System

Abstract

To provide a system for automatically generating a virtual item matched with campaign contents or a company image.SOLUTION: A system according to an embodiment includes a generation AI and an image generation AI. The generation AI includes a plan generation unit, an image generation prompt generation unit, and a virtual item generation unit. A plan generation part generates a plan on the basis of campaign contents and a company image. The image generation prompt generation unit generates an image generation prompt based on the plan generated by the plan generation unit. The virtual item generator generates a virtual item based on the image generating prompt generated by the image generating prompt generator.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, there was a problem in that it was very costly and time-consuming to create virtual items that matched the campaign content and corporate image.

[0005] The system according to the embodiment aims to automatically generate virtual items that match the campaign content and corporate image. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI and an image generation AI. The generation AI includes a plan generation unit, an image generation prompt generation unit, and a virtual item generation unit. The plan generation unit generates a plan based on campaign content and a corporate image. The image generation prompt generation unit generates an image generation prompt based on the plan generated by the plan generation unit. The virtual item generation unit generates a virtual item based on the image generation prompt generated by the image generation prompt generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate virtual items that match the campaign content and corporate image. [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) The virtual item generation system according to an embodiment of the present invention utilizes generation AI and image generation AI to generate project proposals and image generation prompts that match campaign content and corporate image, and the image generation AI then automatically creates virtual items such as characters and stamps based on these. This reduces the cost and effort required for design and production, and contributes to increasing brand awareness and the number of users.

[0029] A virtual item generation system according to an embodiment includes a generation AI and an image generation AI. The generation AI includes a plan generation unit that generates a plan based on campaign content and corporate image, and an image generation prompt generation unit that generates image generation prompts based on the plan generated by the plan generation unit. For example, the generation AI analyzes information input by a user, such as the campaign objective, target demographic, and corporate brand image, and then proposes an optimal plan based on the information. The generation AI also generates prompts to be used by the image generation AI based on the plan. The image generation AI includes a virtual item generation unit that generates a virtual item based on the image generation prompt generated by the image generation prompt generation unit. For example, if the generation AI generates a prompt for a "pop and colorful character," the image generation AI generates a specific character design based on the prompt. This allows the virtual item generation system to reduce design and production costs and effort, contributing to increased brand awareness and user numbers.

[0030] The proposal generation unit can analyze the user's past campaign data, extract successful elements, and reflect them in new proposals. In the proposal generation unit, for example, the generation AI analyzes the user's past campaign data and extracts successful elements. For example, it identifies elements that received high engagement in past campaigns and reflects them in new proposals. The generation AI also analyzes past campaign data, extracts successful elements, and reflects them in new proposals. For example, if a particular visual style or messaging is successful, it can incorporate it into the new proposal. The generation AI also analyzes the user's past campaign data, extracts successful elements, and reflects them in the new proposal. For example, it can incorporate elements that were effective for a specific target demographic into the new proposal. This makes it possible to generate new proposals by utilizing past success stories.

[0031] The plan generation unit can analyze market trends in real time and generate plan proposals based on the latest trends. In the plan generation unit, for example, the generation AI analyzes market trends in real time and generates plan proposals based on the latest trends. For example, it proposes plan proposals that incorporate current fashion and lifestyle trends. The generation AI also analyzes market trends in real time and generates plan proposals based on the latest trends. For example, it proposes plan proposals based on themes and hashtags that are trending on social media. The generation AI also analyzes market trends in real time and generates plan proposals based on the latest trends. For example, it proposes plan proposals that incorporate the latest technology and product trends. This makes it possible to generate plan proposals based on the latest market trends.

[0032] The proposal generation unit can analyze campaign data from different industries and generate proposals that apply success stories from those different industries. For example, the generation AI in the proposal generation unit analyzes campaign data from different industries and generates proposals that apply success stories from those different industries. For example, success stories from the fashion industry are applied to a campaign in the food industry. The generation AI also analyzes campaign data from different industries and generates proposals that apply success stories from those different industries. For example, success stories from the technology industry are applied to a campaign in the education industry. The generation AI also analyzes campaign data from different industries and generates proposals that apply success stories from those different industries. For example, success stories from the entertainment industry are applied to a campaign in the healthcare industry. This makes it possible to generate new proposals by applying success stories from different industries.

[0033] The proposal generation unit can simultaneously generate multiple different proposals based on user input, allowing them to be compared and considered. The proposal generation unit, for example, enables the generation AI to simultaneously generate multiple different proposals based on user input, allowing them to be compared and considered. For example, it may propose multiple proposals based on different target audiences or themes. The generation AI can also simultaneously generate multiple different proposals based on user input, allowing them to be compared and considered. For example, it may propose multiple proposals with different visual styles or messaging. The generation AI can also simultaneously generate multiple different proposals based on user input, allowing them to be compared and considered. For example, it may propose multiple proposals based on different budgets or schedules. This allows multiple proposals to be simultaneously generated, compared and considered.

[0034] The virtual item generation unit can generate virtual items that reflect the user's past design trends. For example, the image generation AI in the virtual item generation unit analyzes the user's past design trends and generates virtual items that reflect them. For example, it creates a character that incorporates the user's favorite colors and styles. The image generation AI can also analyze the user's past design trends and generate virtual items that reflect them. For example, it can create stamps that incorporate design elements that the user has used in the past. The image generation AI can also analyze the user's past design trends and generate virtual items that reflect them. For example, it can create virtual items that incorporate the user's favorite themes and motifs. This makes it possible to generate virtual items that reflect the user's past design trends.

[0035] The virtual item generation unit can generate virtual items that automatically incorporate the user's brand colors and logo. For example, the image generation AI in the virtual item generation unit generates virtual items that automatically incorporate the user's brand colors and logo. For example, the brand logo is incorporated into a character's costume. The image generation AI also generates virtual items that automatically incorporate the user's brand colors and logo. For example, the brand colors are used as backgrounds or decorations. The image generation AI also generates virtual items that automatically incorporate the user's brand colors and logo. For example, the brand logo is designed as part of a stamp. In this way, virtual items that automatically incorporate the user's brand colors and logo can be generated.

[0036] The virtual item generation unit can generate virtual items that incorporate design elements from different cultures and regions. In the virtual item generation unit, for example, the image generation AI generates virtual items that incorporate design elements from different cultures and regions. For example, a character that incorporates traditional Japanese design elements is created. The image generation AI also generates virtual items that incorporate design elements from different cultures and regions. For example, a stamp with an African ethnic costume motif is created. The image generation AI also generates virtual items that incorporate design elements from different cultures and regions. For example, a virtual item that incorporates European architectural styles is created. In this way, virtual items that incorporate design elements from different cultures and regions can be generated.

[0037] The virtual item generation unit can generate virtual items with dynamic animation elements added. The virtual item generation unit adds dynamic animation elements to virtual items generated by, for example, an image generation AI. For example, it incorporates animations that cause characters to move or change facial expressions. It also adds dynamic animation elements to virtual items generated by, for example, an image generation AI. For example, it incorporates animations that cause stamps to rotate or scale. It also adds dynamic animation elements to virtual items generated by, for example, an image generation AI. For example, it incorporates animations that cause the background to change or effects to be added. In this way, it is possible to generate virtual items with dynamic animation elements added.

[0038] Generative AI and image generation AI can work together to automate the entire design process and minimize manual work. Generative AI and image generation AI can work together to automate the entire design process, for example, by building a system that handles everything from generating proposals to creating virtual items. Generative AI and image generation AI can also work together to automate the entire design process, for example, by automatically generating design proposals based on user input and then creating virtual items based on those proposals. Generative AI and image generation AI can also work together to automate the entire design process, for example, by automatically correcting and improving designs, minimizing manual work. This can automate the entire design process and minimize manual work.

[0039] Generative AI can analyze past design data and propose the most efficient design process. For example, generative AI can analyze past design data and propose the most efficient design process. For example, it can propose an optimal design flow based on past success stories. Generative AI can also analyze past design data and propose the most efficient design process. For example, it can propose a process that minimizes the number of design iterations and frequency of revisions. Generative AI can also analyze past design data and propose the most efficient design process. For example, it can propose a process to reduce errors in the early stages of design. This makes it possible to analyze past design data and propose the most efficient design process.

[0040] Generative AI and image generation AI operate on a cloud-based system and can be used by multiple users simultaneously. Generative AI and image generation AI, for example, can operate on a cloud-based system to build a system that can be used by multiple users simultaneously. For example, design projects can be shared and collaborated on in the cloud. Generative AI and image generation AI can also operate on a cloud-based system to build a system that can be used by multiple users simultaneously. For example, design progress can be shared in real time and feedback can be received. Generative AI and image generation AI can also operate on a cloud-based system to build a system that can be used by multiple users simultaneously. For example, team members in different locations can work on designs simultaneously. This allows generative AI and image generation AI to operate on a cloud-based system to build a system that can be used by multiple users simultaneously.

[0041] Generative AI and image generation AI can be used on different devices (smartphones, tablets, PCs). Generative AI and image generation AI can, for example, build systems that can be used on different devices. For example, they can allow the same design project to be accessed on a smartphone, tablet, and PC. Generative AI and image generation AI can also build systems that can be used on different devices. For example, they can seamlessly synchronize design data between devices, allowing you to continue working anywhere. Generative AI and image generation AI can also build systems that can be used on different devices. For example, they can provide an interface optimized for each device, improving usability. This allows them to be used on different devices.

[0042] Generative AI can incorporate elements into project proposals that will promote spreading on social media. For example, generative AI can incorporate elements into project proposals that will promote spreading on social media. For example, it can suggest share buttons and hashtags. Generative AI can also incorporate elements into project proposals that will promote spreading on social media. For example, it can incorporate viral marketing techniques. Generative AI can also incorporate elements into project proposals that will promote spreading on social media. For example, it can suggest collaboration with influencers. This makes it possible to incorporate elements that will promote spreading on social media.

[0043] Image generation AI can reflect user feedback in virtual items. For example, image generation AI can reflect user feedback in virtual items. For example, it can improve the design based on user opinions. Image generation AI can also reflect user feedback in virtual items. For example, it can create items that incorporate the user's preferences and requests. Image generation AI can also reflect user feedback in virtual items. For example, it can prioritize the creation of popular designs based on user ratings. This makes it possible to generate virtual items that reflect user feedback.

[0044] Generative AI and image generation AI can work together to automate deployment on different platforms (social media, websites, apps). Generative AI and image generation AI can work together to automate deployment on different platforms, for example, by running a campaign simultaneously on social media, websites, and apps. Generative AI and image generation AI can also work together to automate deployment on different platforms, for example, by automatically generating content optimized for each platform. Generative AI and image generation AI can also work together to automate deployment on different platforms, for example, by generating messages tailored to the user demographics of each platform. This makes it possible to automate deployment on different platforms.

[0045] The generation AI can generate multiple project proposals tailored to different target groups and select the most suitable one. For example, the generation AI can generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals for young people, seniors, and families. The generation AI can also generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals tailored to different target groups for each region. The generation AI can also generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals tailored to target groups with specific interests and concerns. This makes it possible to generate multiple project proposals tailored to different target groups and select the most suitable one.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] Generative AI can analyze a user's past design data and suggest the most efficient design process. For example, it can suggest the optimal design flow based on past success stories. Generative AI can also analyze past design data and suggest the most efficient design process. For example, it can suggest a process that minimizes the number of design iterations and frequency of revisions. Generative AI can also analyze past design data and suggest the most efficient design process. For example, it can suggest a process to reduce errors in the early stages of design. This makes it possible to analyze past design data and suggest the most efficient design process.

[0048] Generative AI and image generation AI operate on a cloud-based system, allowing multiple users to use them simultaneously. For example, design projects can be shared and collaborated on in the cloud. Generative AI and image generation AI also operate on a cloud-based system, allowing multiple users to use them simultaneously. For example, design progress can be shared in real time and feedback can be received. Generative AI and image generation AI also operate on a cloud-based system, allowing multiple users to use them simultaneously. For example, team members in different locations can work on designs simultaneously. This allows them to operate on a cloud-based system, allowing multiple users to use them simultaneously.

[0049] Generative AI and image generation AI can be used on different devices (smartphones, tablets, PCs). For example, it allows the same design project to be accessed on a smartphone, tablet, and PC. Generative AI and image generation AI can also build systems that can be used on different devices. For example, it can seamlessly synchronize design data between devices, allowing you to continue working anywhere. Generative AI and image generation AI can also build systems that can be used on different devices. For example, it can provide an interface optimized for each device, improving usability. This allows it to be used on different devices.

[0050] Generative AI can incorporate elements into project proposals that will help spread the word on social media. For example, it can suggest share buttons and hashtags. Generative AI can also incorporate elements into project proposals that will help spread the word on social media. For example, it can incorporate viral marketing techniques. Generative AI can also incorporate elements into project proposals that will help spread the word on social media. For example, it can suggest collaboration with influencers. This makes it possible to incorporate elements that will help spread the word on social media.

[0051] Image generation AI can reflect user feedback in virtual items. For example, it can improve the design based on user opinions. Image generation AI can also reflect user feedback in virtual items. For example, it can create items that incorporate the user's preferences and requests. Image generation AI can also reflect user feedback in virtual items. For example, it can prioritize the creation of popular designs based on user ratings. This makes it possible to generate virtual items that reflect user feedback.

[0052] Generative AI and image generation AI can work together to automate deployment on different platforms (social media, websites, apps). For example, a campaign can be run simultaneously on social media, websites, and apps. Generative AI and image generation AI can also work together to automate deployment on different platforms. For example, they can automatically generate content optimized for each platform. Generative AI and image generation AI can also work together to automate deployment on different platforms. For example, they can generate messages tailored to the user demographics of each platform. This makes it possible to automate deployment on different platforms.

[0053] Generative AI can generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals for young people, seniors, and families. Generative AI can also generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals tailored to different target groups for each region. Generative AI can also generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals tailored to target groups with specific interests and concerns. This allows it to generate multiple project proposals tailored to different target groups and select the most suitable one.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The proposal generation unit generates proposals based on the campaign content and corporate image. For example, it analyzes information entered by the user, such as the campaign objectives, target demographic, and corporate brand image, and then proposes optimal proposals based on that information. Step 2: The image generation prompt generation unit generates an image generation prompt based on the proposal generated by the proposal generation unit. For example, the generation AI generates a prompt such as "a pop and colorful character." Step 3: The virtual item generator generates a virtual item based on the image generation prompt generated by the image generation prompt generator. For example, the image generation AI generates a specific character design based on the prompt "a pop and colorful character."

[0056] (Example 2) The virtual item generation system according to an embodiment of the present invention utilizes generation AI and image generation AI to generate project proposals and image generation prompts that match campaign content and corporate image, and the image generation AI then automatically creates virtual items such as characters and stamps based on these. This reduces the cost and effort required for design and production, and contributes to increasing brand awareness and the number of users.

[0057] A virtual item generation system according to an embodiment includes a generation AI and an image generation AI. The generation AI includes a plan generation unit that generates a plan based on campaign content and corporate image, and an image generation prompt generation unit that generates image generation prompts based on the plan generated by the plan generation unit. For example, the generation AI analyzes information input by a user, such as the campaign objective, target demographic, and corporate brand image, and then proposes an optimal plan based on the information. The generation AI also generates prompts to be used by the image generation AI based on the plan. The image generation AI includes a virtual item generation unit that generates a virtual item based on the image generation prompt generated by the image generation prompt generation unit. For example, if the generation AI generates a prompt for a "pop and colorful character," the image generation AI generates a specific character design based on the prompt. This allows the virtual item generation system to reduce design and production costs and effort, contributing to increased brand awareness and user numbers.

[0058] The proposal generation unit can analyze the user's past campaign data, extract successful elements, and reflect them in new proposals. In the proposal generation unit, for example, the generation AI analyzes the user's past campaign data and extracts successful elements. For example, it identifies elements that received high engagement in past campaigns and reflects them in new proposals. The generation AI also analyzes past campaign data, extracts successful elements, and reflects them in new proposals. For example, if a particular visual style or messaging is successful, it can incorporate it into the new proposal. The generation AI also analyzes the user's past campaign data, extracts successful elements, and reflects them in the new proposal. For example, it can incorporate elements that were effective for a specific target demographic into the new proposal. This makes it possible to generate new proposals by utilizing past success stories.

[0059] The plan generation unit can analyze market trends in real time and generate plan proposals based on the latest trends. In the plan generation unit, for example, the generation AI analyzes market trends in real time and generates plan proposals based on the latest trends. For example, it proposes plan proposals that incorporate current fashion and lifestyle trends. The generation AI also analyzes market trends in real time and generates plan proposals based on the latest trends. For example, it proposes plan proposals based on themes and hashtags that are trending on social media. The generation AI also analyzes market trends in real time and generates plan proposals based on the latest trends. For example, it proposes plan proposals that incorporate the latest technology and product trends. This makes it possible to generate plan proposals based on the latest market trends.

[0060] The plan generation unit can use the emotion estimation function to analyze the emotional state of the user and generate a plan that elicits positive emotions. The plan generation unit, for example, uses the emotion estimation function to analyze the emotional state of the user and generate a plan that elicits positive emotions. For example, a plan that incorporates elements that make the user feel joy or excitement is proposed. Also, the emotion estimation function is used to analyze the emotional state of the user and generate a plan that elicits positive emotions. For example, a plan that incorporates elements that make the user feel relaxed or at ease is proposed. Also, the emotion estimation function is used to analyze the emotional state of the user and generate a plan that elicits positive emotions. For example, a plan that incorporates elements that make the user feel moved or empathized is proposed. In this way, a plan that elicits positive emotions can be generated based on the emotional state of the user.

[0061] The proposal generation unit can analyze campaign data from different industries and generate proposals that apply success stories from those different industries. For example, the generation AI in the proposal generation unit analyzes campaign data from different industries and generates proposals that apply success stories from those different industries. For example, success stories from the fashion industry are applied to a campaign in the food industry. The generation AI also analyzes campaign data from different industries and generates proposals that apply success stories from those different industries. For example, success stories from the technology industry are applied to a campaign in the education industry. The generation AI also analyzes campaign data from different industries and generates proposals that apply success stories from those different industries. For example, success stories from the entertainment industry are applied to a campaign in the healthcare industry. This makes it possible to generate new proposals by applying success stories from different industries.

[0062] The proposal generation unit can simultaneously generate multiple different proposals based on user input, allowing them to be compared and considered. The proposal generation unit, for example, enables the generation AI to simultaneously generate multiple different proposals based on user input, allowing them to be compared and considered. For example, it may propose multiple proposals based on different target audiences or themes. The generation AI can also simultaneously generate multiple different proposals based on user input, allowing them to be compared and considered. For example, it may propose multiple proposals with different visual styles or messaging. The generation AI can also simultaneously generate multiple different proposals based on user input, allowing them to be compared and considered. For example, it may propose multiple proposals based on different budgets or schedules. This allows multiple proposals to be simultaneously generated, compared and considered.

[0063] The plan generation unit can use the emotion estimation function to analyze the emotion of the user when entering input in real time and propose an optimal plan. The plan generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering input in real time and propose an optimal plan. For example, if the user is excited, an energetic plan is proposed. Also, the emotion estimation function is used to analyze the emotion of the user when entering input in real time and propose an optimal plan. For example, if the user is relaxed, a calm plan is proposed. Also, the emotion estimation function is used to analyze the emotion of the user when entering input in real time and propose an optimal plan. For example, if the user is concentrating, a detailed plan is proposed. In this way, an optimal plan can be proposed based on the emotion of the user when entering input.

[0064] The virtual item generation unit can generate virtual items that reflect the user's past design trends. For example, the image generation AI in the virtual item generation unit analyzes the user's past design trends and generates virtual items that reflect them. For example, it creates a character that incorporates the user's favorite colors and styles. The image generation AI can also analyze the user's past design trends and generate virtual items that reflect them. For example, it can create stamps that incorporate design elements that the user has used in the past. The image generation AI can also analyze the user's past design trends and generate virtual items that reflect them. For example, it can create virtual items that incorporate the user's favorite themes and motifs. This makes it possible to generate virtual items that reflect the user's past design trends.

[0065] The virtual item generation unit can generate virtual items that automatically incorporate the user's brand colors and logo. For example, the image generation AI in the virtual item generation unit generates virtual items that automatically incorporate the user's brand colors and logo. For example, the brand logo is incorporated into a character's costume. The image generation AI also generates virtual items that automatically incorporate the user's brand colors and logo. For example, the brand colors are used as backgrounds or decorations. The image generation AI also generates virtual items that automatically incorporate the user's brand colors and logo. For example, the brand logo is designed as part of a stamp. In this way, virtual items that automatically incorporate the user's brand colors and logo can be generated.

[0066] The virtual item generation unit can use the emotion estimation function to generate a virtual item incorporating design elements according to the user's emotional state. The virtual item generation unit, for example, uses the emotion estimation function to generate a virtual item incorporating design elements according to the user's emotional state. For example, if the user is happy, bright colors and a fun design are incorporated. The emotion estimation function can also be used to generate a virtual item incorporating design elements according to the user's emotional state. For example, if the user is relaxed, calm colors and a simple design are incorporated. The emotion estimation function can also be used to generate a virtual item incorporating design elements according to the user's emotional state. For example, if the user is excited, energetic colors and a dynamic design are incorporated. In this way, it is possible to generate a virtual item incorporating design elements according to the user's emotional state.

[0067] The virtual item generation unit can generate virtual items that incorporate design elements from different cultures and regions. In the virtual item generation unit, for example, the image generation AI generates virtual items that incorporate design elements from different cultures and regions. For example, a character that incorporates traditional Japanese design elements is created. The image generation AI also generates virtual items that incorporate design elements from different cultures and regions. For example, a stamp with an African ethnic costume motif is created. The image generation AI also generates virtual items that incorporate design elements from different cultures and regions. For example, a virtual item that incorporates European architectural styles is created. In this way, virtual items that incorporate design elements from different cultures and regions can be generated.

[0068] The virtual item generation unit can generate virtual items with dynamic animation elements added. The virtual item generation unit adds dynamic animation elements to virtual items generated by, for example, an image generation AI. For example, it incorporates animations that cause characters to move or change facial expressions. It also adds dynamic animation elements to virtual items generated by, for example, an image generation AI. For example, it incorporates animations that cause stamps to rotate or scale. It also adds dynamic animation elements to virtual items generated by, for example, an image generation AI. For example, it incorporates animations that cause the background to change or effects to be added. In this way, it is possible to generate virtual items with dynamic animation elements added.

[0069] The virtual item generation unit can use the emotion estimation function to generate a virtual item incorporating design elements that evoke the most positive emotions in the user. The virtual item generation unit, for example, uses the emotion estimation function to generate a virtual item incorporating design elements that evoke the most positive emotions in the user. For example, by incorporating colors and shapes that make the user feel joy. The emotion estimation function can also be used to generate a virtual item incorporating design elements that evoke the most positive emotions in the user. For example, by incorporating design elements that make the user feel a sense of security. The emotion estimation function can also be used to generate a virtual item incorporating design elements that evoke the most positive emotions in the user. For example, by incorporating design elements that make the user feel excited. In this way, it is possible to generate a virtual item incorporating design elements that evoke the most positive emotions in the user.

[0070] Generative AI and image generation AI can work together to automate the entire design process and minimize manual work. Generative AI and image generation AI can work together to automate the entire design process, for example, by building a system that handles everything from generating proposals to creating virtual items. Generative AI and image generation AI can also work together to automate the entire design process, for example, by automatically generating design proposals based on user input and then creating virtual items based on those proposals. Generative AI and image generation AI can also work together to automate the entire design process, for example, by automatically correcting and improving designs, minimizing manual work. This can automate the entire design process and minimize manual work.

[0071] Generative AI can analyze past design data and propose the most efficient design process. For example, generative AI can analyze past design data and propose the most efficient design process. For example, it can propose an optimal design flow based on past success stories. Generative AI can also analyze past design data and propose the most efficient design process. For example, it can propose a process that minimizes the number of design iterations and frequency of revisions. Generative AI can also analyze past design data and propose the most efficient design process. For example, it can propose a process to reduce errors in the early stages of design. This makes it possible to analyze past design data and propose the most efficient design process.

[0072] The generative AI can use its emotion estimation function to propose a design process to reduce user stress. For example, the generative AI can use its emotion estimation function to propose a design process to reduce user stress. For example, it can adjust the design flow so that the user can work in a relaxing environment. It can also use its emotion estimation function to propose a design process to reduce user stress. For example, it can identify points that cause users stress and propose a process to avoid them. It can also use its emotion estimation function to propose a design process to reduce user stress. For example, it can propose a process to prioritize incorporating design elements that evoke positive emotions in users. This makes it possible to propose a design process to reduce user stress.

[0073] Generative AI and image generation AI operate on a cloud-based system and can be used by multiple users simultaneously. Generative AI and image generation AI, for example, can operate on a cloud-based system to build a system that can be used by multiple users simultaneously. For example, design projects can be shared and collaborated on in the cloud. Generative AI and image generation AI can also operate on a cloud-based system to build a system that can be used by multiple users simultaneously. For example, design progress can be shared in real time and feedback can be received. Generative AI and image generation AI can also operate on a cloud-based system to build a system that can be used by multiple users simultaneously. For example, team members in different locations can work on designs simultaneously. This allows generative AI and image generation AI to operate on a cloud-based system to build a system that can be used by multiple users simultaneously.

[0074] Generative AI and image generation AI can be used on different devices (smartphones, tablets, PCs). Generative AI and image generation AI can, for example, build systems that can be used on different devices. For example, they can allow the same design project to be accessed on a smartphone, tablet, and PC. Generative AI and image generation AI can also build systems that can be used on different devices. For example, they can seamlessly synchronize design data between devices, allowing you to continue working anywhere. Generative AI and image generation AI can also build systems that can be used on different devices. For example, they can provide an interface optimized for each device, improving usability. This allows them to be used on different devices.

[0075] The generative AI can use the emotion estimation function to provide an environment in which the user can work most efficiently. The generative AI can, for example, use the emotion estimation function to provide an environment in which the user can work most efficiently. For example, the work environment can be adjusted according to the user's emotional state. The generative AI can also use the emotion estimation function to provide an environment in which the user can work most efficiently. For example, music and lighting can be adjusted to help the user concentrate. The generative AI can also use the emotion estimation function to provide an environment in which the user can work most efficiently. For example, the interface can be customized to help the user relax. This makes it possible to provide an environment in which the user can work most efficiently.

[0076] Generative AI can incorporate elements into project proposals that will promote spreading on social media. For example, generative AI can incorporate elements into project proposals that will promote spreading on social media. For example, it can suggest share buttons and hashtags. Generative AI can also incorporate elements into project proposals that will promote spreading on social media. For example, it can incorporate viral marketing techniques. Generative AI can also incorporate elements into project proposals that will promote spreading on social media. For example, it can suggest collaboration with influencers. This makes it possible to incorporate elements that will promote spreading on social media.

[0077] Image generation AI can reflect user feedback in virtual items. For example, image generation AI can reflect user feedback in virtual items. For example, it can improve the design based on user opinions. Image generation AI can also reflect user feedback in virtual items. For example, it can create items that incorporate the user's preferences and requests. Image generation AI can also reflect user feedback in virtual items. For example, it can prioritize the creation of popular designs based on user ratings. This makes it possible to generate virtual items that reflect user feedback.

[0078] The generation AI can use the emotion estimation function to generate a brand message that the user will most empathize with. The generation AI, for example, uses the emotion estimation function to generate a brand message that the user will most empathize with. For example, it suggests a message based on the user's emotional state. The emotion estimation function can also be used to generate a brand message that the user will most empathize with. For example, it can prioritize the generation of messages that the user has positive emotions about. The emotion estimation function can also be used to generate a brand message that the user will most empathize with. For example, it can adjust the content of the message based on the user's emotional data. This makes it possible to generate a brand message that the user will most empathize with.

[0079] Generative AI and image generation AI can work together to automate deployment on different platforms (social media, websites, apps). Generative AI and image generation AI can work together to automate deployment on different platforms, for example, by running a campaign simultaneously on social media, websites, and apps. Generative AI and image generation AI can also work together to automate deployment on different platforms, for example, by automatically generating content optimized for each platform. Generative AI and image generation AI can also work together to automate deployment on different platforms, for example, by generating messages tailored to the user demographics of each platform. This makes it possible to automate deployment on different platforms.

[0080] The generation AI can generate multiple project proposals tailored to different target groups and select the most suitable one. For example, the generation AI can generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals for young people, seniors, and families. The generation AI can also generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals tailored to different target groups for each region. The generation AI can also generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals tailored to target groups with specific interests and concerns. This makes it possible to generate multiple project proposals tailored to different target groups and select the most suitable one.

[0081] The generation AI can use the emotion estimation function to generate a brand message that evokes the most positive emotions in users and distribute it widely. The generation AI can, for example, use the emotion estimation function to generate a brand message that evokes the most positive emotions in users and distribute it widely. For example, it can adjust the message based on the user's emotion data. It can also use the emotion estimation function to generate a brand message that evokes the most positive emotions in users and distribute it widely. For example, it can generate a message that incorporates elements that elicit positive emotions. It can also use the emotion estimation function to generate a brand message that evokes the most positive emotions in users and distribute it widely. For example, it can preferentially distribute messages with high emotion scores. This allows it to generate a brand message that evokes the most positive emotions in users and distribute it widely.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] Generative AI can analyze a user's past design data and suggest the most efficient design process. For example, it can suggest the optimal design flow based on past success stories. Generative AI can also analyze past design data and suggest the most efficient design process. For example, it can suggest a process that minimizes the number of design iterations and frequency of revisions. Generative AI can also analyze past design data and suggest the most efficient design process. For example, it can suggest a process to reduce errors in the early stages of design. This makes it possible to analyze past design data and suggest the most efficient design process.

[0084] Using its emotion estimation function, the generative AI can propose a design process to reduce user stress. For example, it can adjust the design flow so that the user can work in a relaxing environment. The generative AI can also use its emotion estimation function to propose a design process to reduce user stress. For example, it can identify points that cause users stress and propose a process to avoid them. The generative AI can also use its emotion estimation function to propose a design process to reduce user stress. For example, it can propose a process to prioritize incorporating design elements that users feel positive about. This makes it possible to propose a design process to reduce user stress.

[0085] Generative AI and image generation AI operate on a cloud-based system, allowing multiple users to use them simultaneously. For example, design projects can be shared and collaborated on in the cloud. Generative AI and image generation AI also operate on a cloud-based system, allowing multiple users to use them simultaneously. For example, design progress can be shared in real time and feedback can be received. Generative AI and image generation AI also operate on a cloud-based system, allowing multiple users to use them simultaneously. For example, team members in different locations can work on designs simultaneously. This allows them to operate on a cloud-based system, allowing multiple users to use them simultaneously.

[0086] Generative AI and image generation AI can be used on different devices (smartphones, tablets, PCs). For example, it allows the same design project to be accessed on a smartphone, tablet, and PC. Generative AI and image generation AI can also build systems that can be used on different devices. For example, it can seamlessly synchronize design data between devices, allowing you to continue working anywhere. Generative AI and image generation AI can also build systems that can be used on different devices. For example, it can provide an interface optimized for each device, improving usability. This allows it to be used on different devices.

[0087] The generative AI can use its emotion estimation function to provide an environment in which the user can work most efficiently. For example, it can adjust the work environment according to the user's emotional state. The generative AI can also use its emotion estimation function to provide an environment in which the user can work most efficiently. For example, it can adjust the music and lighting to help the user concentrate. The generative AI can also use its emotion estimation function to provide an environment in which the user can work most efficiently. For example, it can customize the interface to help the user relax. This allows it to provide an environment in which the user can work most efficiently.

[0088] Generative AI can incorporate elements into project proposals that will help spread the word on social media. For example, it can suggest share buttons and hashtags. Generative AI can also incorporate elements into project proposals that will help spread the word on social media. For example, it can incorporate viral marketing techniques. Generative AI can also incorporate elements into project proposals that will help spread the word on social media. For example, it can suggest collaboration with influencers. This makes it possible to incorporate elements that will help spread the word on social media.

[0089] Image generation AI can reflect user feedback in virtual items. For example, it can improve the design based on user opinions. Image generation AI can also reflect user feedback in virtual items. For example, it can create items that incorporate the user's preferences and requests. Image generation AI can also reflect user feedback in virtual items. For example, it can prioritize the creation of popular designs based on user ratings. This makes it possible to generate virtual items that reflect user feedback.

[0090] The generation AI can use its emotion estimation function to generate a brand message that users will most empathize with. For example, it can suggest a message based on the user's emotional state. It can also use its emotion estimation function to generate a brand message that users will most empathize with. For example, it can prioritize the generation of messages that users have positive emotions about. It can also use its emotion estimation function to generate a brand message that users will most empathize with. For example, it can adjust the content of the message based on the user's emotional data. This makes it possible to generate a brand message that users will most empathize with.

[0091] Generative AI and image generation AI can work together to automate deployment on different platforms (social media, websites, apps). For example, a campaign can be run simultaneously on social media, websites, and apps. Generative AI and image generation AI can also work together to automate deployment on different platforms. For example, they can automatically generate content optimized for each platform. Generative AI and image generation AI can also work together to automate deployment on different platforms. For example, they can generate messages tailored to the user demographics of each platform. This makes it possible to automate deployment on different platforms.

[0092] Generative AI can generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals for young people, seniors, and families. Generative AI can also generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals tailored to different target groups for each region. Generative AI can also generate multiple project proposals tailored to different target groups and select the most suitable one. For example, it can propose project proposals tailored to target groups with specific interests and concerns. This allows it to generate multiple project proposals tailored to different target groups and select the most suitable one.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The proposal generation unit generates proposals based on the campaign content and corporate image. For example, it analyzes information entered by the user, such as the campaign objectives, target demographic, and corporate brand image, and then proposes optimal proposals based on that information. Step 2: The image generation prompt generation unit generates an image generation prompt based on the proposal generated by the proposal generation unit. For example, the generation AI generates a prompt such as "a pop and colorful character." Step 3: The virtual item generator generates a virtual item based on the image generation prompt generated by the image generation prompt generator. For example, the image generation AI generates a specific character design based on the prompt "a pop and colorful character."

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, 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 robot 414 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.

[0140] 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.

[0141] 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.

[0142] 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 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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, to avoid confusion and 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.

[0161] 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. [Explanation of symbols]

[0162] 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. Equipped with generation AI and image generation AI, The generated AI is a proposal creation unit that creates proposals based on campaign content and corporate image; an image generation prompt generation unit that generates an image generation prompt based on the project proposal generated by the project proposal generation unit; a virtual item generation unit that generates a virtual item based on the image generation prompt generated by the image generation prompt generation unit; A system characterized by:

2. The project proposal generation unit Analyze users' past campaign data, extract successful elements, and incorporate them into new project proposals.

2. The system of claim 1.

3. The project proposal generation unit Analyze market trends in real time and generate proposals based on the latest trends 2. The system of claim 1.

4. The project proposal generation unit Analyzing the user's emotional state and generating a proposal that elicits positive emotions 2. The system of claim 1.

5. The project proposal generation unit Analyze campaign data from different industries and generate proposals based on successful cases 2. The system of claim 1.

6. The project proposal generation unit Based on user input, multiple different project proposals can be generated simultaneously and compared.

2. The system of claim 1.

7. The project proposal generation unit Analyzes the user's emotions in real time as they input information and proposes the most suitable proposal.

2. The system of claim 1.

8. The virtual item generation unit Generate virtual items that reflect the user's past design trends 2. The system of claim 1.

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