system

Abstract

The system according to this embodiment aims to efficiently recruit and manage volunteers and to alleviate the shortage of participants in local festivals and other events. [Solution] The system according to the embodiment comprises a recruitment management unit, a virtual volunteer unit, a fundraising unit, and a promotion generation unit. The recruitment management unit recruits and manages volunteers. The virtual volunteer unit proposes roles for virtual volunteers based on the volunteers recruited by the recruitment management unit. The fundraising unit coordinates crowdfunding and sponsorship. The promotion generation unit creates promotional materials using generation AI.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the recruitment and management of volunteers have not been carried out efficiently, and there is a particular problem of a serious shortage of manpower in local festivals and the like.

[0005] The system according to the embodiment aims to perform efficient volunteer recruitment and management and eliminate the shortage of manpower in local festivals and the like.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recruitment management unit, a virtual volunteer unit, a fundraising unit, and a promotion generation unit. The recruitment management unit recruits and manages volunteers. The virtual volunteer unit proposes roles for virtual volunteers based on the volunteers recruited by the recruitment management unit. The fundraising unit coordinates crowdfunding and sponsorship. The promotion generation unit creates promotional materials using generation AI. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently recruit and manage volunteers, thereby alleviating the shortage of participants in local festivals and other events. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​volunteer management system according to an embodiment of the present invention is a system designed to alleviate the burden on festival organizers in rural areas facing an aging population and to support the continuation of festivals. This system uses AI to centrally manage the recruitment, management, and scheduling of volunteers. The AI ​​considers the volunteers' areas of expertise and available time to propose the optimal division of roles. Furthermore, it provides a role for virtual volunteers who can participate from remote locations to support festival operations. It also introduces a mechanism to supplement operating funds by linking crowdfunding and sponsorships. By utilizing generative AI, it automatically generates attractive crowdfunding campaigns, social media posts, and posters, and sends individually personalized messages to facilitate effective communication and attract support. Furthermore, it uses generative AI to create promotional videos and graphic materials to gain supporters. This system enables efficient recruitment and management of volunteers, even in areas where securing volunteers is difficult, and allows for support from remote locations. It also enables the continuous holding of local festivals by securing operating funds through crowdfunding and sponsorships. For example, the AI ​​volunteer management system includes a recruitment management department that handles the recruitment and management of volunteers. The recruitment management department considers the volunteers' areas of expertise and available time to propose the optimal division of roles. Furthermore, it includes a virtual volunteer department, creating a role for virtual volunteers who can participate remotely. The fundraising department will coordinate crowdfunding and sponsorships. The promotion generation department will use generation AI to automatically generate attractive crowdfunding campaigns, social media posts, and posters. The promotion generation department will also use generation AI to create promotional videos and graphic materials. As a result, the AI ​​volunteer management system will be able to efficiently recruit and manage volunteers, propose roles for virtual volunteers, raise funds, and create promotional materials.

[0029] The AI ​​volunteer management system according to this embodiment comprises a recruitment management unit, a virtual volunteer unit, a fundraising unit, and a promotion generation unit. The recruitment management unit recruits and manages volunteers. For example, the recruitment management unit recruits volunteers using an online platform. The recruitment management unit can also contact volunteers using email notifications. Furthermore, the recruitment management unit can adjust volunteers' schedules using a schedule management system. For example, the recruitment management unit considers the volunteers' areas of expertise and availability to propose the most suitable role assignments. The virtual volunteer unit proposes roles for virtual volunteers. For example, the virtual volunteer unit provides online support. The virtual volunteer unit can also propose remote work. Furthermore, the virtual volunteer unit can provide technical requirements to facilitate participation from remote locations. For example, the virtual volunteer unit supports participation from remote locations using online conferencing tools. The fundraising unit integrates crowdfunding and sponsorship. For example, the fundraising unit plans crowdfunding campaigns. The fundraising unit can also provide methods for recruiting sponsors. Furthermore, the fundraising unit can raise funds using a crowdfunding platform. For example, the fundraising department raises funds through crowdfunding campaigns. The promotion generation department creates promotional materials using generational AI. The promotion generation department can, for example, automatically generate attractive crowdfunding campaigns using generational AI. The promotion generation department can also automatically generate social media posts using generational AI. Furthermore, the promotion generation department can also automatically generate posters using generational AI. For example, the promotion generation department can create promotional videos using generational AI. As a result, the AI ​​volunteer management system according to this embodiment can efficiently recruit and manage volunteers, propose roles for virtual volunteers, raise funds, and create promotional materials.

[0030] The Recruitment Management Department is responsible for recruiting and managing volunteers. For example, it recruits volunteers using online platforms. Specifically, it provides an environment that makes it easy for prospective volunteers to register through dedicated websites and applications. This allows prospective volunteers to enter their profiles, areas of expertise, and desired activities. The Recruitment Management Department can also contact volunteers via email notifications. For example, it can send emails with volunteer activity schedules and important announcements, ensuring volunteers stay informed. Furthermore, the Recruitment Management Department can adjust volunteer schedules using a schedule management system. For example, it can propose optimal role assignments considering volunteers' areas of expertise and availability. This reduces the burden on volunteers and enables more efficient activities. The schedule management system automatically adjusts volunteer availability and activity locations, preventing duplication and waste. It can also collect volunteer feedback to improve activities. This allows the Recruitment Management Department to efficiently recruit and manage volunteers and improve the quality of volunteer activities.

[0031] The Virtual Volunteer Department proposes roles for virtual volunteers. For example, it provides online support, such as remote customer support, technical support, and educational assistance. It can also propose remote work, such as data entry, translation, and research. Furthermore, the Virtual Volunteer Department can provide technical requirements to facilitate remote participation. For instance, it supports remote participation using online conferencing tools, creating an environment where diverse volunteers can participate without geographical constraints. The Virtual Volunteer Department provides the technical support volunteers need to ensure smooth operations, such as guiding them on how to use online conferencing tools and install necessary software for remote work. Additionally, the Virtual Volunteer Department can propose roles tailored to volunteers' skills and experience and provide appropriate training. This allows the Virtual Volunteer Department to support volunteers in effectively working remotely and broaden the scope of volunteer activities.

[0032] The fundraising department coordinates crowdfunding and sponsorship. For example, it plans crowdfunding campaigns. Specifically, it raises funds by clearly defining the project's goals and content and offering attractive rewards to supporters. The fundraising department can also provide methods for recruiting sponsors. For example, it proposes sponsorship to companies and organizations and receives contributions of money or goods. Furthermore, the fundraising department can raise funds using crowdfunding platforms. For example, it raises funds through crowdfunding campaigns. This allows the fundraising department to efficiently raise the funds necessary to realize the project. The fundraising department monitors the progress of crowdfunding campaigns in real time and strengthens promotional activities as needed. It also values ​​communication with supporters and builds trust by regularly reporting on the project's progress. In addition, the fundraising department can share success stories and know-how to support the fundraising activities of other projects. In this way, the fundraising department can build a sustainable fundraising mechanism and contribute to the success of projects.

[0033] The Promotion Generation Department utilizes AI to create promotional materials. For example, it can automatically generate compelling crowdfunding campaigns using AI. Specifically, it generates effective taglines and visual materials based on the project's content and goals. The Promotion Generation Department can also automatically generate social media posts using AI. For example, it can disseminate project progress and event information in a timely manner to increase engagement with supporters and followers. Furthermore, the Promotion Generation Department can automatically generate posters using AI. For example, it can automatically create designs and layouts that match the project's theme, and support printing and digital distribution. The Promotion Generation Department also uses AI to create promotional videos. For example, it can generate videos and animations that convey the project's appeal, providing visually appealing content. This allows the Promotion Generation Department to conduct effective promotional activities to increase project awareness and attract more supporters. The AI ​​learns from past success stories and trends to generate optimal promotional materials, ensuring that content always reflects the latest information. This allows the Promotion Generation Department to create promotional materials efficiently and effectively, contributing to the success of the project.

[0034] The recruitment management department can propose the most suitable role assignments by considering volunteers' areas of expertise and availability. For example, the department can collect volunteers' areas of expertise through surveys. It can also collect volunteers' availability using a schedule management system. Furthermore, the department can propose the most suitable role assignments using a skill matching algorithm. For instance, it can propose roles based on volunteers' areas of expertise. This ensures that the optimal role assignments are proposed based on volunteers' areas of expertise and availability.

[0035] The virtual volunteer department can establish roles for virtual volunteers who can participate remotely. For example, the virtual volunteer department can support remote participation using online conferencing tools. It can also provide remote access methods. Furthermore, the virtual volunteer department can provide online support. For instance, the virtual volunteer department can provide technical requirements to facilitate remote participation. This establishes roles for virtual volunteers who can participate remotely.

[0036] The fundraising department can coordinate crowdfunding campaigns and sponsorships. For example, the fundraising department can plan crowdfunding campaigns. It can also provide methods for recruiting sponsors. Furthermore, the fundraising department can raise funds using crowdfunding platforms. For instance, the fundraising department raises funds through crowdfunding campaigns. This effectively links crowdfunding campaigns and sponsorships.

[0037] The promotion generation unit can automatically generate attractive crowdfunding campaigns, social media posts, and posters using generation AI. For example, the promotion generation unit can automatically generate crowdfunding campaigns using generation AI. It can also automatically generate social media posts using generation AI. Furthermore, it can automatically generate posters using generation AI. For instance, the promotion generation unit can create attractively designed crowdfunding campaigns using generation AI. This allows for the automatic generation of compelling promotional materials using generation AI.

[0038] The promotional content creation unit can create promotional videos and graphic materials using generative AI. For example, the unit can create promotional videos using generative AI. It can also create graphic materials using generative AI. Furthermore, the unit can create attractively designed promotional videos using generative AI. For example, the unit can create high-quality promotional videos using generative AI. This demonstrates how promotional videos and graphic materials are created using generative AI.

[0039] The recruitment management department can analyze volunteers' past participation history and select the most suitable recruitment method. For example, it can select a similar recruitment method based on the success stories of past volunteers. It can also select recruitment methods that are effective for specific times of day or days of the week based on past participation history. Furthermore, it can analyze past participation history to select the most suitable recruitment method for a specific event. For example, it can analyze past participation history data to select the most suitable recruitment method. This ensures that the optimal recruitment method is selected based on past participation history.

[0040] The recruitment management department can filter volunteer recruitment based on users' current living situations and areas of interest. For example, it can suggest relevant volunteer activities based on users' occupations and hobbies. It can also recruit volunteers at times that are convenient for users to participate in, according to their lifestyle. Furthermore, the recruitment management department can suggest volunteer activities that will interest users based on their areas of interest. For example, it can analyze user survey data and suggest the most suitable volunteer activities. This allows for filtering based on users' living situations and areas of interest.

[0041] The recruitment management department can prioritize recruiting highly relevant volunteers by considering the user's geographical location when recruiting volunteers. For example, the recruitment management department can prioritize volunteer activities close to the user's place of residence. It can also prioritize volunteer activities along the user's commute route. Furthermore, it can prioritize volunteer activities related to places the user frequently visits. For example, the recruitment management department can analyze the user's GPS data and propose the most suitable volunteer activities. This ensures that highly relevant volunteers are recruited based on the user's geographical location.

[0042] The recruitment management department can analyze users' social media activity when recruiting volunteers and recruit relevant volunteers. For example, the recruitment management department can suggest volunteer activities related to events that users have shown interest in on social media. It can also suggest volunteer activities that the user's social media friends are participating in. Furthermore, the recruitment management department can suggest volunteer activities that might interest users based on the content of their social media posts. For example, the recruitment management department can analyze users' social media data and suggest the most suitable volunteer activities. This ensures that relevant volunteers are recruited based on the user's social media activity.

[0043] The Virtual Volunteer Department can analyze the past activity history of virtual volunteers and suggest the most suitable roles. For example, the Virtual Volunteer Department can suggest similar roles based on the roles of successful virtual volunteers in the past. Furthermore, the Virtual Volunteer Department can suggest roles that utilize specific skills based on past activity history. In addition, the Virtual Volunteer Department can analyze past activity history and suggest the most effective roles. For example, the Virtual Volunteer Department analyzes past activity history data and suggests the most suitable roles. This ensures that the optimal role is suggested based on past activity history.

[0044] The Virtual Volunteer Department can filter virtual volunteer roles based on the user's current skills and areas of interest. For example, it can suggest relevant virtual volunteer roles based on the user's skills. It can also suggest virtual volunteer roles that are of interest to the user based on their areas of interest. Furthermore, it can suggest virtual volunteer roles of appropriate difficulty level according to the user's current skill level. For example, the Virtual Volunteer Department can analyze user survey data and suggest the most suitable virtual volunteer role. This filters the roles based on the user's skills and areas of interest.

[0045] The Virtual Volunteer Department can prioritize suggesting highly relevant roles when proposing virtual volunteer roles, taking into account the user's geographical location. For example, the Virtual Volunteer Department can prioritize suggesting virtual volunteer roles related to the user's place of residence. It can also prioritize suggesting virtual volunteer roles related to the user's commute route. Furthermore, it can prioritize suggesting virtual volunteer roles related to places the user frequently visits. For instance, the Virtual Volunteer Department analyzes the user's GPS data to suggest the most suitable virtual volunteer role. This ensures that highly relevant roles are prioritized based on the user's geographical location.

[0046] The Virtual Volunteer Department can analyze a user's social media activity and suggest relevant roles when proposing virtual volunteer roles. For example, it can suggest virtual volunteer roles related to events the user has shown interest in on social media. It can also suggest virtual volunteer roles that the user's social media friends are participating in. Furthermore, the Virtual Volunteer Department can suggest interesting virtual volunteer roles based on the content of the user's social media posts. For example, the Virtual Volunteer Department analyzes the user's social media data and suggests the most suitable virtual volunteer role. This ensures that relevant roles are suggested based on the user's social media activity.

[0047] The fundraising department can analyze past fundraising history and select the most suitable fundraising method. For example, the fundraising department can select a similar method based on past successful crowdfunding campaigns. Furthermore, the fundraising department can select methods that are effective at specific times of day or on specific days of the week based on past fundraising history. In addition, the fundraising department can analyze past fundraising history to select the most effective method. For example, the fundraising department can analyze past fundraising history data to select the most suitable fundraising method. This ensures that the optimal fundraising method is selected based on past fundraising history.

[0048] The fundraising department can filter fundraising options based on the user's current lifestyle and areas of interest. For example, it can suggest relevant fundraising methods based on the user's occupation and hobbies. It can also schedule fundraising at times convenient for the user, aligning with their lifestyle. Furthermore, the fundraising department can suggest fundraising methods that appeal to the user based on their areas of interest. For instance, it can analyze user survey data to suggest the most suitable fundraising method, effectively filtering options based on the user's lifestyle and areas of interest.

[0049] The fundraising department can prioritize suggesting highly relevant fundraising methods when a user is seeking funding, taking into account their geographical location. For example, the fundraising department can prioritize suggesting fundraising methods related to the user's place of residence. It can also prioritize suggesting fundraising methods related to the user's commute route. Furthermore, it can prioritize suggesting fundraising methods related to places the user frequently visits. For instance, the fundraising department can analyze the user's GPS data to suggest the most suitable fundraising method. This ensures that highly relevant fundraising methods are suggested based on the user's geographical location.

[0050] The fundraising department can analyze a user's social media activity and suggest relevant fundraising methods during the fundraising process. For example, the fundraising department can suggest fundraising methods related to projects the user has shown interest in on social media. It can also suggest fundraising methods related to projects supported by the user's social media friends. Furthermore, the fundraising department can suggest fundraising methods that are of interest to the user based on the content of their social media posts. For example, the fundraising department can analyze the user's social media data and suggest the most suitable fundraising method. This ensures that relevant fundraising methods are suggested based on the user's social media activity.

[0051] The promotion generation unit can adjust the level of detail in promotional materials based on the importance of the target. For example, for important events, the AI ​​generates promotional materials containing detailed information. For less important events, the AI ​​can also generate promotional materials containing concise information. Furthermore, the AI ​​can generate promotional materials containing only the necessary information for a specific target audience. For example, the promotion generation unit generates optimal promotional materials based on the importance of the event. This adjusts the level of detail in the materials based on the importance of the target audience.

[0052] The promotion generation unit can apply different generation algorithms depending on the target category when generating promotional materials. For example, in the case of event promotion, the AI ​​generates visually appealing materials. Furthermore, in the case of product promotion, the AI ​​can generate materials that emphasize the product's features. Additionally, in the case of service promotion, the AI ​​can generate materials that emphasize the convenience of the service. For example, the promotion generation unit applies the optimal generation algorithm depending on the target category. This ensures that different generation algorithms are applied depending on the target category.

[0053] The promotion generation unit can prioritize promotional materials based on their respective submission deadlines. For example, in the case of an urgent event, the AI ​​can immediately generate promotional materials. Furthermore, for events scheduled further in the future, the AI ​​can generate promotional materials systematically. Additionally, the AI ​​can prioritize the generation of promotional materials when the submission deadline is approaching. For instance, the unit generates the most suitable promotional materials based on the event's submission deadline. This ensures that the priority of materials is determined based on the target submission date.

[0054] The promotion generation unit can adjust the order of promotional materials based on their relevance to the target audience. For example, the AI ​​can generate promotional materials with important information placed first. The AI ​​can also generate promotional materials with highly relevant information prioritized. Furthermore, the AI ​​can generate promotional materials tailored to the target audience, placing the most interesting information first. For instance, the promotion generation unit generates optimal promotional materials based on the relevance of the information. This adjusts the order of the materials based on their relevance to the target audience.

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

[0056] The recruitment management department can monitor the health of volunteers and adjust their roles based on their health status. For example, if a volunteer is feeling fatigued, the recruitment management department can suggest lighter tasks. Conversely, if a volunteer is healthy, the recruitment management department can suggest more important roles. Furthermore, if a volunteer is feeling unwell, the recruitment management department can suggest rest. This ensures that the optimal role assignments are suggested based on the volunteer's health condition.

[0057] A virtual volunteer organization can provide training programs to support volunteer skill development. For example, it can offer online courses to help volunteers acquire new skills. It can also host webinars to provide opportunities for expert guidance. Furthermore, it can conduct skill tests to assess volunteer skill levels, thereby supporting volunteer skill improvement.

[0058] The fundraising department can implement reward programs that utilize local specialty products. For example, the fundraising department can offer local specialty products as rewards to crowdfunding supporters. The fundraising department can also propose promotions utilizing local specialty products to sponsors. Furthermore, the fundraising department can plan events utilizing local specialty products to promote fundraising. This will lead to the implementation of reward programs that leverage local specialty products.

[0059] The recruitment management department can implement incentive programs to improve volunteer motivation. For example, it could award points based on volunteer activity hours, allowing volunteers to earn rewards by accumulating points. The department could also recognize outstanding volunteers and present them with certificates of appreciation. Furthermore, it could promote volunteer achievements on social media to increase social awareness. This, in turn, can boost volunteer motivation.

[0060] The fundraising department can collaborate with local businesses to conduct joint crowdfunding campaigns. For example, the fundraising department can work with local businesses to conduct joint promotions. It can also offer the businesses' products or services as rewards. Furthermore, the fundraising department can leverage the businesses' networks to attract a wide range of supporters. This allows for the implementation of crowdfunding campaigns in collaboration with local businesses.

[0061] The recruitment management department can implement programs to involve volunteers' families and friends. For example, the department can propose volunteer activities that can be participated in together with family and friends. They can also plan events for family and friends to encourage participation in volunteer activities. Furthermore, the department can send thank-you messages to family and friends to gain their understanding and cooperation in volunteer activities. This effectively implements programs that involve volunteers' families and friends.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The recruitment management department is responsible for recruiting and managing volunteers. For example, they can recruit volunteers using online platforms and contact them via email notifications. They can also use a schedule management system to coordinate volunteers' schedules and propose optimal role assignments considering volunteers' strengths and availability. Step 2: The virtual volunteer team proposes roles for virtual volunteers. For example, they suggest online support or remote work, and provide technical requirements to facilitate participation from remote locations. They can also support remote participation using online conferencing tools. Step 3: The fundraising department integrates crowdfunding and sponsorship. For example, they plan crowdfunding campaigns and provide methods for recruiting sponsors. They raise funds using crowdfunding platforms and collect funds through crowdfunding campaigns. Step 4: The promotion generation unit uses generation AI to create promotional materials. For example, it can use generation AI to automatically generate attractive crowdfunding campaigns and automatically create social media posts and posters. It can also use generation AI to create promotional videos.

[0064] (Example of form 2) The AI ​​volunteer management system according to an embodiment of the present invention is a system designed to alleviate the burden on festival organizers in rural areas facing an aging population and to support the continuation of festivals. This system uses AI to centrally manage the recruitment, management, and scheduling of volunteers. The AI ​​considers the volunteers' areas of expertise and available time to propose the optimal division of roles. Furthermore, it provides a role for virtual volunteers who can participate from remote locations to support festival operations. It also introduces a mechanism to supplement operating funds by linking crowdfunding and sponsorships. By utilizing generative AI, it automatically generates attractive crowdfunding campaigns, social media posts, and posters, and sends individually personalized messages to facilitate effective communication and attract support. Furthermore, it uses generative AI to create promotional videos and graphic materials to gain supporters. This system enables efficient recruitment and management of volunteers, even in areas where securing volunteers is difficult, and allows for support from remote locations. It also enables the continuous holding of local festivals by securing operating funds through crowdfunding and sponsorships. For example, the AI ​​volunteer management system includes a recruitment management department that handles the recruitment and management of volunteers. The recruitment management department considers the volunteers' areas of expertise and available time to propose the optimal division of roles. Furthermore, it includes a virtual volunteer department, creating a role for virtual volunteers who can participate remotely. The fundraising department will coordinate crowdfunding and sponsorships. The promotion generation department will use generation AI to automatically generate attractive crowdfunding campaigns, social media posts, and posters. The promotion generation department will also use generation AI to create promotional videos and graphic materials. As a result, the AI ​​volunteer management system will be able to efficiently recruit and manage volunteers, propose roles for virtual volunteers, raise funds, and create promotional materials.

[0065] The AI ​​volunteer management system according to this embodiment comprises a recruitment management unit, a virtual volunteer unit, a fundraising unit, and a promotion generation unit. The recruitment management unit recruits and manages volunteers. For example, the recruitment management unit recruits volunteers using an online platform. The recruitment management unit can also contact volunteers using email notifications. Furthermore, the recruitment management unit can adjust volunteers' schedules using a schedule management system. For example, the recruitment management unit considers the volunteers' areas of expertise and availability to propose the most suitable role assignments. The virtual volunteer unit proposes roles for virtual volunteers. For example, the virtual volunteer unit provides online support. The virtual volunteer unit can also propose remote work. Furthermore, the virtual volunteer unit can provide technical requirements to facilitate participation from remote locations. For example, the virtual volunteer unit supports participation from remote locations using online conferencing tools. The fundraising unit integrates crowdfunding and sponsorship. For example, the fundraising unit plans crowdfunding campaigns. The fundraising unit can also provide methods for recruiting sponsors. Furthermore, the fundraising unit can raise funds using a crowdfunding platform. For example, the fundraising department raises funds through crowdfunding campaigns. The promotion generation department creates promotional materials using generational AI. The promotion generation department can, for example, automatically generate attractive crowdfunding campaigns using generational AI. The promotion generation department can also automatically generate social media posts using generational AI. Furthermore, the promotion generation department can also automatically generate posters using generational AI. For example, the promotion generation department can create promotional videos using generational AI. As a result, the AI ​​volunteer management system according to this embodiment can efficiently recruit and manage volunteers, propose roles for virtual volunteers, raise funds, and create promotional materials.

[0066] The Recruitment Management Department is responsible for recruiting and managing volunteers. For example, it recruits volunteers using online platforms. Specifically, it provides an environment that makes it easy for prospective volunteers to register through dedicated websites and applications. This allows prospective volunteers to enter their profiles, areas of expertise, and desired activities. The Recruitment Management Department can also contact volunteers via email notifications. For example, it can send emails with volunteer activity schedules and important announcements, ensuring volunteers stay informed. Furthermore, the Recruitment Management Department can adjust volunteer schedules using a schedule management system. For example, it can propose optimal role assignments considering volunteers' areas of expertise and availability. This reduces the burden on volunteers and enables more efficient activities. The schedule management system automatically adjusts volunteer availability and activity locations, preventing duplication and waste. It can also collect volunteer feedback to improve activities. This allows the Recruitment Management Department to efficiently recruit and manage volunteers and improve the quality of volunteer activities.

[0067] The Virtual Volunteer Department proposes roles for virtual volunteers. For example, it provides online support, such as remote customer support, technical support, and educational assistance. It can also propose remote work, such as data entry, translation, and research. Furthermore, the Virtual Volunteer Department can provide technical requirements to facilitate remote participation. For instance, it supports remote participation using online conferencing tools, creating an environment where diverse volunteers can participate without geographical constraints. The Virtual Volunteer Department provides the technical support volunteers need to ensure smooth operations, such as guiding them on how to use online conferencing tools and install necessary software for remote work. Additionally, the Virtual Volunteer Department can propose roles tailored to volunteers' skills and experience and provide appropriate training. This allows the Virtual Volunteer Department to support volunteers in effectively working remotely and broaden the scope of volunteer activities.

[0068] The fundraising department coordinates crowdfunding and sponsorship. For example, it plans crowdfunding campaigns. Specifically, it raises funds by clearly defining the project's goals and content and offering attractive rewards to supporters. The fundraising department can also provide methods for recruiting sponsors. For example, it proposes sponsorship to companies and organizations and receives contributions of money or goods. Furthermore, the fundraising department can raise funds using crowdfunding platforms. For example, it raises funds through crowdfunding campaigns. This allows the fundraising department to efficiently raise the funds necessary to realize the project. The fundraising department monitors the progress of crowdfunding campaigns in real time and strengthens promotional activities as needed. It also values ​​communication with supporters and builds trust by regularly reporting on the project's progress. In addition, the fundraising department can share success stories and know-how to support the fundraising activities of other projects. In this way, the fundraising department can build a sustainable fundraising mechanism and contribute to the success of projects.

[0069] The Promotion Generation Department utilizes AI to create promotional materials. For example, it can automatically generate compelling crowdfunding campaigns using AI. Specifically, it generates effective taglines and visual materials based on the project's content and goals. The Promotion Generation Department can also automatically generate social media posts using AI. For example, it can disseminate project progress and event information in a timely manner to increase engagement with supporters and followers. Furthermore, the Promotion Generation Department can automatically generate posters using AI. For example, it can automatically create designs and layouts that match the project's theme, and support printing and digital distribution. The Promotion Generation Department also uses AI to create promotional videos. For example, it can generate videos and animations that convey the project's appeal, providing visually appealing content. This allows the Promotion Generation Department to conduct effective promotional activities to increase project awareness and attract more supporters. The AI ​​learns from past success stories and trends to generate optimal promotional materials, ensuring that content always reflects the latest information. This allows the Promotion Generation Department to create promotional materials efficiently and effectively, contributing to the success of the project.

[0070] The recruitment management department can propose the most suitable role assignments by considering volunteers' areas of expertise and availability. For example, the department can collect volunteers' areas of expertise through surveys. It can also collect volunteers' availability using a schedule management system. Furthermore, the department can propose the most suitable role assignments using a skill matching algorithm. For instance, it can propose roles based on volunteers' areas of expertise. This ensures that the optimal role assignments are proposed based on volunteers' areas of expertise and availability.

[0071] The virtual volunteer department can establish roles for virtual volunteers who can participate remotely. For example, the virtual volunteer department can support remote participation using online conferencing tools. It can also provide remote access methods. Furthermore, the virtual volunteer department can provide online support. For instance, the virtual volunteer department can provide technical requirements to facilitate remote participation. This establishes roles for virtual volunteers who can participate remotely.

[0072] The fundraising department can coordinate crowdfunding campaigns and sponsorships. For example, the fundraising department can plan crowdfunding campaigns. It can also provide methods for recruiting sponsors. Furthermore, the fundraising department can raise funds using crowdfunding platforms. For instance, the fundraising department raises funds through crowdfunding campaigns. This effectively links crowdfunding campaigns and sponsorships.

[0073] The promotion generation unit can automatically generate attractive crowdfunding campaigns, social media posts, and posters using generation AI. For example, the promotion generation unit can automatically generate crowdfunding campaigns using generation AI. It can also automatically generate social media posts using generation AI. Furthermore, it can automatically generate posters using generation AI. For instance, the promotion generation unit can create attractively designed crowdfunding campaigns using generation AI. This allows for the automatic generation of compelling promotional materials using generation AI.

[0074] The promotional content creation unit can create promotional videos and graphic materials using generative AI. For example, the unit can create promotional videos using generative AI. It can also create graphic materials using generative AI. Furthermore, the unit can create attractively designed promotional videos using generative AI. For example, the unit can create high-quality promotional videos using generative AI. This demonstrates how promotional videos and graphic materials are created using generative AI.

[0075] The recruitment management department can estimate a user's emotions and adjust the timing of volunteer recruitment based on those emotions. For example, if a user is feeling stressed, the department can recruit volunteers during a time when the user can relax. Conversely, if a user is excited, the department can immediately recruit volunteers to encourage active participation. Furthermore, if a user is tired, the department can recruit volunteers after they have rested. For instance, the department can analyze user emotion data and recruit volunteers at the optimal time. This adjusts the timing of volunteer recruitment based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The recruitment management department can analyze volunteers' past participation history and select the most suitable recruitment method. For example, it can select a similar recruitment method based on the success stories of past volunteers. It can also select recruitment methods that are effective for specific times of day or days of the week based on past participation history. Furthermore, it can analyze past participation history to select the most suitable recruitment method for a specific event. For example, it can analyze past participation history data to select the most suitable recruitment method. This ensures that the optimal recruitment method is selected based on past participation history.

[0077] The recruitment management department can filter volunteer recruitment based on users' current living situations and areas of interest. For example, it can suggest relevant volunteer activities based on users' occupations and hobbies. It can also recruit volunteers at times that are convenient for users to participate in, according to their lifestyle. Furthermore, the recruitment management department can suggest volunteer activities that will interest users based on their areas of interest. For example, it can analyze user survey data and suggest the most suitable volunteer activities. This allows for filtering based on users' living situations and areas of interest.

[0078] The recruitment management department can estimate a user's emotions and prioritize volunteer recruitment based on those emotions. For example, if a user is excited, the recruitment management department will prioritize suggesting important volunteer activities. It can also prioritize suggesting lighter volunteer activities if the user is relaxed. Furthermore, if a user is stressed, it can prioritize suggesting relaxing volunteer activities. For instance, the recruitment management department analyzes the user's emotional data and suggests the most suitable volunteer activities. This determines the priority of volunteer recruitment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The recruitment management department can prioritize recruiting highly relevant volunteers by considering the user's geographical location when recruiting volunteers. For example, the recruitment management department can prioritize volunteer activities close to the user's place of residence. It can also prioritize volunteer activities along the user's commute route. Furthermore, it can prioritize volunteer activities related to places the user frequently visits. For example, the recruitment management department can analyze the user's GPS data and propose the most suitable volunteer activities. This ensures that highly relevant volunteers are recruited based on the user's geographical location.

[0080] The recruitment management department can analyze users' social media activity when recruiting volunteers and recruit relevant volunteers. For example, the recruitment management department can suggest volunteer activities related to events that users have shown interest in on social media. It can also suggest volunteer activities that the user's social media friends are participating in. Furthermore, the recruitment management department can suggest volunteer activities that might interest users based on the content of their social media posts. For example, the recruitment management department can analyze users' social media data and suggest the most suitable volunteer activities. This ensures that relevant volunteers are recruited based on the user's social media activity.

[0081] The virtual volunteer system can estimate the user's emotions and adjust the virtual volunteer's role based on those emotions. For example, if the user is relaxed, the virtual volunteer system can suggest a virtual volunteer role that involves light tasks. If the user is excited, the virtual volunteer system can suggest a virtual volunteer role that involves important tasks. Furthermore, if the user is stressed, the virtual volunteer system can suggest a virtual volunteer role that involves relaxing tasks. For example, the virtual volunteer system can analyze the user's emotional data and suggest the optimal virtual volunteer role. This adjusts the virtual volunteer's role based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The Virtual Volunteer Department can analyze the past activity history of virtual volunteers and suggest the most suitable roles. For example, the Virtual Volunteer Department can suggest similar roles based on the roles of successful virtual volunteers in the past. Furthermore, the Virtual Volunteer Department can suggest roles that utilize specific skills based on past activity history. In addition, the Virtual Volunteer Department can analyze past activity history and suggest the most effective roles. For example, the Virtual Volunteer Department analyzes past activity history data and suggests the most suitable roles. This ensures that the optimal role is suggested based on past activity history.

[0083] The Virtual Volunteer Department can filter virtual volunteer roles based on the user's current skills and areas of interest. For example, it can suggest relevant virtual volunteer roles based on the user's skills. It can also suggest virtual volunteer roles that are of interest to the user based on their areas of interest. Furthermore, it can suggest virtual volunteer roles of appropriate difficulty level according to the user's current skill level. For example, the Virtual Volunteer Department can analyze user survey data and suggest the most suitable virtual volunteer role. This filters the roles based on the user's skills and areas of interest.

[0084] The virtual volunteer team can estimate the user's emotions and prioritize virtual volunteer roles based on those emotions. For example, if the user is excited, the virtual volunteer team will prioritize suggesting important virtual volunteer roles. If the user is relaxed, the virtual volunteer team can also prioritize suggesting lighter virtual volunteer roles. Furthermore, if the user is stressed, the virtual volunteer team can prioritize suggesting relaxing virtual volunteer roles. For instance, the virtual volunteer team analyzes the user's emotional data and suggests the most suitable virtual volunteer role. This determines the priority of virtual volunteer roles based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The Virtual Volunteer Department can prioritize suggesting highly relevant roles when proposing virtual volunteer roles, taking into account the user's geographical location. For example, the Virtual Volunteer Department can prioritize suggesting virtual volunteer roles related to the user's place of residence. It can also prioritize suggesting virtual volunteer roles related to the user's commute route. Furthermore, it can prioritize suggesting virtual volunteer roles related to places the user frequently visits. For instance, the Virtual Volunteer Department analyzes the user's GPS data to suggest the most suitable virtual volunteer role. This ensures that highly relevant roles are prioritized based on the user's geographical location.

[0086] The Virtual Volunteer Department can analyze a user's social media activity and suggest relevant roles when proposing virtual volunteer roles. For example, it can suggest virtual volunteer roles related to events the user has shown interest in on social media. It can also suggest virtual volunteer roles that the user's social media friends are participating in. Furthermore, the Virtual Volunteer Department can suggest interesting virtual volunteer roles based on the content of the user's social media posts. For example, the Virtual Volunteer Department analyzes the user's social media data and suggests the most suitable virtual volunteer role. This ensures that relevant roles are suggested based on the user's social media activity.

[0087] The fundraising department can estimate user emotions and adjust the timing of crowdfunding campaigns based on those emotions. For example, if a user is excited, the fundraising department can launch the campaign immediately. It can also launch the campaign at an appropriate time if the user is relaxed. Furthermore, if the user is stressed, the fundraising department can launch the campaign during a time when they can relax. For instance, the fundraising department analyzes user emotion data and launches the campaign at the optimal time. This adjusts the timing of crowdfunding campaigns based on user emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The fundraising department can analyze past fundraising history and select the most suitable fundraising method. For example, the fundraising department can select a similar method based on past successful crowdfunding campaigns. Furthermore, the fundraising department can select methods that are effective at specific times of day or on specific days of the week based on past fundraising history. In addition, the fundraising department can analyze past fundraising history to select the most effective method. For example, the fundraising department can analyze past fundraising history data to select the most suitable fundraising method. This ensures that the optimal fundraising method is selected based on past fundraising history.

[0089] The fundraising department can filter fundraising options based on the user's current lifestyle and areas of interest. For example, it can suggest relevant fundraising methods based on the user's occupation and hobbies. It can also schedule fundraising at times convenient for the user, aligning with their lifestyle. Furthermore, the fundraising department can suggest fundraising methods that appeal to the user based on their areas of interest. For instance, it can analyze user survey data to suggest the most suitable fundraising method, effectively filtering options based on the user's lifestyle and areas of interest.

[0090] The fundraising department can estimate the user's emotions and determine fundraising priorities based on those emotions. For example, if the user is excited, the fundraising department will prioritize suggesting important fundraising methods. It can also prioritize suggesting lighter fundraising methods if the user is relaxed. Furthermore, if the user is stressed, it can prioritize suggesting relaxing fundraising methods. For instance, the fundraising department analyzes the user's emotional data and suggests the optimal fundraising method. This determines fundraising priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The fundraising department can prioritize suggesting highly relevant fundraising methods when a user is seeking funding, taking into account their geographical location. For example, the fundraising department can prioritize suggesting fundraising methods related to the user's place of residence. It can also prioritize suggesting fundraising methods related to the user's commute route. Furthermore, it can prioritize suggesting fundraising methods related to places the user frequently visits. For instance, the fundraising department can analyze the user's GPS data to suggest the most suitable fundraising method. This ensures that highly relevant fundraising methods are suggested based on the user's geographical location.

[0092] The fundraising department can analyze a user's social media activity and suggest relevant fundraising methods during the fundraising process. For example, the fundraising department can suggest fundraising methods related to projects the user has shown interest in on social media. It can also suggest fundraising methods related to projects supported by the user's social media friends. Furthermore, the fundraising department can suggest fundraising methods that are of interest to the user based on the content of their social media posts. For example, the fundraising department can analyze the user's social media data and suggest the most suitable fundraising method. This ensures that relevant fundraising methods are suggested based on the user's social media activity.

[0093] The promotion generation unit can estimate the user's emotions and adjust the expression of promotional materials based on the estimated emotions. For example, if the user is relaxed, the AI ​​can generate promotional materials with a calm tone. If the user is excited, the AI ​​can also generate promotional materials with an energetic tone. Furthermore, if the user is stressed, the AI ​​can generate promotional materials with a relaxing tone. For example, the promotion generation unit analyzes the user's emotional data and generates optimal promotional materials. This adjusts the expression of the promotional materials based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The promotion generation unit can adjust the level of detail in promotional materials based on the importance of the target. For example, for important events, the AI ​​generates promotional materials containing detailed information. For less important events, the AI ​​can also generate promotional materials containing concise information. Furthermore, the AI ​​can generate promotional materials containing only the necessary information for a specific target audience. For example, the promotion generation unit generates optimal promotional materials based on the importance of the event. This adjusts the level of detail in the materials based on the importance of the target audience.

[0095] The promotion generation unit can apply different generation algorithms depending on the target category when generating promotional materials. For example, in the case of event promotion, the AI ​​generates visually appealing materials. Furthermore, in the case of product promotion, the AI ​​can generate materials that emphasize the product's features. Additionally, in the case of service promotion, the AI ​​can generate materials that emphasize the convenience of the service. For example, the promotion generation unit applies the optimal generation algorithm depending on the target category. This ensures that different generation algorithms are applied depending on the target category.

[0096] The promotion generation unit can estimate the user's emotions and adjust the length of the promotional material based on the estimated emotions. For example, if the user is in a hurry, the AI ​​can generate short, concise promotional material. If the user is relaxed, the AI ​​can generate longer promotional material with detailed explanations. Furthermore, if the user is excited, the AI ​​can generate promotional material with visually stimulating effects. For example, the promotion generation unit analyzes the user's emotional data and generates optimal promotional material. This adjusts the length of the promotional material based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The promotion generation unit can prioritize promotional materials based on their respective submission deadlines. For example, in the case of an urgent event, the AI ​​can immediately generate promotional materials. Furthermore, for events scheduled further in the future, the AI ​​can generate promotional materials systematically. Additionally, the AI ​​can prioritize the generation of promotional materials when the submission deadline is approaching. For instance, the unit generates the most suitable promotional materials based on the event's submission deadline. This ensures that the priority of materials is determined based on the target submission date.

[0098] The promotion generation unit can adjust the order of promotional materials based on their relevance to the target audience. For example, the AI ​​can generate promotional materials with important information placed first. The AI ​​can also generate promotional materials with highly relevant information prioritized. Furthermore, the AI ​​can generate promotional materials tailored to the target audience, placing the most interesting information first. For instance, the promotion generation unit generates optimal promotional materials based on the relevance of the information. This adjusts the order of the materials based on their relevance to the target audience.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The recruitment management department can monitor the health of volunteers and adjust their roles based on their health status. For example, if a volunteer is feeling fatigued, the recruitment management department can suggest lighter tasks. Conversely, if a volunteer is healthy, the recruitment management department can suggest more important roles. Furthermore, if a volunteer is feeling unwell, the recruitment management department can suggest rest. This ensures that the optimal role assignments are suggested based on the volunteer's health condition.

[0101] A virtual volunteer organization can provide training programs to support volunteer skill development. For example, it can offer online courses to help volunteers acquire new skills. It can also host webinars to provide opportunities for expert guidance. Furthermore, it can conduct skill tests to assess volunteer skill levels, thereby supporting volunteer skill improvement.

[0102] The fundraising department can implement reward programs that utilize local specialty products. For example, the fundraising department can offer local specialty products as rewards to crowdfunding supporters. The fundraising department can also propose promotions utilizing local specialty products to sponsors. Furthermore, the fundraising department can plan events utilizing local specialty products to promote fundraising. This will lead to the implementation of reward programs that leverage local specialty products.

[0103] The promotional material generation unit can estimate the user's emotions and adjust the colors of the promotional materials based on those emotions. For example, if the user is relaxed, the AI ​​can generate promotional materials with calming colors. If the user is excited, the AI ​​can generate promotional materials with vibrant colors. Furthermore, if the user is stressed, the AI ​​can generate promotional materials with calming colors. In this way, the colors of the promotional materials are adjusted based on the user's emotions.

[0104] The recruitment management department can implement incentive programs to improve volunteer motivation. For example, it could award points based on volunteer activity hours, allowing volunteers to earn rewards by accumulating points. The department could also recognize outstanding volunteers and present them with certificates of appreciation. Furthermore, it could promote volunteer achievements on social media to increase social awareness. This, in turn, can boost volunteer motivation.

[0105] The virtual volunteer system can estimate the user's emotions and adjust the virtual volunteer's feedback based on those emotions. For example, if the user is relaxed, the virtual volunteer will provide feedback in a calm tone. If the user is excited, the virtual volunteer can provide feedback in an energetic tone. Furthermore, if the user is stressed, the virtual volunteer can provide feedback in a relaxing tone. In this way, the virtual volunteer's feedback is adjusted based on the user's emotions.

[0106] The fundraising department can collaborate with local businesses to conduct joint crowdfunding campaigns. For example, the fundraising department can work with local businesses to conduct joint promotions. It can also offer the businesses' products or services as rewards. Furthermore, the fundraising department can leverage the businesses' networks to attract a wide range of supporters. This allows for the implementation of crowdfunding campaigns in collaboration with local businesses.

[0107] The promotional material generation unit can estimate the user's emotions and adjust the audio of the promotional material based on those emotions. For example, if the user is relaxed, the AI ​​can generate promotional material with a calm voice. If the user is excited, the AI ​​can generate promotional material with an energetic voice. Furthermore, if the user is stressed, the AI ​​can generate promotional material with a relaxing voice. In this way, the audio of the promotional material is adjusted based on the user's emotions.

[0108] The recruitment management department can implement programs to involve volunteers' families and friends. For example, the department can propose volunteer activities that can be participated in together with family and friends. They can also plan events for family and friends to encourage participation in volunteer activities. Furthermore, the department can send thank-you messages to family and friends to gain their understanding and cooperation in volunteer activities. This effectively implements programs that involve volunteers' families and friends.

[0109] The promotional material generation unit can estimate the user's emotions and adjust the tempo of the promotional material based on those emotions. For example, if the user is relaxed, the AI ​​can generate promotional material with a relaxed tempo. If the user is excited, the AI ​​can generate promotional material with a fast tempo. Furthermore, if the user is stressed, the AI ​​can generate promotional material with a relaxing tempo. In this way, the tempo of the promotional material is adjusted based on the user's emotions.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The recruitment management department is responsible for recruiting and managing volunteers. For example, they can recruit volunteers using online platforms and contact them via email notifications. They can also use a schedule management system to coordinate volunteers' schedules and propose optimal role assignments considering volunteers' strengths and availability. Step 2: The virtual volunteer team proposes roles for virtual volunteers. For example, they suggest online support or remote work, and provide technical requirements to facilitate participation from remote locations. They can also support remote participation using online conferencing tools. Step 3: The fundraising department integrates crowdfunding and sponsorship. For example, they plan crowdfunding campaigns and provide methods for recruiting sponsors. They raise funds using crowdfunding platforms and collect funds through crowdfunding campaigns. Step 4: The promotion generation unit uses generation AI to create promotional materials. For example, it can use generation AI to automatically generate attractive crowdfunding campaigns and automatically create social media posts and posters. It can also use generation AI to create promotional videos.

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the recruitment management unit, virtual volunteer unit, fundraising unit, and promotion generation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the recruitment management unit is implemented by the control unit 46A of the smart device 14 and can recruit volunteers using an online platform and contact volunteers using email notifications. The virtual volunteer unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports participation from remote locations using online conferencing tools. The fundraising unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and plans crowdfunding campaigns and raises funds using a crowdfunding platform. The promotion generation unit is implemented by, for example, the control unit 46A of the smart device 14 and automatically generates attractive crowdfunding campaigns, SNS posts, posters, and promotional videos using generation AI. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the recruitment management unit, virtual volunteer unit, fundraising unit, and promotion generation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the recruitment management unit is implemented by the control unit 46A of the smart glasses 214 and can recruit volunteers using an online platform and contact volunteers using email notifications. The virtual volunteer unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports participation from remote locations using online conferencing tools. The fundraising unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and plans crowdfunding campaigns and raises funds using a crowdfunding platform. The promotion generation unit is implemented by, for example, the control unit 46A of the smart glasses 214 and automatically generates attractive crowdfunding campaigns, SNS posts, posters, and promotional videos using generation AI. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the recruitment management unit, virtual volunteer unit, fundraising unit, and promotion generation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the recruitment management unit is implemented by the control unit 46A of the headset terminal 314 and can recruit volunteers using an online platform and contact volunteers using email notifications. The virtual volunteer unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports participation from remote locations using online conferencing tools. The fundraising unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and plans crowdfunding campaigns and raises funds using a crowdfunding platform. The promotion generation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and automatically generates attractive crowdfunding campaigns, SNS posts, posters, and promotional videos using generation AI. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 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.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the recruitment management unit, virtual volunteer unit, fundraising unit, and promotion generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the recruitment management unit is implemented by the control unit 46A of the robot 414 and can recruit volunteers using an online platform and contact volunteers using email notifications. The virtual volunteer unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports participation from remote locations using online conferencing tools. The fundraising unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and plans crowdfunding campaigns and raises funds using a crowdfunding platform. The promotion generation unit is implemented by, for example, the control unit 46A of the robot 414 and automatically generates attractive crowdfunding campaigns, SNS posts, posters, and promotional videos using generation AI. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) The Recruitment Management Department is responsible for recruiting and managing volunteers, Based on the volunteers recruited by the aforementioned recruitment management department, the virtual volunteer department proposes the roles of virtual volunteers. The fundraising department coordinates crowdfunding and sponsorships, It includes a promotion generation unit that creates promotional materials using generation AI. A system characterized by the following features. (Note 2) The aforementioned recruitment management department, We will propose the most suitable division of roles, taking into account the volunteers' areas of expertise and available time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned virtual volunteer club, We will create a role for virtual volunteers who can participate from remote locations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned fundraising department, Link crowdfunding campaigns and sponsorships. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned promotion generation unit, We use AI to automatically generate compelling crowdfunding campaigns, social media posts, and posters. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned promotion generation unit, Create promotional videos and graphic materials using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recruitment management department, The system estimates user sentiment and adjusts the timing of volunteer recruitment based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recruitment management department, Analyze volunteers' past participation history to select the most suitable recruitment method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recruitment management department, When recruiting volunteers, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recruitment management department, It estimates user sentiment and determines the priority of volunteers to recruit based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recruitment management department, When recruiting volunteers, we prioritize recruiting highly relevant volunteers by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recruitment management department, When recruiting volunteers, we analyze users' social media activity and recruit relevant volunteers. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned virtual volunteer club, It estimates the user's emotions and adjusts the role of virtual volunteers based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned virtual volunteer club, We analyze the past activity history of virtual volunteers and suggest the most suitable roles. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned virtual volunteer club, When proposing virtual volunteer roles, filtering is performed based on the user's current skills and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned virtual volunteer club, It estimates user emotions and prioritizes virtual volunteers based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned virtual volunteer club, When proposing virtual volunteer roles, the system prioritizes suggesting highly relevant roles by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned virtual volunteer club, When proposing virtual volunteer roles, we analyze users' social media activity and suggest relevant roles. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned fundraising department, We estimate user sentiment and adjust the timing of crowdfunding campaigns based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned fundraising department, Analyze past fundraising history and select the optimal fundraising method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned fundraising department, When raising funds, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned fundraising department, It estimates user sentiment and determines funding priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned fundraising department, When raising funds, we prioritize suggesting the most relevant fundraising methods by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned fundraising department, When raising funds, we analyze users' social media activity and propose relevant fundraising methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned promotion generation unit, We estimate user emotions and adjust the presentation of promotional materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned promotion generation unit, When generating promotional materials, adjust the level of detail based on the importance of the subject. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned promotion generation unit, When generating promotional materials, different generation algorithms are applied depending on the target category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned promotion generation unit, It estimates user sentiment and adjusts the length of promotional materials based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned promotion generation unit, When generating promotional materials, prioritize the materials based on the target submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned promotion generation unit, When generating promotional materials, adjust the order of materials based on their relevance to the target audience. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Recruitment Management Department is responsible for recruiting and managing volunteers, Based on the volunteers recruited by the aforementioned recruitment management department, the virtual volunteer department proposes the roles of virtual volunteers. The fundraising department coordinates crowdfunding and sponsorships, It includes a promotion generation unit that creates promotional materials using generation AI. A system characterized by the following features.

2. The aforementioned recruitment management department, We will propose the most suitable division of roles, taking into account the volunteers' areas of expertise and available time. The system according to feature 1.

3. The aforementioned virtual volunteer club, We will create a role for virtual volunteers who can participate from remote locations. The system according to feature 1.

4. The aforementioned fundraising department, Link crowdfunding campaigns and sponsorships. The system according to feature 1.

5. The aforementioned promotion generation unit, We use AI to automatically generate attractive crowdfunding campaigns, social media posts, and posters. The system according to feature 1.

6. The aforementioned promotion generation unit, Create promotional videos and graphic materials using generative AI. The system according to feature 1.

7. The aforementioned recruitment management department, The system estimates user sentiment and adjusts the timing of volunteer recruitment based on that estimated sentiment. The system according to feature 1.

8. The aforementioned recruitment management department, Analyze volunteers' past participation history to select the most suitable recruitment method. The system according to feature 1.

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