, and the number of channels is (n1+1).

[0128] The output of the ClassVN layer 251 is converted into a plurality of judgment values ​​Class1-1 to Class1-2 for known classes and a judgment value Class1-UN indicating an unknown class. These judgment values ​​are usually normalized by a softmax function. Specifically, for example, a judgment value for each class can be obtained by performing an operation in which the vector length of the output vector of each node of the ClassVN layer 251 is calculated from the output vector of the node, and the vector length of each node is normalized by a softmax function. As described above, the activation value aj obtained by the above formula (4) is a value corresponding to the vector length of the output vector ML+1j and is normalized. Therefore, the activation value aj in each node of the ClassVN layer 251 may be output and used as it is as a judgment value for each class.

[0129] In the above-described embodiment, a vector neural network that obtains an output vector by the calculation of the above formulas (2) to (5) is used as the machine learning models 201 and 202. However, instead of this, a capsule network disclosed in U.S. Patent No. 5,210,798 or International Publication WO 2019 / 083553 may be used. Also, a neural network that uses only scalar neurons may be used.

[0130] The method of generating the known feature spectrum groups KS1 and KS2 and the method of generating output data of intermediate layers such as the ConvVN1 layer are not limited to the above embodiment, and for example, these data may be generated using the Kmeans method. Also, these data may be generated using transformations such as PCA, ICA, Fisher, etc. Also, the transformation methods of the known feature spectrum group KSG and the output data of the intermediate layers may be different.

[0131] Next, a processing method of the printing condition setting step (step S50) will be described. In the printing condition setting step (step S50), printing conditions are set according to the determined ink type and the determined medium type. Figure 25 is a flowchart showing the detailed processing method in the printing condition setting step (step S50). The printing conditions are derived from a printing condition setting table PPT (FIG. 26) stored in the memory unit 120. The printing condition setting table PPT is table data in which the calculation unit 110 integrates the ink / printing condition data 106 (at least one of the control parameters, maintenance mode, ICC profile, and printing mode) with the medium / printing setting table PST (FIG. 13) to calculate printing conditions corresponding to ink types and medium types. When there are items with different printing conditions between the ink type and the medium type, it is possible to set whether to give priority to the printing conditions held by the ink type or the printing conditions held by the medium type. For example, in the case of a combination of ink type A and medium type A-1, the printing condition setting table PPT is referenced and Pt11 is derived, but the output profile is set to PO10 corresponding to ink type A, the device link profile is set to PD10 corresponding to ink type A, and the heater temperature is set to the conditions corresponding to ink type A. In addition, in the case of a combination of ink type B and medium type A-1, the printing condition setting table PPT is referenced and Pt21 is derived, but the output profile is set to PR1 corresponding to medium type A-1, the device link profile is set to DL1 corresponding to medium type A-1, and the heater temperature is set to the conditions corresponding to medium type A-1. Furthermore, in the case of a combination of ink type C and medium type A-1, the printing condition setting table PPT is referenced and Pt31 is derived, but the output profile is set to P030 corresponding to ink type C, the device link profile is set to DL1 corresponding to medium type A-1, and the heater temperature is set to a condition corresponding to ink type C. Furthermore, in the case of a combination of ink type D and medium type A-1, the printing condition setting table PPT is referenced and Pt41 is derived, but the output profile is set to PR1 corresponding to medium type A-1, the device link profile is set to PD40 corresponding to ink type D, and the heater temperature is set to a condition corresponding to ink type D. Although the output profile, device link profile, and heater temperature have been explained as examples, the same applies to the other items, and the printing conditions held by the ink type and the printing conditions held by the medium type can be combined and set. It should be noted that, instead of setting conditions linked to ink types or medium types, conditions to be used for a combination of a specific ink type and a specific medium type may be set. Also, it is possible to notify the operator that there are different printing conditions depending on the ink type and the medium type, and to allow the operator to select and set the conditions.

[0132] In step S710, the calculation unit 110 determines whether there are printing conditions locally. Specifically, it determines whether a printing condition setting table PPT corresponding to the determined ink type and medium type is stored in the information processing device 20 (e.g., a personal computer) used by the user.

[0133] If it is determined that the printing conditions exist locally (YES), the process proceeds to step S720, and if it is determined that the printing conditions do not exist locally (NO), the process proceeds to step S730.

[0134] In step S720, the calculation unit 110 sets the printing conditions based on the printing condition setting table PPT, and causes the display unit 150 to display the set printing conditions.

[0135] Meanwhile, in step S730, printing conditions are searched for on the cloud via communication unit 130. Then, the process proceeds to step S720, where calculation unit 110 sets the searched printing conditions. Thereafter, the print processing unit 112 executes printing according to the set print conditions.

[0136] As described above, according to this embodiment, it is possible to determine the ink type and medium type, and to set appropriate printing conditions (for example, control parameters, maintenance mode, ICC profile, print mode) according to the combination of ink type and medium type. [Explanation of symbols]

[0137] 1...printing condition setting system, 20...information processing device, 30, 31...spectroscopic analysis device, 102...ink type discriminator, 104...teaching data, 105...training model, 106...ink and printing condition data, 110...calculation unit, 112...print processing unit, 114...classification processing unit, 116...print setting creation unit, 117...learning unit, 118...discriminator management unit, 120...storage unit, 130...communication unit, 140...input unit, 150...display unit, 201...machine learning model, 202...machine learning model, 203... Machine learning model, 261...similarity calculation unit, 262...similarity calculation unit, 2001...printing device, 2002...control unit, 2010...cartridge, 2020...carriage, 2030...mounting unit, 2040...head, 2045...platen, 2052...carriage motor, 2054...drive belt, 2055...guide rod, 2056...transport motor, 2058...transport roller, 2060...maintenance unit, 2061...capping unit, 2062...cap, 2063...wiping unit.

Claims

1. A printing condition setting method for setting printing conditions in a printing device by using an information processing device, comprising: an ink type learning step of performing machine learning of an ink type discriminator using physical property information and an ink type identifier of the ink used in the printing device; a medium type learning step of performing machine learning of a medium type discriminator using characteristic information and medium type identification information of a medium used in the printing device; a printing condition setting step of setting the printing conditions according to the ink type determined by the ink type determiner and the medium type determined by the medium type determiner, the ink type is determined by inputting ink observation data obtained by a first spectroscopic analysis device capable of spectroscopic measurement of ink to the ink type discriminator, and based on output data output from the ink type discriminator; the medium type is determined based on output data output from the medium type discriminator, by inputting target spectral data obtained by a second spectroscopic analysis device that is executed by a user of the printing device and that is capable of spectroscopic measurement of a medium into the medium type discriminator; The printing conditions are set using a printing condition setting table, which is table data in which printing conditions corresponding to the ink type and the medium type are calculated.

2. 2. The printing condition setting method according to claim 1, A printing condition setting method, wherein the printing conditions are control parameters of the printing device.

3. 2. The printing condition setting method according to claim 1, The printing condition setting method, wherein the printing condition is a maintenance mode of the printing device.

4. 2. The printing condition setting method according to claim 1, The printing condition setting method, wherein the printing condition is an ICC profile of the printing device.

5. 2. The printing condition setting method according to claim 1, A printing condition setting method, wherein the printing condition is a printing mode of the printing device.

6. 6. The printing condition setting method according to claim 1, further comprising: A printing condition setting method, wherein a machine learning method of the ink type discriminator in the ink type learning step is different from a machine learning method of the medium type discriminator in the medium type learning step.

7. A printing condition setting system for setting printing conditions in a printing device, comprising: an ink type learning unit that performs machine learning of an ink type discriminator using ink physical property information and an ink type identifier; a medium type learning unit that performs machine learning of a medium type discriminator using the medium characteristic information and the medium type identification information; a printing condition setting unit that sets the printing conditions according to the ink type determined by the ink type determining unit and the medium type determined by the medium type determining unit, the ink type is determined by inputting ink observation data obtained by a first spectroscopic analysis device capable of spectroscopic measurement of ink to the ink type discriminator, and based on output data output from the ink type discriminator; the medium type is determined based on output data output from the medium type discriminator, by inputting target spectral data obtained by a second spectroscopic analysis device that is executed by a user of the printing device and that is capable of spectroscopic measurement of a medium into the medium type discriminator; The printing conditions are set using a printing condition setting table, which is table data in which printing conditions corresponding to the ink type and the medium type are calculated.

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