Electronic apparatus

Abstract

To provide a technique in an electronic apparatus that can store setting values for a plurality of setting targets and can reduce the time and effort for input operations of the setting values.SOLUTION: An MFP 1 can receive input of organization configuration information indicating a relationship among a plurality of users, and a user prompt indicating a relation between each user and a setting value of function restriction, and input the received input data and a unique prompt 26 to a trained model of a generative AI server 200. The unique prompt 26 is data indicating, on the basis of the input data, an instruction to demand outputting a setting value to be recommended for each user as output data in a format that can be interpreted by the MFP 1. The trained model has been trained to generate the output data on the basis of the input data. The MFP 1 can store, in a memory 12, the setting value to be recommended for every user indicated in the output data output from the trained model, in association with the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technical field disclosed in this specification relates to an electronic device capable of storing setting values ​​for a plurality of setting targets. [Background technology]

[0002] In recent years, printers equipped with multiple functions such as a copy function, a printer function, a scanner function, and a facsimile function have become known, as disclosed in Patent Document 1. Furthermore, a technology is also known for such multi-function printers to have a function for identifying users and to set whether each function can be used by each user. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-336402 Summary of the Invention [Problem to be solved by the invention]

[0004] When a printer administrator or user needs to input setting values ​​for multiple setting targets into an electronic device, the task of inputting these setting values ​​is time-consuming. [Means for solving the problem]

[0005] An electronic device made for the purpose of solving this problem is an electronic device comprising a user interface, a communication interface, and a memory, and is provided with a plurality of setting targets, and is capable of associating a setting value with each of the setting targets and storing it in the memory, and is configured to be able to operate in accordance with the setting value stored in the memory, the electronic device is further capable of accessing a server that stores a trained model via the communication interface, and the trained model has been trained to generate output data based on input data, the electronic device is further capable of executing an input data reception process that receives first user input data and second user input data via the user interface, the first user input data is data in a format that can be interpreted by the trained model, is data that indicates a plurality of elements and is data that indicates a relationship between the elements, and some of the elements include at least some of the setting targets out of the plurality of setting targets, and the second user input data is The trained model is data in an interpretable format, and is data indicating the relationship between each of the setting targets indicated as part of the elements of the first user input data and the setting value; the electronic device is further capable of executing an input process to input the first user input data and the second user input data accepted in the input data acceptance process and unique input data into the trained model via the communication interface; the unique input data is data indicating an instruction to output, based on the first user input data and the second user input data, the recommended setting value for each of the setting targets indicated as part of the elements of the first user input data as output data in an interpretable format by the electronic device; and the electronic device is further configured to be capable of executing a setting process to store, in the memory, the recommended setting value for each of the setting targets indicated in the output data output from the trained model, in association with each of the setting targets, after executing the input process.

[0006] The electronic device disclosed in this specification accepts, via a user interface, first user input data indicating the relationship between multiple elements including a setting target and second user input data indicating the relationship between the setting target and a setting value, and inputs the accepted data together with unique input data into a trained model. The unique input data is data indicating instructions to output recommended setting values ​​for each setting target in a format interpretable by the electronic device. The electronic device then stores the setting values ​​for each setting target in memory based on the output data from the trained model. This allows setting values ​​to be stored for multiple setting targets, reducing the user's effort in inputting setting values.

[0007] A control method for realizing the functions of the electronic device, a computer program, and a computer-readable storage medium storing the computer program are also novel and useful. [Effects of the Invention]

[0008] The technology disclosed in this specification realizes a technology for an electronic device that can store setting values ​​for a plurality of setting targets, and reduces the effort required to input these setting values. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is an explanatory diagram showing an overview of a system according to an embodiment of the present invention; [Figure 2] 10 is a flowchart illustrating an example of a procedure for a setting information reception process. [Figure 3] FIG. 10 is an explanatory diagram showing an example of a function restriction input field. [Figure 4] FIG. 10 is an explanatory diagram showing an example of a user list input field. [Figure 5] 10 is a flowchart showing the procedure of a setting support process. [Figure 6] FIG. 10 is an explanatory diagram showing an example of an input data receiving field. [Figure 7] FIG. 10 is an explanatory diagram showing an example of organizational structure information. [Figure 8]10 is a flowchart showing the procedure of a setting confirmation process. [Figure 9] FIG. 10 is an explanatory diagram showing an example of a preview screen. [Figure 10] 10 is a flowchart showing the procedure of a setting support process. DETAILED DESCRIPTION OF THE INVENTION

[0010] A first embodiment of an electronic device will be described in detail below with reference to the accompanying drawings. This specification discloses a multifunction peripheral (hereinafter referred to as "MFP") having various functions including an image forming function.

[0011] As shown in Fig. 1, the MFP 1 of this embodiment includes a controller 10 including a CPU 11 and a memory 12. The MFP 1 also includes a user interface (hereinafter referred to as "user IF") 13, a communication interface (hereinafter referred to as "communication IF") 14, a print engine 15, and a scanner 16, which are electrically connected to the controller 10. The MFP 1 is an example of an electronic device. The print engine 15 and the scanner 16 are examples of image forming engines. The print engine 15 is capable of printing as an image formation, and the scanner 16 is capable of reading as an image formation.

[0012] The CPU 11 of the MFP 1 executes various processes in accordance with programs read from the memory 12 and based on user operations. The memory 12 of the MFP 1 stores various programs and data, including an operating system (hereinafter referred to as "OS") 21, programs and data for implementing an embedded web server function (hereinafter referred to as "EWS") 22, function restriction information 23, user information 24, and a setting program 25. The MFP 1 can also store a unique prompt 26 and structure information 27. The memory 12 is also used as a work area when various processes are executed. A buffer provided in the CPU 11 is also an example of the memory 12. Details of the programs and data will be described later.

[0013] Note that an example of memory 12 is not limited to a ROM, RAM, HDD, etc. built into MFP 1, but may also be a storage medium that is readable and writable by CPU 11. A computer-readable storage medium is a non-transitory medium. In addition to the above examples, non-transitory media also include recording media such as CD-ROMs and DVD-ROMs. Non-transitory media are also tangible media. On the other hand, an electrical signal carrying a program downloaded from a server on the Internet is a computer-readable signal medium, which is a type of computer-readable medium, but is not included in non-transitory computer-readable storage media.

[0014] The user IF 13 includes hardware for displaying a screen for notifying the user of information and hardware for accepting operations by the user. The user IF 13 may include a touch panel having a screen display function and an operation acceptance function, or may include a combination of a display and hardware buttons, etc.

[0015] The communication IF 14 includes hardware for communicating with external devices, and includes functions compatible with communication standards such as Wi-Fi (registered trademark), Ethernet (registered trademark), and USB.

[0016] The MFP1 can be connected to a personal computer (hereinafter referred to as "PC") 3 via local wireless communication, local LAN communication, etc. The PC 3 has a browser 31 and can access the EWS 22 of the MFP1 via the browser 31. When the MFP1 receives access to the EWS 22 from the PC 3, it can send html data to the PC 3 for displaying an image on the browser 31. While displaying an image based on the html data received from the EWS 22 of the MFP1, the PC 3 can receive a user operation via the browser 31 and can send data indicating the received operation to the MFP1. When the MFP1 receives data indicating an operation via the browser 31 from the PC 3, it can execute processing based on that operation. In this case, the EWS 22 that provides the html data is an example of a user interface of the MFP1.

[0017] The MFP 1 can also connect to the Internet 100 via the communication IF 14 and can access the generation AI server 200 via the Internet 100. The generation AI server 200 is an example of a server that stores trained models. The trained models stored in the generation AI server 200 have been trained in advance using various types of data so as to generate output data based on input data. The generation AI server 200 also has a chat function. When a question is input to the chat function, the generation AI server 200 can output an answer generated by the trained model based on the input question to the device that input the question. Note that, although a question in Japanese (hereinafter referred to as a "prompt") will be exemplified below, the language used as the prompt is not limited to Japanese. The generation AI server 200 is, for example, a server incorporating OpenAI's ChatGPT.

[0018] The MFP 1 of this embodiment is shared by multiple users. The MFP 1 can store multiple pieces of information about users who can use the MFP 1 as user information 24, and when a login instruction is received, can perform login authentication based on the user information 24. The MFP 1 also has multiple groups to which each user belongs, and can store information about the group to which each user belongs in the user information 24.

[0019] The MFP 1 of this embodiment has multiple image forming functions, including, for example, printing, copying, scanning, fax sending, fax receiving, and uploading and downloading via cloud connection. The MFP 1 can accept settings for function restrictions that restrict the use of each function by each user. Specifically, the MFP 1 can store, as function restriction information 23, setting values ​​that indicate whether each of the multiple image forming functions is available for use for each group to which each user belongs.

[0020] As a result, when the MFP 1 accepts a login instruction from a user and succeeds in login authentication, it identifies the group to which the logged-in user belongs based on the user information 24, and acquires the setting values ​​set for that group based on the function restriction information 23. The MFP 1 is configured to operate in accordance with the acquired setting values. For example, if information indicating that printing is available but scanning is not available for the group to which the logged-in user belongs is stored, the MFP 1 enables printing operations by the logged-in user, but disables scanning operations by the logged-in user.

[0021] Next, a description will be given of a setting procedure that can be executed by the MFP 1. The following processing basically refers to processing by the CPU 11 in accordance with instructions written in a program. In other words, in the following description, processes such as "judging," "extracting," "selecting," "calculating," "deciding," "identifying," "acquiring," "receiving," and "control" refer to CPU processing. Processing by the CPU also includes hardware control using an OS API. In this specification, the operation of each program will be described without mentioning the OS. In other words, in the following description, a statement to the effect that "Program B controls Hardware C" may also mean that "Program B controls Hardware C using the OS API." Furthermore, CPU processing in accordance with instructions written in a program may be described in abbreviated terms. For example, it may be described as "performed by the CPU." Furthermore, CPU processing in accordance with instructions written in a program may be described in abbreviated terms, such as "performed by Program A."

[0022] Note that "obtaining" is used as a concept that does not require a request. In other words, the process of receiving data without the CPU requesting it is also included in the concept of "the CPU obtaining data." Furthermore, "data" in this specification is represented as a bit string that can be read by a computer. Data with the same substantial meaning but different formats will be treated as the same data. The same applies to "information" in this specification. Furthermore, "requesting" and "instructing" are concepts that indicate outputting information indicating a request or an instruction to the other party. Furthermore, information indicating a request or an instruction will also be simply referred to as a "request" or "instruction."

[0023] Furthermore, the process by a CPU to determine whether information A indicates event B is sometimes conceptually described as "determining whether event B is true from information A." The process by a CPU to determine whether information A indicates event B or event C is sometimes conceptually described as "determining whether event B or event C is true from information A."

[0024] The procedure for the setting information reception process executed by the MFP1 will be described with reference to the flowchart in Figure 2. The setting information reception process is executed by the CPU 11 of the MFP1 when a setting start instruction for making various settings on the MFP1 is received. The following describes settings related to function restrictions among the various settings of the MFP1, and omits descriptions of other settings. Furthermore, the contents described below as displays and operations on the user IF 13 may also be displays and operations via the browser 31 of the PC 3 by the EWS 22, as described above.

[0025] The MFP 1 may be able to accept settings related to function restrictions only when it accepts a login from a user with administrator privileges. In the following, the user who issues an instruction to start the setting information acceptance process and performs the setting operation is referred to as the "administrator," and multiple users who use the MFP 1 after the settings are made are referred to as "users."

[0026] When the CPU 11 receives an instruction to start setting, it displays a setting menu including options for accepting settings related to function restrictions on the user IF 13 (S101) and accepts the administrator's selection (S102). The setting menu includes, for example, a function restriction button 41 for accepting an instruction to set function restrictions, a user list setting button 42 for accepting an instruction to register to a user list, and a setting support button 43 for accepting an instruction for setting support, as shown in FIG. 3. Note that the CPU 11 can accept various settings in addition to settings related to function restrictions in the setting menu. In S101, the CPU 11 displays a screen including an initial image of the setting menu instead of the input field 51.

[0027] When it is determined that an operation by the administrator on the function restriction button 41 in the setting menu has been accepted (S102: function restriction), the CPU 11 causes the user IF 13 to display an input field 51 for accepting input of information to be stored in the function restriction information 23 (S111), as shown in Fig. 3. As the input field 51, the CPU 11 displays, for example, a group name 511 and function information 512 in association with each other.

[0028] Group name 511 is a field that accepts input of a name indicating each group when multiple users are divided into multiple groups. The group name input in group name 511 is a name that is arbitrarily set by the administrator. The group name may be, for example, the name of the department to which the user belongs, such as general affairs department, development department, or sales department, or the name of a job title, such as executive, full-time employee, or part-time worker, or may be a classification name that classifies each user based on years of service, qualifications, etc. The "general mode" in group name 511 is a mode that does not require authentication and can be used by anyone. In other words, the MFP 1 considers a user who is not logged in to be a "general mode" user.

[0029] Function information 512 is a field for accepting input of setting values, which are information on whether each of the multiple image forming functions of MFP 1 is available for use, for each group indicated by group name 511. Function information 512 has multiple items corresponding to each function of MFP 1, and a check box is provided for each item. Information accepted in each check box indicates whether the item is available for use. Items that can be accepted in function information 512 include the image forming functions such as printing, copying, and scanning described above, and may also include restrictions on the number of prints, restrictions on color printing and monochrome printing, and restrictions on default values ​​for print settings, etc. Note that FIG. 3 shows an example of input field 51 in an initial state, where the group name is blank and all items are available.

[0030] Then, the CPU 11 accepts input operations for the group name 511 and the function information 512 (S112). S112 is an example of a setting acceptance process. When manually inputting, the administrator needs to input the name of each group in the group name 511, and further set whether or not to check each item in the function information 512 for each group.

[0031] The CPU 11 determines whether an operation to confirm the information input in the input field 51 has been received (S113). The instruction to confirm the information in S113 is an example of a setting instruction. For example, the CPU 11 determines that a confirmation operation has been received when an operation on the OK button 515 has been received. If it determines that a confirmation operation has been received (S113: YES), the CPU 11 stores the information being displayed in the input field 51 as function restriction information 23 (see FIG. 1) in the memory 12 (S115). That is, the CPU 11 stores information indicating each group name input in the group name 511 and information indicating the setting value of the function restriction indicated by whether or not each item in the function information 512 is checked, in association with each other.

[0032] After setting the function restriction information 23 via an operation on the input field 51, the administrator operates the user list setting button 42 shown in Fig. 3. When it is determined that the administrator's operation on the user list setting button 42 in the setting menu has been accepted (S102: User List), the CPU 11 causes the user IF 13 to display an input field 52 for accepting information about each user to be stored in the user information 24 (S121), as shown in Fig. 4, for example. The CPU 11 displays the input field 52 in which the fields for accepting input of, for example, a user name 521, a password 522, a user ID 523, and a group 524 are associated with each other.

[0033] User name 521 and password 522 are information used for user login authentication. User ID 523 is identification information that identifies each user. Group 524 is information indicating a group to which this user belongs, and is information indicating one of the group names accepted in group name 511 of input field 51 (see FIG. 3) described above. CPU 11 may, for example, display each group name entered in group name 511 as a choice in group 524, and accept the designation of a group by selection from the choices.

[0034] Then, the CPU 11 accepts input operations into each field of the input field 52 (S122). S122 is an example of a setting acceptance process. For example, as shown in FIG. 4, the administrator enters the user name "User SA," enters the password and user ID corresponding to "User SA," and enters the "General Affairs Department," the department to which the user indicated by "User SA" belongs, as the group name. The administrator then enters information for the user name "User SB" in the same manner. FIG. 4 shows a state in which information about the user name "User SB" is being entered; in this state, the user ID and group name for "User SB" have not yet been entered. In this way, when entering information manually, the administrator needs to enter the user name 521, password 522, and user ID 523 for each user, and select a group 524.

[0035] The CPU 11 determines whether an operation to confirm the information input in the input field 52 has been received (S123). The instruction to confirm the information in S123 is an example of a setting instruction. For example, the CPU 11 determines that a confirmation operation has been received when an operation on the OK button 525 has been received. If it determines that a confirmation operation has been received (S123: YES), the CPU 11 stores the information received in the input field 52 as user information 24 (see FIG. 1) in the memory 12 (S125). The CPU 11 associates at least each user identified by a pair of the user name 521 and the password 522 with the group name received in the group 524 and stores the information as user information 24.

[0036] In this way, by storing function restriction information 23 and user information 24 in memory 12, the administrator of MFP 1 can set, for each user, setting values ​​indicating whether each item of various functions of MFP 1 can be used. A user is an example of a setting target. S115 and S125 are examples of manual setting processing. Since MFP 1 can accept setting values ​​for each group, even if there are many users, setting values ​​for each user can be stored via the group.

[0037] However, especially when the organization is large and there are many users and groups, the amount of information to be entered increases, placing a heavy burden on the administrator. The MFP1 may have a function to read information to be entered into each input field shown in Figures 3 and 4 from a CSV file prepared by the administrator. However, even in this case, the administrator must write the information to be entered into each input field into a file, which places a heavy burden on the administrator.

[0038] The CPU 11 may be able to accept an instruction to cancel operations on the input field 51 or the input field 52. Also, although the example has been described in which the administrator performs an operation to set the function restriction information 23 and then an operation to set the user information 24, the operations may be performed in the reverse order.

[0039] On the other hand, if it is determined that an operation by the administrator to the setting support button 43 has been accepted in the setting menu displayed in S101 (S102: setting support), the CPU 11 executes setting support processing (S131). The procedure of the setting support processing will be described with reference to the flowchart shown in FIG.

[0040] The CPU 11 displays an input data acceptance field for setting support on the user IF 13 (S201) and accepts input from the administrator. S201 is an example of an input data acceptance process. For example, as shown in FIG. 6, the CPU 11 displays an input data acceptance field 61 including a first input button 611 for accepting input of organization configuration information, a second input field 612 for accepting input of a prompt, an execute button 615, and a cancel button 616.

[0041] First input button 611 is a button that accepts selection of data indicating organizational configuration information. The data indicating organizational configuration information includes information indicating the relationship between the user name of each user who uses MFP1 and the group name indicating the group to which the user belongs, and is data in which the relationship between the user name and the group name is shown in a diagram or table. User names and group names are examples of data indicating elements, and information indicating the group to which each user belongs is an example of data indicating the relationship between each element. The input data selected by first input button 611 is an example of first user input data.

[0042] If the group names are the names of the departments to which the group belongs, such as "General Affairs Department," "Development Department," and "Sales Department," the administrator can select, as data indicating organizational structure information, image data showing each department name associated with each user's username, such as "User SA," as shown in FIG. 7. As shown in FIG. 7, the organizational structure information has a hierarchical structure including group names and the usernames of users belonging to the groups. The organizational structure information is an example of hierarchical structure data indicating a hierarchical structure, where the group names are an example of elements in the first hierarchical level, and the usernames are an example of elements in the second hierarchical level, which is a lower level belonging to the first hierarchical level.

[0043] The input data selected by the first input button 611 is data that will be input to the trained model of the generation AI server 200 in a later procedure. The trained model of the generation AI server 200 may be able to analyze image data and interpret the meaning of the image data. In this case, the trained model can interpret from the input image data that it is an organizational chart of an organization consisting of multiple groups, including the usernames of users belonging to each group. The trained model of the generation AI server 200 may also be able to analyze data including text information and interpret the meaning of the data. In this case, the trained model can interpret from the data including text information describing information equivalent to FIG. 7 that it is text information indicating an organization consisting of multiple groups, including the usernames of users belonging to each group.

[0044] The format of the image data selected by the first input button 611 may be JPEG, PNG, GIF, PDF, etc. The format of the data including text information selected by the first input button 611 may be PDF, CSV, XLS, or an unstructured format such as text in which an explanation of an organizational chart is written in sentences. A prompt is also an example of data that can include unstructured text information.

[0045] Furthermore, the input data selected with the first input button 611 may be data including information on all users to be set, or may be data including information on some of the users to be set. The input data may be, for example, image data representing only the general affairs department among the information shown in FIG. 7. Furthermore, with the first input button 611, the CPU 11 may be able to accept the selection of multiple pieces of input data. For example, when image data representing only the general affairs department, image data representing only the development department, and image data representing only the sales department are selected, the CPU 11 may use all of the image data as input data. Furthermore, the input data does not have to include a password and a user ID.

[0046] The second input field 612 is a field for accepting a user prompt, which is a prompt expressed in text form by inputting information about function restrictions to be set for each group. The user prompt entered in the second input field 612 is data indicating the relationship between each group name included in the organizational structure information selected with the first input button 611 and the setting value of the function restrictions to be assigned to the group indicated by that group name. The input data accepted in the second input field 612 is an example of second user input data. The second input field 612 is an example of a prompt input screen.

[0047] The user prompt entered in the second input field 612 is data indicating the relationship between each user included in the organizational structure information selected with the first input button 611 and the setting value of the function restriction via the group to which the user belongs. For example, in the organizational structure information shown in Fig. 7, when groups are formed by the names of the departments to which the users belong, the user prompt entered in the second input field 612 may be, for example, the following sentence: "Please assign permissions so that the General Affairs and Development departments can only print, and the Sales department can only print and scan."

[0048] Because the MFP1 can accept function restriction setting values ​​for each group, the administrator can input information indicating the setting values ​​for each group as a user prompt in the second input field 612. Also, for example, if data indicating an organizational structure already exists, the administrator can specify the data indicating the organizational structure by selecting it with the first input button 611. Therefore, even if there are a large number of users, the effort required for users to input information can be reduced. Furthermore, because the function restriction setting values ​​can be specified in natural language for each group as a user prompt, even an administrator who is unfamiliar with setting up the MFP1 can easily input data. In other words, there is a high degree of freedom in the data that can be selected with the first input button 611 and the data that can be entered in the second input field 612, making the MFP1 easy to use.

[0049] Note that the input data selected with the first input button 611 and the input data entered in the second input field 612 may be of the same format. For example, the CPU 11 may be able to accept the input of a prompt instead of accepting the selection of image data with the first input button 611. The CPU 11 may also be able to accept the selection of data in the second input field 612, for example, may be able to accept the selection of data indicating an organizational structure. If the input data selected with the first input button 611 and the input data entered in the second input field 612 may be of the same format, it will be easier for the administrator to prepare the input data.

[0050] Then, the CPU 11 determines whether or not an operation on the execute button 615 has been accepted in the input data acceptance field 61 being displayed (S203). If it is determined that an operation on the execute button 615 has not been accepted (S203: NO), the CPU 11 determines whether or not an operation on the cancel button 616 has been accepted (S204). If it is determined that an operation on the cancel button 616 has not been accepted (S204: NO), the CPU 11 waits until an operation on the execute button 615 or the cancel button 616 is accepted.

[0051] If it is determined that an operation on the execute button 615 has been accepted (S203: YES), the CPU 11 determines whether the input data selected with the first input button 611 and the input data entered in the second input field 612 are appropriate (S206). If it is determined that the input data are inappropriate (S206: NO), the CPU 11 displays an error message indicating that there is an error in the input (S207), and proceeds to S201 to display the input data acceptance field 61 again. The CPU 11 may highlight the input portion with an error. For example, if no data is selected with the first input button 611, if the data selected with the first input button 611 is unreadable data, or if no data is entered in the second input field 612, the CPU 11 determines that the input data is inappropriate.

[0052] If it is determined that the two pieces of input data are appropriate (S206: YES), the CPU 11 acquires the specific prompt 26 and the structure information 27 (S211). If the specific prompt 26 and the structure information 27 are stored in the memory 12 (see FIG. 1), the CPU 11 acquires them by reading them from the memory 12. The specific prompt 26 is an example of specific input data.

[0053] Unique prompt 26 is data indicating an instruction to request that setting values ​​of function restrictions recommended for each user be output as output data in a format that can be interpreted by MFP 1, based on two pieces of input data. Unique prompt 26 includes an instruction to generate output data based on the input data, an instruction to output the generated output data to MFP 1, and an instruction to limit the format of the generated output data.

[0054] Structural information 27 is data indicating a format of information that can be interpreted by the MFP 1, and is data indicating the structure of information that can be used to set function restrictions on the MFP 1. If the MFP 1 has a setting function that stores information in the above-mentioned function restriction information 23 and user information 24 based on setting data described in JSON format, structural information 27 may be information indicating a JSON schema. In this case, the JSON schema includes parameter information that specifies a format that can be input to the MFP 1. For example, it includes information specifying that a character string is to be entered in group name 511 in input field 51 (see FIG. 3) and user name 521 in input field 52 (see FIG. 4).

[0055] When the structural information 27 is information indicating a json schema, the specific prompt 26 is data indicating an instruction to output output data in which information indicating recommended function restriction setting values ​​is arranged in a json format, which is a format that can be interpreted by the MFP1, for each user indicated in the organizational structure information selected by the first input button 611, based on the user prompt entered in the second input field 612.

[0056] In this case, the specific prompt 26 may be written, for example, as follows: "Based on two pieces of input data, output data that conforms to the JSON schema format. The output data should be JSON information only. In the JSON information, describe the user name and group name in a correspondence based on the input data. In the userlist JSON schema, username corresponds to the user name. groupname corresponds to the group to which the user belongs. Based on the input data, describe availability information in restrictedfunction, corresponding to the group name. groupname in the JSON schema, groupname corresponds to the group, print corresponds to printing, copy corresponds to copying, faxsend corresponds to sending a fax, faxrecv corresponds to receiving a fax, etc. For print, copy, faxsend, faxrecv, etc., enter 1 if available, or 0 if unavailable."

[0057] Note that the unique prompt 26 and the structural information 27 may be stored in the memory 12 at the time of shipping from the factory, or may be acquired by the MFP 1 from an external device such as a server after accepting an administrator's operation on the setting support button 43 (see FIG. 3 or 4) on the setting menu displayed in S101 of the setting information acceptance process (see FIG. 2). Also, the unique prompt 26 and the structural information 27 may be different information for each model.

[0058] The CPU 11 inputs the two pieces of input data received in the input data reception field 61 displayed in S201, and the unique prompt 26 and structural information 27 acquired in S211, into the trained model by passing them to the generation AI server 200 (S212). The CPU 11 may pass the four pieces of data together to the generation AI server 200, or may pass them in order, starting with some of the data. S212 is an example of input processing.

[0059] The generation AI server 200 may perform various processes on the data received from the MFP 1 without significantly altering its meaning before inputting it into the trained model. For example, the generation AI server 200 may perform filtering, such as a process that emphasizes the features of the input data or a process that removes noise from the input data. In this specification, the case where data transmitted by the MFP 1 is input to the trained model after undergoing various processes is also included in the category of the MFP 1 inputting data to the trained model.

[0060] The trained model of the generation AI server 200 generates an answer based on each input prompt and each data, and outputs the generated answer to the MFP1. Therefore, after executing S212, the CPU 11 can receive the answer generated by the trained model (S213). In other words, by passing four pieces of data to the trained model, namely the two pieces of data input by the administrator and the two pieces of data acquired by the MFP1, it is expected that the trained model will produce output data in a format usable by the MFP1.

[0061] As with input data, the generation AI server 200 may perform various processes on the data output by the trained model without significantly altering its meaning, and then transmit the data to the MFP 1. In this specification, the case where the MFP 1 receives data that has been subjected to various processes after being output by the trained model is also included in the category of the MFP 1 receiving data output by the trained model.

[0062] The CPU 11 determines whether the received answer conforms to the structure specified by the structure information 27 (S215). If it is determined that the answer conforms to the specified structure (S215: YES), the CPU 11 acquires the output data output this time from the generation AI server 200 as temporary setting information (S216).

[0063] The provisional configuration information that conforms to the specified structure is data in JSON format. The userlist of the provisional configuration information describes a user name such as "User SA" and a group name such as "General Affairs Department" that are associated based on the input data. For example, "User SA" to "User SC" are associated with the "General Affairs Department," and "User KA" to "User KB" are associated with the "Development Department." Furthermore, the username of the provisional configuration information describes "User SA" and the like that was shown in the data indicating the organizational structure. The groupname describes "General Affairs Department" and the like that was shown in the data indicating the organizational structure.

[0064] Additionally, restrictedfunction describes information indicating whether or not each item, such as printing or copying, is permitted, in association with a group name such as "General Affairs Department," based on the input data. Specifically, if printing is permitted, "1" is written in print, and if printing is not permitted, "0" is written in print. For example, in the case of the input data described above, "1" is written for "General Affairs Department" and "Development Department" only for printing, indicating that it is permitted, and "0" is written for other functions, such as copying, indicating that they are not permitted.

[0065] On the other hand, if it is determined that the response is not compatible (S215: NO), the CPU 11 discards the currently obtained response. For example, if the MFP 1 cannot interpret the output data or if the obtained response is not written in JSON format despite the input of information indicating a JSON schema as the structure information 27, the CPU 11 determines that the response is not compatible.

[0066] If it is determined in S215 that the data are not compatible, the CPU 11 determines whether the number of times the four data passed in S212 have been passed to the generation AI server 200 to request an answer has exceeded a specified number of times (S218). If it is determined that the specified number of times has not been exceeded (S218: NO), the CPU 11 passes the same four data as last time to the generation AI server 200 again (S212). Since the trained model of the generation AI server 200 may output a different answer from the previous time even when the same data is input, an answer different from the previous one can be expected. The specified number of times is, for example, three times.

[0067] If it is determined that an answer that matches the specified structure has not been obtained despite inputting the specified number of times (S218: YES), the CPU 11 displays an error screen indicating that an appropriate answer has not been obtained (S219). For example, the CPU 11 may display a message prompting the user to change the input data and input it again.

[0068] After S216, or after S219, or if it is determined that an operation on the cancel button 616 has been accepted in the input data acceptance field 61 (see FIG. 6) displayed in S201 (S204: YES), the CPU 11 terminates the setting support process and returns to the setting information acceptance process of FIG. 2.

[0069] Returning to the explanation of the setting information reception process in Figure 2, after the setting support process in S131, the CPU 11 determines whether or not temporary setting information has been acquired (S132). If it is determined in S216 of the setting support process that the output data output from the generation AI server 200 has been acquired as temporary setting information (S132: YES), the CPU 11 executes setting confirmation processing (S133). The procedure of the setting confirmation processing will be explained with reference to the flowchart in Figure 8.

[0070] In the setting confirmation process, the CPU 11 causes the user IF 13 to display a preview screen showing the function restriction information 23 and the user information 24 in a state in which the provisional setting information acquired in S216 of the setting support process has been set as function restriction information for the MFP 1 (S301). S301 is an example of display processing. For example, as shown in FIG. 9, the CPU 11 displays a preview screen 71 including provisionally set function restriction 711, a provisionally set user list 712, an apply button 715, and a not apply button 716 based on the provisional setting information. The function restriction 711 shown in FIG. 9 shows a state in which the provisional setting information has been applied to the input field 51 (see FIG. 3). The user list 712 shown in FIG. 9 shows a state in which the provisional setting information has been applied to the input field 52 (see FIG. 4).

[0071] The CPU 11 accepts an operation by the administrator on the displayed preview screen 71 (S302). Specifically, the CPU 11 can accept an operation indicating an instruction to modify the displayed function restriction 711 or user list 712, and an operation on the apply button 715 or the not apply button 716. Although some parts are omitted in FIG. 9, the user list 712 on the preview screen 71 may include input fields for a password and a user ID, similar to the input field 52 for the user list shown in FIG. 4. In this case, the administrator can input a password and a user ID on the preview screen 71.

[0072] The CPU 11 copies the temporary setting information acquired in S216 of the setting support process to RAM and can change the copied information in response to an input operation by the administrator. For example, when an operation is received in each input field of the function restriction 711 or the user list 712 on the preview screen 71 (S302: input operation), the CPU 11 reflects the operation on the preview screen 71 and receives further instructions.

[0073] When an operation on the Apply button 715 is accepted (S302: Apply), the CPU 11 stores the provisional setting information in the memory 12 as confirmed setting information (S315). S315 is an example of setting processing. Note that if the provisional setting information has not been changed by an input operation by the administrator, the CPU 11 stores the provisional setting information acquired in S216 of the setting support processing in the memory 12 as is in S315. In this case, the provisional setting information becomes confirmed setting information. In other words, the Apply button 715 can also be said to be a button that accepts a selection of whether or not to store in the memory 12 the setting value displayed based on the information indicated in the output data output from the trained model. S302, which accepts an operation on the Apply button 715, is an example of preview processing.

[0074] If the provisional setting information has been changed by the administrator's input operation, the CPU 11 stores the changed information as setting information in S315. The MFP 1 can accept, on the preview screen 71, a correction instruction for the answer received from the trained model. Therefore, even if there is an error in part of the provisional setting information obtained from the trained model, the administrator can obtain appropriate setting information by simply correcting that error, without having to query the trained model again. Also, as shown in FIG. 9, the preview screen 71 has a similar configuration to the input screen for the aforementioned manual input, i.e., the input field 51 (see FIG. 3) that accepts the function restriction information 23 and the input field 52 (see FIG. 4) that accepts the user information 24. Therefore, the administrator can easily check or correct the output data output from the trained model.

[0075] On the other hand, when an operation on the "do not apply" button 716 is received (S302: do not apply), the CPU 11 discards the temporary setting information (S316). In this case, the CPU 11 does not store the setting information in the memory 12.

[0076] The MFP 1 does not immediately confirm the setting information output from the trained model, but displays it as provisional setting information on the preview screen 71 and accepts a selection from the administrator as to whether or not to confirm the content, i.e., whether or not to store it in the memory 12. In other words, the MFP 1 determines whether or not to store the setting information obtained from the trained model in the memory 12, in accordance with the selection accepted on the preview screen 71. Therefore, if there is an error in the setting information obtained from the trained model, the possibility of the erroneous setting information being stored as is is reduced.

[0077] The MFP 1 can accept manual settings for function restrictions via input field 51 (see FIG. 3) or input field 52 (see FIG. 4), but instead of performing such input operations, temporary setting information can be acquired and stored in memory 12 by inputting organizational structure information and a user prompt into first input button 611 and second input field 612. Therefore, instead of inputting the user names of many users, the administrator only needs to prepare data indicating the organizational structure, thereby reducing the effort required for input operations.

[0078] After S315 or S316, the CPU 11 hides the currently displayed preview screen 71 (S317), ends the setting confirmation process, and returns to the setting information reception process.

[0079] Even if the format of the output data output from the generation AI server 200 is appropriate and YES is determined in S215 of the setting support process (see FIG. 5), there is a possibility that the information structure will not match when attempting to set the function restriction information 23 and user information 24 based on that output data. If the CPU 11 determines in S301 that the preview screen cannot be displayed appropriately, it may notify an error or may execute the setting support process again.

[0080] Returning to the explanation of the procedure for the setting information reception process in Figure 2. After any of S115, S125, and S133, or if it is determined that appropriate output data has not been obtained from the generation AI server 200 during the setting support process (S132: NO), the CPU 11 determines whether an instruction to end the setting has been received (S141). If it is determined that an instruction to end the setting has not been received (S141: NO), the CPU 11 proceeds to S101 and displays the setting menu.

[0081] 3 and 4, the setting menu includes a plurality of options other than those shown in Fig. 3 and 4, and the CPU 11 can also accept an instruction to end the setting while the setting menu is displayed. If it is determined that an instruction to end the setting has been accepted (S141: YES), the CPU 11 ends the setting information acceptance process.

[0082] As described above in detail, the MFP 1 of the first embodiment receives, via an interface such as the EWS 22 or the user IF 13, organizational structure information indicating the relationship between each target user and the group to which the user belongs, and a user prompt indicating the setting of functional restrictions for each group. The MFP 1 then inputs the received data, along with the unique prompt 26 and structure information 27 prepared in advance, into the trained model. The unique prompt 26 and structure information 27 are information indicating instructions to output recommended setting values ​​for each user in a format interpretable by the MFP 1. The MFP 1 can then store information indicating the setting values ​​for each user in the memory 12 based on the output data from the trained model. This eliminates the need to input information for each user, even when storing setting values ​​for a large number of users, thereby reducing the administrator's workload for inputting setting values.

[0083] Next, a second embodiment of the electronic device will be described in detail with reference to the accompanying drawings. The second embodiment differs from the first embodiment only in the structural information and unique prompts input to the trained model, and the procedures of each process are the same as those of the first embodiment. In the following, the same reference numerals are used for the same configurations and procedures as those of the first embodiment, and descriptions thereof will be omitted.

[0084] The MFP 1 of the second embodiment uses html data instead of a json schema as structure information 27. For example, depending on the model, the MFP 1 may not have a json schema that indicates the structure of information that can be used to set function restrictions. Even such an MFP 1, if it has an EWS 22, for example, has html data for displaying, on the browser 31, an input field 51 (see FIG. 3) that accepts input of function restriction information 23 and an input field 52 (see FIG. 4) that accepts input of user information 24. The MFP 1 of the second embodiment passes this html data to the generation AI server 200 as structure information 27. The input field 51 and the input field 52 are examples of a list screen, and the html data is an example of screen data.

[0085] When HTML data is used as the structural information 27, the specific prompt is expressed differently from when a JSON schema is used. The specific prompt 26 of the second embodiment includes an instruction to determine the setting item from the HTML data, and is data indicating an instruction to output recommended setting values ​​based on the input data as output data in a format that can be interpreted by the MFP1.

[0086] The trained model can interpret HTML data. For example, the trained model can determine which description in the HTML data indicates whether a specific checkbox is checked. Based on two pieces of input data entered by the administrator, the trained model can obtain information that is posted when the HTML data is changed to a state in which recommended setting values ​​are written.

[0087] When the structural information is html data, the specific prompt 26 is written as follows, for example: Please follow the steps below (1 to 3) in order to output the answer, according to the constraints. ### Constraints When the html data is changed and the changed html data is sent, output the POST value in JSON format. ### Execution procedure Step 1: From the input html data, identify each item included in the function restriction input field and the user list input field. Step 2: Based on the organizational configuration information and user prompts, determine the recommended setting values ​​for each item determined in step 1, and format them in a way that is appropriate for each item. Step 3: When the information formatted in step 2 is entered into each item of the HTML data in the browser, the values ​​that are posted are output in JSON format.

[0088] Even when using the structure information 27, which is html data, and the above-described specific prompt 26, the CPU 11 displays the setting menu (see FIG. 3) (S101 in FIG. 2) and accepts input from the administrator, as in the first embodiment. Then, when an operation on the setting support button 43 is accepted (S102: setting support), the CPU 11 executes the setting support process (see FIG. 5) (S131), as in the first embodiment. In S203 of the setting support process, the administrator simply enters the same data as the above-described organizational structure information and user prompt into the above-described input data acceptance field 61 (see FIG. 6), as in the first embodiment.

[0089] In this embodiment, in S211 of the setting support process, the CPU 11 acquires the structural information 27, which is html data, and the above-mentioned specific prompt 26. Then, in S212, the CPU 11 passes the two pieces of input data entered by the administrator, the above-mentioned specific prompt 26, and the structural information 27, which is html data, to the generation AI server 200.

[0090] The html data held by the EWS 22 is also information indicating setting items that can be accepted by the MFP 1. In other words, the MFP 1 acquires the html data and the unique prompt 26, and passes them, along with input data entered by the administrator, to the trained model, thereby obtaining output data in JSON format indicating information that can be set by the MFP 1.

[0091] The setting contents indicated by the JSON format output data output to the MFP 1 by the trained model of this embodiment are basically the same as those of the first embodiment. For example, the JSON format output data includes a description in which a user name such as "User SA" is associated with a group name such as "General Affairs Department" based on the input data from the MFP 1. For example, the JSON format output data includes information indicating whether or not printing, copying, etc. are available, associated with a group name such as "General Affairs Department," based on the input data from the MFP 1.

[0092] After completing the setting support process at S131, the CPU 11 returns to S101 and displays the setting menu. When the function restriction button 41 is operated on the setting menu, the CPU 11 displays an input field similar to the input field 51 shown in Fig. 3 and containing information such as a group name based on the acquired output data. When the user list setting button 42 is operated on the setting menu, the CPU 11 displays an input field similar to the input field 52 shown in Fig. 4 and containing information such as a user name based on the acquired output data.

[0093] The CPU 11 can accept additional input or correction operations in each of the displayed input fields, just as in the case of manual input. Furthermore, when an operation on the OK buttons 515 and 525 is accepted, the CPU 11 stores the information being displayed in each input field as setting information in the memory 12. In this case, the process of displaying an input field having the same configuration as the input field 51 shown in Fig. 3 and the process of displaying an input field having the same configuration as the input field 52 shown in Fig. 4 are examples of preview processing.

[0094] If the MFP 1 can interpret html data and obtain setting information from the html data, a unique prompt indicating an instruction to output the html data as output data may be used. In this case, for example, step 3 of the unique prompt 26 may be written as "output html data in a state where the information formatted in step 2 has been entered into each item of the html data on the browser."

[0095] As described above in detail, the MFP1 of the second embodiment also easily realizes a setting function that uses a trained model. The MFP1 of the second embodiment uses html data as structural information 27, allowing a unique prompt common to each model to be used. As with the first embodiment, the MFP1 of the second embodiment eliminates the need to input information about each user, even when storing setting values ​​for a large number of users, thereby reducing the administrator's workload for inputting setting values.

[0096] On the other hand, structural information 27 indicating a JSON schema is information used for various settings such as setting function restrictions in the MFP 1, and includes information indicating configurable setting items. In the case of the MFP 1 of the first embodiment, there is a high possibility that output data in JSON format indicating information that can be set in the MFP 1 itself can be obtained.

[0097] Next, a third embodiment of an electronic device will be described in detail with reference to the accompanying drawings. The MFP1 of the third embodiment differs from the MFP1 of the first and second embodiments in that, instead of inputting structural information 27 into a trained model, a function that can be handled by the MFP1 is called from the trained model and information corresponding to the structural information 27 is returned to the trained model. The only processing in the third embodiment that differs from the first embodiment is the setting support processing, and the same configurations and procedures as those of the first embodiment will be denoted by the same reference numerals and will be described briefly.

[0098] Similar to the MFP1 of the first embodiment, when the MFP1 of the third embodiment receives an instruction to start setting, it displays a setting menu (see FIG. 3) in S101 of the setting information reception process. Then, when the CPU 11 determines that an operation on the setting support button 43 has been received in the displayed setting menu (S102: setting support), it executes the setting support process (S131). The procedure of the setting support process of the third embodiment will be described with reference to the flowchart of FIG.

[0099] The MFP1 of the third embodiment causes the trained model to make a function call. To do this, the CPU 11 first sets the function to be called (S401). The MFP1 of this embodiment sets an item acquisition function and a preview setting function as functions. The item acquisition function is a function that is called with the model name of the MFP1 as an argument, and responds with information on a list of items that can be set in that model. The preview setting function is a function that is called with the value to be set for each item as an argument, and previews the setting results and accepts setting instructions.

[0100] The MFP 1 sets information including the names of these functions, details of the functions, information to be passed as arguments, and information about the arguments in the trained model via the API (application programming interface) of the generation AI server 200. This enables the trained model to call these functions. Note that the MFP 1 may store the information about these functions to be set in the trained model via the API in advance in the memory 12, or may obtain it from a server or the like.

[0101] For convenience, we have stated that function information is registered in the trained model, but this concept also includes registering function information in the generation AI server 200 equipped with the trained model. Also, for convenience, we have stated that the trained model calls a function of the MFP1, but this concept also includes the generation AI server 200 equipped with the trained model calling a function of the MFP1 as part of processing related to the trained model.

[0102] Furthermore, CPU 11 sets a specific prompt describing a procedure for using a function call (S402). In this case, the specific prompt is expressed differently from when structure information is used. Specifically, the specific prompt in the third embodiment is data indicating an instruction to call a function provided in MFP 1 and use the response to the function to output recommended setting values ​​based on input data in a format that can be set in MFP 1.

[0103] A specific prompt when using a function call is written as follows, for example: Please follow the steps below (1 to 6) in order to output the answer, according to the constraints. ### Constraints After all steps are completed, please respond with a completion message. Otherwise, please make a function call or respond with an error message if an error occurs. ### Execution procedure Step 1. Call the item acquisition function of the target device to obtain a list of configurable items. Step 2: Based on the obtained list of configurable items, determine the items that can be set in the function restriction information and user list. Step 3: Determine recommended setting values ​​for the target configurable items from the organization configuration information and user prompts. Step 4. The setting value determined in step 3 is formatted to be suitable as an argument for the preview setting function. Step 5. Call the preview setting function of the target device using the setting value formatted in step 4 as an argument. Step 6: Reply with the result of step 5.

[0104] Then, CPU 11 accepts input of user input data (S403). As in the first embodiment, the user input data is, for example, organization configuration information and a user prompt. Note that S401 to S402 and S403 may be performed in reverse order. Furthermore, MFP 1 may store the unique prompt for the function in memory 12 in advance, or may obtain it from a server or the like.

[0105] The CPU 11 inputs the unique prompt for the function set in S402 and the two pieces of input data received in S403 to the generation AI server 200 (S405). Note that in this embodiment, there is no need to pass the structural information 27 to the generation AI server 200. The trained model can execute processing in a specified procedure based on the unique prompt for the input function.

[0106] The CPU 11 determines whether an instruction to execute a function has been received from the trained model (S411). For example, in step 1, the trained model calls an item acquisition function of the MFP 1. If it is determined that the item acquisition function has been called from the trained model (S411: YES), the CPU 11 executes the item acquisition function (S412) and, as a result, responds to the trained model with a list of configurable items (S413). Specifically, the MFP 1 passes information indicating a list of setting items that can be set in the MFP 1 to the trained model. S411 when the item acquisition function is called is an example of a request to send structural information. The list of configurable items is an example of structural information. The MFP 1 has a transmission function that transmits the list of configurable items to the request source when the item acquisition function is called.

[0107] The list information of configurable items that the MFP1 responds with is specifically information indicating which functions can be set to be usable or unusable. The MFP1 passes information indicating functions such as print, copy, and scan, which are functions whose usability can be set, to the trained model. The MFP1 does not need to pass information indicating functions whose usability cannot be set to the trained model. For example, if the MFP does not have a facsimile communication function, it can pass list information that does not include items for fax transmission and fax reception to the trained model.

[0108] If it is determined that the function has not been called (S411: NO), the CPU 11 determines whether or not an end message has been received (S421). If it is determined that an end message has not been received (S421: NO), the CPU 11 determines whether or not an error message has been received (S422). If it is determined that an error message has not been received (S422: NO), the CPU 11 waits until it receives either an instruction to execute the function (S411), an end message (S421), or an error message (S422).

[0109] After obtaining the list of configurable items in step 1, the trained model executes steps 2 to 4 in order. If an abnormality occurs during the processing of steps 2 to 4, the trained model outputs an error message based on the constraints.

[0110] If the processes of steps 2 to 4 are successful, the trained model calls the preview setting function in step 5. If it is determined that the preview setting function has been called from the trained model (S411: YES), CPU 11 executes the preview setting function (S412). If the arguments passed from the trained model when the preview setting function was called are in an appropriate format, CPU 11 can execute the preview setting function to properly display the preview. If the preview display is successful, CPU 11 responds to the trained model with information indicating that the preview display was successful (S413). The setting value passed to MFP 1 as an argument of the preview setting function is an example of output data.

[0111] If the input data entered by the administrator is the same as that in the first embodiment, the setting contents indicated by the setting values ​​that the trained model passes to the MFP1 as arguments to the preview setting function will basically be the same as those in the first embodiment. For example, the setting values ​​include setting contents in which a user name such as "User SA" is associated with a group name such as "General Affairs Department" based on the input data from the MFP1. Also, for example, the setting values ​​include setting contents indicating whether printing, copying, etc. are available, associated with a group name such as "General Affairs Department," based on the input data from the MFP1.

[0112] In this embodiment, the trained model passes, as an argument of the preview setting function, setting values ​​indicating the availability of only the functions indicated in the list information of configurable items passed from the MFP 1 to the MFP 1. For example, if the MFP does not have a facsimile communication function, list information that does not include items for fax transmission or fax reception is passed to the trained model, and the trained model passes, as an argument of the preview setting function, setting values ​​that do not include information indicating the availability of fax transmission or fax reception to the MFP.

[0113] In this case, the trained model responds with an end message in step 6 based on the information acquired in S413. If an end message is received from the trained model (S421: YES), the CPU 11 ends the setting support process. In this case, the argument passed from the trained model when the preview setting function is called is an example of output data. If the display is successful, the CPU 11 may store the setting value in the memory 12.

[0114] On the other hand, if the argument passed when the preview setting function is called is not in an appropriate format, the CPU 11 fails to display the preview. Even if the preview display is successful, the CPU 11 may be able to accept an instruction not to apply the preview on the currently displayed preview screen. If the display fails or an instruction not to apply the preview is accepted, the CPU 11 responds with information indicating the failure to the trained model in step S413.

[0115] If a failure is responded, the trained model responds with an error message in step 6. If an error message is received from the trained model (S422: YES), CPU 11 displays an error (S423) and terminates the setting support process. In this case, CPU 11 discards the argument passed from the trained model when the preview setting function was called. Note that CPU 11 may proceed to S405 and pass each piece of data to the generation AI server 200 again.

[0116] The unique prompt for the function may not include step 6 of the execution procedure described above. If the preview setting function called in step 5 is successfully executed, the CPU 11 may execute a setting confirmation process (see FIG. 8), and if the preview setting function fails to be executed, an error message may be displayed.

[0117] As described above in detail, even with the MFP1 of the third embodiment, when setting values ​​for multiple users are to be stored in the setting work related to function restrictions, it is not necessary to input information about each user, and the administrator's workload for inputting setting values ​​can be reduced. In this embodiment, by using functions, it is not necessary to prepare structural information 27 in advance, and it is also not necessary to prepare different unique prompts for each model.

[0118] On the other hand, the MFP1 of the first embodiment and the MFP1 of the second embodiment require the preparation of a unique prompt 26 and structural information 27, but the interaction with the trained model is only required once when inputting data, making the processing simpler.

[0119] Note that the embodiments are merely illustrative and do not limit the present invention in any way. Therefore, the technology disclosed in this specification can naturally be improved and modified in various ways without departing from the spirit and scope of the present invention. For example, the electronic device may be any device that has a communication interface, can communicate with the generation AI server 200, and can store various setting values ​​in memory, and is not limited to devices with image formation functions. In other words, the electronic device is not limited to the MFP 1, but may also be a single-function printer, copier, scanner, fax machine, embroidery machine, machine tool (cutting machine, polishing machine, laser machine), or fluid ejection device (inkjet device, etc.). Furthermore, the configurable setting items and their setting values ​​may vary depending on the functions possessed by the electronic device. For example, the setting value is not limited to information indicating whether a function is available, but may also be information indicating the range of setting values ​​that can be set when the function is used.

[0120] Furthermore, the display format shown in each embodiment is not limited to the illustrated example. For example, while Fig. 9 shows an example in which the function restriction 711 and the user list 712 are displayed side by side on the preview screen 71, the CPU 11 may display them side by side on the top and bottom. Furthermore, the CPU 11 may display a preview screen including either the function restriction 711 or the user list 712 and a switching button, and may switch between displaying the function restriction 711 and the user list 712 by accepting an operation on the switching button.

[0121] Although each of the embodiments has been described with reference to an example in which each user using the MFP 1 is identified by a user name, they may also be identified by a user ID. For example, the JSON information's userlist may be configured to associate a userid corresponding to a user ID with a groupname, rather than a username corresponding to a user name. In this case, the administrator may input organizational structure information containing the user ID as input data. In this case, the organizational structure information may not necessarily include the user name. In this case, the preview screen 71 (see FIG. 9) of the first embodiment, the input fields 51 and 52 based on the output data of the second embodiment, and the preview display using the preview setting function of the third embodiment may display the user ID instead of the user name, and may allow the user name to be entered.

[0122] Also, the setting confirmation process (see FIG. 8) may be omitted. In that case, when the CPU 11 obtains an answer that matches the structure specified in the setting support process (see FIG. 5) from the trained model, it may store the setting values ​​in the memory 12 based on that answer. However, it is preferable to display a preview before storing the setting values, since this can eliminate the possibility that setting values ​​that are significantly different from the administrator's intentions will be set.

[0123] In addition, in the setting confirmation process of each embodiment, not only can the user select whether or not to apply the setting in the preview display, but also instructions for correction can be accepted (S302 in FIG. 8), but instructions for correction do not have to be accepted. In other words, if there is an error, the temporary setting information may not be stored in the memory 12, and the process may be started over from the beginning.

[0124] In addition, in the setting support process of each embodiment, the same data is repeatedly input into the trained model up to a specified number of times (if NO at S218 in Figure 5, proceed to S212), but instead of repeatedly inputting the data, CPU 11 may terminate the process and become able to accept instructions again.

[0125] Furthermore, for example, in each of the embodiments, an example has been shown in which multiple users are divided into multiple groups and function restriction settings are accepted for each group, but the MFP 1 may be capable of accepting settings for each individual user, rather than being limited to settings for each group. For example, the MFP 1 may be capable of displaying an input field that combines the input field 51 shown in Fig. 3 and the input field 52 shown in Fig. 4. Furthermore, instead of the preview screen 71 shown in Fig. 9, the MFP 1 may display a preview screen in which provisional setting information is associated with each user.

[0126] In addition, in each embodiment, a configuration that uses a trained model of the generation AI server 200 is exemplified, but instead of a trained model, it can also be applied to a program created based on coding by an engineer (programmer) in which processing such as decision steps is described.

[0127] Furthermore, in any flowchart or sequence diagram disclosed in each embodiment, the execution order of multiple processes in any multiple steps can be changed or executed in parallel as desired, as long as no contradictions occur in the processing content.

[0128] The processes disclosed in the embodiments may be executed by a single CPU, multiple CPUs, hardware such as an ASIC, or a combination thereof. The processes disclosed in the embodiments may be realized in various ways, such as a recording medium on which a program for executing the processes is recorded, or a method. [Explanation of symbols]

[0129] 1 MFP 12 Memory 13 User Interface 14 Communication Interface 15 Print Engine 16 Scanner 200 Generation AI Server

Claims

1. A user interface; a communication interface; Memory and An electronic device comprising: A plurality of setting targets are provided, and a setting value can be associated with each of the setting targets and stored in the memory, and the device is configured to be able to operate in accordance with the setting value stored in the memory, The electronic device further comprises: A server that stores a trained model is accessible via the communication interface, and the trained model has been trained to generate output data based on input data; The electronic device further comprises: an input data receiving process for receiving first user input data and second user input data via the user interface; The first user input data is data in a format that can be interpreted by the trained model, is data indicating a plurality of elements, and is data indicating a relationship between the elements, and some of the elements include at least some of the setting targets among the plurality of setting targets; The second user input data is data in a format that can be interpreted by the trained model, and is data that indicates a relationship between each of the setting targets indicated as part of the elements of the first user input data and the setting value; The electronic device further comprises: An input process can be executed to input the first user input data and the second user input data accepted in the input data acceptance process and unique input data to the trained model via the communication interface; the unique input data is data indicating an instruction to request that the electronic device output, based on the first user input data and the second user input data, the recommended setting values ​​for each of the setting targets indicated as part of the elements of the first user input data, as output data in a format that can be interpreted by the electronic device; The electronic device further comprises: After the input process is executed, a setting process can be executed in which the setting values ​​recommended for each setting target indicated in the output data output from the trained model are associated with each setting target and stored in the memory. Electronic equipment configured to:

2. 10. The electronic device according to claim 1, An image forming engine is provided, causing the image forming engine to form an image in accordance with the setting values ​​stored in the memory; Electronic equipment configured to:

3. 3. The electronic device according to claim 2, the image forming engine is capable of at least one of printing and reading as image formation, The electronic device includes: having a plurality of image forming functions including at least one of printing and reading; The setting value indicating whether or not the plurality of image forming functions can be used can be stored in the memory in association with each user, the setting value being set to users who can use the electronic device; In a state where a user is identified, an operation for an available image forming function among the plurality of image forming functions is enabled and an operation for an unavailable image forming function is disabled based on the setting value associated with the identified user, the first user input data includes, as the plurality of elements, a user who can use the electronic device and a group to which the user belongs, and is data indicating a relationship between the user and the group; the second user input data is data indicating a relationship between the group and the setting value indicating whether each of the plurality of image forming functions is available, the unique input data is data indicating an instruction to request that the electronic device output, based on the first user input data and the second user input data, the setting values ​​recommended for each of the users indicated as part of the elements of the first user input data, as the output data in a format that can be interpreted by the electronic device; The electronic device further comprises: In the setting process, the setting value for each user indicated in the output data output from the trained model is associated with each of the users indicated as part of the elements of the first user input data, and stored in the memory. Electronic equipment configured to:

4. 3. The electronic device according to claim 2, The electronic device includes: After the input process is executed, when the output data output from the trained model is acquired, a preview process is executed in which the setting values ​​recommended for each setting target indicated in the output data are displayed via the user interface, and a selection is made as to whether or not to store the displayed setting values ​​in the memory; When the selection to store the setting value in the memory is accepted in the preview process, the setting process is executed; When the selection not to store the setting value in the memory is accepted in the preview process, the setting process is not executed. Electronic equipment configured to:

5. 5. The electronic device according to claim 4, The electronic device includes: In the preview process, the setting values ​​recommended for each of the setting targets shown in the output data are displayed via the user interface, and a selection is received as to whether or not to modify the displayed setting values ​​and to store the displayed setting values; When the selection to store the setting values ​​in the memory is received after the correction of the setting values ​​is received in the preview process, the setting process stores the corrected setting values ​​in the memory in association with each of the setting targets. Electronic equipment configured to:

6. 3. The electronic device according to claim 2, The electronic device includes: a setting reception process that provides an input field for receiving an input of the setting target and an input field for receiving an input of the setting value via the user interface and receives an input operation for each of the input fields; a manual setting process for storing the setting values ​​input in the setting reception process in the memory in association with each of the input setting targets; is executable, In the input processing, the first user input data and the second user input data accepted in the input data acceptance processing and the unique input data are input to the trained model via the communication interface without accepting an input operation in the input field; In the setting process, the setting values ​​recommended for each of the setting targets indicated in the output data output from the trained model are associated with each of the setting targets and stored in the memory without accepting an input operation into the input field. Electronic equipment configured to:

7. 7. An electronic device according to claim 6, The setting value associated with the setting target includes a plurality of items, and the setting value for each of the items can be stored in the memory; The electronic device includes: In the setting reception process, the input field for receiving input of the setting target and the input field for receiving input for each of the items for the setting value are displayed via the user interface, and an input operation for each of the input fields is received; In the manual setting process, the setting values ​​for each of the items input in the setting reception process are stored in the memory in association with each of the setting targets; In the input processing, the first user input data and the second user input data accepted in the input data acceptance processing and the unique input data are input to the trained model without accepting an input operation in the input field; the second user input data is data indicating a relationship between each of the setting targets indicated as a part of the elements of the first user input data and the setting value for each of the items, The electronic device further comprises: In the setting process, the setting values ​​for each of the items recommended for each of the setting targets indicated in the output data output from the trained model are associated with each of the setting targets and stored in the memory without accepting an input operation into the input field. Electronic equipment configured to:

8. 8. An electronic device according to claim 7, The electronic device includes: After the input process is executed, when the output data output from the trained model is acquired, a display process is executed to display, via the user interface, the setting target indicated in the output data and the setting value for each of the items indicated in the output data. Electronic equipment configured to:

9. 8. An electronic device according to claim 7, the image forming engine is capable of at least one of printing and reading; The electronic device includes: having a plurality of image forming functions including at least one of printing and reading; It is possible to store in the memory, for each user, a user who can use the electronic device as the setting target, each of the plurality of image forming functions as the item, and the setting value indicating whether each item can be used or not associated with the item; When a user is identified, an operation for an available image forming function among the plurality of image forming functions is enabled and an operation for an unavailable image forming function is disabled based on the setting value associated with the identified user, In the setting reception process, the input field for receiving the input from the user and the input field for receiving the input of the setting value indicating whether or not each of the image forming functions is available are displayed via the user interface, and an input operation is received for each of the input fields for each of the users and each of the image forming functions; In the manual setting process, the setting values ​​for each of the image forming functions input in the setting acceptance process are stored in the memory in association with each of the users; In the input processing, the first user input data and the second user input data accepted in the input data acceptance processing and the unique input data are input to the trained model without accepting an input operation of the setting value for each of the image forming functions; the first user input data includes, as the plurality of elements, a user who can use the electronic device and a group to which the user belongs, and is data indicating a relationship between the user and the group; the second user input data is data indicating a relationship between the group and the setting value indicating whether each of the plurality of image forming functions is available; the unique input data is data indicating an instruction to request that the electronic device output, based on the first user input data and the second user input data, the setting values ​​recommended for each of the users indicated as part of the elements of the first user input data, as the output data in a format that can be interpreted by the electronic device; The electronic device further comprises: In the setting process, the setting values ​​for each user indicated in the output data output from the trained model are associated with each of the users indicated as part of the elements of the first user input data, without accepting an input operation for each of the image forming functions, and are stored in the memory. Electronic equipment configured to:

10. 3. The electronic device according to claim 2, The electronic device includes: the input data receiving process displays an input screen for a prompt via the user interface, and receives, as the second user input data, a user prompt that is a prompt input on the input screen; the unique input data is a prompt indicating an instruction to request that the electronic device output, based on the first user input data and the second user input data, the recommended setting values ​​for each of the setting targets indicated as part of the elements of the first user input data, in the form of output data that can be interpreted by the electronic device; The electronic device further comprises: In the input processing, the first user input data accepted in the input data acceptance processing, the user prompt entered on the input screen, and a unique prompt that is the unique input data are input to the trained model via the communication interface. Electronic equipment configured to:

11. 11. The electronic device according to claim 10, The memory stores the unique prompt before the user prompt is input on the input screen. Electronic equipment configured to:

12. 3. The electronic device according to claim 2, The electronic device includes: a setting function for, when receiving a setting instruction based on setting data in which the setting targets and the setting values ​​are arranged in a predetermined structure, storing the setting values ​​included in the setting data in the memory in association with the setting targets included in the setting data; the unique input data is data indicating an instruction to request that the recommended setting values ​​for each of the setting targets indicated as part of the elements of the first user input data be output as the output data arranged in the predetermined structure used in the setting function, based on the first user input data and the second user input data; The electronic device further comprises: In the setting process, the output data output from the trained model is processed by the setting function, and the setting values ​​recommended for each setting target indicated in the output data are associated with each setting target and stored in the memory. Electronic equipment configured to:

13. 3. The electronic device according to claim 2, The electronic device includes: a transmission function of, when receiving a transmission request for structure information indicating a structure of the setting value, transmitting the structure information to a source of the transmission request; the specific input data indicates a procedure up to outputting the output data, the procedure including a procedure of outputting the transmission request for the structural information, then generating the output data based on the acquired structural information, and outputting the generated output data; The electronic device further comprises: After executing the input process, when the transmission request of the structural information is acquired from the trained model, the structural information is transmitted to the trained model using the transmission function; After executing the input process, when the output data is acquired from the trained model, execute the setting process; In the setting process, the setting values ​​recommended for each setting target indicated in the output data output from the trained model and generated by the trained model according to the structure of the setting values ​​indicated in the structure information are associated with each setting target and stored in the memory. Electronic equipment configured to:

14. 3. The electronic device according to claim 2, The electronic device includes: a list screen displaying the setting values ​​stored in the memory in association with the setting targets can be displayed via the user interface; In the input processing, the first user input data and the second user input data accepted in the input data acceptance processing, screen data indicating a configuration of the list screen, and the unique input data are input to the trained model via the communication interface; the unique input data is data indicating an instruction to request that the electronic device output the recommended setting values ​​for each of the setting targets indicated as part of the elements of the first user input data based on the first user input data, the second user input data, and the screen data, in a format that can be interpreted by the electronic device; The electronic device further comprises: In the setting process, the setting values ​​recommended for each setting target are associated with each setting target based on the list screen shown in the output data output from the trained model, and are stored in the memory. Electronic equipment configured to:

15. 3. The electronic device according to claim 2, The memory stores the unique input data, In the input processing, the first user input data and the second user input data accepted in the input data acceptance processing and the unique input data read from the memory are input to the trained model via the communication interface. Electronic equipment configured to:

16. 3. The electronic device according to claim 2, the first user input data is hierarchical structure data indicating a hierarchical structure including, as the plurality of elements, elements of a first hierarchy and elements of a second hierarchy that is a lower hierarchy belonging to the first hierarchy, and some of the elements of the second hierarchy include at least some of the setting targets among the plurality of setting targets; the second user input data is data indicating a relationship between each of the elements of the first hierarchy indicated as a part of the elements of the first user input data and the setting value, the specific input data is data indicating an instruction to request that the electronic device output, based on the first user input data and the second user input data, the recommended setting values ​​for each of the elements in the second hierarchy indicated as part of the elements of the first user input data, in the output data in a format that can be interpreted by the electronic device; Electronic equipment configured to:

17. 17. The electronic device according to claim 16, The format that can be interpreted by the trained model includes image data, At least one of the first user input data and the second user input data is image data. Electronic equipment configured to:

18. 17. The electronic device according to claim 16, The format that the trained model can interpret includes a prompt; At least one of the first user input data and the second user input data is a prompt. Electronic equipment configured to:

19. 17. The electronic device according to claim 16, The formats that the trained model can interpret include image data and prompts; the first user input data is image data; the second user input data is a prompt. Electronic equipment configured to:

20. 17. The electronic device according to claim 16, The first user input data and the second user input data are data of the same format that can be interpreted by the trained model. Electronic equipment configured to:

21. 3. The electronic device according to claim 2, the first user input data is image data showing an image in which the plurality of elements are written, and is data showing a relationship between the elements written in the image data, and some of the elements written in the image data include at least some of the setting targets among the plurality of setting targets; the second user input data is data indicating a relationship between each of the setting targets indicated as a part of an element written in the image data, which is the first user input data, and the setting value; The unique input data is data indicating an instruction to request that the electronic device output, based on the first user input data and the second user input data, the recommended setting values ​​for each of the setting targets indicated as part of the elements written in the image data, which is the first user input data, in the form of output data that can be interpreted by the electronic device. Electronic equipment configured to:

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