Image forming apparatus, image forming method, and storage medium

Abstract

To provide an image forming apparatus, an image forming method, and a program capable of improving color conversion accuracy.SOLUTION: A reading control unit that causes a reading device to perform a reading operation on a color sample printed matter serving as a sample of color matching and acquires read image data of the color sample printed matter, an estimation request unit that requests estimation of a profile using a learned model in a machine learning server based on the read image data acquired by the reading control unit, a reception unit that receives a second profile estimated from a first profile and the read image data by the machine learning server using the learned model in response to a request from the estimation request unit, and a printing control unit that performs printing control by applying the second profile to document data.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an image forming apparatus, an image forming method, and a program. [Background technology]

[0002] There is known a technique for generating an ICC (International Color Consortium) profile from an image read by a reading device.

[0003] As a technology for generating such an ICC profile, a technology has been disclosed in which a trained model generated by machine learning using the type of printing medium, the amount of color material per unit area, and the input image as training data is used to create a color conversion profile that includes information relating to the correspondence between coordinate values ​​in color space and the amount of color material (for example, Patent Document 1). Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology described in Patent Document 1 adjusts the color conversion profile according to the difference in the maximum amount of color material (solid density) for each model, and is not a color conversion profile for matching the hue of a specific color, so there is a problem in that the color conversion accuracy for that specific color in the image is not guaranteed.

[0005] The present invention has been made in view of the above, and has an object to provide an image forming apparatus, an image forming method, and a program that can improve color conversion accuracy. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the object, the present invention is characterized by comprising a reading control unit that causes a reading device to perform a reading operation on a color sample printout that serves as a sample for color matching and acquires the read image data of the color sample printout; an estimation request unit that requests estimation of a profile using a trained model in a machine learning server based on the read image data acquired by the reading control unit; a receiving unit that receives a second profile estimated by the machine learning server from a first profile and the read image data using the trained model in response to a request from the estimation request unit; and a printing control unit that applies the second profile to manuscript data to perform printing control. [Effects of the Invention]

[0007] According to the present invention, it is possible to improve the color conversion accuracy. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of the overall configuration of an information processing system according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of the image forming apparatus according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of a hardware configuration of a machine learning server or the like according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a functional block configuration of the information processing system according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating the overall operation of the information processing system according to the embodiment. [Figure 6] FIG. 6 is a flowchart showing an example of the flow of operations of the information processing system according to the embodiment. [Figure 7] FIG. 7 is a flowchart showing another example of the flow of operations of the information processing system according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating the overall operation of an information processing system according to a modified example. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of an image forming apparatus, an image forming method, and a program according to the present invention will be described in detail with reference to the drawings. Furthermore, the present invention is not limited to the following embodiments, and the components in the following embodiments include those that would be easily conceived by a person skilled in the art, those that are substantially the same, and those that are within the scope of what is called equivalents. Furthermore, various omissions, substitutions, modifications, and combinations of the components can be made without departing from the spirit of the following embodiments.

[0010] (Overall configuration of information processing system) 1 is a diagram showing an example of the overall configuration of an information processing system according to an embodiment, and the overall configuration of the information processing system 1 according to this embodiment will be described with reference to FIG.

[0011] The information processing system 1 shown in FIG. 1 is a system that generates an ICC profile (hereinafter sometimes simply referred to as a profile) using a trained model generated by machine learning from scanned image data of a color sample printout that serves as a sample for color matching, and performs color conversion on document data using the ICC profile. The profile includes, for example, a lookup table for color conversion from a specific color space to a different color space. As shown in FIG. 1, the information processing system 1 includes an image forming apparatus 10, a machine learning server 20, and a data server 30. The image forming apparatus 10, the machine learning server 20, and the data server 30 are capable of data communication with each other via a network N. As shown in FIG. 1, the machine learning server 20 and the data server 30 are server devices configured within a cloud system.

[0012] The image forming device 10 is an image forming device that performs a reading process on a color sample printout, and then performs color conversion on the original data using a profile estimated by a trained model in the machine learning server 20 based on the read image data, and then prints the data.

[0013] The machine learning server 20 is a server that estimates (generates) a profile to be applied to the image forming device 10 using a trained model generated by supervised machine learning using input data including previously generated profiles, scanned image data of color sample printouts, and the number of years of use, temperature, and humidity in the environment in which the color sample printouts were scanned.

[0014] The data server 30 is a server that collects, for example, past profiles applied to an image forming device 10, scanned image data of color sample printouts scanned by the image forming device 10, and the number of years of use, temperature, and humidity in the environment in which the color sample printouts were scanned.

[0015] (Hardware configuration of image forming device) 2 is a diagram showing an example of the hardware configuration of the image forming apparatus according to the embodiment, and the hardware configuration of the image forming apparatus according to the embodiment will be described with reference to FIG.

[0016] As shown in FIG. 2, the image forming apparatus 10 includes a controller 910, a short-range communication circuit 920, an engine control unit 930, an operation panel 940, a network I / F 950, and a sensor 960.

[0017] The controller 910 includes a CPU (Central Processing Unit) 901, which is the main part of the computer, a MEM-P (system memory) 902, a NB (north bridge) 903, a SB (south bridge) 904, an ASIC (Application Specific Integrated Circuit) 906, a MEM-C (local memory) 907, an HDD (Hard Disk Drive) controller 908, and an HD 909. Of these, the NB 903 and the ASIC 906 are connected by an AGP (Accelerated Graphics Port) bus 921.

[0018] The CPU 901 is a computing device that performs overall control of the image forming apparatus 10. The NB 903 is a bridge that connects the CPU 901 with the MEM-P 902, the SB 904, and the AGP bus 921, and includes a memory controller that controls reading and writing to and from the MEM-P 902, a PCI (Peripheral Component Interconnect) master, and an AGP target.

[0019] The MEM-P 902 includes a ROM (Read Only Memory) 902a, which is memory for storing programs and data that realize the functions of the controller 910, and a RAM (Random Access Memory) 902b, which is used for expanding the programs and data and as a drawing memory during memory printing. The programs stored in the RAM 902b may be provided by being recorded in an installable or executable file format on a computer-readable recording medium such as a CD-ROM (Compact Disc Read Only Memory), CD-R (Compact Disc Recordable), or DVD.

[0020] The SB 904 is a bridge for connecting the NB 903 with PCI devices, peripheral devices, etc. The ASIC 906 is an integrated circuit (IC) for image processing applications that has hardware elements for image processing and serves as a bridge connecting the AGP bus 921, PCI bus 922, HDD controller 908, and MEM-C 907. The ASIC 906 includes a PCI target and AGP master, an arbiter (ARB) that forms the core of the ASIC 906, a memory controller that controls the MEM-C 907, multiple direct memory access controllers (DMACs) that perform image data rotation using hardware logic, etc., and a PCI unit that transfers data between the scanner unit 931 and printer unit 932 via the PCI bus 922. A universal serial bus (USB) interface or an Institute of Electrical and Electronics Engineers 1394 (IEEE 1394) interface may be connected to the ASIC 906.

[0021] The MEM-C 907 is a local memory used as an image buffer for copying and a code buffer. The HD 909 is a storage for storing image data, font data used during printing, and forms. The HDD controller 908 is a controller that controls reading and writing of data from and to the HD 909 under the control of the CPU 901. The HDD controller 908 and the HD 909 may be SSDs (Solid State Drives).

[0022] The AGP bus 921 is a bus interface for a graphics accelerator card proposed to speed up graphics processing, and by directly accessing the MEM-P 902 at high throughput, the graphics accelerator card can be made faster.

[0023] The short-range communication circuit 920 is a communication circuit such as NFC (Near Field Communication) or Bluetooth. The short-range communication circuit 920 is electrically connected to the ASIC 906 via a PCI bus 922. An antenna 920a for wireless communication is connected to the short-range communication circuit 920.

[0024] The engine control unit 930 includes a scanner unit 931 (reading device) that reads an original, and a printer unit 932 that performs a printing operation. The scanner unit 931 and the printer unit 932 include image processing functions such as error diffusion or gamma conversion.

[0025] The operation panel 940 is equipped with a panel display section 940a such as a touch panel that displays the current setting values ​​or selection screens, etc., and accepts input from the user, and hard keys 940b consisting of a numeric keypad that accepts setting values ​​for image formation conditions such as density setting conditions, and a start key that accepts a copy start instruction.

[0026] Image forming apparatus 10 can sequentially switch among document box function, copy function, printer function, and fax function using the application switching key on operation panel 940. When the document box function is selected, the device enters document box mode, when the copy function is selected, the device enters copy mode, when the printer function is selected, the device enters printer mode, and when the fax function is selected, the device enters fax mode.

[0027] The network I / F 950 is an interface for data communication via a network, and is an interface capable of communication conforming to, for example, Ethernet (registered trademark), TCP (Transmission Control Protocol) / IP (Internet Protocol), etc. The network I / F 950 is electrically connected to the ASIC 906 via the PCI bus 922.

[0028] The sensor 960 is, for example, a sensor provided inside the image forming apparatus 10 to detect the temperature, humidity, etc. of the inside. Note that the sensor 960 may be installed outside the image forming apparatus 10 (for example, on the surface of the housing of the image forming apparatus 10) to detect the temperature, humidity, etc. of the environment in which the image forming apparatus 10 is installed.

[0029] Note that the hardware configuration of the information processing device constituting the image forming device 10 shown in FIG. 2 is an example, and it is not necessary to include all of the components shown in FIG. 2, or it may include other components.

[0030] (Hardware configuration of machine learning servers, etc.) 3 is a diagram showing an example of the hardware configuration of the machine learning server etc. according to the embodiment. The hardware configuration of the machine learning server 20 according to the present embodiment will be described with reference to FIG.

[0031] As shown in Figure 3, the machine learning server 20 includes a CPU 701, a ROM 702, a RAM 703, an auxiliary storage device 705, a media drive 707, a display 708, a network I / F 709, a keyboard 711, a mouse 712, and a DVD (Digital Versatile Disc) drive 714.

[0032] The CPU 701 is a computing device that controls the overall operation of the machine learning server 20. The ROM 702 is a non-volatile storage device that stores programs for the machine learning server 20. The RAM 703 is a volatile storage device that is used as a work area for the CPU 701.

[0033] The auxiliary storage device 705 is a storage device such as an HDD or SSD that stores various data, programs, and the like.

[0034] The media drive 707 is a device that controls reading and writing of data from and to a recording medium 706 such as a flash memory under the control of the CPU 701 .

[0035] The display 708 is a display device configured with a liquid crystal or organic EL (Electro-Luminescence) display, etc., that displays various information such as a cursor, a menu, a window, characters, or an image.

[0036] The network I / F 709 is an interface for communicating data with external devices such as the image forming apparatus 10 and the data server 30 via a network. The network I / F 709 is, for example, a network interface card (NIC) that supports Ethernet and is capable of wired or wireless communication compliant with TCP / IP or the like.

[0037] The keyboard 711 is an input device for selecting letters, numbers, and various instructions, moving the cursor, etc. The mouse 712 is an input device for selecting and executing various instructions, selecting a processing target, moving the cursor, etc.

[0038] The DVD drive 714 is a device that controls reading and writing of data from and to a DVD 713 such as a DVD-ROM or a DVD-R (Digital Versatile Disk Recordable) as an example of a removable storage medium.

[0039] The above-mentioned CPU 701, ROM 702, RAM 703, auxiliary storage device 705, media drive 707, display 708, network I / F 709, keyboard 711, mouse 712 and DVD drive 714 are communicatively connected to each other via a bus 710 such as an address bus and a data bus.

[0040] The hardware configuration of the machine learning server 20 shown in Figure 3 is an example, and does not necessarily include all of the components shown in Figure 3, or may include other components. For example, the machine learning server 20 may include a GPU. Furthermore, the machine learning server 20 is not limited to being configured as a single information processing device as shown in Figure 3, but may be configured as multiple information processing devices.

[0041] The data server 30 also conforms to the hardware configuration shown in FIG.

[0042] (Configuration and operation of functional blocks of information processing systems) Fig. 4 is a diagram showing an example of the configuration of functional blocks of an information processing system according to an embodiment. Fig. 5 is a diagram explaining the overall operation of the information processing system according to an embodiment. The configuration and operation of the functional blocks of the information processing system 1 according to this embodiment will be explained with reference to Figs. 4 and 5.

[0043] As shown in FIG. 4, the image forming device 10 includes a communication unit 101, a reading control unit 102, a device status detection unit 103 (status detection unit), an estimation request unit 104, a job control unit 105 (print control unit), and a memory unit 106.

[0044] The communication unit 101 is a functional unit that performs data communication with the machine learning server 20 via the network N via the network I / F 950.

[0045] The reading control unit 102 is a functional unit that causes the scanner unit 931 to perform a reading operation on a color sample printout or the like (step S11 in FIG. 5). As a result, the reading control unit 102 acquires read image data of the color sample printout. The read image data is composed of color values ​​of the hues included in the color sample printout.

[0046] The device status detection unit 103 is a functional unit that detects status information relating to the status of the image forming device 10 (e.g., the number of years of use, temperature, humidity, etc. of the image forming device 10) (step S14 in FIG. 5). For example, the device status detection unit 103 acquires the number of years of use based on the number of years of use measured by the image forming device 10. The device status detection unit 103 also acquires the temperature and humidity of the image forming device 10 detected by the sensor 960.

[0047] The estimation request unit 104 is a functional unit that requests the machine learning server 20 to estimate a profile using a trained model based on the scanned image data of the color sample printout acquired by the read control unit 102 and the age, temperature, and humidity of the image forming device 10 detected by the device state detection unit 103. Specifically, the estimation request unit 104 transmits a profile estimation request to the machine learning server 20 via the communication unit 101, along with the scanned image data and the age, temperature, and humidity of the image forming device 10 (steps S12 and S15 in FIG. 5). Then, the estimation request unit 104 receives the profile estimated (generated) by the machine learning server 20 ("profile (2)" shown in FIG. 5) via the communication unit 101 (step S20 in FIG. 5) and stores it in the storage unit 106. Note that the "receiving unit" in the present invention corresponds to the estimation request unit 104 or the communication unit 101.

[0048] The job control unit 105 is a functional unit that applies the profile received by the estimation request unit 104 to the manuscript data (i.e., performs color conversion using the profile) and controls printing on the printer unit 932 (step S21 in FIG. 5). This makes it possible to create a printed matter that achieves colors similar to those of the color sample printed matter.

[0049] The storage unit 106 is a functional unit that stores the read image data acquired by the read control unit 102 and the profile acquired by the estimation request unit 104. The storage unit 106 is realized by the HD 909 shown in FIG.

[0050] The above-described communication unit 101, reading control unit 102, device status detection unit 103, estimation request unit 104, and job control unit 105 are realized by executing a program by the CPU 901 shown in Fig. 2. Note that at least some of the communication unit 101, reading control unit 102, device status detection unit 103, estimation request unit 104, and job control unit 105 may be realized by a hardware circuit such as an FPGA (Field-Programmable Gate Array) or an ASIC.

[0051] Note that the functional units of the image forming apparatus 10 shown in FIG. 4 are conceptual representations of their functions, and are not limited to such configurations. For example, the multiple functional units illustrated as independent functional units in the image forming apparatus 10 shown in FIG. 4 may be configured as a single functional unit. On the other hand, the function of a single functional unit in the image forming apparatus 10 shown in FIG. 4 may be divided into multiple units and configured as multiple functional units. Furthermore, the functional units of the image forming apparatus 10 do not need to be configured as distinct software modules as shown in FIG. 4; it is sufficient that the functions of the functional units as a whole are realized by executing a program in the image forming apparatus 10.

[0052] As shown in FIG. 4, the machine learning server 20 includes a communication unit 201, a learning data generation unit 202, a machine learning unit 203, an estimation unit 204, and a storage unit 205.

[0053] The communication unit 201 is a functional unit that performs data communication via the network I / F 709 between the image forming apparatus 10 and the data server 30 over the network N. For example, the communication unit 201 receives, from the data server 30, scanned image data of a color sample printout, as well as the age of use, temperature, and humidity of the environment in which the color sample printout was scanned. The communication unit 201 also receives, from the image forming apparatus 10, scanned image data of the color sample printout, as well as the age of use, temperature, and humidity of the image forming apparatus 10 (steps S13 and S16 in FIG. 5).

[0054] The learning data generation unit 202 is a functional unit that generates learning data (teacher data) to be used in machine learning by the machine learning unit 203 from previously generated profiles, scanned image data of color sample prints received from the data server 30 by the communication unit 201, and the number of years of use, temperature, and humidity in the environment in which the color sample prints were scanned.

[0055] The machine learning unit 203 is a functional unit that performs machine learning using the training data (teacher data) generated by the training data generation unit 202, and generates a trained model for estimating a profile. The machine learning unit 203 stores the generated trained model in the storage unit 205.

[0056] The estimation unit 204 is a functional unit that, upon receiving a profile estimation request from the image forming apparatus 10 via the communication unit 201, estimates (generates) a profile to be applied to the image forming apparatus 10 ("profile (2)" shown in FIG. 5) using a trained model generated by the machine learning unit 203, using a previously estimated profile ("profile (1)" shown in FIG. 5) as input data, as well as the scanned image data of the color sample printout received along with the estimation request, and the age, temperature, and humidity of the image forming apparatus 10 as input data (step S17 in FIG. 5). Note that the "first profile" in the present invention corresponds to a profile used as input data for the trained model, and the "second profile" corresponds to a profile estimated using the trained model. The estimation unit 204 transmits the estimated profile to the image forming apparatus 10 via the communication unit 201 (step S18 in FIG. 5) and stores it in the storage unit 205 (step S19 in FIG. 5).

[0057] The storage unit 205 is a functional unit that stores the training data generated by the training data generation unit 202, the trained model generated by the machine learning unit 203, and the profile estimated by the estimation unit 204. The storage unit 205 is realized by the auxiliary storage device 705 shown in FIG. 3.

[0058] The communication unit 201, the learning data generation unit 202, the machine learning unit 203, and the estimation unit 204 are realized by executing a program by the CPU 701 shown in Fig. 3. Note that at least some of the communication unit 201, the learning data generation unit 202, the machine learning unit 203, and the estimation unit 204 may be realized by a hardware circuit such as an FPGA or an ASIC.

[0059] Note that the functional units of the machine learning server 20 shown in Figure 4 are conceptual representations of their functions, and are not limited to this configuration. For example, the multiple functional units illustrated as independent functional units in the machine learning server 20 shown in Figure 4 may be configured as a single functional unit. On the other hand, the function of a single functional unit in the machine learning server 20 shown in Figure 4 may be divided into multiple units and configured as multiple functional units. Furthermore, the functional units of the machine learning server 20 do not need to be configured as distinct software modules as shown in Figure 4; it is sufficient that the functions of the functional units as a whole are realized by executing a program on the machine learning server 20.

[0060] As shown in FIG. 4, the data server 30 includes a communication unit 301, a data collection and provision unit 302, and a storage unit 303.

[0061] The communication unit 301 is a functional unit that performs data communication with the machine learning server 20 via the network N via a network I / F 709.

[0062] The data collection and provision unit 302 is a functional unit that collects scanned image data of color sample prints, as well as the years of use, temperature, and humidity of the environment in which the color sample prints were scanned, from the image forming apparatus 10 or the like via the communication unit 301. The data collection and provision unit 302 stores the collected data in the storage unit 303. Furthermore, when machine learning is performed in the machine learning server 20, the data collection and provision unit 302 reads out the data from the storage unit 303 and provides it via the communication unit 301.

[0063] The storage unit 303 is a functional unit that stores the scanned image data of the color sample prints collected by the data collection and provision unit 302, as well as the years of use, temperature, humidity, etc. in the environment in which the color sample prints were scanned. The storage unit 303 is realized by the auxiliary storage device 705 shown in FIG. 3.

[0064] The communication unit 301 and the data collection and provision unit 302 described above are realized by a program being executed by the CPU 701 shown in Fig. 3. Note that at least a part of the communication unit 301 and the data collection and provision unit 302 may be realized by a hardware circuit such as an FPGA or an ASIC.

[0065] Note that the functional units of the data server 30 shown in FIG. 4 are conceptual representations of functions, and are not limited to such configurations. For example, the multiple functional units illustrated as independent functional units in the data server 30 shown in FIG. 4 may be configured as a single functional unit. On the other hand, the function of a single functional unit in the data server 30 shown in FIG. 4 may be divided into multiple units and configured as multiple functional units. Furthermore, the functional units of the data server 30 do not need to be configured as distinct software modules as shown in FIG. 4; it is sufficient that the functions of the functional units as a whole are realized by executing a program in the data server 30.

[0066] Furthermore, as will be described later with reference to FIG. 6 , the age, temperature, and humidity of the image forming apparatus 10 are not necessarily used in the machine learning performed by the machine learning unit 203 and the estimation process performed by the estimation unit 204. In this case, the image forming apparatus 10 may not have the apparatus state detection unit 103. That is, the machine learning unit 203 may perform machine learning using at least previously generated profiles and scanned image data of a color sample printout to generate a trained model for estimating a profile. Furthermore, the estimation unit 204 may use at least previously estimated profiles and scanned image data of a color sample printout received together with an estimation request as input data to estimate (generate) a profile to be applied to the image forming apparatus 10 using the trained model.

[0067] (Operational flow of information processing system) Fig. 6 is a flowchart showing an example of the operation flow of the information processing system according to the embodiment. Fig. 7 is a flowchart showing another example of the operation flow of the information processing system according to the embodiment. First, the operation flow of the information processing system 1 according to the present embodiment will be described with reference to Fig. 6. Fig. 6 describes an operation in which the machine learning and estimation process by the machine learning server 20 do not use the age, temperature, and humidity of the image forming apparatus 10.

[0068] <Step S31> The read control unit 102 of the image forming apparatus 10 causes the scanner unit 931 to perform a read operation on the color sample printout, and acquires read image data of the color sample printout, and then the process proceeds to step S32.

[0069] <Step S32> The estimation request unit 104 of the image forming apparatus 10 requests the machine learning server 20 to estimate a profile using a trained model based on the read image data of the color sample printout acquired by the reading control unit 102. Specifically, the estimation request unit 104 transmits the read image data and a profile estimation request to the machine learning server 20 via the communication unit 101. Then, the process proceeds to step S33.

[0070] <Step S33> When the estimation unit 204 of the machine learning server 20 receives a profile estimation request from the image forming apparatus 10 via the communication unit 201, the estimation unit 204 estimates (generates) a profile to be applied to the image forming apparatus 10 ("profile (2)" shown in FIG. 5) using a previously estimated profile ("profile (1)" shown in FIG. 5) and the scanned image data of the color sample printout received together with the estimation request as input data, using the trained model generated by the machine learning unit 203. Then, the process proceeds to step S34.

[0071] <Step S34> The estimation unit 204 transmits the estimated profile to the image forming apparatus 10 via the communication unit 201 and stores it in the storage unit 205. Then, the estimation request unit 104 receives the profile estimated (generated) by the machine learning server 20 via the communication unit 101 and stores it in the storage unit 106. Then, the process proceeds to step S35.

[0072] <Step S35> The job control unit 105 of the image forming apparatus 10 applies the profile received by the estimation request unit 104 to the document data (that is, performs color conversion using the profile), and performs print control on the printer unit 932.

[0073] Next, with reference to FIG. 7, an operation of using the age, temperature, and humidity of the image forming apparatus 10 in machine learning and estimation processing by the machine learning server 20 will be described.

[0074] <Step S41> The read control unit 102 of the image forming apparatus 10 causes the scanner unit 931 to perform a read operation on the color sample printout, and acquires read image data of the color sample printout, and then the process proceeds to step S42.

[0075] <Step S42> The apparatus status detection unit 103 of the image forming apparatus 10 detects the age, temperature, and humidity of the image forming apparatus 10 as status information related to the image forming apparatus 10. Then, the process proceeds to step S43.

[0076] <Step S43> The estimation request unit 104 of the image forming apparatus 10 requests the machine learning server 20 to estimate a profile using a trained model based on the scanned image data of the color sample printout acquired by the reading control unit 102, and the age, temperature, and humidity of the image forming apparatus 10 detected by the apparatus state detection unit 103. Specifically, the estimation request unit 104 transmits a profile estimation request to the machine learning server 20 via the communication unit 101, along with the scanned image data and the age, temperature, and humidity of the image forming apparatus 10. Then, the process proceeds to step S44.

[0077] <Step S44> When the estimation unit 204 of the machine learning server 20 receives a profile estimation request from the image forming device 10 via the communication unit 201, it uses a previously estimated profile ("profile (1)" shown in FIG. 5) as well as the scanned image data of the color sample printout received together with the estimation request, and the number of years of use, temperature, and humidity of the image forming device 10 as input data, and estimates (generates) a profile ("profile (2)" shown in FIG. 5) to be applied to the image forming device 10 using the trained model generated by the machine learning unit 203. Then, the process proceeds to step S45.

[0078] <Step S45> The estimation unit 204 transmits the estimated profile to the image forming apparatus 10 via the communication unit 201 and stores it in the storage unit 205. Then, the estimation request unit 104 receives the profile estimated (generated) by the machine learning server 20 via the communication unit 101 and stores it in the storage unit 106. Then, the process proceeds to step S46.

[0079] <Step S46> The job control unit 105 of the image forming apparatus 10 applies the profile received by the estimation request unit 104 to the document data (that is, performs color conversion using the profile), and performs print control on the printer unit 932.

[0080] As described above, in the image forming apparatus 10 according to this embodiment, the read control unit 102 causes the scanner unit 931 to perform a read operation on a color sample printout serving as a sample for color matching and acquires the read image data of the color sample printout, the estimation request unit 104 requests the machine learning server 20 to estimate a profile using a trained model based on the read image data acquired by the read control unit 102, the communication unit 101 receives a profile estimated by the machine learning server 20 using the trained model from a previously estimated profile and the read image data in response to the request from the estimation request unit 104, and the job control unit 105 applies the profile received by the communication unit 101 to the manuscript data to perform printing control. As a result, even if the color sample printout contains few color values, the color conversion accuracy can be improved by applying the estimated profile.

[0081] Furthermore, in the image forming apparatus 10 according to this embodiment, the apparatus status detection unit 103 detects status information relating to the status of the image forming apparatus 10, the estimation request unit 104 requests the machine learning server 20 to estimate a profile using a trained model based on the read image data acquired by the read control unit 102 and the status information detected by the apparatus status detection unit 103, and the communication unit 101 receives, in response to the request from the estimation request unit 104, profiles estimated in the past and a profile estimated by the machine learning server 20 using the trained model from the read image data and the status information. In this way, by using the status information relating to the status of the image forming apparatus 10 in the estimation process using the trained model, it is possible to further improve the color conversion accuracy.

[0082] (Variation) The information processing system 1 according to this modification will be described, focusing on the differences from the information processing system 1 according to the above-described embodiment. In this modification, an operation for reusing a profile estimated by the machine learning server 20 will be described. Note that the overall configuration of the information processing system 1 according to this modification, the hardware configuration of the image forming apparatus 10, the machine learning server 20, and the data server 30, and the configuration of the functional blocks of the information processing system 1 are the same as those described in the above-described embodiment.

[0083] 8 is a diagram illustrating the overall operation of an information processing system according to a modified example. The overall operation of the information processing system 1 according to this modified example will be described with reference to FIG. 8, focusing on the differences from the overall operation of the information processing system 1 according to the above-described embodiment.

[0084] The estimation unit 204 reads the estimated and saved profile ("profile (2)" shown in FIG. 8) from the storage unit 205, feeds back the profile, and reuses it in the estimation process of a new profile using the trained model. That is, the estimation unit 204 reuses the estimated profile as "profile (1)" shown in FIG. 8, and estimates a new profile using the trained model.

[0085] This allows high color conversion accuracy to be maintained by reusing the profile, even when, for example, it is desired to match the color to that of a specific image forming apparatus 10 before replacement.

[0086] In the above-described embodiments and modifications, when at least one of the functional units of the image forming apparatus 10, the machine learning server 20, and the data server 30 is realized by executing a program, the program is provided in advance in a ROM or the like. In the above-described embodiments and modifications, the programs executed by the image forming apparatus 10, the machine learning server 20, and the data server 30 may be provided by being recorded in an installable or executable file format on a computer-readable recording medium such as a CD-ROM (Compact Disc Read Only Memory), a flexible disk (FD), a CD-R (Compact Disk-Recordable), or a DVD (Digital Versatile Disc). In the above-described embodiments and modifications, the programs executed by the image forming apparatus 10, the machine learning server 20, and the data server 30 may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network. In the above-described embodiments and modifications, the programs executed by the image forming apparatus 10, the machine learning server 20, and the data server 30 may be provided or distributed via a network such as the Internet. Furthermore, in the above-described embodiments and variations, the programs executed by the image forming device 10, the machine learning server 20, and the data server 30 are modularly structured to include at least one of the functional units described above, and in terms of actual hardware, the CPUs 701 and 901 read and execute the programs from the above-described storage devices (e.g., auxiliary storage device 705, HD 909, etc.), thereby loading and generating the above-described functional units onto the main storage device.

[0087] The aspects of the present invention are as follows. <1> a reading control unit that causes a reading device to perform a reading operation on a color sample printout that serves as a sample for color matching, and acquires read image data of the color sample printout; an estimation request unit that requests a machine learning server to estimate a profile using a trained model based on the read image data acquired by the read control unit; a receiving unit that receives a second profile estimated by the machine learning server using the trained model from the first profile and the scanned image data in response to a request from the estimation request unit; a print control unit that applies the second profile to the document data to perform print control; The image forming apparatus is provided with the above. <2> The method further includes the step of: storing the second profile received by the receiving unit; <1> 2. The image forming apparatus according to claim 1, wherein: <3> a status detection unit that detects status information relating to a status of the image forming apparatus; the estimation request unit requests the machine learning server to estimate a profile using the trained model based on the read image data acquired by the read control unit and the state information detected by the state detection unit; and the receiving unit receives the first profile and the second profile estimated by the machine learning server using the trained model from the scanned image data and the status information in response to a request from the estimation request unit. <1> or <2> 2. The image forming apparatus according to claim 1, wherein: <4> The status information is the number of years of use, temperature, and humidity of the image forming apparatus. <3> 2. The image forming apparatus according to claim 1, wherein: <5> the machine learning server reuses the estimated second profile as the first profile and estimates a new second profile using the trained model. <1> or <2> 2. The image forming apparatus according to claim 1, wherein: <6> The trained model is generated by supervised learning. <1> or <2> 2. The image forming apparatus according to claim 1, wherein: <7> a reading control step of causing a reading device to perform a reading operation on a color sample printout serving as a sample for color matching, and acquiring read image data of the color sample printout; an estimation request step of requesting a profile estimation using a trained model in a machine learning server based on the acquired scanned image data; a receiving step of receiving a second profile estimated by the machine learning server using the trained model from the first profile and the scanned image data in response to the request made by the estimation request step; a print control step of applying the second profile to the manuscript data to perform print control; The image forming method has the following features. <8> On the computer, a reading control step of causing a reading device to perform a reading operation on a color sample printout serving as a sample for color matching, and acquiring read image data of the color sample printout; an estimation request step of requesting a profile estimation using a trained model in a machine learning server based on the acquired scanned image data; a receiving step of receiving a second profile estimated by the machine learning server using the trained model from the first profile and the scanned image data in response to the request made by the estimation request step; a print control step of applying the second profile to the manuscript data to perform print control; This is a program for executing the above. [Explanation of symbols]

[0088] 1. Information Processing Systems 10 Image forming device 20 Machine Learning Server 30 Data Server 101 Communications Department 102 Reading control unit 103 Device status detection unit 104 Estimation request part 105 Job control section 106 Storage section 201 Communications Department 202 Learning Data Generation Unit 203 Machine Learning Department 204 Estimation section 205 Storage section 301 Communications Department 302 Data Collection and Provision Department 303 Storage section 701 CPU 702 ROM 703 RAM 705 Auxiliary storage 706 Recording Media 707 Media Drive 708 Display 709 Network I / F 710 Bus 711 keyboard 712 Mouse 713 DVD 714 DVD drive 901 CPU 902 MEM-P 902a ROM 902b RAM 903 NB 904SB 906 ASIC 907 MEM-C 908 HDD Controller 909 HD 910 Controller 920 Near field communication circuit 920a antenna 921 AGP Bus 922 PCI bus 930 Engine control unit 931 Scanner 932 Printer section 940 Operation Panel 940a Panel display 940b hard key 950 Network I / F 960 Sensors N Network [Prior art documents] [Patent documents]

[0089] [Patent Document 1] Patent Publication No. 2021-019278

Claims

1. a reading control unit that causes a reading device to perform a reading operation on a color sample printout that serves as a sample for color matching, and acquires read image data of the color sample printout; an estimation request unit that requests a machine learning server to estimate a profile using a trained model based on the read image data acquired by the read control unit; a receiving unit that receives a second profile estimated by the machine learning server using the trained model from the first profile and the scanned image data in response to a request from the estimation request unit; a print control unit that applies the second profile to the document data to perform print control; An image forming apparatus comprising:

2. The image forming apparatus according to claim 1 , further comprising a storage unit that stores the second profile received by the receiving unit.

3. a status detection unit that detects status information relating to a status of the image forming apparatus; the estimation request unit requests the machine learning server to estimate a profile using the trained model based on the read image data acquired by the read control unit and the state information detected by the state detection unit; and The image forming apparatus according to claim 1 or 2, wherein the receiving unit receives the second profile estimated by the machine learning server using the trained model from the first profile, the scanned image data, and the status information in response to a request from the estimation request unit.

4. 4. The image forming apparatus according to claim 3, wherein the status information includes the number of years of use of the image forming apparatus, the temperature, and the humidity.

5. The image forming apparatus according to claim 1 , wherein the machine learning server reuses the estimated second profile as the first profile and estimates a new second profile using the trained model.

6. The image forming apparatus according to claim 1 , wherein the trained model is generated by supervised learning.

7. a reading control step of causing a reading device to perform a reading operation on a color sample printout serving as a sample for color matching, and acquiring read image data of the color sample printout; an estimation request step of requesting a profile estimation using a trained model in a machine learning server based on the acquired scanned image data; a receiving step of receiving a second profile estimated by the machine learning server using the trained model from the first profile and the scanned image data in response to the request made by the estimation request step; a print control step of applying the second profile to the document data to perform print control; An image forming method comprising the steps of:

8. On the computer, a reading control step of causing a reading device to perform a reading operation on a color sample printout serving as a sample for color matching, and acquiring read image data of the color sample printout; an estimation request step of requesting a profile estimation using a trained model in a machine learning server based on the acquired scanned image data; a receiving step of receiving a second profile estimated by the machine learning server using the trained model from the first profile and the scanned image data in response to the request made by the estimation request step; a print control step of applying the second profile to the document data to perform print control; A program to execute.

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