System

Abstract

An object of a system according to an embodiment is to facilitate establishment and operation of political parties in virtual spaces and to support efficient policy making and election activities.SOLUTION: This system is provided with a party headquarters constructing and operating part, a generation AI supporting part, and various participation method providing parts. The party headquarters construction and operation unit constructs and operates the party headquarters in the virtual space. The generation AI support part operates the party headquarters constructed by the party headquarters construction and operation part. The various participation method providing unit performs policy making and election activities supported by the generation AI supporting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was not easy to establish and operate political parties in virtual space, and there was a lack of support for efficient policy formulation and election campaigns.

[0005] The system according to the embodiment aims to facilitate the establishment and operation of political parties in virtual space and to support efficient policy planning and election campaigns. [Means for solving the problem]

[0006] The system according to the embodiment comprises a party headquarters construction and operation department, a generation AI support department, and a diverse participation method provision department. The party headquarters construction and operation department constructs and operates a party headquarters in a virtual space. The generation AI support department operates the party headquarters constructed by the party headquarters construction and operation department. The diverse participation method provision department carries out policy planning and election campaigns supported by the generation AI support department. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate the establishment and operation of political parties in virtual space and support efficient policy planning and election campaigns. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 political party establishment platform according to an embodiment of the present invention is a system that utilizes the metaverse and generative AI to allow anyone to easily establish and operate a fictional political party. This system builds and operates a party headquarters in a virtual space, and generative AI supports policy formulation and election campaigns, providing various ways to participate from various devices. As a result, the political party establishment platform transcends physical constraints, allowing party members from across the country and around the world to gather together and efficiently conduct policy formulation and election campaigns.

[0029] A political party establishment platform according to an embodiment includes a virtual party headquarters construction and operation unit, a generation AI support unit, and a unit providing various participation methods. The virtual party headquarters construction and operation unit constructs a party headquarters. For example, a user can design a virtual building in the metaverse and arrange conference rooms and offices. The generation AI support unit supports policy planning and election campaigns. For example, the generation AI receives prompts containing instructions from the user, collects policy information, and provides analysis results. The generation AI also develops election strategies and proposes effective campaigns during election campaigns. The various participation methods unit enables participation from various devices. For example, users can use a VR headset to immerse themselves in the virtual space and experience the virtual world. They can also access and participate in activities from anywhere using a PC or smartphone. This allows the political party establishment platform to easily establish and operate a fictional political party by utilizing the metaverse and generation AI. For example, party members from across the country and around the world can gather together to efficiently conduct policy planning and election campaigns, transcending physical constraints.

[0030] The Virtual Space Party Headquarters Construction and Management Department can be equipped with a function that allows users to design virtual buildings and arrange conference rooms and offices. For example, when a user designs a party headquarters in the metaverse, the Virtual Space Party Headquarters Construction and Management Department uses an emotion estimation function to collect emotional data from party members and automatically generate designs and layouts based on that data. For example, it can select colors and layouts that will help party members relax. This allows users to design virtual buildings and arrange conference rooms and offices.

[0031] The generative AI support unit can have the function of receiving prompts containing instructions from a user, collecting information on policies, and providing analysis results. For example, the generative AI support unit receives prompts containing instructions from a user, collects information on policies, and provides analysis results. For example, the generative AI collects relevant data in response to a policy-related question entered by a user and provides analysis results. In addition, during election campaigns, the generative AI analyzes voter data for each electoral district and identifies target voter demographics. For example, the generative AI analyzes the age groups and voting trends for each electoral district and proposes effective election strategies. This enables the generative AI to collect information on policies and provide analysis results.

[0032] The diverse participation method providing unit may have a function of immersing a user in a virtual space using a VR headset to provide a realistic experience. For example, the diverse participation method providing unit allows a user to immerse themselves in a virtual space using a VR headset to provide a realistic experience. For example, a user can wear a VR headset to access the party headquarters in the metaverse and interact with other party members in real time. In addition, a user can hold a meeting in a virtual conference room and hold policy discussions through a realistic experience. This allows a user to immerse themselves in a virtual space using a VR headset to provide a realistic experience.

[0033] The generation AI support unit can be equipped with the function of analyzing voter data for each electoral district and identifying the target voter demographic. For example, the generation AI analyzes voter data for each electoral district and identifies the target voter demographic. For example, the generation AI analyzes the age groups and voting trends for each electoral district and proposes effective election strategies. The generation AI also analyzes voters' interests and voting behavior and sends effective messages to the target voter demographic. This makes it possible to analyze voter data for each electoral district and identify the target voter demographic.

[0034] The unit for providing various participation methods can have a function in which, for participation methods using a PC or smartphone, the generation AI analyzes the user's behavioral history and suggests the optimal participation method. For example, the unit for providing various participation methods has the generation AI collect user behavioral history data and suggest the optimal participation method based on that data. For example, the generation AI suggests a device and participation method suitable for the user based on past participation history. The generation AI also analyzes the user's behavioral patterns and suggests the optimal time and method of participation. This allows the generation AI to analyze the user's behavioral history and suggest the optimal participation method for participation methods using a PC or smartphone.

[0035] The diverse participation method providing unit may have a function for developing technology to realize a seamless experience between different devices in a participation method using a VR / AR device. The diverse participation method providing unit may develop technology to realize a seamless experience between a VR / AR device and a PC or smartphone, for example. For example, the unit may build a system that synchronizes data between different devices in real time. The unit may also develop technology to maintain consistency in the user interface. This allows the development of technology to realize a seamless experience between different devices in a participation method using a VR / AR device.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] A party establishment platform can also have an education department, which has the function of providing political education to party members and voters. For example, the education department could hold political lectures and workshops in a virtual space to help party members acquire the knowledge necessary for policymaking and election activities. The education department could also use generative AI to analyze the learning progress of individual party members and propose optimal learning plans. This would help improve party members' skills and support more effective party management.

[0038] A political party establishment platform can also have a fundraising department, which has the function of efficiently raising funds for the party's operations. For example, the fundraising department can connect with a crowdfunding platform to solicit donations from people who support the party's activities. The fundraising department can also use generative AI to analyze donor data and develop effective donation campaigns. This strengthens the party's financial base and ensures sustainable operations.

[0039] A political party establishment platform can also have a media relations department, which has the function of widely publicizing the party's activities. For example, the media relations department can use generative AI to automatically generate press releases and news articles about the party's activities and distribute them to various media outlets. The media relations department can also link with social media platforms to disseminate information about the party's activities in real time. This can increase the party's name recognition and supporters.

[0040] The party establishment platform can also be equipped with a feedback collection unit, which has the function of collecting opinions and requests from party members and voters. For example, the feedback collection unit can hold surveys and opinion exchange meetings in a virtual space to directly hear the voices of party members and voters. The feedback collection unit can also use generative AI to analyze the collected data and reflect it in the party's policies and activities. This allows the party's activities to reflect the opinions of more people, making it easier to gain support.

[0041] The party establishment platform can also be equipped with a health management unit, which has the function of managing the health status of party members. For example, the health management unit can connect with wearable devices to collect and analyze party members' health data. The health management unit can also use generative AI to provide advice and health plans based on party members' health status. This can help party members maintain their health while participating in activities.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The Virtual Party Headquarters Construction and Operation Department builds the party headquarters. For example, users can design a virtual building in the metaverse and arrange conference rooms and offices. Step 2: The Generative AI Support Unit supports policy formulation and election campaigns. For example, the Generative AI receives prompts containing instructions from the user, collects information about policies, and provides analysis results. The Generative AI also develops election strategies and proposes effective campaigns. Step 3: The diverse participation method provision unit allows participation from various devices. For example, users can use a VR headset to immerse themselves in a virtual space and have a realistic experience. They can also access and participate in activities from anywhere using a PC or smartphone.

[0044] (Example 2) The political party establishment platform according to an embodiment of the present invention is a system that utilizes the metaverse and generative AI to allow anyone to easily establish and operate a fictional political party. This system builds and operates a party headquarters in a virtual space, and generative AI supports policy formulation and election campaigns, providing various ways to participate from various devices. As a result, the political party establishment platform transcends physical constraints, allowing party members from across the country and around the world to gather together and efficiently conduct policy formulation and election campaigns.

[0045] A political party establishment platform according to an embodiment includes a virtual party headquarters construction and operation unit, a generation AI support unit, and a unit providing various participation methods. The virtual party headquarters construction and operation unit constructs a party headquarters. For example, a user can design a virtual building in the metaverse and arrange conference rooms and offices. The generation AI support unit supports policy planning and election campaigns. For example, the generation AI receives prompts containing instructions from the user, collects policy information, and provides analysis results. The generation AI also develops election strategies and proposes effective campaigns during election campaigns. The various participation methods unit enables participation from various devices. For example, users can use a VR headset to immerse themselves in the virtual space and experience the virtual world. They can also access and participate in activities from anywhere using a PC or smartphone. This allows the political party establishment platform to easily establish and operate a fictional political party by utilizing the metaverse and generation AI. For example, party members from across the country and around the world can gather together to efficiently conduct policy planning and election campaigns, transcending physical constraints.

[0046] The Virtual Space Party Headquarters Construction and Management Department can be equipped with a function that allows users to design virtual buildings and arrange conference rooms and offices. For example, when a user designs a party headquarters in the metaverse, the Virtual Space Party Headquarters Construction and Management Department uses an emotion estimation function to collect emotional data from party members and automatically generate designs and layouts based on that data. For example, it can select colors and layouts that will help party members relax. This allows users to design virtual buildings and arrange conference rooms and offices.

[0047] The generative AI support unit can have the function of receiving prompts containing instructions from a user, collecting information on policies, and providing analysis results. For example, the generative AI support unit receives prompts containing instructions from a user, collects information on policies, and provides analysis results. For example, the generative AI collects relevant data in response to a policy-related question entered by a user and provides analysis results. In addition, during election campaigns, the generative AI analyzes voter data for each electoral district and identifies target voter demographics. For example, the generative AI analyzes the age groups and voting trends for each electoral district and proposes effective election strategies. This enables the generative AI to collect information on policies and provide analysis results.

[0048] The diverse participation method providing unit may have a function of immersing a user in a virtual space using a VR headset to provide a realistic experience. For example, the diverse participation method providing unit allows a user to immerse themselves in a virtual space using a VR headset to provide a realistic experience. For example, a user can wear a VR headset to access the party headquarters in the metaverse and interact with other party members in real time. In addition, a user can hold a meeting in a virtual conference room and hold policy discussions through a realistic experience. This allows a user to immerse themselves in a virtual space using a VR headset to provide a realistic experience.

[0049] The generation AI support unit can be equipped with the function of analyzing voter data for each electoral district and identifying the target voter demographic. For example, the generation AI analyzes voter data for each electoral district and identifies the target voter demographic. For example, the generation AI analyzes the age groups and voting trends for each electoral district and proposes effective election strategies. The generation AI also analyzes voters' interests and voting behavior and sends effective messages to the target voter demographic. This makes it possible to analyze voter data for each electoral district and identify the target voter demographic.

[0050] The generation AI support unit can be equipped with a function that uses the emotion estimation function to make policy proposals based on the emotions of party members and voters. For example, the generation AI support unit uses the emotion estimation function to collect emotional data on party members and voters and make policy proposals based on that data. For example, the generation AI analyzes the emotional data of party members and voters and prioritizes proposing policies that elicit positive emotions. The generation AI also proposes policies that meet the interests and needs of party members and voters based on the emotional data. This makes it possible to use the emotion estimation function to make policy proposals based on the emotions of party members and voters.

[0051] The multiple participation method providing unit can have a function of providing an interactive experience based on the user's emotions using a VR / AR device. The multiple participation method providing unit, for example, uses a VR / AR device to collect user emotion data in real time and provide an interactive experience based on the data. For example, the multiple participation method providing unit automatically generates a virtual environment that allows the user to relax. Also, the unit customizes interactions within the virtual space according to the user's emotions. This makes it possible to provide an interactive experience based on the user's emotions using a VR / AR device.

[0052] The unit for providing various participation methods can have a function in which, for participation methods using a PC or smartphone, the generation AI analyzes the user's behavioral history and suggests the optimal participation method. For example, the unit for providing various participation methods has the generation AI collect user behavioral history data and suggest the optimal participation method based on that data. For example, the generation AI suggests a device and participation method suitable for the user based on past participation history. The generation AI also analyzes the user's behavioral patterns and suggests the optimal time and method of participation. This allows the generation AI to analyze the user's behavioral history and suggest the optimal participation method for participation methods using a PC or smartphone.

[0053] The diverse participation method providing unit may have a function for developing technology to realize a seamless experience between different devices in a participation method using a VR / AR device. The diverse participation method providing unit may develop technology to realize a seamless experience between a VR / AR device and a PC or smartphone, for example. For example, the unit may build a system that synchronizes data between different devices in real time. The unit may also develop technology to maintain consistency in the user interface. This allows the development of technology to realize a seamless experience between different devices in a participation method using a VR / AR device.

[0054] The diverse participation method providing unit may have a function of using an emotion estimation function to provide a customized interface based on the user's emotion in a participation method using a VR / AR device. The diverse participation method providing unit may, for example, use the emotion estimation function to provide a customized interface based on the user's emotion data. For example, the diverse participation method providing unit may automatically generate an interface design that allows the user to relax. The unit may also customize the interface layout and functions according to the user's emotion. This allows the emotion estimation function to provide a customized interface based on the user's emotion in a participation method using a VR / AR device.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] A party establishment platform can also have an education department, which has the function of providing political education to party members and voters. For example, the education department could hold political lectures and workshops in a virtual space to help party members acquire the knowledge necessary for policymaking and election activities. The education department could also use generative AI to analyze the learning progress of individual party members and propose optimal learning plans. This would help improve party members' skills and support more effective party management.

[0057] A political party establishment platform can also have a fundraising department, which has the function of efficiently raising funds for the party's operations. For example, the fundraising department can connect with a crowdfunding platform to solicit donations from people who support the party's activities. The fundraising department can also use generative AI to analyze donor data and develop effective donation campaigns. This strengthens the party's financial base and ensures sustainable operations.

[0058] A political party establishment platform can also have a media relations department, which has the function of widely publicizing the party's activities. For example, the media relations department can use generative AI to automatically generate press releases and news articles about the party's activities and distribute them to various media outlets. The media relations department can also link with social media platforms to disseminate information about the party's activities in real time. This can increase the party's name recognition and supporters.

[0059] The party establishment platform can also be equipped with a feedback collection unit, which has the function of collecting opinions and requests from party members and voters. For example, the feedback collection unit can hold surveys and opinion exchange meetings in a virtual space to directly hear the voices of party members and voters. The feedback collection unit can also use generative AI to analyze the collected data and reflect it in the party's policies and activities. This allows the party's activities to reflect the opinions of more people, making it easier to gain support.

[0060] The party establishment platform can also be equipped with a health management unit, which has the function of managing the health status of party members. For example, the health management unit can connect with wearable devices to collect and analyze party members' health data. The health management unit can also use generative AI to provide advice and health plans based on party members' health status. This can help party members maintain their health while participating in activities.

[0061] The political party establishment platform can further use an emotion estimation function to provide a function for improving the motivation of party members. For example, the emotion estimation function can be used to collect emotional data of party members and provide messages and rewards to increase motivation based on that data. The emotion estimation function can also be used to monitor the stress levels of party members and provide a relaxing environment as needed. This can maintain the motivation of party members and support their effective activities.

[0062] The political party establishment platform can further use the emotion estimation function to provide a function for maximizing the effectiveness of election campaigns. For example, the emotion estimation function can be used to collect voter emotion data and optimize the content and timing of election campaigns based on that data. The emotion estimation function can also be used to monitor the emotional state of party members during election campaigns and provide support as needed. This can maximize the effectiveness of election campaigns and support election success.

[0063] The party establishment platform can further use emotion estimation to provide a function for grasping voter reactions to party policies in real time. For example, the emotion estimation function can be used to collect voter emotional data and evaluate the acceptability of policies based on that data. The emotion estimation function can also be used to detect negative reactions to policies early on and take swift countermeasures. This allows the party's policies to be adjusted so that they are supported by more voters.

[0064] The party establishment platform can further utilize an emotion estimation function to provide a function for smoother communication within the party. For example, the emotion estimation function can be used to collect emotional data on communication between party members and suggest ways to improve communication based on that data. The emotion estimation function can also be used to quickly detect emotional discrepancies between party members and take appropriate measures. This can facilitate smoother communication within the party and improve teamwork.

[0065] The political party establishment platform can further use the emotion estimation function to maximize the effectiveness of party events. For example, the emotion estimation function can be used to collect emotional data of event participants in real time and adjust the progress of the event based on that data. The emotion estimation function can also be used to analyze the emotional data of participants after the event ends and identify areas for improvement for the next event. This can make party events more effective and satisfying for participants.

[0066] The processing flow of the second embodiment will be briefly explained below.

[0067] Step 1: The Virtual Party Headquarters Construction and Operation Department builds the party headquarters. For example, users can design a virtual building in the metaverse and arrange conference rooms and offices. Step 2: The Generative AI Support Unit supports policy formulation and election campaigns. For example, the Generative AI receives prompts containing instructions from the user, collects information about policies, and provides analysis results. The Generative AI also develops election strategies and proposes effective campaigns. Step 3: The diverse participation method provision unit allows participation from various devices. For example, users can use a VR headset to immerse themselves in a virtual space and have a realistic experience. They can also access and participate in activities from anywhere using a PC or smartphone.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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).

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0081] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0087] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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."

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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]

[0135] 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. The Party Headquarters Construction and Management Department in virtual space, A generation AI support department that operates the party headquarters built by the party headquarters construction and operation department in the virtual space; and a diverse participation method providing unit for carrying out policy planning and election activities supported by the generation AI support unit. A system characterized by:

2. The Party Headquarters Construction and Operation Department in the virtual space will: Allows users to design virtual buildings and populate them with meeting rooms and offices 2. The system of claim 1.

3. The generation AI support unit Equipped with the ability to analyze voter data for each electoral district and identify target voter demographics 2. The system of claim 1.

4. The various participation method providing unit: In the participation method using a PC or smartphone, the generation AI has a function of analyzing the user's behavioral history and proposing the optimal participation method.

2. The system of claim 1.

5. The generation AI support unit It has the ability to make policy proposals based on the sentiments of party members and voters.

2. The system of claim 1.

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