system

Abstract

An object of the system according to the embodiment is to enable a private shop to easily create an advertisement, a menu table, and a poster utilizing the SNS.SOLUTION: A system includes a reception unit, a proposal unit, a correction unit, and an output unit. The reception unit receives a request for creating advertisement contents, a menu table, or a poster from a user. The proposal unit analyzes the information received by the reception unit and proposes a design or wording. The correction unit is used by the user to confirm and correct the design and wording proposed by the proposal unit. The output unit is used to post the design or wording corrected by the correction unit on an SNS or to print and use the design or wording.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult for independent shops to easily create advertising campaigns, menus, and posters using social media.

[0005] The system according to the embodiment aims to enable independent shops to easily create advertisements, menus, and posters using social media. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a correction unit, and an output unit. The reception unit accepts requests from users to create advertising content, menus, or posters. The proposal unit analyzes the information accepted by the reception unit and proposes designs and wording. The correction unit allows the user to confirm and correct the designs and wording proposed by the proposal unit. The output unit allows the design and wording corrected by the correction unit to be posted to social networking sites or printed and used. [Effects of the Invention]

[0007] The system according to the embodiment allows independent shops to easily create advertisements, menus, and posters using social media. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A generative AI system according to an embodiment of the present invention allows independent businesses, such as restaurants, to easily create advertising materials, menus, and posters for use on social media. The system accepts requests from users to create advertising content, menus, and posters. The system analyzes the user's input and proposes appropriate designs and wording. The generated designs and wording can be reviewed and edited by the user, and can ultimately be posted to social media or printed for use. For example, the system can create advertisements for new menu items or seasonal campaign information by inputting user information about the store, the content the user wants to advertise, and menu details. The system generates optimal designs and wording based on social media trends and the user's store information. For example, the system can propose posters and menus incorporating popular designs and catchphrases on social media. The generated designs and wording can be reviewed and edited by the user, changing colors, fonts, and wording. Finally, the generated designs and wording can be posted to social media or printed for use. This allows independent businesses, such as restaurants, to easily create effective advertising materials, menus, and posters. As a result, the generative AI system will make it easier for restaurants and other independent businesses to use social media to advertise, create menus, and create posters, which is expected to improve customer attraction and increase sales. For example, by effectively disseminating information about new menu items or seasonal campaigns on social media, it will be possible to appeal to many customers. In addition, the designs and wording suggested by the generative AI reflect social media trends, which will attract the interest of more people.

[0029] A generation AI system according to an embodiment includes a reception unit, a proposal unit, a correction unit, and an output unit. The reception unit accepts requests from users to create advertising content, menus, and posters. The user can input, for example, store information, the content they want to advertise, and menu details. The proposal unit uses a generation AI to analyze the information accepted by the reception unit and propose appropriate designs and wording. The proposal unit generates optimal designs and wording based on, for example, social media trends and the user's store information. The generation AI generates designs and wording using a text generation AI (e.g., LLM) or a multimodal generation AI. The correction unit provides an interface that allows the user to confirm and correct the designs and wording proposed by the proposal unit. The correction unit provides an interface that allows the user to easily change designs by, for example, drag and drop. The user can confirm the designs and wording proposed by the generation AI and make corrections as necessary. The output unit provides data for posting the designs and wording corrected by the correction unit to social media or for printing and using them. The output unit has, for example, a function to post the generated design and text to social media and a function to provide printing data. This allows the generation AI system according to the embodiment to easily create advertising content, menus, and posters. For example, the generation AI system generates designs and text that reflect social media trends based on information entered by the user, and after the user confirms and modifies them, the system can post them to social media or print them for use.

[0030] The suggestion unit can generate designs or wording based on social media trends or the user's store information. The suggestion unit generates designs and wording based on social media trends, for example. For example, the suggestion unit generates designs and wording by referring to trends on Twitter (registered trademark) or popular posts on Instagram (registered trademark). The suggestion unit can also generate designs and wording based on the user's store information. For example, the suggestion unit generates optimal designs and wording based on information such as the store's location, business hours, and services offered. This allows the suggestion unit to generate designs and wording that reflect social media trends. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input social media trend information into the generation AI, which then generates designs and wording.

[0031] The suggestion unit can generate a design based on a store logo or color registered in advance by the user. For example, the suggestion unit generates a design based on a store logo registered in advance by the user. For example, the suggestion unit generates a design based on the image format and color specifications of the logo registered by the user. The suggestion unit can also generate a design based on the store colors registered in advance by the user. For example, the suggestion unit generates a design based on RGB values ​​and a color palette specified by the user. This allows the suggestion unit to generate a design that matches the brand image of the store. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input information about the logo and color registered by the user into the generation AI, which then generates a design.

[0032] The correction unit can provide an interface that allows a user to change the design by dragging and dropping. For example, the correction unit can provide an interface that allows a user to change the design by dragging and dropping. For example, the correction unit can allow a user to move or resize design elements by dragging and dropping. The correction unit can also provide options for a user to change the color or font of the design. For example, the correction unit can provide an interface that allows a user to select from a color palette or change the font style. This allows the correction unit to easily correct the design. Some or all of the above-mentioned processing in the correction unit may be performed using AI or may be performed without using AI. For example, the correction unit can input the user's corrections into AI, which can then suggest corrections.

[0033] The output unit may provide a function for posting the generated design or text to a social networking site (SNS). For example, the output unit may provide an option for a user to post the generated design or text to a social networking site (SNS), such as Facebook (registered trademark), Twitter, or Instagram. The output unit may also provide an interface for a user to add a caption or hashtag when posting to the SNS. For example, the output unit may provide an option for a user to enter a caption or select a hashtag. This allows the output unit to easily post to the SNS. Some or all of the above-described processing in the output unit may be performed using AI, or may be performed without AI. For example, the output unit may input the generated design or text to AI, which may then automate posting to the SNS.

[0034] The output unit can provide data for printing the generated design or text. For example, the output unit provides an option for saving the generated design or text in PDF format or a high-resolution image format. The output unit can also provide an interface for the user to specify the resolution and size of the data to be printed. For example, the output unit provides options for the user to select the resolution or specify the print size. This allows the output unit to easily obtain the data to be printed. Some or all of the above-described processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit can input the generated design or text to AI, which then generates the data to be printed.

[0035] The reception unit can analyze the user's past request history and select the reception method. The reception unit, for example, analyzes the user's past request history and selects the optimal reception method. For example, the reception unit prioritizes receiving requests during time periods when the user frequently made requests in the past. The reception unit can also prioritize suggesting request methods (voice, text, etc.) that the user has used in the past. The reception unit can also accept requests on specific days of the week or time periods based on the user's past request history. This allows the reception unit to provide the optimal reception method based on the user's past request history. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past request history into a generation AI, which can select the optimal reception method.

[0036] The reception unit can perform filtering based on the user's current store status and campaign information at the time of reception. For example, the reception unit performs filtering based on the user's current store status and campaign information at the time of reception. For example, if the user's store is busy, the reception unit postpones the request. Furthermore, if the user's store is running a campaign, the reception unit can also prioritize receiving requests related to the campaign. Furthermore, if the user's store is quiet, the reception unit can immediately receive the request. This allows the reception unit to receive the optimal request based on the user's store status and campaign information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's store status and campaign information into the generation AI, and the generation AI can perform filtering.

[0037] The reception unit can select the optimal reception means according to the user's input method (voice, text, image, etc.) at the time of reception. The reception unit, for example, selects the reception means according to the user's input method at the time of reception. For example, when the user makes a request by voice, the reception unit uses voice recognition to receive the request. Furthermore, when the user makes a request by text, the reception unit can also use text analysis to receive the request. Furthermore, when the user makes a request by image, the reception unit can also use image analysis to receive the request. This allows the reception unit to provide the optimal reception means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI, which can select the optimal reception means.

[0038] The reception unit can prioritize receiving highly relevant requests based on the user's geographical location information when receiving a request. For example, the reception unit prioritizes receiving highly relevant requests by taking the user's geographical location information into consideration when receiving a request. For example, the reception unit prioritizes receiving requests from stores in the user's neighborhood. Furthermore, if the user is running a campaign in a specific area, the reception unit can prioritize receiving requests from that area. Furthermore, the reception unit can postpone requests from stores far from the user. This allows the reception unit to receive the most appropriate request based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into a generation AI, which can select highly relevant requests.

[0039] The reception unit can receive requests based on the user's social media activity at the time of reception. For example, the reception unit analyzes the user's social media activity at the time of reception and receives related requests. For example, the reception unit prioritizes receiving content that the user wants to promote on social media. The reception unit can also analyze the content posted by the user on social media and prioritize receiving related requests. The reception unit can also receive related requests by referring to the activities of the user's friends on social media. This allows the reception unit to receive optimal requests based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can select related requests.

[0040] The reception unit can customize the reception method based on the user's past feedback at the time of reception. For example, the reception unit customizes the reception method by reflecting the user's past feedback at the time of reception. For example, the reception unit preferentially suggests reception methods that have been well-received by users in the past. The reception unit can also improve the reception method based on the user's past feedback. The reception unit can also simplify the reception procedure by reflecting the user's past feedback. This allows the reception unit to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI, which can select the optimal reception method.

[0041] The suggestion unit can adjust the details of the suggestion based on trends on social media when making a suggestion. For example, the suggestion unit can adjust the level of detail of the suggestion based on trends on social media when making a suggestion. For example, the suggestion unit makes a suggestion that incorporates a design that is popular on social media. The suggestion unit can also make a suggestion using a catchphrase that is popular on social media. The suggestion unit can also adjust the level of detail of the suggestion based on trends on social media. This allows the suggestion unit to make optimal suggestions based on trends on social media. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input information about trends on social media into the generation AI, which can then adjust the level of detail of the suggestion.

[0042] The suggestion unit can apply a suggestion algorithm according to the user's store information when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms according to the user's store information when making a suggestion. For example, the suggestion unit can suggest an optimal design according to the industry of the user's store. The suggestion unit can also suggest appropriate wording according to the size of the user's store. The suggestion unit can also make suggestions specific to the area based on the location information of the user's store. This allows the suggestion unit to make optimal suggestions based on the user's store information. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's store information into a generation AI, which can then apply an optimal suggestion algorithm.

[0043] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion based on designs previously adopted by the user. The suggestion unit can also analyze the user's past suggestion results and suggest an optimal design. The suggestion unit can also improve the accuracy of wording by referring to the user's past suggestion results. This allows the suggestion unit to make an optimal suggestion based on the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI or may be performed without using a generation AI. For example, the suggestion unit can input the user's past suggestion results into the generation AI, which can improve the accuracy of the suggestion.

[0044] The suggestion unit, when making a suggestion, can determine the order of suggestions based on the time of posting on the SNS. For example, the suggestion unit, when making a suggestion, can determine the order of suggestions based on the time of posting on the SNS. For example, the suggestion unit makes suggestions based on a time period when there are many posts on the SNS. The suggestion unit can also make suggestions based on a time period when there are few posts on the SNS. The suggestion unit can also determine the order of suggestions based on the time of posting on the SNS. This allows the suggestion unit to make optimal suggestions based on the time of posting on the SNS. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without using the generation AI. For example, the suggestion unit can input information about the time of posting on the SNS to the generation AI, and the generation AI can determine the order of suggestions.

[0045] The suggestion unit can adjust the order of suggestions based on information about the user's store when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on information about the user's store when making suggestions. For example, the suggestion unit prioritizes suggestions related to the industry of the user's store. The suggestion unit can also adjust the order of suggestions based on the size of the user's store. The suggestion unit can also adjust the order of suggestions based on location information of the user's store. This allows the suggestion unit to make suggestions in an optimal order based on the relevance of the user's store. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input information about the user's store into a generation AI, which can adjust the order of suggestions.

[0046] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's knowledge level when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's knowledge level when making a proposal. For example, if the user has specialized knowledge, the suggestion unit can make a proposal that uses a lot of technical terminology. Also, if the user does not have specialized knowledge, the suggestion unit can make a proposal that explains things in simple terms. Also, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's expertise level. This allows the suggestion unit to make a proposal using optimal technical terminology according to the user's expertise level. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI or may be performed without using a generation AI. For example, the suggestion unit can input user knowledge level information into the generation AI, and the generation AI can adjust the use of technical terminology in the proposal.

[0047] The correction unit can select a correction method based on the user's past correction history when making corrections. For example, the correction unit can analyze the user's past correction history and select an optimal correction method when making corrections. For example, the correction unit can suggest an optimal correction method based on corrections made by the user in the past. The correction unit can also analyze the user's past correction history and select an optimal correction method. The correction unit can also improve the accuracy of corrections by referring to the user's past correction history. This allows the correction unit to provide an optimal correction method based on the user's past correction history. Some or all of the above-mentioned processing in the correction unit may be performed using AI or may be performed without using AI. For example, the correction unit can input the user's past correction history into a generation AI, which can select an optimal correction method.

[0048] The correction unit can customize the correction method based on the user's current store situation at the time of correction. For example, the correction unit customizes the correction method based on the user's current store situation at the time of correction. For example, the correction unit can provide simple correction options when the user's store is busy. The correction unit can also provide detailed correction options when the user's store is quiet. The correction unit can also customize the correction method according to the user's store situation. This allows the correction unit to provide the optimal correction method based on the user's store situation. Some or all of the above-mentioned processing in the correction unit may be performed using AI or may be performed without using AI. For example, the correction unit can input the user's store situation data to the generation AI, which can select the optimal correction method.

[0049] The correction unit can improve the correction method based on user feedback during correction. For example, the correction unit improves the correction method by reflecting user feedback during correction. For example, the correction unit improves correction options based on user feedback. The correction unit can also simplify the correction procedure by reflecting user feedback. The correction unit can also improve the accuracy of correction by referring to user feedback. This allows the correction unit to provide an optimal correction method based on user feedback. Some or all of the above-mentioned processing in the correction unit may be performed using AI or may be performed without using AI. For example, the correction unit can input user feedback data into the generation AI, which can improve the correction method.

[0050] The correction unit can select a correction method based on the user's geographical location information when making corrections. For example, the correction unit selects the optimal correction method by taking the user's geographical location information into consideration when making corrections. For example, the correction unit prioritizes correction requests from stores nearby the user. Furthermore, if the user is running a campaign in a specific area, the correction unit can also prioritize correction requests from that area. Furthermore, the correction unit can postpone correction requests from stores far from the user. This allows the correction unit to provide the optimal correction method based on the user's geographical location information. Some or all of the above-mentioned processing in the correction unit may be performed using AI, or may be performed without using AI. For example, the correction unit can input the user's geographical location information into a generation AI, which can select the optimal correction method.

[0051] The correction unit can suggest correction measures based on the user's social media activity during correction. For example, the correction unit can analyze the user's social media activity during correction and suggest correction measures. For example, the correction unit prioritizes correcting content that the user wants to promote on social media. The correction unit can also analyze the content posted by the user on social media and suggest related corrections. The correction unit can also suggest related corrections based on the activity of the user's friends on social media. This allows the correction unit to provide optimal correction measures based on the user's social media activity. Some or all of the above-mentioned processing in the correction unit may be performed using AI or without AI. For example, the correction unit can input the user's social media activity data into a generation AI, which can then suggest optimal correction measures.

[0052] The correction unit can customize the correction method based on the user's past feedback when correcting. For example, the correction unit customizes the correction method by reflecting the user's past feedback when correcting. For example, the correction unit preferentially suggests correction methods that have been well-received by users in the past. The correction unit can also improve the correction method based on the user's past feedback. The correction unit can also simplify the correction procedure by reflecting the user's past feedback. This allows the correction unit to provide an optimal correction method based on the user's past feedback. Some or all of the above-described processing in the correction unit may be performed using AI or may be performed without using AI. For example, the correction unit can input the user's past feedback data into the generation AI, which can select the optimal correction method.

[0053] The output unit can select an output method based on the user's past output history at the time of output. For example, the output unit can analyze the user's past output history and select the optimal output method at the time of output. For example, the output unit can suggest the optimal output method based on output methods used by the user in the past. The output unit can also analyze the user's past output history and select the optimal output method. The output unit can also improve the accuracy of the output by referring to the user's past output history. This allows the output unit to provide the optimal output method based on the user's past output history. Some or all of the above-mentioned processing in the output unit may be performed using AI or may be performed without using AI. For example, the output unit can input the user's past output history into a generation AI, which can select the optimal output method.

[0054] The output unit can customize the output means based on the user's current store situation at the time of output. The output unit, for example, customizes the output means based on the user's current store situation at the time of output. For example, the output unit can provide simple output options when the user's store is busy. The output unit can also provide detailed output options when the user's store is quiet. The output unit can also customize the output means according to the user's store situation. This allows the output unit to provide the optimal output means based on the user's store situation. Some or all of the above-mentioned processing in the output unit may be performed using AI or may be performed without using AI. For example, the output unit can input the user's store situation data to a generation AI, which can select the optimal output means.

[0055] The output unit can improve the output method based on user feedback at the time of output. For example, the output unit improves the output method by reflecting user feedback at the time of output. For example, the output unit improves output options based on user feedback. The output unit can also simplify the output procedure by reflecting user feedback. The output unit can also improve the accuracy of the output by referring to user feedback. This allows the output unit to provide an optimal output method based on user feedback. Some or all of the above-mentioned processing in the output unit may be performed using AI or may be performed without using AI. For example, the output unit can input user feedback data to a generation AI, which can then improve the output method.

[0056] The output unit can select an output method based on the user's geographical location information at the time of output. For example, the output unit selects the optimal output method by taking the user's geographical location information into consideration at the time of output. For example, the output unit prioritizes output requests from stores nearby the user. Furthermore, if the user is running a campaign in a specific area, the output unit can prioritize output requests from that area. Furthermore, the output unit can postpone output requests from stores far away from the user. This allows the output unit to provide the optimal output method based on the user's geographical location information. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit can input the user's geographical location information to a generation AI, which can select the optimal output method.

[0057] The output unit can suggest an output means based on the user's social media activity at the time of output. For example, the output unit can analyze the user's social media activity and suggest an output means at the time of output. For example, the output unit prioritizes output of content that the user wants to promote on social media. The output unit can also analyze the content posted by the user on social media and suggest related output. The output unit can also suggest related output with reference to the activity of the user's friends on social media. This allows the output unit to provide the optimal output means based on the user's social media activity. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit can input the user's social media activity data into a generation AI, which can then suggest the optimal output means.

[0058] The output unit can customize the output method based on the user's past feedback at the time of output. For example, the output unit customizes the output method by reflecting the user's past feedback at the time of output. For example, the output unit preferentially suggests output methods that have been well-received by users in the past. The output unit can also improve the output method based on the user's past feedback. The output unit can also simplify the output procedure by reflecting the user's past feedback. This allows the output unit to provide an optimal output method based on the user's past feedback. Some or all of the above-described processing in the output unit may be performed using AI or may be performed without using AI. For example, the output unit can input the user's past feedback data into a generation AI, which can select the optimal output method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The suggestion unit can analyze the user's past purchase history and customize the content of the suggestions. For example, the suggestion unit can suggest related new products and services based on products and services the user has purchased in the past. The suggestion unit can also analyze the user's preference trends from the user's purchase history and suggest designs and wording that the user is likely to be interested in. Furthermore, the suggestion unit can make suggestions tailored to specific seasons or events based on the user's purchase history. This allows the suggestion unit to make optimal suggestions based on the user's purchase history.

[0061] The correction unit can analyze the user's correction history and suggest corrections. For example, the correction unit can suggest similar corrections based on corrections made by the user in the past. The correction unit can also analyze trends in preferred designs and wording from the user's correction history and suggest corrections that the user prefers. Furthermore, the correction unit can also suggest corrections tailored to specific seasons or events based on the user's correction history. This allows the correction unit to suggest optimal corrections based on the user's correction history.

[0062] The output unit can analyze the user's past output history and customize the output method. For example, the output unit can suggest a similar output format based on the output format the user has used in the past. The output unit can also analyze the user's preferred output options from the user's output history and suggest an output method that the user prefers. Furthermore, the output unit can suggest an output method suited to a specific season or event based on the user's output history. In this way, the output unit can provide the optimal output method based on the user's output history.

[0063] The suggestion unit can analyze the user's social media activity and customize the suggestion content. For example, the suggestion unit can make relevant suggestions based on the content the user has shown interest in on social media. The suggestion unit can also analyze the content posted by the user on social media and suggest designs and wording that the user prefers. Furthermore, the suggestion unit can make relevant suggestions based on the activity of the user's friends on social media. This allows the suggestion unit to make optimal suggestions based on the user's social media activity.

[0064] The output unit can select an output method taking into consideration the user's geographical location information. For example, the output unit can prioritize output requests from stores in the user's neighborhood. Also, if the user is running a campaign in a specific area, the output unit can prioritize output requests from that area. Furthermore, the output unit can postpone output requests from stores far away from the user. This allows the output unit to provide the optimal output method based on the user's geographical location information.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives requests from users to create advertisement content, menus, and posters. Users can input, for example, store information, the content they want to advertise, and menu details. Step 2: The proposal unit uses a generation AI to analyze the information received by the reception unit and propose appropriate designs and wording. The proposal unit generates optimal designs and wording based on, for example, social media trends and the user's store information. The generation AI generates designs and wording using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The correction unit provides an interface for the user to review and modify the design and wording proposed by the proposal unit. The correction unit provides an interface that allows the user to easily change the design, for example, by dragging and dropping. The user can review the design and wording proposed by the generation AI and make modifications as necessary. Step 4: The output unit posts the design and text corrected by the correction unit to a social networking site or provides data for printing and use. The output unit, for example, has a function for posting the generated design and text to a social networking site or a function for providing data for printing.

[0067] (Example 2) A generative AI system according to an embodiment of the present invention allows independent businesses, such as restaurants, to easily create advertising materials, menus, and posters for use on social media. The system accepts requests from users to create advertising content, menus, and posters. The system analyzes the user's input and proposes appropriate designs and wording. The generated designs and wording can be reviewed and edited by the user, and can ultimately be posted to social media or printed for use. For example, the system can create advertisements for new menu items or seasonal campaign information by inputting user information about the store, the content the user wants to advertise, and menu details. The system generates optimal designs and wording based on social media trends and the user's store information. For example, the system can propose posters and menus incorporating popular designs and catchphrases on social media. The generated designs and wording can be reviewed and edited by the user, changing colors, fonts, and wording. Finally, the generated designs and wording can be posted to social media or printed for use. This allows independent businesses, such as restaurants, to easily create effective advertising materials, menus, and posters. As a result, the generative AI system will make it easier for restaurants and other independent businesses to use social media to advertise, create menus, and create posters, which is expected to improve customer attraction and increase sales. For example, by effectively disseminating information about new menu items or seasonal campaigns on social media, it will be possible to appeal to many customers. In addition, the designs and wording suggested by the generative AI reflect social media trends, which will attract the interest of more people.

[0068] A generation AI system according to an embodiment includes a reception unit, a proposal unit, a correction unit, and an output unit. The reception unit accepts requests from users to create advertising content, menus, and posters. The user can input, for example, store information, the content they want to advertise, and menu details. The proposal unit uses a generation AI to analyze the information accepted by the reception unit and propose appropriate designs and wording. The proposal unit generates optimal designs and wording based on, for example, social media trends and the user's store information. The generation AI generates designs and wording using a text generation AI (e.g., LLM) or a multimodal generation AI. The correction unit provides an interface that allows the user to confirm and correct the designs and wording proposed by the proposal unit. The correction unit provides an interface that allows the user to easily change designs by, for example, drag and drop. The user can confirm the designs and wording proposed by the generation AI and make corrections as necessary. The output unit provides data for posting the designs and wording corrected by the correction unit to social media or for printing and using them. The output unit has, for example, a function to post the generated design and text to social media and a function to provide printing data. This allows the generation AI system according to the embodiment to easily create advertising content, menus, and posters. For example, the generation AI system generates designs and text that reflect social media trends based on information entered by the user, and after the user confirms and modifies them, the system can post them to social media or print them for use.

[0069] The suggestion unit can generate designs or wording based on social media trends or the user's store information. The suggestion unit generates designs and wording based on social media trends, for example. For example, the suggestion unit generates designs and wording by referring to Twitter trends or popular Instagram posts. The suggestion unit can also generate designs and wording based on the user's store information. For example, the suggestion unit generates optimal designs and wording based on information such as the store's location, business hours, and services offered. This allows the suggestion unit to generate designs and wording that reflect social media trends. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input social media trend information into the generation AI, which then generates designs and wording.

[0070] The suggestion unit can generate a design based on a store logo or color registered in advance by the user. For example, the suggestion unit generates a design based on a store logo registered in advance by the user. For example, the suggestion unit generates a design based on the image format and color specifications of the logo registered by the user. The suggestion unit can also generate a design based on the store colors registered in advance by the user. For example, the suggestion unit generates a design based on RGB values ​​and a color palette specified by the user. This allows the suggestion unit to generate a design that matches the brand image of the store. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input information about the logo and color registered by the user into the generation AI, which then generates a design.

[0071] The correction unit can provide an interface that allows a user to change the design by dragging and dropping. For example, the correction unit can provide an interface that allows a user to change the design by dragging and dropping. For example, the correction unit can allow a user to move or resize design elements by dragging and dropping. The correction unit can also provide options for a user to change the color or font of the design. For example, the correction unit can provide an interface that allows a user to select from a color palette or change the font style. This allows the correction unit to easily correct the design. Some or all of the above-mentioned processing in the correction unit may be performed using AI or may be performed without using AI. For example, the correction unit can input the user's corrections into AI, which can then suggest corrections.

[0072] The output unit may provide a function for posting the generated design or text to a social networking site (SNS). For example, the output unit may provide an option for a user to post the generated design or text to a social networking site (SNS), such as Facebook, Twitter, or Instagram. The output unit may also provide an interface for a user to add a caption or hashtag when posting to the SNS. For example, the output unit may provide an option for a user to enter a caption or select a hashtag. This allows the output unit to easily post to the SNS. Some or all of the above-described processing in the output unit may be performed using AI, or may be performed without AI. For example, the output unit may input the generated design or text to AI, which may then automate posting to the SNS.

[0073] The output unit can provide data for printing the generated design or text. For example, the output unit provides an option for saving the generated design or text in PDF format or a high-resolution image format. The output unit can also provide an interface for the user to specify the resolution and size of the data to be printed. For example, the output unit provides options for the user to select the resolution or specify the print size. This allows the output unit to easily obtain the data to be printed. Some or all of the above-described processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit can input the generated design or text to AI, which then generates the data to be printed.

[0074] The reception unit can estimate a user's emotions and adjust the timing of accepting requests for creating advertising content, menus, and posters based on the estimated user emotions. The reception unit, for example, estimates a user's emotions and adjusts the timing of accepting requests for creating advertising content, menus, and posters based on the estimated user emotions. For example, if a user is feeling stressed, the reception unit adjusts the reception of requests to a time when the user is able to relax. Furthermore, if a user is excited, the reception unit can immediately accept requests and respond quickly. Furthermore, if a user is tired, the reception unit can adjust the reception of requests to the next day. This allows the reception unit to accept requests at the optimal timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input user emotion data into a generation AI, which then estimates the emotion.

[0075] The reception unit can analyze the user's past request history and select the reception method. The reception unit, for example, analyzes the user's past request history and selects the optimal reception method. For example, the reception unit prioritizes receiving requests during time periods when the user frequently made requests in the past. The reception unit can also prioritize suggesting request methods (voice, text, etc.) that the user has used in the past. The reception unit can also accept requests on specific days of the week or time periods based on the user's past request history. This allows the reception unit to provide the optimal reception method based on the user's past request history. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past request history into a generation AI, which can select the optimal reception method.

[0076] The reception unit can perform filtering based on the user's current store status and campaign information at the time of reception. For example, the reception unit performs filtering based on the user's current store status and campaign information at the time of reception. For example, if the user's store is busy, the reception unit postpones the request. Furthermore, if the user's store is running a campaign, the reception unit can also prioritize receiving requests related to the campaign. Furthermore, if the user's store is quiet, the reception unit can immediately receive the request. This allows the reception unit to receive the optimal request based on the user's store status and campaign information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's store status and campaign information into the generation AI, and the generation AI can perform filtering.

[0077] The reception unit can select the optimal reception means according to the user's input method (voice, text, image, etc.) at the time of reception. The reception unit, for example, selects the reception means according to the user's input method at the time of reception. For example, when the user makes a request by voice, the reception unit uses voice recognition to receive the request. Furthermore, when the user makes a request by text, the reception unit can also use text analysis to receive the request. Furthermore, when the user makes a request by image, the reception unit can also use image analysis to receive the request. This allows the reception unit to provide the optimal reception means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI, which can select the optimal reception means.

[0078] The reception unit can estimate the user's emotions and determine the order of requests to be received based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and determine the order of requests to be received based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize requests that will help the user relax. Also, if the user is excited, the reception unit can prioritize requests that require immediate attention. Also, if the user is tired, the reception unit can prioritize simple requests. This allows the reception unit to receive requests in an optimal order of priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0079] The reception unit can prioritize receiving highly relevant requests based on the user's geographical location information when receiving a request. For example, the reception unit prioritizes receiving highly relevant requests by taking the user's geographical location information into consideration when receiving a request. For example, the reception unit prioritizes receiving requests from stores in the user's neighborhood. Furthermore, if the user is running a campaign in a specific area, the reception unit can prioritize receiving requests from that area. Furthermore, the reception unit can postpone requests from stores far from the user. This allows the reception unit to receive the most appropriate request based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into a generation AI, which can select highly relevant requests.

[0080] The reception unit can receive requests based on the user's social media activity at the time of reception. For example, the reception unit analyzes the user's social media activity at the time of reception and receives related requests. For example, the reception unit prioritizes receiving content that the user wants to promote on social media. The reception unit can also analyze the content posted by the user on social media and prioritize receiving related requests. The reception unit can also receive related requests by referring to the activities of the user's friends on social media. This allows the reception unit to receive optimal requests based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can select related requests.

[0081] The reception unit can customize the reception method based on the user's past feedback at the time of reception. For example, the reception unit customizes the reception method by reflecting the user's past feedback at the time of reception. For example, the reception unit preferentially suggests reception methods that have been well-received by users in the past. The reception unit can also improve the reception method based on the user's past feedback. The reception unit can also simplify the reception procedure by reflecting the user's past feedback. This allows the reception unit to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI, which can select the optimal reception method.

[0082] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, if the user is relaxed, the suggestion unit can make a suggestion using soft expression. If the user is excited, the suggestion unit can make a suggestion using strong expression. If the user is tired, the suggestion unit can make a suggestion using simple and easy-to-understand expression. This allows the suggestion unit to make a suggestion using an optimal expression based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can adjust the way the suggestion is expressed.

[0083] The suggestion unit can adjust the details of the suggestion based on trends on social media when making a suggestion. For example, the suggestion unit can adjust the level of detail of the suggestion based on trends on social media when making a suggestion. For example, the suggestion unit makes a suggestion that incorporates a design that is popular on social media. The suggestion unit can also make a suggestion using a catchphrase that is popular on social media. The suggestion unit can also adjust the level of detail of the suggestion based on trends on social media. This allows the suggestion unit to make optimal suggestions based on trends on social media. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input information about trends on social media into the generation AI, which can then adjust the level of detail of the suggestion.

[0084] The suggestion unit can apply a suggestion algorithm according to the user's store information when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms according to the user's store information when making a suggestion. For example, the suggestion unit can suggest an optimal design according to the industry of the user's store. The suggestion unit can also suggest appropriate wording according to the size of the user's store. The suggestion unit can also make suggestions specific to the area based on the location information of the user's store. This allows the suggestion unit to make optimal suggestions based on the user's store information. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's store information into a generation AI, which can then apply an optimal suggestion algorithm.

[0085] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion based on designs previously adopted by the user. The suggestion unit can also analyze the user's past suggestion results and suggest an optimal design. The suggestion unit can also improve the accuracy of wording by referring to the user's past suggestion results. This allows the suggestion unit to make an optimal suggestion based on the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI or may be performed without using a generation AI. For example, the suggestion unit can input the user's past suggestion results into the generation AI, which can improve the accuracy of the suggestion.

[0086] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can make short, to-the-point suggestions. If the user is relaxed, the suggestion unit can make longer suggestions with detailed explanations. If the user is excited, the suggestion unit can make visually stimulating suggestions. This allows the suggestion unit to make suggestions with an optimal length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can adjust the length of the suggestions.

[0087] The suggestion unit, when making a suggestion, can determine the order of suggestions based on the time of posting on the SNS. For example, the suggestion unit, when making a suggestion, can determine the order of suggestions based on the time of posting on the SNS. For example, the suggestion unit makes suggestions based on a time period when there are many posts on the SNS. The suggestion unit can also make suggestions based on a time period when there are few posts on the SNS. The suggestion unit can also determine the order of suggestions based on the time of posting on the SNS. This allows the suggestion unit to make optimal suggestions based on the time of posting on the SNS. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without using the generation AI. For example, the suggestion unit can input information about the time of posting on the SNS to the generation AI, and the generation AI can determine the order of suggestions.

[0088] The suggestion unit can adjust the order of suggestions based on information about the user's store when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on information about the user's store when making suggestions. For example, the suggestion unit prioritizes suggestions related to the industry of the user's store. The suggestion unit can also adjust the order of suggestions based on the size of the user's store. The suggestion unit can also adjust the order of suggestions based on location information of the user's store. This allows the suggestion unit to make suggestions in an optimal order based on the relevance of the user's store. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input information about the user's store into a generation AI, which can adjust the order of suggestions.

[0089] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's knowledge level when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's knowledge level when making a proposal. For example, if the user has specialized knowledge, the suggestion unit can make a proposal that uses a lot of technical terminology. Also, if the user does not have specialized knowledge, the suggestion unit can make a proposal that explains things in simple terms. Also, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's expertise level. This allows the suggestion unit to make a proposal using optimal technical terminology according to the user's expertise level. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI or may be performed without using a generation AI. For example, the suggestion unit can input user knowledge level information into the generation AI, and the generation AI can adjust the use of technical terminology in the proposal.

[0090] The correction unit can estimate the user's emotion and adjust the correction method based on the estimated user's emotion. For example, the correction unit can estimate the user's emotion and adjust the correction method based on the estimated user's emotion. For example, the correction unit can provide detailed correction options when the user is relaxed. The correction unit can also provide simple correction options when the user is in a hurry. The correction unit can also provide visually stimulating correction options when the user is excited. This allows the correction unit to perform correction in an optimal manner according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the correction unit may be performed using AI or without AI. For example, the correction unit can input the user's emotion data into the generation AI, which can then adjust the correction method.

[0091] The correction unit can select a correction method based on the user's past correction history when making corrections. For example, the correction unit can analyze the user's past correction history and select an optimal correction method when making corrections. For example, the correction unit can suggest an optimal correction method based on corrections made by the user in the past. The correction unit can also analyze the user's past correction history and select an optimal correction method. The correction unit can also improve the accuracy of corrections by referring to the user's past correction history. This allows the correction unit to provide an optimal correction method based on the user's past correction history. Some or all of the above-mentioned processing in the correction unit may be performed using AI or may be performed without using AI. For example, the correction unit can input the user's past correction history into a generation AI, which can select an optimal correction method.

[0092] The correction unit can customize the correction method based on the user's current store situation at the time of correction. For example, the correction unit customizes the correction method based on the user's current store situation at the time of correction. For example, the correction unit can provide simple correction options when the user's store is busy. The correction unit can also provide detailed correction options when the user's store is quiet. The correction unit can also customize the correction method according to the user's store situation. This allows the correction unit to provide the optimal correction method based on the user's store situation. Some or all of the above-mentioned processing in the correction unit may be performed using AI or may be performed without using AI. For example, the correction unit can input the user's store situation data to the generation AI, which can select the optimal correction method.

[0093] The correction unit can improve the correction method based on user feedback during correction. For example, the correction unit improves the correction method by reflecting user feedback during correction. For example, the correction unit improves correction options based on user feedback. The correction unit can also simplify the correction procedure by reflecting user feedback. The correction unit can also improve the accuracy of correction by referring to user feedback. This allows the correction unit to provide an optimal correction method based on user feedback. Some or all of the above-mentioned processing in the correction unit may be performed using AI or may be performed without using AI. For example, the correction unit can input user feedback data into the generation AI, which can improve the correction method.

[0094] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user emotions. For example, the correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user emotions. For example, if the user is nervous, the correction unit can prioritize corrections that will relax the user. Furthermore, if the user is excited, the correction unit can prioritize corrections that require immediate action. Furthermore, if the user is tired, the correction unit can prioritize simple corrections. This allows the correction unit to perform corrections with optimal priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the correction unit may be performed using AI, or may be performed without using AI. For example, the correction unit can input the user's emotion data into the generation AI, which can then determine the priority of corrections.

[0095] The correction unit can select a correction method based on the user's geographical location information when making corrections. For example, the correction unit selects the optimal correction method by taking the user's geographical location information into consideration when making corrections. For example, the correction unit prioritizes correction requests from stores nearby the user. Furthermore, if the user is running a campaign in a specific area, the correction unit can also prioritize correction requests from that area. Furthermore, the correction unit can postpone correction requests from stores far from the user. This allows the correction unit to provide the optimal correction method based on the user's geographical location information. Some or all of the above-mentioned processing in the correction unit may be performed using AI, or may be performed without using AI. For example, the correction unit can input the user's geographical location information into a generation AI, which can select the optimal correction method.

[0096] The correction unit can suggest correction measures based on the user's social media activity during correction. For example, the correction unit can analyze the user's social media activity during correction and suggest correction measures. For example, the correction unit prioritizes correcting content that the user wants to promote on social media. The correction unit can also analyze the content posted by the user on social media and suggest related corrections. The correction unit can also suggest related corrections based on the activity of the user's friends on social media. This allows the correction unit to provide optimal correction measures based on the user's social media activity. Some or all of the above-mentioned processing in the correction unit may be performed using AI or without AI. For example, the correction unit can input the user's social media activity data into a generation AI, which can then suggest optimal correction measures.

[0097] The correction unit can customize the correction method based on the user's past feedback when correcting. For example, the correction unit customizes the correction method by reflecting the user's past feedback when correcting. For example, the correction unit preferentially suggests correction methods that have been well-received by users in the past. The correction unit can also improve the correction method based on the user's past feedback. The correction unit can also simplify the correction procedure by reflecting the user's past feedback. This allows the correction unit to provide an optimal correction method based on the user's past feedback. Some or all of the above-described processing in the correction unit may be performed using AI or may be performed without using AI. For example, the correction unit can input the user's past feedback data into the generation AI, which can select the optimal correction method.

[0098] The output unit can estimate the user's emotion and adjust the output method based on the estimated user's emotion. For example, the output unit can estimate the user's emotion and adjust the output method based on the estimated user's emotion. For example, the output unit can provide detailed output options when the user is relaxed. The output unit can also provide simple output options when the user is in a hurry. The output unit can also provide visually stimulating output options when the user is excited. This allows the output unit to perform an optimal output method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the output unit may be performed using AI or without AI. For example, the output unit can input user's emotion data to a generation AI, which can then adjust the output method.

[0099] The output unit can select an output method based on the user's past output history at the time of output. For example, the output unit can analyze the user's past output history and select the optimal output method at the time of output. For example, the output unit can suggest the optimal output method based on output methods used by the user in the past. The output unit can also analyze the user's past output history and select the optimal output method. The output unit can also improve the accuracy of the output by referring to the user's past output history. This allows the output unit to provide the optimal output method based on the user's past output history. Some or all of the above-mentioned processing in the output unit may be performed using AI or may be performed without using AI. For example, the output unit can input the user's past output history into a generation AI, which can select the optimal output method.

[0100] The output unit can customize the output means based on the user's current store situation at the time of output. The output unit, for example, customizes the output means based on the user's current store situation at the time of output. For example, the output unit can provide simple output options when the user's store is busy. The output unit can also provide detailed output options when the user's store is quiet. The output unit can also customize the output means according to the user's store situation. This allows the output unit to provide the optimal output means based on the user's store situation. Some or all of the above-mentioned processing in the output unit may be performed using AI or may be performed without using AI. For example, the output unit can input the user's store situation data to a generation AI, which can select the optimal output means.

[0101] The output unit can improve the output method based on user feedback at the time of output. For example, the output unit improves the output method by reflecting user feedback at the time of output. For example, the output unit improves output options based on user feedback. The output unit can also simplify the output procedure by reflecting user feedback. The output unit can also improve the accuracy of the output by referring to user feedback. This allows the output unit to provide an optimal output method based on user feedback. Some or all of the above-mentioned processing in the output unit may be performed using AI or may be performed without using AI. For example, the output unit can input user feedback data to a generation AI, which can then improve the output method.

[0102] The output unit can estimate the user's emotions and determine the order of output based on the estimated user emotions. For example, the output unit can estimate the user's emotions and determine the order of output based on the estimated user emotions. For example, if the user is nervous, the output unit can prioritize output that will relax the user. Furthermore, if the user is excited, the output unit can prioritize output that requires an immediate response. Furthermore, if the user is tired, the output unit can prioritize simple output. This allows the output unit to perform output in an optimal order of priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without AI. For example, the output unit can input the user's emotion data to a generation AI, which can determine the order of output.

[0103] The output unit can select an output method based on the user's geographical location information at the time of output. For example, the output unit selects the optimal output method by taking the user's geographical location information into consideration at the time of output. For example, the output unit prioritizes output requests from stores nearby the user. Furthermore, if the user is running a campaign in a specific area, the output unit can prioritize output requests from that area. Furthermore, the output unit can postpone output requests from stores far away from the user. This allows the output unit to provide the optimal output method based on the user's geographical location information. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit can input the user's geographical location information to a generation AI, which can select the optimal output method.

[0104] The output unit can suggest an output means based on the user's social media activity at the time of output. For example, the output unit can analyze the user's social media activity and suggest an output means at the time of output. For example, the output unit prioritizes output of content that the user wants to promote on social media. The output unit can also analyze the content posted by the user on social media and suggest related output. The output unit can also suggest related output with reference to the activity of the user's friends on social media. This allows the output unit to provide the optimal output means based on the user's social media activity. Some or all of the above-mentioned processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit can input the user's social media activity data into a generation AI, which can then suggest the optimal output means.

[0105] The output unit can customize the output method based on the user's past feedback at the time of output. For example, the output unit customizes the output method by reflecting the user's past feedback at the time of output. For example, the output unit preferentially suggests output methods that have been well-received by users in the past. The output unit can also improve the output method based on the user's past feedback. The output unit can also simplify the output procedure by reflecting the user's past feedback. This allows the output unit to provide an optimal output method based on the user's past feedback. Some or all of the above-described processing in the output unit may be performed using AI or may be performed without using AI. For example, the output unit can input the user's past feedback data into a generation AI, which can select the optimal output method. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, proposal unit, correction unit, and output unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts requests from users to create advertising content, menus, and posters via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the information accepted by the reception unit using a generation AI to propose appropriate designs and wording. The correction unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to confirm and correct the proposed designs and wording. The output unit posts the revised designs and wording to social media or provides print data via the output device 40 of the smart device 14 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, suggestion unit, correction unit, and output unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit accepts requests from users to create advertising content, menus, and posters via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information accepted by the reception unit using a generation AI to suggest appropriate designs and wording. The correction unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to confirm and correct the proposed design and wording. The output unit posts the corrected design and wording to SNS or provides print data via the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, proposal unit, correction unit, and output unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit accepts requests from users to create advertising content, menus, and posters via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information accepted by the reception unit using a generation AI to propose appropriate designs and wording. The correction unit is realized by the control unit 46A of the headset terminal 314 and provides an interface for the user to confirm and correct the proposed design and wording. The output unit posts the revised design and wording to SNS or provides print data via the display 343 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, proposal unit, correction unit, and output unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives requests from users to create advertising content, menus, and posters via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generation AI to propose appropriate designs and wording. The correction unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to confirm and correct the proposed design and wording. The output unit posts the revised design and wording to SNS or provides print data via the speaker 240 of the robot 414 or the communication I / F 26 of the data processing device 12.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The suggestion unit can analyze the user's past purchase history and customize the content of the suggestions. For example, the suggestion unit can suggest related new products and services based on products and services the user has purchased in the past. The suggestion unit can also analyze the user's preference trends from the user's purchase history and suggest designs and wording that the user is likely to be interested in. Furthermore, the suggestion unit can make suggestions tailored to specific seasons or events based on the user's purchase history. This allows the suggestion unit to make optimal suggestions based on the user's purchase history.

[0108] The suggestion unit can estimate the user's emotion and adjust the timing of the suggestion based on the estimated user's emotion. For example, the suggestion unit can delay the timing of making the suggestion when the user is relaxed. Also, the suggestion unit can make the suggestion immediately when the user is excited. Furthermore, the suggestion unit can postpone the suggestion until the next day when the user is tired. This allows the suggestion unit to make the suggestion at the optimal timing according to the user's emotion.

[0109] The correction unit can analyze the user's correction history and suggest corrections. For example, the correction unit can suggest similar corrections based on corrections made by the user in the past. The correction unit can also analyze trends in preferred designs and wording from the user's correction history and suggest corrections that the user prefers. Furthermore, the correction unit can also suggest corrections tailored to specific seasons or events based on the user's correction history. This allows the correction unit to suggest optimal corrections based on the user's correction history.

[0110] The correction unit can estimate the user's emotion and adjust the correction interface based on the estimated user's emotion. For example, the correction unit can provide detailed correction options when the user is relaxed. The correction unit can also provide simple correction options when the user is in a hurry. Furthermore, the correction unit can provide visually stimulating correction options when the user is excited. This allows the correction unit to perform correction with an optimal interface according to the user's emotion.

[0111] The output unit can analyze the user's past output history and customize the output method. For example, the output unit can suggest a similar output format based on the output format the user has used in the past. The output unit can also analyze the user's preferred output options from the user's output history and suggest an output method that the user prefers. Furthermore, the output unit can suggest an output method suited to a specific season or event based on the user's output history. In this way, the output unit can provide the optimal output method based on the user's output history.

[0112] The output unit can estimate the user's emotion and adjust the timing of the output based on the estimated user's emotion. For example, the output unit can delay the output when the user is relaxed. Alternatively, the output unit can immediately output when the user is excited. Furthermore, the output unit can postpone the output until the next day when the user is tired. This allows the output unit to output at the optimal timing according to the user's emotion.

[0113] The suggestion unit can analyze the user's social media activity and customize the suggestion content. For example, the suggestion unit can make relevant suggestions based on the content the user has shown interest in on social media. The suggestion unit can also analyze the content posted by the user on social media and suggest designs and wording that the user prefers. Furthermore, the suggestion unit can make relevant suggestions based on the activity of the user's friends on social media. This allows the suggestion unit to make optimal suggestions based on the user's social media activity.

[0114] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user's emotions. For example, if the user is nervous, the correction unit can prioritize corrections that will relax the user. Also, if the user is excited, the correction unit can prioritize corrections that require immediate attention. Furthermore, if the user is tired, the correction unit can prioritize simple corrections. This allows the correction unit to perform corrections with optimal priority according to the user's emotions.

[0115] The output unit can select an output method taking into consideration the user's geographical location information. For example, the output unit can prioritize output requests from stores in the user's neighborhood. Also, if the user is running a campaign in a specific area, the output unit can prioritize output requests from that area. Furthermore, the output unit can postpone output requests from stores far away from the user. This allows the output unit to provide the optimal output method based on the user's geographical location information.

[0116] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. For example, if the user is relaxed, the suggestion unit can make suggestions using soft expressions. If the user is excited, the suggestion unit can also make suggestions using forceful expressions. Furthermore, if the user is tired, the suggestion unit can also make suggestions using simple and easy-to-understand expressions. This allows the suggestion unit to make suggestions using the optimal expression method according to the user's emotion.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives requests from users to create advertisement content, menus, and posters. Users can input, for example, store information, the content they want to advertise, and menu details. Step 2: The proposal unit uses a generation AI to analyze the information received by the reception unit and propose appropriate designs and wording. The proposal unit generates optimal designs and wording based on, for example, social media trends and the user's store information. The generation AI generates designs and wording using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The correction unit provides an interface for the user to review and modify the design and wording proposed by the proposal unit. The correction unit provides an interface that allows the user to easily change the design, for example, by dragging and dropping. The user can review the design and wording proposed by the generation AI and make modifications as necessary. Step 4: The output unit posts the design and text corrected by the correction unit to a social networking site or provides data for printing and use. The output unit, for example, has a function for posting the generated design and text to a social networking site or a function for providing data for printing.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives requests from users to create advertisement content, menus, or posters; a proposal unit that analyzes the information received by the reception unit and proposes designs and wordings; a correction unit for allowing a user to confirm and correct the design and wording proposed by the proposal unit; and an output unit for posting the design and wording corrected by the correction unit to a social networking service or for printing and using the same. A system characterized by:

2. The proposal unit Generate designs or text based on social media trends or user store information 2. The system of claim 1.

3. The proposal unit Generate designs based on the store logo or colors registered by the user in advance 2. The system of claim 1.

4. The correction unit Provides a drag-and-drop interface for design changes 2. The system of claim 1.

5. The output unit Provides the ability to post generated designs or text to social media 2. The system of claim 1.

6. The output unit Providing data for printing the generated design or wording 2. The system of claim 1.

7. The reception unit To estimate a user's emotions and adjust the timing of accepting requests for the creation of advertisement content, menus, or posters based on the estimated user's emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past request history and select the reception method 2. The system of claim 1.

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