Image forming system, image forming method, image forming apparatus, and image forming program

Abstract

To propose an image forming system, an image forming method, an image forming apparatus, and an image forming program capable of forming an image desired by a provider on a medium.SOLUTION: An image forming system according to an embodiment of the present disclosure includes a terminal and an image forming apparatus, and the terminal includes a reception unit that receives an input from a user. The image forming apparatus includes: an acquisition unit configured to acquire input information related to the received input from the user; a determination unit configured to determine whether an image generated by an artificial intelligence (AI) based on the acquired input information can be formed on the medium based on the input information and a policy of a lending side related to a medium lent to the user from a lending side; and a formation unit configured to form on the medium the image determined to be capable of being formed on the medium.SELECTED DRAWING: Figure 4

Description

[Technical Field]

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

[0002] There is known a technology for forming an image on a medium such as a card according to the content of an input received from a user (for example, Patent Document 1 below). As an example, there is known a technology for forming a user's facial photograph on a credit card (for example, Patent Document 2 below). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-331782 [Patent Document 2] Patent No. 3369994 Summary of the Invention [Problem to be solved by the invention]

[0004] The conventional technology can form an image on a medium that a user desires. However, the image that a user desires is not necessarily the image that the provider of the medium to the user desires. Therefore, the conventional technology may form an image on the medium that the provider does not desire.

[0005] For example, depending on the content of a user's input, conventional technology may cause images that are contrary to public order and morals, such as violent images, to be printed on a credit card, which is an image that the provider does not want. In this case, if the user presents the credit card to an outside party when using the credit card, the credit card company that provides the credit card may be held liable or lose credibility. Thus, conventional technology not only causes images that the provider does not want to be printed on the medium, but may also damage the provider's reputation.

[0006] Therefore, the present disclosure proposes an image forming system, an image forming method, an image forming apparatus, and an image forming program that can form an image desired by a provider on a medium. [Means for solving the problem]

[0007] In order to solve the above problems, the image forming system of the present disclosure comprises a terminal and an image forming device, wherein the terminal comprises a reception unit that receives input from a user, and the image forming device comprises an acquisition unit that acquires input information regarding the received input from the user, a determination unit that determines whether an image generated by a generation AI (Artificial Intelligence) based on the acquired input information can be formed on the medium based on the input information and the lending side's policy regarding the medium to be loaned to the user by the lending side, and a formation unit that forms on the medium an image that has been determined to be formable on the medium. [Effects of the Invention]

[0008] According to one aspect of the embodiment, an image desired by a provider can be formed on a medium. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 2 is a diagram for explaining an outline of processing according to the first embodiment. [Figure 2] FIG. 2 is a diagram for explaining a specific example of processing according to the first embodiment. [Figure 3] FIG. 2 is a flowchart illustrating a processing flow according to the first embodiment. [Figure 4] FIG. 2 is a block diagram showing an example of the configuration of a server and a user terminal according to the first embodiment. [Figure 5] FIG. 3 is a sequence diagram showing a processing procedure according to the first embodiment. [Figure 6] FIG. 10 is a flowchart illustrating a process flow according to the second embodiment. [Figure 7]FIG. 10 is a block diagram showing an example of the configuration of a server and a user terminal according to a second embodiment. [Figure 8] FIG. 10 is a flowchart illustrating a process flow according to the third embodiment. [Figure 9] FIG. 10 is a block diagram showing an example of the configuration of a server and a user terminal according to a third embodiment. [Figure 10] FIG. 10 is a flowchart illustrating a process flow according to a fourth embodiment. [Figure 11] FIG. 10 is a block diagram showing an example of the configuration of a server and a user terminal according to a fourth embodiment. [Figure 12] FIG. 2 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of a server. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the following embodiments, the same components are designated by the same reference numerals, and redundant description will be omitted.

[0011] (1. First embodiment) (1-1. Overview of Processing According to the First Embodiment) 1 is a diagram for explaining an overview of processing according to the first embodiment. An image forming system 1 includes a server 100, a user terminal 200, and an issuer terminal 300. Each device included in the image forming system 1 can transmit and receive data to and from each other via wireless communication or the like.

[0012] Server 100 is an example of an image forming apparatus according to the present disclosure. Server 100 forms image 10 on a medium in response to input from a user. In FIG. 1, when user terminal 200 receives a prompt input from the user, such as a keyword or text related to image 10, server 100 receives the prompt from user terminal 200. Server 100 then forms image 10, which is generated by a generation AI based on the prompt, on a credit card.

[0013] User terminal 200 is an example of a terminal according to the present disclosure. For example, user terminal 200 is a smartphone. In FIG. 1, user terminal 200 accepts input from a user regarding membership registration for the user to become a credit card member and input from the user regarding image 10 to be formed on the credit card. Membership registration is a procedure carried out before image 10 is formed on the credit card.

[0014] The issuer terminal 300 is a terminal of an issuer. The issuer is a company that issues credit cards. The issuer terminal 300 is also an example of a terminal on the provider side that provides media to users. For example, the issuer terminal 300 is a personal computer.

[0015] In Fig. 1, if the issuer determines during the membership screening that the user is suitable to be a credit card member, the issuer terminal 300 transmits formation data 20 that authorizes the formation of an image on the credit card to the server 100. The membership screening, like the membership registration, is a procedure that is carried out before the formation of the image 10 on the credit card.

[0016] The following describes the processing of steps S1 to S6 in which the server 100, the user terminal 200, and the issuer terminal 300 generate the image 10 and print the image 10 on the credit card in parallel with the processing related to the membership registration and membership screening.

[0017] 1, server 100 is connected to the issuer's membership application site via an API (Application Programming Interface). This allows a seamless process from the user's membership registration to image generation by server 100. The membership application site includes a personal information input screen, which is a screen for the user to register for a credit card, and an illustration generation screen, which is a screen for generating image 10 to be formed on the credit card.

[0018] In step S1, the user terminal 200 accepts input of personal information by the user on a personal information input screen. For example, the user terminal 200 accepts input of personal information by the user on a personal information input screen displayed on the display unit of the user terminal 200.

[0019] In step S2, the user terminal 200 accepts an input from the user to the illustration generation screen. For example, the user terminal 200 accepts an input of a prompt such as a keyword or text from the user to the illustration generation screen.

[0020] When user terminal 200 has accepted all of the user's inputs on the illustration generation screen, the user's membership application is completed in step S4. After the user's membership application is completed, user terminal 200 transmits input information regarding the inputs accepted from the user on the illustration generation screen to server 100.

[0021] In step S3, the server 100 generates the image 10 using an image generation AI service, which is a service that generates an image using a generation AI. For example, the server 100 acquires input information by receiving it from the user terminal 200. The server 100 inputs the input information into the generation AI, and generates output information output from the generation AI as the image 10. The server 100 links user information, such as the user's personal information and input information, with the generated image 10 using a unique value.

[0022] In step S5, the issuer performs a membership screening of the user. If the issuer judges that the user is suitable as a credit card member, the issuer terminal 300 transmits to the server 100, based on the input information entered by the issuer, formation data 20 that allows the server 100 to form the image 10 on the credit card.

[0023] In step S6, the server 100 forms the image 10 generated in step S3 on a credit card. For example, when the server 100 receives the forming data 20 from the issuer terminal 300, the server 100 forms the image 10 on the credit card.

[0024] Next, a specific example of the above-mentioned processing will be described with reference to Fig. 2. Fig. 2 is a diagram for explaining a specific example of the processing according to the first embodiment.

[0025] In step S11, the user terminal 200 accepts an application for membership registration from the user 30. For example, the user terminal 200 accepts input of personal information for membership registration from the user 30. Specifically, the user terminal 200 accepts input of personal information from the user 30 into a blank UI (User Interface) on a personal information input screen 40 displayed on the display unit. The personal information input screen 40 is one of the web pages of the membership application site of the issuer 50, who is the user of the issuer terminal 300.

[0026] In step S12, server 100 generates image 70 to be formed on credit card 60. For example, after user 30 applies for membership (after receiving personal information from user 30 for membership registration), user terminal 200 transitions the screen displayed on the display unit from personal information input screen 40 to an image generation page, which is a web page for image generation.

[0027] Image 70 is an example of Image 10 generated by the AI ​​based on prompts such as keywords and text, including "night view," "futuristic," and "beautiful." The image generation page is one of the web pages of a membership application site. The image generation page is also an example of an illustration generation screen.

[0028] Next, the user terminal 200 accepts input from the user 30 for the UI of blank spaces on the image generation page. For example, the user terminal 200 accepts input of prompts such as "night view," "futuristic," and "beautiful." The user terminal 200 transmits input information regarding the input accepted from the user 30 to the server 100.

[0029] The server 100 acquires input information by receiving the input information from the user terminal 200. The server 100 inputs the input information to the generation AI, and generates output information output from the generation AI as an image. For example, the server 100 inputs prompts such as "night view," "futuristic," and "beautiful" to the generation AI, and generates an image 70 in response to these prompts.

[0030] In step S13, the issuer 50 performs a membership screening of the user 30. If the issuer 50 judges that the user 30 is appropriate as a member, the issuer terminal 300 transmits, to the server 100, formation data that permits the image 70 to be formed on the credit card 60, based on the input information entered by the issuer 50.

[0031] In step S14, server 100 forms image 70 generated in step S12 on credit card 60. For example, suppose server 100 receives a request from user 30 via a membership application site or the like to form image 70 on credit card 60. In this case, server 100 forms image 70 on credit card 60 by means of printing, engraving, or the like using POD (Print On Demand).

[0032] As described above, server 100 creates an image on a credit card that is generated by a generation AI based on prompts such as keywords and text. This allows even users who are not accustomed to drawing pictures to easily create an original image of their choice on a credit card.

[0033] However, image generation using generative AI does not necessarily result in the image that the issuer desires. Therefore, even if image generation using generative AI can create an image that the user desires on the credit card, there is a risk that an image that is not desired by the issuer that provides the credit card to the user may be created on the credit card.

[0034] For example, if a user inputs a keyword such as "blood," image generation using generative AI may result in violent images or other images that violate public order and morals being displayed on the credit card. In this case, if the user presents the credit card to an outside party when using it, the issuer may be held liable or lose credibility. In this way, image generation using generative AI may not only result in images that the issuer does not want displayed on the medium, but may also damage the issuer's reputation.

[0035] In particular, images generated by generative AI have a high degree of freedom. Furthermore, when generating images using generative AI, simply inputting keywords such as "blood" into the AI ​​can easily generate violent images or other images that violate public order and morals. Therefore, when generating images using generative AI, there is a high possibility that images that issuers do not want will be created on credit cards.

[0036] Furthermore, the issuer is not simply a provider of credit cards to users, but a lender that lends the credit card to users while retaining ownership of the card. Furthermore, credit cards are not used internally by users personally, but are used by presenting them to external parties. For this reason, when a generation AI generates media to be lent by a lender, the above-mentioned problems may become more pronounced.

[0037] For example, even if a violent image is printed on a credit card due to keywords entered by a user, there is a risk that the issuer, who is the owner of the credit card, may be perceived by the public as having issued the credit card with a violent image printed on it.

[0038] Therefore, the server 100 and the user terminal 200 perform the following processing to form an image desired by a provider such as an issuer on a medium such as a credit card.

[0039] The user terminal 200 accepts input from a user. The server 100 acquires input information related to the accepted input from the user. The server 100 determines whether an image generated by the generation AI based on the acquired input information can be formed on a medium based on the input information and the lending party's policy. The server 100 also forms, on the medium, an image that has been determined to be formable on the medium.

[0040] The "lender's policy" refers to the policy, ideas, and rules of the lender, such as an issuer, regarding the media that the lender lends to the user. For example, the lender's policy may be a policy that prevents images that may be damaging to reputation, etc. from being formed on the media that the lender lends to the user. Specifically, the lender's policy may be a policy that prevents images from being formed on the media that include character strings that should be excluded when generating images, images that are contrary to public order and morals, images that may infringe on the rights of others, such as copyright, design rights, or trademark rights, or images that evoke politically dangerous ideas. Specific examples of these lender's policies will be described later.

[0041] An example of the processing of the server 100 and the user terminal 200 for achieving the above object will be described below with reference to Fig. 3. Fig. 3 is a flow diagram for explaining the processing flow according to the first embodiment. In step S21, the user terminal 200 accepts input of keywords 80 related to the image to be generated from the user.

[0042] In step S22, the server 100 acquires the keyword 80 accepted in step S21. For example, the server 100 acquires the keyword 80 by receiving the keyword 80 from the user terminal 200.

[0043] Next, the server 100 determines whether the image generated by the generation AI based on the acquired keywords 80 can be used on a credit card based on the keywords 80 and the issuer's policy regarding credit cards loaned to users by the issuer. "Images generated by the generation AI" includes not only images already generated by the generation AI, but also images that have not yet been generated by the generation AI but may be generated in the future. Figure 3 explains an example of an image that may be generated by the generation AI in the future.

[0044] For example, if the keywords 80 are "night view," "futuristic," and "beautiful," the server 100 estimates that the image 10 that may be generated by the generation AI does not violate the issuer's policy. Therefore, the server 100 determines that the image 10 can be used on a credit card (step S22; OK). The server 100 then proceeds to step S23.

[0045] As another example, if the acquired keyword 80 is the name of an anime character, the server 100 presumes that the image that may be generated violates the issuer's policy of not allowing anything that may infringe copyright to be formed on a medium, and therefore the server 100 determines that the image cannot be formed on a credit card (step S22; NG).

[0046] As another example, if the keyword 80 is "swastika," the server 100 presumes that the image that may be generated violates the issuer's policy of not allowing images evoking politically dangerous ideas to be printed on credit cards. Therefore, the server 100 determines that the image cannot be printed on a credit card (step S22; NG).

[0047] If the server 100 determines that the image cannot be formed on the credit card (step S22; NG), the process returns to step S21.

[0048] In step S23, server 100 generates an image determined to be suitable for a credit card using the generation AI based on the acquired input information. For example, if the acquired keywords 80 are "night view," "futuristic," and "beautiful," server 100 generates image 10 using the output information output from the generation AI.

[0049] In step S24, server 100 registers the generated image 10 in database 90. Next, server 100 forms the registered image 10 on a credit card. For example, suppose server 100 receives a request from user 30 via a membership application site to form image 10 on a credit card. In this case, server 100 forms image 10 generated by the POD on the credit card by means of printing, engraving, or the like.

[0050] In this way, the server 100 forms on the medium only the image 10 that is determined to be formable on the medium based on the input information and the lending party's policy, thereby preventing the formation of an image 10 that violates the lending party's policy. Therefore, the server 100 can form on the medium, such as a credit card, an image 10 that the provider desires, such as a safe and secure image 10 desired by an issuer.

[0051] (1-2. Configuration of Server and User Terminal According to First Embodiment) Next, a description will be given of the configuration of the server 100 and the user terminal 200 according to the first embodiment. Fig. 4 is a block diagram showing an example of the configuration of the server and the user terminal according to the first embodiment.

[0052] (Server configuration) As shown in FIG. 4, the server 100 includes a communication unit 110, a storage unit 120, and a control unit .

[0053] The communication unit 110 is realized by, for example, a network interface controller or the like. The communication unit 110 is connected to a network N such as the Internet by wire or wirelessly, and transmits and receives information to and from the user terminal 200 and the issuer terminal 300 via the network N. For example, the communication unit 110 transmits and receives information using communication standards and communication technologies such as Wi-Fi (registered trademark), SIM (Subscriber Identity Module), and LPWA (Low Power Wide Area). Specifically, the communication unit 110 receives input information from the user terminal 200.

[0054] The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 includes a generated AI 121 and an image storage unit 122. The storage unit 120 also stores the provider's policy, such as the issuer's policy.

[0055] The generation AI 121 is an AI that outputs an image in response to input information. For example, the generation AI 121 is an arbitrary and publicly known trained model that has been trained using the input information as an input variable and an image as an output variable. The image storage unit 122 stores images generated by the generation AI 121. For example, the image storage unit 122 stores a database in which images that have been determined to be suitable for use on a credit card, among the images generated by the generation AI 121, are registered.

[0056] The control unit 130 is realized by, for example, a CPU (Central Processing Unit), MPU (Micro Processing Unit), GPU (Graphics Processing Unit), etc. executing a program stored in the storage unit 120 using RAM, etc. as a work area. The control unit 130 is also a controller, and is realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

[0057] As shown in FIG. 4, the control unit 130 includes an acquisition unit 131, a determination unit 132, a generation unit 133, a registration unit 134, and a formation unit 135.

[0058] The acquisition unit 131 acquires various information such as input information from a user accepted by the user terminal 200. For example, when the communication unit 110 receives input information from the user terminal 200, the acquisition unit 131 acquires the input information from the communication unit 110.

[0059] The acquisition unit 131 can also acquire formation data that permits an image to be formed on a medium such as a credit card. For example, when the communication unit 110 receives the formation data from the issuer terminal 300, the acquisition unit 131 acquires the formation data from the communication unit 110.

[0060] The determination unit 132 determines whether the image generated by the generation AI 121 based on the input information acquired by the acquisition unit 131 can be formed on a medium based on the input information and the lending party's policy. As an example, if the acquired keywords are "night view," "futuristic," and "beautiful," the determination unit 132 estimates that the image that may be generated by the generation AI 121 does not violate the issuer's policy. Therefore, the determination unit 132 determines that the image can be formed on a credit card.

[0061] As another example, if the acquired keyword is the name of an anime character or a "swastika," the judgment unit 132 determines that the image that may be generated cannot be formed on a credit card, because the image violates the lending party's copyright and political policies.

[0062] The generation unit 133 generates an image determined to be formable on a credit card based on the acquired input information using the generation AI 121. For example, if the acquired keywords are "night view," "futuristic," and "beautiful," the generation unit 133 generates an image based on the output information output from the generation AI 121.

[0063] The registration unit 134 registers in the database the images generated by the generation unit 133. For example, the registration unit 134 registers in the database, among the images generated by the generation AI 121, images that are determined to be formable on a credit card.

[0064] The forming unit 135 forms on a medium an image from the database registered by the registration unit 134. For example, suppose that the forming unit 135 receives a request from a user via a membership application site to form an image registered in the database onto a credit card. In this case, the forming unit 135 forms the image onto the credit card by means of printing, engraving, or the like using a print-on-demand (POD) system.

[0065] (User terminal configuration) 4, the user terminal 200 includes a communication unit 210, a display unit 220, and a control unit 230. The user terminal 200 may further include a storage unit.

[0066] The communication unit 210 is realized by, for example, a network interface controller or the like. The communication unit 210 is connected to a network N such as the Internet by wire or wirelessly, and transmits and receives information to and from the server 100, the issuer terminal 300, and the like via the network N. For example, the communication unit 210 transmits and receives information using a communication standard or communication technology such as Wi-Fi, SIM, or LPWA. Specifically, the communication unit 210 transmits input information from a user, which is received by a receiving unit 231 (described later), to the communication unit 110 of the server 100.

[0067] The display unit 220 is realized by, for example, a touch panel of a smartphone or a desktop of a personal computer.

[0068] The control unit 230 is realized by, for example, a CPU, MPU, GPU, etc. executing a program using RAM, etc. as a work area. The control unit 230 is also a controller, and is realized by, for example, an integrated circuit such as an ASIC or FPGA.

[0069] 4, control unit 230 includes reception unit 231. Reception unit 231 receives input from a user. For example, reception unit 231 receives input of personal information from a user into a blank UI on a personal information input screen displayed on display unit 220, or input of keywords or the like from a user into a blank UI on an illustration generation screen displayed on display unit 220.

[0070] (1-3. Processing Procedure According to the First Embodiment) The processing procedure according to the first embodiment will be described with reference to Fig. 5. Fig. 5 is a sequence diagram showing the processing procedure according to the first embodiment.

[0071] In step S31, the receiving unit 231 of the user terminal 200 receives an input from the user. For example, the receiving unit 231 receives an input such as a keyword from the user in a blank UI of the illustration generation screen displayed on the display unit 220 of the user terminal 200.

[0072] In step S32, the communication unit 210 of the user terminal 200 transmits input information regarding the input from the user accepted by the acceptance unit 231 to the server 100. The communication unit 110 of the server 100 also receives the input information from the communication unit 210. For example, the communication unit 210 transmits to the communication unit 110 a keyword that the user has entered into a blank UI on the illustration generation screen. The communication unit 110 also receives the keyword from the communication unit 210.

[0073] In step S33, the acquiring unit 131 of the server 100 acquires the input information from the user accepted by the accepting unit 231. For example, the acquiring unit 131 acquires the keyword received by the communication unit 110.

[0074] In step S34, the judgment unit 132 of the server 100 judges whether the image generated by the generation AI 121 based on the input information acquired by the acquisition unit 131 can be formed on a medium based on the input information and the lending party's policy.

[0075] For example, if the acquired keywords are "night view," "futuristic," and "beautiful," the determination unit 132 estimates that the image that may be generated by the generation AI 121 does not violate the issuer's policy. Therefore, the determination unit 132 determines that the image can be formed on a credit card (step S34; Yes). In this case, the server 100 proceeds to step S35.

[0076] As another example, if the acquired keyword is the name of an anime character or a "swastika," the determination unit 132 determines that the image that may be generated is in violation of the lending party's copyright and political policies and therefore cannot be formed on a credit card (step S34; No). In this case, the server 100 returns to step S31. For example, the communication unit 110 transmits information to the communication unit 210 indicating that the image cannot be formed. The communication unit 210 receives the information. Then, the reception unit 231 receives input from the user again.

[0077] In step S35, the generation unit 133 of the server 100 generates an image determined to be formable on a credit card based on the acquired input information using the generation AI 121. For example, if the acquired keywords are "night view," "futuristic," and "beautiful," the generation unit 133 generates an image based on the output information output from the generation AI 121.

[0078] In step S36, the registration unit 134 of the server 100 registers in the database the images generated by the generation unit 133. For example, the registration unit 134 registers in the database, among the images generated by the generation AI 121, the images that are determined to be suitable for use on a credit card.

[0079] In step S37, the forming unit 135 forms on a medium the image in the database registered by the registration unit 134. For example, when the forming unit 135 receives a request from a user via a membership application site or the like to form an image registered in the database onto a credit card, the forming unit 135 forms the image on the credit card by means of printing, engraving, or the like in the Pod.

[0080] (2. Second Embodiment) (2-1. Overview of Processing According to Second Embodiment) The server 100 can also determine whether an image to be generated can be formed on a medium based on a lending party's policy of not forming an image on a medium based on a negative prompt, which is a character string including elements that should be excluded when generating an image. An overview of the processing of the image forming system 1A according to the second embodiment will be described below with reference to FIG. 6. FIG. 6 is a flow chart for explaining the processing flow according to the second embodiment.

[0081] 6, image forming system 1A includes a server 100A instead of server 100. Steps S41, S44, and S45 are similar to steps S21, S23, and S24, and therefore will not be described.

[0082] In step S42, the server 100A passes the keyword 80 acquired from the user terminal 200 through a negative prompt filter. The "negative prompt filter" is a filter that determines a first threshold value for the safety of the keyword 80 that can be input to the generation AI based on the lender's policy of not allowing images based on negative prompts to be formed on the medium. The first threshold value is a value related to the safety that is generally recognized as being acceptable for external presentation. The first threshold value is adjusted by fine-tuning the classification model, etc.

[0083] In step S43, the server 100A determines whether the image to be generated can be formed on the medium based on the lending side's policy of not forming an image based on a negative prompt on the medium. For example, the server 100A determines whether the keyword 80 passed through the negative prompt filter includes a negative prompt.

[0084] As an example, when the server 100A determines that the keyword 80 does not include a negative prompt such as a prohibited word (step S43; OK), it determines that the image to be generated can be formed on the medium. Specifically, when the keyword 80 does not include a negative prompt such as a prohibited word, the server 100A calculates that the numerical value related to the safety of the keyword 80 is equal to or greater than the first threshold. Then, the server 100A determines that the keyword 80 can be input to the generation AI. In this case, the server 100A proceeds to step S44.

[0085] Furthermore, when the server 100A determines that the keyword 80 includes a negative prompt such as a prohibited word (step S43; NG), it determines that the image to be generated cannot be formed on the medium. Specifically, when the keyword 80 includes a negative prompt such as a prohibited word, the server 100A calculates that the numerical value related to the safety level of the keyword 80 is less than the first threshold. It determines that the keyword 80 cannot be input to the generation AI. In this case, the server 10A returns to step S41.

[0086] In this way, server 100A forms on the medium only the image 10 that does not violate the policy of not allowing images based on negative prompts to be formed on the medium, thereby preventing the image 10 based on negative prompts from being formed on the medium. Therefore, server 100A can form on the medium, such as a credit card, the image 10 that is more desired by the provider, such as an issuer.

[0087] (2-2. Configuration of Server and User Terminal According to Second Embodiment) Next, a description will be given of the configurations of the server 100A and the user terminal 200 according to the second embodiment. Fig. 7 is a block diagram showing an example of the configuration of the server and the user terminal according to the second embodiment.

[0088] 7, the server 100A includes a control unit 130A instead of the control unit 130. The control unit 130A includes a determination unit 132A instead of the determination unit 132.

[0089] The determination unit 132A determines whether the image to be generated can be formed on the medium based on the lending side's policy of not forming an image on the medium based on a character string including elements that should be excluded when generating an image.

[0090] For example, the determination unit 132A passes keywords, which are input information from the user acquired by the acquisition unit 131, through a negative prompt filter. Then, the determination unit 132A determines whether or not a negative prompt is included in the keywords that have passed through the negative prompt filter.

[0091] As an example, if the determination unit 132A determines that the keyword does not include a negative prompt such as a prohibited word, it determines that the generated image can be formed on the medium. Specifically, if the keyword does not include a negative prompt, the determination unit 132A calculates that the numerical value related to the keyword safety level is equal to or greater than the first threshold of the negative prompt filter. Then, the determination unit 132A determines that the keyword can be input to the generation AI.

[0092] Furthermore, when the determination unit 132A determines that the keyword contains a negative prompt such as a prohibited word, it determines that the image to be generated cannot be formed on the medium. Specifically, when the keyword contains a negative prompt such as a prohibited word, the determination unit 132A calculates that the numerical value related to the safety level of the keyword is less than the first threshold. Then, the determination unit 132A determines that the keyword cannot be input to the generation AI.

[0093] In the above example, if the keyword contains at least one negative prompt such as a prohibited word, the determination unit 132A determines that the numerical value related to the safety level of the keyword is less than the first threshold value. However, the determination by the determination unit 132A is not limited to the above example.

[0094] For example, when the keyword contains a negative prompt, the judgment unit 132A can make the above-mentioned judgment using a demerit system that deducts points from a numerical value related to safety, and can also judge that if multiple negative prompts are included, the numerical value will be less than the first threshold value.

[0095] In this case, the determination unit 132A may, for example, set a numerical value for the safety level, which is a value to be deducted from a predetermined numerical value, for each negative prompt. This allows the server 100A to determine that keywords that are safe overall can be input to the generation AI, thereby preventing the server 100A from unfairly determining that a keyword cannot be input to the generation AI.

[0096] The determination unit 132A can also make the above-mentioned determination based on the keyword combination and context. In this case, even if the safety value of each individual keyword is low, if the total of the safety values ​​is equal to or greater than the first threshold value depending on the combination with other keywords and the context, the determination unit 132A determines that the keyword combination can be input to the generation AI.

[0097] For example, when a first keyword that may represent a discriminatory term is combined with a second keyword that has a high degree of safety, and the combined keywords do not represent a discriminatory term, the determination unit 132A determines that the total numerical value relating to the safety is equal to or greater than the first threshold value. In this way, the determination unit 132A can more effectively prevent an unfair determination that a keyword cannot be input to the generation AI by calculating the total numerical value based on the keyword combination using a point-addition method or the like.

[0098] (3. Third Embodiment) (3-1. Overview of Processing According to the Third Embodiment) The server 100 can also determine whether the image to be generated can be formed on the medium based on the lender's policy of not allowing images based on anything that violates public order and morals to be formed on the medium. An overview of the processing of the image forming system 1B according to the third embodiment will be described below with reference to FIG. 8. FIG. 8 is a flow chart for explaining the processing flow according to the third embodiment.

[0099] 8, the image forming system 1B includes a server 100B instead of the server 100. Steps S51, S52, and S54 are similar to steps S21, S23, and S24, and therefore will not be described.

[0100] Unlike image forming system 1A, which determines whether an image can be formed on a medium at a prompt stage before the image is generated, image forming system 1B determines whether an image can be formed on a medium at a stage after the image is generated.

[0101] In step S53, the server 100B determines whether the image to be generated can be formed on the medium based on a policy that prohibits the formation of images based on anything that violates public order and morals on the medium. "Offensive images" include violent or sexual content that has no social appropriateness.

[0102] In step S53, for example, the server 100B passes the image generated by the generation AI through a public order and morals filter. The "public order and morals filter" is a filter that determines a second threshold value for the safety level of the generated image based on a policy of not allowing images based on anything that violates public order and morals to be formed on a medium. The second threshold value is a value related to the safety level that is generally recognized as not violating public order and morals. The second threshold value is adjusted by fine-tuning the classification model, etc.

[0103] The numerical value relating to the safety level of the generated image is deducted when, for example, violent images such as blood or missing body parts, sexual images such as swimsuits or nudes, etc. are detected. In this case, when a violent image or a sexual image, etc. is detected from the generated image, server 100B calculates the sum of the numerical values ​​so that the predetermined numerical value is deducted below the second threshold value.

[0104] When determining whether the generated image contains sexual images among images based on content contrary to public order and morals, the server 100B determines whether the proportion of skin color in a predetermined part of the person contained in the generated image is equal to or less than a third threshold value, which is a proportion that is not generally recognized as sexual.

[0105] Specifically, server 100B determines whether the proportion of veil pink, light ochre, or the like in the woman's chest, lower front abdomen, buttocks, etc. included in the generated image is equal to or less than a third threshold. The predetermined part can be identified, for example, by a known object detection technique based on feature points.

[0106] If server 100B determines that the skin color ratio of a specific part of the person is equal to or less than the third threshold, it estimates that the generated image is likely to not include a person with a lot of exposed skin, such as a person in a swimsuit or nude. In this case, server 100B determines that the image does not include sexually explicit images. Server 100B then calculates that the safety level of the image is equal to or greater than the second threshold. Server 100B then determines that the generated image can be formed on a medium (step S53; OK). In this case, server 100B proceeds to step S54.

[0107] If the server 100B determines that the proportion of skin color in a specific part of the person is greater than the third threshold, it estimates that the generated image is likely to include a person with extensive skin exposure. In this case, the server 100B determines that the image includes sexually explicit material. The server 100B also calculates that the safety level of the image is lower than the second threshold. The server 100B then determines that the generated image cannot be formed on the medium (step S53; NG). In this case, the server 100B returns to step S51.

[0108] In this way, server 100B forms on the medium only images 10 that do not violate the policy of not allowing images based on anything offensive to public order and morals to be formed on the medium, thereby preventing images 10 that are offensive to public order and morals from being formed on the medium. Therefore, server 100B can form on the medium, such as a credit card, the image 10 that is most desired by a provider such as an issuer.

[0109] (3-2. Configuration of Server and User Terminal According to Third Embodiment) Next, a description will be given of the configurations of the server 100B and the user terminal 200 according to the third embodiment. Fig. 9 is a block diagram showing an example of the configuration of the server and the user terminal according to the third embodiment.

[0110] 9, the server 100B includes a control unit 130B instead of the control unit 130. The control unit 130B includes a determination unit 132B instead of the determination unit 132.

[0111] The determining unit 132B determines whether or not the image to be generated can be formed on the medium, based on the lending side's policy of not allowing images based on anything that violates public order and morals to be formed on the medium.

[0112] For example, the determination unit 132B passes the image generated by the generation AI through a public order and morals filter. When determining whether the generated image contains violent images instead of or in addition to sexual images among images based on content contrary to public order and morals, the determination unit 132B determines whether the proportion of red, such as dark red, contained in the generated image is equal to or less than a fourth threshold. The fourth threshold is generally a proportion that is recognized as not violent, such as a small amount of blood.

[0113] When determining whether a generated image contains a violent image among images based on things contrary to public order and morals, the determination unit 132B can also determine whether a predetermined body part of an animal such as a person is missing. For example, the determination unit 132B determines whether a predetermined body part is missing by determining whether a predetermined body part is detected using a publicly known object detection technology based on feature points, except when a predetermined body part such as an arm or leg protrudes from the area of ​​the image.

[0114] If the proportion of red in the generated image is equal to or less than a fourth threshold and a predetermined part is detected in the generated image, the determination unit 132B determines that the image does not contain a violent image. Then, the determination unit 132B determines that the numerical value relating to the safety level of the image is equal to or greater than a second threshold at which the image can be formed on a medium.

[0115] If the proportion of red in the generated image is greater than a fourth threshold or if a predetermined part is not detected in the generated image, the determination unit 132B determines that the image contains a violent image. Furthermore, the determination unit 132B determines that the numerical value related to the safety level of the image is less than a second threshold. Then, the determination unit 132B determines that the generated image cannot be formed on a medium.

[0116] In the above example, when a violent image or a sexual image is detected from the generated image, the determination unit 132B calculates the sum of the predetermined numerical values ​​so that the predetermined numerical value is reduced to less than the second threshold value. However, the determination by the determination unit 132B is not limited to the above example.

[0117] For example, even if the proportion of red contained in the generated image is greater than the fourth threshold, the judgment unit 132B may not judge the generated image to be a violent image if the generated image is a medical image, such as an image of blood donation.

[0118] Specifically, when the determination unit 132B determines that the proportion of white in a specific part of the person is equal to or greater than a fifth threshold, it estimates that the generated image is likely to include a medical professional or the like wearing a white coat. The fifth threshold is the value at which a person is generally recognized as wearing a white coat. In this case, the determination unit 132B determines that the image does not include a violent image. Then, the determination unit 132B calculates a value related to the safety level of the image to be equal to or greater than a second threshold. Then, the determination unit 132B determines that the generated image can be formed on a medium.

[0119] (4. Fourth Embodiment) (4-1. Overview of Processing According to the Fourth Embodiment) The server 100 can also determine whether the image to be generated can be formed on the medium based on the lender's policy of not allowing images based on both negative prompts and images that are contrary to public order and morals to be formed on the medium. An overview of the processing of the image forming system 1C according to the fourth embodiment will be described below with reference to FIG. 10. FIG. 10 is a flow chart for explaining the processing flow according to the fourth embodiment.

[0120] 10, image forming system 1C includes server 100C instead of server 100. Steps S61, S64, and S66 are similar to steps S21, S23, and S24, and therefore their explanations will be omitted. Steps S62 and S63 are similar to steps S42 and S43, and therefore their explanations will be omitted. Step S65 is similar to step S53, and therefore its explanation will be omitted.

[0121] Through the processing of steps S61 to S66, the server 100C forms on the medium only images 10 that do not violate the policy of not allowing both images based on negative prompts and images based on things that are contrary to public order and morals to be formed on the medium. For example, the server 100C determines that only images that do not include prohibited words in the keywords entered by the user and do not include sexual or violent images can be formed on the medium.

[0122] This allows server 100C to prevent image 10 that violates the policy of not allowing both images based on negative prompts and images based on material that violates public order and morals to be formed on the medium from being formed on the medium. Therefore, server 100C can form, on the medium such as a credit card, an image 10 that is more desired by a provider such as an issuer, compared to when the policy of excluding negative prompts and the policy of excluding material that violates public order and morals are used separately.

[0123] (4-2. Configuration of Server and User Terminal According to Fourth Embodiment) The following describes the configurations of the server 100C and the user terminal 200 according to the fourth embodiment. Fig. 11 is a block diagram showing an example of the configuration of the server and the user terminal according to the fourth embodiment.

[0124] 11, the server 100C includes a control unit 130C instead of the control unit 130. The control unit 130C includes a determination unit 132C instead of the determination unit 132.

[0125] The determination unit 132C determines whether the image to be generated can be formed on the medium based on the lender's policy of not allowing images based on both negative prompts and images that are contrary to public order and morals to be formed on the medium. For example, the determination unit 132C determines that only images that do not include prohibited words in the keywords entered by the user and do not include sexual or violent images can be formed on the medium.

[0126] (5. Modifications of the embodiment) Each of the above-described embodiments may involve various modifications. For example, in each of the above-described embodiments, the lending party is an issuer. However, the lending party is not particularly limited as long as it is a party that lends a medium. For example, when lending a credit card to a user, the lending party is not limited to an issuer that issues the credit card, but may be a party that belongs to the credit card company. Furthermore, the lending party may be a bank or the like that lends a cash card to a user.

[0127] In each of the above-described embodiments, the medium is a credit card. However, the medium is not particularly limited as long as it is a medium on which an image can be formed as a loaned item loaned by the loaning party. For example, the medium may be a physical medium such as the face of a cash card, employee ID card, or health insurance card, or may be an electronic medium that does not exist as a physical object, such as a digital card.

[0128] (6. Other Embodiments) The processing according to the above-described embodiment may be implemented in various different forms other than the above-described embodiment.

[0129] For example, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using a known method. Furthermore, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0130] As an example, a process can be added to Fig. 1. Specifically, after the image 10 is generated by the generation AI in step S3, and before the issuer transmits the formation data 20 to the server 100 in step S5, the server 100 can perform a process of transmitting the image 10 to the user terminal 200 or the issuer terminal 300.

[0131] As a result, the user terminal 200 can display the image 10 received from the server 100 on the display unit, thereby allowing the user to confirm whether the image 10 is what the user wants. For example, if the image 10 of a "sweet potato" is generated even though the user wants an image of a "potato," the user terminal 200 can display the image 10 of a "sweet potato" on the display unit, thereby allowing the user to recognize that the image 10 is not what the user wants.

[0132] Furthermore, the issuer terminal 300 can display the image 10 received from the server 100 on the display unit, thereby allowing the issuer to confirm whether the image 10 is undesirable to the issuer. For example, if the issuer does not want discriminatory images but a discriminatory image 10 is mistakenly generated based on a keyword, the issuer terminal 300 can display the discriminatory image 10 on the display unit, allowing the issuer to recognize that the image 10 is undesirable to the issuer.

[0133] As a result, the server 100 can only perform the formation of the image 10 on a medium such as a credit card if the user or issuer has confirmed the image 10 and given permission to do so, thereby preventing the formation of the image 10 from being performed against the wishes of the user or issuer.

[0134] 1, the server 100 may transmit a request to issue a credit card on which the image 10 is formed to the issuer terminal 300. This allows the issuer terminal 300 to grant authorization or the like to the medium, such as a credit card, on which the image 10 is formed by the server 100, thereby enabling the medium.

[0135] The processing procedure of Figure 3 can also be modified. In Figure 3, before the generation AI generates an image in step S23, server 100 determines whether the image generated by the generation AI can be printed on a credit card based on input information such as keywords entered into user terminal 200 and the lender's policy. As a result, if server 100 determines that the image cannot be printed on a credit card, it can omit the generation AI from generating the image, which may reduce the overall processing load.

[0136] However, after the image is generated by the generation AI in step S23, the server 100 can also determine whether the image can be formed on a medium based on the lending party's policy, etc. This prevents the server 100 from erroneously determining that an image estimated from keywords, etc. cannot be formed on a medium, even if the server 100 determines that the actually generated image can be formed on a medium such as a credit card.

[0137] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0138] For example, the components having the functions of judgment unit 132A in judgment unit 132C, the components having the functions of judgment unit 132B in generation unit 133 and judgment unit 132C, and the components having the functions of registration unit 134 can be distributed across multiple servers.

[0139] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0140] Furthermore, the effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0141] (7. Effects of the image forming system according to the present disclosure) As described above, the image forming system (image forming system 1 in the embodiment) according to the present disclosure includes a terminal (user terminal 200 in the embodiment) and an image forming apparatus (server 100 in the embodiment). The terminal includes a reception unit (reception unit 231 in the embodiment) that receives input from a user. The image forming apparatus includes an acquisition unit (acquisition unit 131 in the embodiment), a determination unit (determination unit 132 in the embodiment), and a formation unit (formation unit 135 in the embodiment).

[0142] The acquisition unit acquires input information regarding the accepted input from the user. The determination unit determines whether an image generated by a generation AI (generation AI 121 in this embodiment) based on the acquired input information can be formed on a medium based on the input information and the lending side's policy regarding the medium to be lent to the user by the lending side. The formation unit forms, on the medium, an image determined to be formable on the medium.

[0143] In this way, the image forming system according to the present disclosure forms on a medium only those images that are determined to be formable on the medium based on the input information and the lending party's policy, thereby preventing images that violate the lending party's policy from being formed on the medium. Therefore, the image forming system can form on a medium such as a credit card an image that the provider desires, such as a safe and secure image desired by the issuer.

[0144] (8. Hardware Configuration) Information devices such as the server 100 and user terminal 200 according to the above-described embodiments are realized by a computer 1000 having a configuration as shown in Fig. 12, for example. The server 100 according to the embodiment will be described below as an example. Fig. 12 is a hardware configuration diagram showing an example of a computer that realizes the functions of a server. The computer 1000 has a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, an HDD (Hard Disk Drive) 1400, a communication interface 1500, and an input / output interface 1600. The components of the computer 1000 are connected by a bus 1050.

[0145] The CPU 1100 operates and controls each unit based on programs stored in the ROM 1300 or the HDD 1400. For example, the CPU 1100 loads the programs stored in the ROM 1300 or the HDD 1400 into the RAM 1200 and executes processing corresponding to the various programs.

[0146] The ROM 1300 stores boot programs such as a Basic Input Output System (BIOS) executed by the CPU 1100 when the computer 1000 is started, and programs that depend on the hardware of the computer 1000 .

[0147] HDD 1400 is a computer-readable recording medium that non-temporarily records programs executed by CPU 1100 and data used by such programs. Specifically, HDD 1400 is a recording medium that records programs that execute processes related to the present disclosure, which are an example of program data 1450.

[0148] The communication interface 1500 is an interface for connecting the computer 1000 to an external network 1550 (e.g., the Internet). For example, the CPU 1100 receives data from other devices and transmits data generated by the CPU 1100 to other devices via the communication interface 1500.

[0149] The input / output interface 1600 is an interface for connecting the input / output device 1650 and the computer 1000. For example, the CPU 1100 receives data from an input device such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 also transmits data to an output device such as a display, a speaker, or a printer via the input / output interface 1600. The input / output interface 1600 may also function as a media interface for reading programs and the like recorded on a predetermined recording medium. Examples of media include optical recording media such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disk), magneto-optical recording media such as an MO (Magneto-Optical disk), tape media, magnetic recording media, and semiconductor memories.

[0150] For example, when computer 1000 functions as server 100 according to the embodiment, CPU 1100 of computer 1000 executes an image forming program loaded onto RAM 1200 to realize functions of control unit 130, etc. Also, HDD 1400 stores programs for executing processes according to the present disclosure and data in storage unit 120. Note that CPU 1100 reads and executes program data 1450 from HDD 1400, but as another example, these programs may be acquired from another device via external network 1550.

[0151] The above describes the embodiments of the present application in detail based on the drawings, but these are merely examples, and the present invention can be implemented in other forms that include the embodiments described in the Disclosure of the Invention section and that have been modified and improved in various ways based on the knowledge of those skilled in the art. [Explanation of symbols]

[0152] 1. Image forming system 100 servers 110 Communications Department 120 Storage section 121 Generation AI 122 Image storage unit 130 Control Unit 131 Acquisition Department 132 Judgment section 133 Generation part 134 Registration Department 135 Formation part 200 user terminals 210 Communications Department 220 Display section 230 Control Unit 231 Reception Department 300 issuer terminals N Network

Claims

1. A terminal and an image forming device are provided, the terminal includes a reception unit that receives input from a user; the image forming apparatus, an acquisition unit that acquires input information regarding the accepted input from the user; a determination unit that determines whether an image generated by a generation AI (Artificial Intelligence) based on the acquired input information can be formed on the medium based on the input information and a lending side's policy regarding the medium to be lent to the user by the lending side; a forming unit that forms, on the medium, an image that has been determined to be formable on the medium. An image forming system comprising:

2. The computer Accepts input from the user, acquiring input information relating to the received user input; Determine whether the image generated by the generation AI based on the acquired input information can be formed on the medium based on the input information and the lending side's policy regarding the medium to be lent to the user by the lending side; An image determined to be formable on the medium is formed on the medium. An image forming method comprising:

3. an acquisition unit that acquires input information regarding an input from a user; A determination unit that determines whether an image generated by the generation AI based on the acquired input information can be formed on the medium based on the input information and a lending side policy regarding the medium to be lent to the user by the lending side; a forming unit that forms, on the medium, an image that has been determined to be formable on the medium. An image forming apparatus characterized by:

4. Computer, an acquisition unit that acquires input information regarding an input from a user; A determination unit that determines whether an image generated by the generation AI based on the acquired input information can be formed on the medium based on the input information and a lending side policy regarding the medium to be lent to the user by the lending side; a forming unit that forms, on the medium, an image that has been determined to be formable on the medium. An image forming program that causes an image forming apparatus to function.

Images