system

Abstract

The system according to the embodiment aims to efficiently allocate volunteers. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an allocation unit, a monitoring unit, and a reallocation unit. The collection unit collects volunteer registration information. The analysis unit analyzes the information collected by the collection unit. The allocation unit allocates appropriate volunteer activities based on the information analyzed by the analysis unit. The monitoring unit monitors the progress of the volunteer activities allocated by the allocation unit. The reallocation unit reallocates based on the progress monitored by the monitoring unit.

Description

[Technical Field]

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

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

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

[0004] In conventional technology, volunteer allocation is done manually, which makes it difficult to allocate volunteers efficiently.

[0005] The system according to the embodiment aims to efficiently allocate volunteers. [Means for solving the problem]

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, an allocation unit, a monitoring unit, and a reallocation unit. The collection unit collects volunteer registration information. The analysis unit analyzes the information collected by the collection unit. The allocation unit allocates appropriate volunteer activities based on the information analyzed by the analysis unit. The monitoring unit monitors the progress of the volunteer activities allocated by the allocation unit. The reallocation unit reallocates activities based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently allocate volunteers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A volunteer allocation system according to an embodiment of the present invention collects volunteer registration information, analyzes it using AI, and assigns optimal volunteer activities. This volunteer allocation system collects detailed data, such as volunteers' skills and experience, desired activities, past activity history, and qualifications. AI analyzes this information to assign optimal volunteer activities. For example, volunteers with medical skills can be assigned to medical support activities. AI also considers volunteer schedules to efficiently allocate volunteers. Furthermore, the system has a function to monitor the progress of volunteer activities in real time and reallocate volunteers as necessary. This is expected to improve the efficiency and satisfaction of volunteer activities. For example, when collecting volunteer registration information, detailed data, such as volunteers' skills and experience, desired activities, past activity history, and qualifications, is collected. For example, information on volunteers with medical qualifications and volunteers who have previously participated in disaster relief activities is collected. AI then analyzes the collected information. AI analyzes the volunteers' skills, experience, desired activities, and other information to assign optimal volunteer activities. For example, volunteers with medical skills can be assigned to medical support activities. AI also takes volunteer schedules into consideration to allocate volunteers efficiently. Furthermore, it monitors the progress of volunteer activities in real time. AI monitors the progress of volunteer activities and reallocates them as necessary. For example, if a volunteer finishes an activity earlier than planned, it can assign them to the next activity. Finally, it evaluates volunteer satisfaction. AI regularly evaluates volunteer satisfaction and uses this to improve the system. For example, volunteers can provide feedback after their activities, and the system can be improved based on that feedback. This is expected to improve the efficiency of volunteer activities and satisfaction. Volunteers can perform activities that suit their skills and experience, allowing them to carry out their activities more efficiently. Furthermore, having AI manage schedules reduces the burden on volunteers and makes activities more efficient.This allows the volunteer allocation system to collect and analyze volunteer registration information, allocate optimal activities, monitor progress, and reallocate as needed, thereby improving the efficiency of volunteer activities and increasing satisfaction.

[0029] A volunteer allocation system according to an embodiment includes a collection unit, an analysis unit, an allocation unit, a monitoring unit, and a reallocation unit. The collection unit collects registration information about volunteers. The registration information about volunteers includes, but is not limited to, skills, experience, desired activities, past activity history, and qualification information. The collection unit collects, for example, information entered by volunteers through an online form. The collection unit can also retrieve the history of activities in which the volunteers have participated from a database. The collection unit can also verify the qualification information provided by the volunteers and register it in the database. For example, the collection unit automatically saves the information entered by the volunteers in the database and updates it as necessary. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the skills, experience, and desired activities of the volunteers and allocates optimal volunteer activities. For example, the analysis unit uses AI to analyze the volunteers' skill sets and suggest appropriate activities. The analysis unit can also allocate appropriate activities based on the volunteers' past activity history. The analysis unit can also take the volunteers' schedules into account to efficiently allocate volunteers. For example, the analysis unit matches the volunteer's skills with the activity content and suggests the most suitable activity. The allocation unit allocates volunteer activities based on the information analyzed by the analysis unit. For example, the allocation unit allocates medical support activities to volunteers with medical-related skills. The allocation unit can also allocate appropriate activities based on the activity content desired by the volunteer. Furthermore, the allocation unit can also make efficient allocations taking into account the volunteer's schedule. For example, the allocation unit matches the volunteer's skills with the activity content and suggests the most suitable activity. The monitoring unit monitors the progress of the volunteer activity allocated by the allocation unit. For example, the monitoring unit monitors the progress in real time from the time the volunteer starts the activity to the time it ends. For example, the monitoring unit can allocate the next activity when the volunteer finishes the activity. The reallocation unit reallocates volunteers based on the progress monitored by the monitoring unit.The reassignment unit, for example, assigns volunteers to the next activity if they finish their activity earlier than scheduled. The reassignment unit can also periodically evaluate volunteer satisfaction and use the results to improve the system. For example, the reassignment unit improves the system based on feedback provided by volunteers after their activities. As a result, the volunteer assignment system according to this embodiment can improve the efficiency of volunteer activities and increase satisfaction by collecting and analyzing volunteer registration information, assigning optimal activities, monitoring progress, and reassigning as needed.

[0030] The data collection unit can collect detailed data on volunteers' skills and experience, desired activities, past activity history, and qualifications. For example, the data collection unit collects information entered by volunteers through online forms. For example, the data collection unit automatically saves detailed data such as skills and experience, desired activities, past activity history, and qualifications entered by volunteers to a database. The data collection unit can also retrieve the history of activities that volunteers have participated in from the database. For example, the data collection unit evaluates the current skills and experience of volunteers based on their past activity history. Furthermore, the data collection unit can verify the qualifications provided by volunteers and register them in the database. For example, the data collection unit verifies medical qualifications and disaster relief experience provided by volunteers and registers them in the database. This allows for more appropriate assignment of activities by collecting detailed data on volunteers. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the information entered by volunteers into AI, and the AI ​​can automatically save it to the database.

[0031] The analysis unit can analyze the collected information and assign appropriate volunteer activities. The analysis unit, for example, analyzes the volunteer's skills, experience, and desired activity content and assigns the most appropriate volunteer activity. For example, the analysis unit can use AI to analyze the volunteer's skill set and suggest appropriate activities. The analysis unit can also assign appropriate activities based on the volunteer's past activity history. For example, the analysis unit can evaluate the volunteer's current skills and experience based on the volunteer's past activity history and suggest appropriate activities. Furthermore, the analysis unit can make efficient assignments taking into account the volunteer's schedule. For example, the analysis unit can match the volunteer's skills with the activity content and suggest the most appropriate activity. In this way, the analysis of the collected information can assign the most appropriate activity to the volunteer. Some or all of the above-mentioned processing by the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input the volunteer's skill set into AI, which can then automatically suggest appropriate activities.

[0032] The allocation unit can assign medical support activities to volunteers with medical skills. For example, the allocation unit assigns medical support activities to volunteers with medical skills. For example, the allocation unit suggests medical support activities based on the medical qualifications and work experience provided by the volunteer. The allocation unit can also assign appropriate activities based on the activity content desired by the volunteer. For example, the allocation unit suggests appropriate activities based on the medical support activity desired by the volunteer. Furthermore, the allocation unit can make efficient allocations taking into account the volunteer's schedule. For example, the allocation unit matches the volunteer's skills with the activity content and suggests optimal activities. This allows the volunteer's skills to be fully utilized by allocating appropriate activities to volunteers with medical skills. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without AI. For example, the allocation unit can input the volunteer's medical qualifications and work experience into AI, which can then automatically suggest medical support activities.

[0033] The monitoring unit can monitor the progress of volunteer activities in real time. For example, the monitoring unit monitors the progress of a volunteer from the time the volunteer starts an activity until the time the volunteer finishes it in real time. For example, the monitoring unit can assign the next activity when the volunteer finishes the activity. The monitoring unit can also monitor the activity status of the volunteer in real time and reallocate the volunteer as necessary. For example, the monitoring unit assigns the next activity if the volunteer finishes the activity earlier than planned. Furthermore, the monitoring unit can regularly evaluate the volunteer's satisfaction and use the results to improve the system. For example, the monitoring unit improves the system based on feedback provided by the volunteer after the activity. In this way, by monitoring the progress of the volunteer activity in real time, it is possible to respond quickly as necessary. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the volunteer's activity status into AI, which can automatically monitor the progress and reallocate the volunteer as necessary.

[0034] The reallocation unit can allocate the next activity to a volunteer if the volunteer finishes an activity earlier than planned. For example, the reallocation unit allocates the next activity to a volunteer if the volunteer finishes an activity earlier than planned. For example, the reallocation unit suggests the next activity if the volunteer finishes an activity earlier than planned. The reallocation unit can also perform efficient reallocation taking into account the volunteer's schedule. For example, the reallocation unit suggests the next activity based on the volunteer's schedule. Furthermore, the reallocation unit can periodically evaluate the volunteer's satisfaction and use the results to improve the system. For example, the reallocation unit improves the system based on feedback provided by the volunteer after the activity. This allows the volunteer's time to be used effectively by allocating the next activity if the volunteer finishes an activity earlier than planned. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation unit can input the volunteer's activity status into AI, which can automatically suggest the next activity.

[0035] The reallocation unit can periodically evaluate the satisfaction of volunteers and use the evaluation results to improve the system. The reallocation unit, for example, periodically evaluates the satisfaction of volunteers and uses the evaluation results to improve the system. For example, the reallocation unit improves the system based on feedback provided by volunteers after their activities. The reallocation unit can also periodically conduct surveys to evaluate the satisfaction of volunteers. For example, the reallocation unit periodically sends surveys to volunteers and collects feedback. The reallocation unit can also analyze the results of activities to evaluate the satisfaction of volunteers. For example, the reallocation unit evaluates the satisfaction based on the results of the volunteers' activities. In this way, periodically evaluating the satisfaction of volunteers can be useful for improving the system. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation unit can input the feedback of volunteers into AI, which can automatically suggest improvements to the system.

[0036] The collection unit can analyze the volunteer's past activity history and select the optimal collection method. For example, the collection unit can prioritize providing an online form to a volunteer who has frequently registered via online forms in the past. For example, the collection unit can prioritize providing an online form to a volunteer who has frequently registered via online forms in the past. The collection unit can also suggest telephone registration to a volunteer who has frequently registered via telephone in the past. For example, the collection unit can suggest telephone registration to a volunteer who has frequently registered via telephone in the past. The collection unit can also recommend in-person registration to a volunteer who has frequently registered in person in the past. For example, the collection unit can recommend in-person registration to a volunteer who has frequently registered in person in the past. This enables efficient information collection by selecting the optimal collection method based on the volunteer's past activity history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the volunteer's past activity history into AI, and the AI ​​can automatically suggest the optimal collection method.

[0037] When collecting registration information, the collection unit can filter the information based on the volunteer's current living situation and areas of interest. For example, if the volunteer is currently a student, the collection unit prioritizes collecting activity information for students. For example, if the volunteer is currently a student, the collection unit prioritizes collecting activity information for students. Furthermore, if the volunteer is a medical professional, the collection unit can collect information related to medical support activities. For example, if the volunteer is a medical professional, the collection unit collects information related to medical support activities. Furthermore, if the volunteer is interested in environmental protection, the collection unit can collect information related to environmental protection activities. For example, if the volunteer is interested in environmental protection, the collection unit collects information related to environmental protection activities. In this way, by filtering information based on the volunteer's living situation and areas of interest, more relevant information can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the volunteer's living situation and areas of interest into AI, which can then automatically filter the information.

[0038] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of volunteers when collecting registration information. For example, if a volunteer lives in an urban area, the data collection unit will prioritize the collection of activity information in urban areas. Similarly, if a volunteer lives in a rural area, the data collection unit can prioritize the collection of activity information in rural areas. Furthermore, if a volunteer lives overseas, the data collection unit can prioritize the collection of activity information in international areas. This allows for the efficient collection of highly relevant information by considering the geographical location of volunteers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the volunteer's geographical location information into the AI, which can then automatically prioritize the collection of highly relevant information.

[0039] When collecting the registration information, the collection unit can analyze the social media activities of the volunteers and collect related information. For example, the collection unit collects related activity information based on content frequently shared by the volunteers on social media. For example, the collection unit collects related activity information based on content frequently shared by the volunteers on social media. The collection unit can also collect related information based on organizations and events that the volunteers follow on social media. For example, the collection unit collects related information based on organizations and events that the volunteers follow on social media. Furthermore, the collection unit can also collect related information based on groups and communities that the volunteers participate in on social media. For example, the collection unit collects related information based on groups and communities that the volunteers participate in on social media. This allows for efficient collection of related information by analyzing the social media activities of the volunteers. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the social media activities of the volunteers into AI, and the AI ​​can automatically collect related information.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the volunteer's skills. For example, the analysis unit provides detailed analysis results to volunteers with advanced skills. For example, the analysis unit provides detailed analysis results to volunteers with advanced skills. The analysis unit can also provide concise analysis results to volunteers with basic skills. For example, the analysis unit provides concise analysis results to volunteers with basic skills. Furthermore, the analysis unit can also provide detailed analysis results related to a particular skill to volunteers who specialize in that skill. For example, the analysis unit provides detailed analysis results related to a particular skill to volunteers who specialize in that skill. In this way, by adjusting the level of detail of the analysis based on the importance of the volunteer's skills, appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the volunteer's skills into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of volunteer. For example, the analysis unit applies an analysis algorithm specialized for medical support activities to medical volunteers. For example, the analysis unit applies an analysis algorithm specialized for medical support activities to medical volunteers. The analysis unit can also apply an analysis algorithm specialized for environmental protection to environmental protection volunteers. For example, the analysis unit applies an analysis algorithm specialized for environmental protection to environmental protection volunteers. The analysis unit can also apply an analysis algorithm specialized for educational support to educational support volunteers. For example, the analysis unit applies an analysis algorithm specialized for educational support to educational support volunteers. This allows for the application of an appropriate analysis algorithm depending on the category of volunteer, thereby providing highly accurate analysis results. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the volunteer category into AI, and the AI ​​can automatically apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time of volunteer registration. For example, the analysis unit prioritizes analyzing information about recently registered volunteers. For example, the analysis unit prioritizes analyzing information about recently registered volunteers. The analysis unit can also prioritize analyzing information about volunteers who have been active for a long time. For example, the analysis unit prioritizes analyzing information about volunteers who have been active for a long time. Furthermore, the analysis unit can also prioritize analyzing information about volunteers related to a specific event or project. For example, the analysis unit prioritizes analyzing information about volunteers related to a specific event or project. This enables efficient analysis by determining the priority of analysis based on the time of volunteer registration. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the time of volunteer registration into AI, and the AI ​​can automatically determine the priority of analysis.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the volunteers. For example, the analysis unit prioritizes analyzing information about volunteers with specific skills. For example, the analysis unit prioritizes analyzing information about volunteers with specific skills. The analysis unit can also prioritize analyzing information about volunteers living in a specific area. For example, the analysis unit prioritizes analyzing information about volunteers living in a specific area. Furthermore, the analysis unit can also prioritize analyzing information about volunteers who are interested in a specific activity. For example, the analysis unit prioritizes analyzing information about volunteers who are interested in a specific activity. This enables efficient analysis by adjusting the order of analysis based on the relevance of the volunteers. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the volunteers into AI, and the AI ​​can automatically adjust the order of analysis.

[0044] The allocating unit can adjust the level of detail of the allocation based on the importance of the volunteer's skills when allocating. For example, the allocating unit may assign detailed tasks to volunteers with advanced skills. For example, the allocating unit may assign detailed tasks to volunteers with advanced skills. The allocating unit may also assign simple tasks to volunteers with basic skills. For example, the allocating unit may assign simple tasks to volunteers with basic skills. Furthermore, the allocating unit may assign detailed tasks related to a specific skill to a volunteer who specializes in that skill. For example, the allocating unit may assign detailed tasks related to a specific skill to a volunteer who specializes in that skill. In this way, by adjusting the level of detail of the allocation based on the importance of the volunteer's skills, appropriate activities can be allocated. Some or all of the above-described processing in the allocating unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocating unit may input the importance of the volunteer's skills into AI, and the AI ​​may automatically adjust the level of detail of the allocation.

[0045] The allocation unit can apply different allocation algorithms depending on the category of volunteers when allocating. For example, the allocation unit applies an allocation algorithm specialized for medical support activities to medical volunteers. For example, the allocation unit applies an allocation algorithm specialized for medical support activities to medical volunteers. The allocation unit can also apply an allocation algorithm specialized for environmental protection to environmental protection volunteers. For example, the allocation unit applies an allocation algorithm specialized for environmental protection to environmental protection volunteers. The allocation unit can also apply an allocation algorithm specialized for education support to educational support volunteers. For example, the allocation unit applies an allocation algorithm specialized for education support to educational support volunteers. This enables highly accurate allocation by applying an appropriate allocation algorithm depending on the category of volunteer. Some or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input the category of volunteers into AI, and the AI ​​can automatically apply an appropriate allocation algorithm.

[0046] The allocation unit can optimally allocate volunteers by taking into account the geographical location information of the volunteers when allocating them. For example, the allocation unit preferentially allocates activities in urban areas to volunteers living in urban areas. For example, the allocation unit preferentially allocates activities in urban areas to volunteers living in urban areas. The allocation unit can also preferentially allocate activities in rural areas to volunteers living in rural areas. For example, the allocation unit preferentially allocates activities in rural areas to volunteers living in rural areas. Furthermore, the allocation unit can also preferentially allocate international activities to volunteers living overseas. For example, the allocation unit preferentially allocates international activities to volunteers living overseas. In this way, optimal activities can be allocated by taking into account the geographical location information of the volunteers. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit inputs the geographical location information of the volunteers into AI, and the AI ​​can automatically perform optimal allocation.

[0047] The allocating unit can analyze the social media activity of the volunteer and allocate relevant activities during allocation. The allocating unit, for example, allocates relevant activities based on content frequently shared on social media. For example, the allocating unit allocates relevant activities based on content frequently shared on social media. The allocating unit can also allocate relevant activities based on organizations or events followed on social media. For example, the allocating unit allocates relevant activities based on organizations or events followed on social media. The allocating unit can also allocate relevant activities based on groups or communities joined on social media. For example, the allocating unit allocates relevant activities based on groups or communities joined on social media. In this way, by analyzing the social media activity of the volunteer, relevant activities can be efficiently allocated. Some or all of the above-described processing in the allocating unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocating unit can input the social media activity of the volunteer into AI, and the AI ​​can automatically allocate relevant activities.

[0048] During monitoring, the monitoring unit can predict current progress by referring to past progress data. For example, the monitoring unit predicts current progress status based on past progress data and notifies volunteers. For example, the monitoring unit predicts current progress status based on past progress data and notifies volunteers. The monitoring unit can also analyze past progress data and issue an alert if current progress is behind schedule. For example, the monitoring unit analyzes past progress data and issues an alert if current progress is behind schedule. Furthermore, the monitoring unit can also evaluate whether current progress is on track by referring to past progress data. For example, the monitoring unit evaluates whether current progress is on track by referring to past progress data. This allows current progress to be predicted by referring to past progress data, enabling efficient monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input past progress data into AI, which can then automatically predict current progress.

[0049] The monitoring unit can apply different monitoring methods to different categories of volunteers during monitoring. For example, the monitoring unit applies a monitoring method specialized for medical support activities to medical volunteers. For example, the monitoring unit applies a monitoring method specialized for medical support activities to medical volunteers. The monitoring unit can also apply a monitoring method specialized for environmental protection to volunteers working in environmental protection activities. For example, the monitoring unit applies a monitoring method specialized for environmental protection to volunteers working in environmental protection activities. The monitoring unit can also apply a monitoring method specialized for educational support to volunteers working in educational support activities. For example, the monitoring unit applies a monitoring method specialized for educational support to volunteers working in educational support activities. This enables highly accurate monitoring by applying an appropriate monitoring method according to the category of volunteer. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the volunteer category into AI, and the AI ​​can automatically apply an appropriate monitoring method.

[0050] During monitoring, the monitoring unit can analyze changes in progress based on the time period during which the volunteer was active. The monitoring unit can analyze changes in progress based on, for example, when the volunteer started the activity. For example, the monitoring unit can analyze changes in progress based on when the volunteer started the activity. The monitoring unit can also analyze changes in progress based on when the volunteer participated in a particular event or project. For example, the monitoring unit can analyze changes in progress based on when the volunteer participated in a particular event or project. The monitoring unit can also analyze changes in progress based on when the volunteer finished the activity. For example, the monitoring unit can analyze changes in progress based on when the volunteer finished the activity. This enables efficient monitoring by analyzing changes in progress based on the time period during which the volunteer was active. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the time period during which the volunteer was active into AI, and the AI ​​can automatically analyze changes in progress.

[0051] During monitoring, the monitoring unit can analyze the progress by referring to market data related to the volunteers. For example, the monitoring unit analyzes the progress by referring to data on the market in which the volunteers are active. For example, the monitoring unit analyzes the progress by referring to data on the market in which the volunteers are active. The monitoring unit can also analyze the progress by referring to market data on the region in which the volunteers are active. For example, the monitoring unit analyzes the progress by referring to market data on the region in which the volunteers are active. Furthermore, the monitoring unit can also analyze the progress by referring to market data on the field in which the volunteers are active. For example, the monitoring unit analyzes the progress by referring to market data on the field in which the volunteers are active. In this way, progress can be efficiently analyzed by referring to the market data related to the volunteers. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input market data related to the volunteers into AI, and the AI ​​can automatically analyze the progress.

[0052] When reallocating tasks, the reallocation unit can analyze the volunteer's past activity history and select an optimal reallocation method. For example, the reallocation unit reallocates a similar activity to a volunteer with a history of successful activities. For example, the reallocation unit reallocates a similar activity to a volunteer with a history of successful activities. The reallocation unit can also reallocate a different activity to a volunteer with a history of unsuccessful activities. For example, the reallocation unit reallocates a different activity to a volunteer with a history of unsuccessful activities. Furthermore, the reallocation unit can also reallocate a volunteer who has demonstrated a particular skill in the past to an activity that can utilize that skill. For example, the reallocation unit reallocates a volunteer who has demonstrated a particular skill in the past to an activity that can utilize that skill. This enables efficient reallocation by selecting an optimal reallocation method based on the volunteer's past activity history. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation department can input the volunteer's past activity history into the AI, which can then automatically select the optimal reallocation method.

[0053] The reallocation unit can customize the reallocation method based on the volunteer's current living situation. For example, if a volunteer is currently a student, the reallocation unit can reallocate them to student-oriented activities. For example, if a volunteer is currently a student, the reallocation unit can reallocate them to student-oriented activities. For example, if a volunteer is currently working, the reallocation unit can reallocate them to activities that can be combined with their work. For example, if a volunteer is currently working, the reallocation unit can reallocate them to activities that can be combined with their family life. For example, if a volunteer is currently married, the reallocation unit can reallocate them to activities that can be combined with their family life. In this way, the burden on volunteers can be reduced by customizing the reallocation method based on their current living situation. Some or all of the above processing in the reallocation unit may be performed using AI, for example, or without using AI. For example, the reallocation unit can input the volunteer's current living situation into the AI, allowing the AI ​​to automatically customize the reallocation method.

[0054] The reallocation unit can select the optimal reallocation method by considering the geographical location information of the volunteers during reallocation. For example, the reallocation unit can prioritize reallocating volunteers living in urban areas to activities in urban areas. For example, the reallocation unit can prioritize reallocating volunteers living in rural areas to activities in rural areas. For example, the reallocation unit can prioritize reallocating volunteers living in rural areas to activities in rural areas. Furthermore, the reallocation unit can prioritize reallocating volunteers living overseas to international activities. For example, the reallocation unit can prioritize reallocating volunteers living overseas to international activities. In this way, the optimal activities can be reallocated by considering the geographical location information of the volunteers. Some or all of the above processing in the reallocation unit may be performed using AI, for example, or without AI. For example, the reallocation unit can input the geographical location information of the volunteers into the AI, and the AI ​​can automatically select the optimal reallocation method.

[0055] During reallocation, the reallocation unit can analyze the social media activity of the volunteer and suggest a reallocation method. The reallocation unit, for example, reallocates related activities based on content frequently shared on social media. For example, the reallocation unit reallocates related activities based on content frequently shared on social media. The reallocation unit can also reallocate related activities based on organizations or events followed on social media. For example, the reallocation unit reallocates related activities based on organizations or events followed on social media. The reallocation unit can also reallocate related activities based on groups or communities joined on social media. For example, the reallocation unit reallocates related activities based on groups or communities joined on social media. In this way, by analyzing the social media activity of the volunteer, related activities can be efficiently reallocated. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation unit can input the social media activity of the volunteer into AI, and the AI ​​can automatically suggest a reallocation method.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The collection unit can analyze the volunteer's past activity history and select the optimal collection method. For example, for a volunteer who has frequently registered via online form in the past, the online form can be provided preferentially. Furthermore, for a volunteer who has frequently registered via telephone in the past, telephone registration can be suggested. Furthermore, for a volunteer who has frequently registered in person in the past, in-person registration can be recommended. This enables efficient information collection by selecting the optimal collection method based on the volunteer's past activity history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the volunteer's past activity history into AI, which can then automatically suggest the optimal collection method.

[0058] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the volunteer. For example, if the volunteer lives in an urban area, activity information in the urban area can be prioritized. Also, if the volunteer lives in a rural area, activity information in the rural area can be prioritized. Furthermore, if the volunteer lives overseas, international activity information can be prioritized. In this way, highly relevant information can be efficiently collected by taking into account the geographical location information of the volunteer. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the volunteer into AI, and the AI ​​can automatically prioritize collecting highly relevant information.

[0059] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the volunteer's skills. For example, detailed analysis results can be provided to volunteers with advanced skills. Brief analysis results can also be provided to volunteers with basic skills. Furthermore, detailed analysis results related to a particular skill can be provided to volunteers who specialize in that skill. In this way, by adjusting the level of detail of the analysis based on the importance of the volunteer's skills, appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the volunteer's skills into AI, which then automatically adjusts the level of detail of the analysis.

[0060] The allocation unit can analyze the social media activity of the volunteer and allocate relevant activities during allocation. For example, the allocation unit can allocate relevant activities based on the content frequently shared on social media. The allocation unit can also allocate relevant activities based on organizations and events followed on social media. Furthermore, the allocation unit can allocate relevant activities based on groups and communities participated in on social media. In this way, by analyzing the social media activity of the volunteer, relevant activities can be allocated efficiently. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input the social media activity of the volunteer into AI, which can then automatically allocate relevant activities.

[0061] During monitoring, the monitoring unit can predict current progress by referring to past progress data. For example, it can predict current progress based on past progress data and notify the volunteer. It can also analyze past progress data and issue an alert if current progress is behind schedule. It can also evaluate whether current progress is on track by referring to past progress data. This allows current progress to be predicted by referring to past progress data, enabling efficient monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input past progress data into AI, which can then automatically predict current progress.

[0062] When reallocating tasks, the reallocation unit can analyze the volunteer's past activity history and select the optimal reallocation method. For example, a volunteer with a history of successful activities can be reallocated to a similar activity. A volunteer with a history of unsuccessful activities can also be reallocated to a different activity. Furthermore, a volunteer who has demonstrated specific skills in the past can be reallocated to an activity that utilizes those skills. This enables efficient reallocation by selecting the optimal reallocation method based on the volunteer's past activity history. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation unit can input the volunteer's past activity history into AI, which can then automatically select the optimal reallocation method.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection department collects the volunteer registration information. The volunteer registration information includes skills, experience, desired activities, past activity history, and qualification information. The collection department collects the information entered by the volunteer through an online form and can also retrieve the history of past activities from a database. The collection department also verifies the qualification information provided by the volunteer and registers it in the database. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the volunteers' skills, experience, and desired activities, and assigns the most appropriate volunteer activities. For example, it uses AI to analyze skill sets and suggests appropriate activities. It also takes into account past activity history and schedules to make efficient assignments. Step 3: The allocation unit assigns volunteer activities based on the information analyzed by the analysis unit. For example, volunteers with medical skills are assigned medical support activities. The allocation is also efficient, taking into account the volunteers' desired activities and schedules. Step 4: The monitoring unit monitors the progress of the volunteer activities allocated by the allocation unit. For example, the monitoring unit can monitor the progress of the volunteers in real time from the time they start their activities to the time they finish them, and allocate the next activity to them when they finish their current activity. Step 5: The Reallocation Department reallocates volunteers based on the progress monitored by the Monitoring Department. For example, if a volunteer finishes an activity earlier than planned, the reallocation department will assign the next activity. The Reallocation Department also periodically evaluates volunteer satisfaction and uses it to improve the system.

[0065] (Example 2) A volunteer allocation system according to an embodiment of the present invention collects volunteer registration information, analyzes it using AI, and assigns optimal volunteer activities. This volunteer allocation system collects detailed data, such as volunteers' skills and experience, desired activities, past activity history, and qualifications. AI analyzes this information to assign optimal volunteer activities. For example, volunteers with medical skills can be assigned to medical support activities. AI also considers volunteer schedules to efficiently allocate volunteers. Furthermore, the system has a function to monitor the progress of volunteer activities in real time and reallocate volunteers as necessary. This is expected to improve the efficiency and satisfaction of volunteer activities. For example, when collecting volunteer registration information, detailed data, such as volunteers' skills and experience, desired activities, past activity history, and qualifications, is collected. For example, information on volunteers with medical qualifications and volunteers who have previously participated in disaster relief activities is collected. AI then analyzes the collected information. AI analyzes the volunteers' skills, experience, desired activities, and other information to assign optimal volunteer activities. For example, volunteers with medical skills can be assigned to medical support activities. AI also takes volunteer schedules into consideration to allocate volunteers efficiently. Furthermore, it monitors the progress of volunteer activities in real time. AI monitors the progress of volunteer activities and reallocates them as necessary. For example, if a volunteer finishes an activity earlier than planned, it can assign them to the next activity. Finally, it evaluates volunteer satisfaction. AI regularly evaluates volunteer satisfaction and uses this to improve the system. For example, volunteers can provide feedback after their activities, and the system can be improved based on that feedback. This is expected to improve the efficiency of volunteer activities and satisfaction. Volunteers can perform activities that suit their skills and experience, allowing them to carry out their activities more efficiently. Furthermore, having AI manage schedules reduces the burden on volunteers and makes activities more efficient.This allows the volunteer allocation system to collect and analyze volunteer registration information, allocate optimal activities, monitor progress, and reallocate as needed, thereby improving the efficiency of volunteer activities and increasing satisfaction.

[0066] A volunteer allocation system according to an embodiment includes a collection unit, an analysis unit, an allocation unit, a monitoring unit, and a reallocation unit. The collection unit collects registration information about volunteers. The registration information about volunteers includes, but is not limited to, skills, experience, desired activities, past activity history, and qualification information. The collection unit collects, for example, information entered by volunteers through an online form. The collection unit can also retrieve the history of activities in which the volunteers have participated from a database. The collection unit can also verify the qualification information provided by the volunteers and register it in the database. For example, the collection unit automatically saves the information entered by the volunteers in the database and updates it as necessary. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the skills, experience, and desired activities of the volunteers and allocates optimal volunteer activities. For example, the analysis unit uses AI to analyze the volunteers' skill sets and suggest appropriate activities. The analysis unit can also allocate appropriate activities based on the volunteers' past activity history. The analysis unit can also take the volunteers' schedules into account to efficiently allocate volunteers. For example, the analysis unit matches the volunteer's skills with the activity content and suggests the most suitable activity. The allocation unit allocates volunteer activities based on the information analyzed by the analysis unit. For example, the allocation unit allocates medical support activities to volunteers with medical-related skills. The allocation unit can also allocate appropriate activities based on the activity content desired by the volunteer. Furthermore, the allocation unit can also make efficient allocations taking into account the volunteer's schedule. For example, the allocation unit matches the volunteer's skills with the activity content and suggests the most suitable activity. The monitoring unit monitors the progress of the volunteer activity allocated by the allocation unit. For example, the monitoring unit monitors the progress in real time from the time the volunteer starts the activity to the time it ends. For example, the monitoring unit can allocate the next activity when the volunteer finishes the activity. The reallocation unit reallocates volunteers based on the progress monitored by the monitoring unit.The reassignment unit, for example, assigns volunteers to the next activity if they finish their activity earlier than scheduled. The reassignment unit can also periodically evaluate volunteer satisfaction and use the results to improve the system. For example, the reassignment unit improves the system based on feedback provided by volunteers after their activities. As a result, the volunteer assignment system according to this embodiment can improve the efficiency of volunteer activities and increase satisfaction by collecting and analyzing volunteer registration information, assigning optimal activities, monitoring progress, and reassigning as needed.

[0067] The data collection unit can collect detailed data on volunteers' skills and experience, desired activities, past activity history, and qualifications. For example, the data collection unit collects information entered by volunteers through online forms. For example, the data collection unit automatically saves detailed data such as skills and experience, desired activities, past activity history, and qualifications entered by volunteers to a database. The data collection unit can also retrieve the history of activities that volunteers have participated in from the database. For example, the data collection unit evaluates the current skills and experience of volunteers based on their past activity history. Furthermore, the data collection unit can verify the qualifications provided by volunteers and register them in the database. For example, the data collection unit verifies medical qualifications and disaster relief experience provided by volunteers and registers them in the database. This allows for more appropriate assignment of activities by collecting detailed data on volunteers. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the information entered by volunteers into AI, and the AI ​​can automatically save it to the database.

[0068] The analysis unit can analyze the collected information and assign appropriate volunteer activities. The analysis unit, for example, analyzes the volunteer's skills, experience, and desired activity content and assigns the most appropriate volunteer activity. For example, the analysis unit can use AI to analyze the volunteer's skill set and suggest appropriate activities. The analysis unit can also assign appropriate activities based on the volunteer's past activity history. For example, the analysis unit can evaluate the volunteer's current skills and experience based on the volunteer's past activity history and suggest appropriate activities. Furthermore, the analysis unit can make efficient assignments taking into account the volunteer's schedule. For example, the analysis unit can match the volunteer's skills with the activity content and suggest the most appropriate activity. In this way, the analysis of the collected information can assign the most appropriate activity to the volunteer. Some or all of the above-mentioned processing by the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input the volunteer's skill set into AI, which can then automatically suggest appropriate activities.

[0069] The allocation unit can assign medical support activities to volunteers with medical skills. For example, the allocation unit assigns medical support activities to volunteers with medical skills. For example, the allocation unit suggests medical support activities based on the medical qualifications and work experience provided by the volunteer. The allocation unit can also assign appropriate activities based on the activity content desired by the volunteer. For example, the allocation unit suggests appropriate activities based on the medical support activity desired by the volunteer. Furthermore, the allocation unit can make efficient allocations taking into account the volunteer's schedule. For example, the allocation unit matches the volunteer's skills with the activity content and suggests optimal activities. This allows the volunteer's skills to be fully utilized by allocating appropriate activities to volunteers with medical skills. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without AI. For example, the allocation unit can input the volunteer's medical qualifications and work experience into AI, which can then automatically suggest medical support activities.

[0070] The monitoring unit can monitor the progress of volunteer activities in real time. For example, the monitoring unit monitors the progress of a volunteer from the time the volunteer starts an activity until the time the volunteer finishes it in real time. For example, the monitoring unit can assign the next activity when the volunteer finishes the activity. The monitoring unit can also monitor the activity status of the volunteer in real time and reallocate the volunteer as necessary. For example, the monitoring unit assigns the next activity if the volunteer finishes the activity earlier than planned. Furthermore, the monitoring unit can regularly evaluate the volunteer's satisfaction and use the results to improve the system. For example, the monitoring unit improves the system based on feedback provided by the volunteer after the activity. In this way, by monitoring the progress of the volunteer activity in real time, it is possible to respond quickly as necessary. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the volunteer's activity status into AI, which can automatically monitor the progress and reallocate the volunteer as necessary.

[0071] The reallocation unit can allocate the next activity to a volunteer if the volunteer finishes an activity earlier than planned. For example, the reallocation unit allocates the next activity to a volunteer if the volunteer finishes an activity earlier than planned. For example, the reallocation unit suggests the next activity if the volunteer finishes an activity earlier than planned. The reallocation unit can also perform efficient reallocation taking into account the volunteer's schedule. For example, the reallocation unit suggests the next activity based on the volunteer's schedule. Furthermore, the reallocation unit can periodically evaluate the volunteer's satisfaction and use the results to improve the system. For example, the reallocation unit improves the system based on feedback provided by the volunteer after the activity. This allows the volunteer's time to be used effectively by allocating the next activity if the volunteer finishes an activity earlier than planned. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation unit can input the volunteer's activity status into AI, which can automatically suggest the next activity.

[0072] The reallocation unit can periodically evaluate the satisfaction of volunteers and use the evaluation results to improve the system. The reallocation unit, for example, periodically evaluates the satisfaction of volunteers and uses the evaluation results to improve the system. For example, the reallocation unit improves the system based on feedback provided by volunteers after their activities. The reallocation unit can also periodically conduct surveys to evaluate the satisfaction of volunteers. For example, the reallocation unit periodically sends surveys to volunteers and collects feedback. The reallocation unit can also analyze the results of activities to evaluate the satisfaction of volunteers. For example, the reallocation unit evaluates the satisfaction based on the results of the volunteers' activities. In this way, periodically evaluating the satisfaction of volunteers can be useful for improving the system. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation unit can input the feedback of volunteers into AI, which can automatically suggest improvements to the system.

[0073] The collection unit can estimate the emotions of the volunteers and adjust the timing of collecting the registered information based on the estimated emotions of the volunteers. For example, when the volunteers are relaxed, the collection unit collects the registered information at a normal timing. For example, when the volunteers are relaxed, the collection unit collects the registered information at a normal timing. Furthermore, when the volunteers are stressed, the collection unit can delay the collection timing so that the volunteers can provide information in a calm state. For example, when the volunteers are stressed, the collection unit can delay the collection timing so that the volunteers can provide information in a calm state. Furthermore, when the volunteers are excited, the collection unit can quickly collect information and obtain detailed information while their emotions are heightened. For example, when the volunteers are excited, the collection unit quickly collects information and obtains detailed information while their emotions are heightened. This allows the collection timing to be adjusted based on the emotions of the volunteers, thereby reducing the burden on the volunteers. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the volunteer's emotions into AI, which may then automatically adjust the collection timing.

[0074] The collection unit can analyze the volunteer's past activity history and select the optimal collection method. For example, the collection unit can prioritize providing an online form to a volunteer who has frequently registered via online forms in the past. For example, the collection unit can prioritize providing an online form to a volunteer who has frequently registered via online forms in the past. The collection unit can also suggest telephone registration to a volunteer who has frequently registered via telephone in the past. For example, the collection unit can suggest telephone registration to a volunteer who has frequently registered via telephone in the past. The collection unit can also recommend in-person registration to a volunteer who has frequently registered in person in the past. For example, the collection unit can recommend in-person registration to a volunteer who has frequently registered in person in the past. This enables efficient information collection by selecting the optimal collection method based on the volunteer's past activity history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the volunteer's past activity history into AI, and the AI ​​can automatically suggest the optimal collection method.

[0075] When collecting registration information, the collection unit can filter the information based on the volunteer's current living situation and areas of interest. For example, if the volunteer is currently a student, the collection unit prioritizes collecting activity information for students. For example, if the volunteer is currently a student, the collection unit prioritizes collecting activity information for students. Furthermore, if the volunteer is a medical professional, the collection unit can collect information related to medical support activities. For example, if the volunteer is a medical professional, the collection unit collects information related to medical support activities. Furthermore, if the volunteer is interested in environmental protection, the collection unit can collect information related to environmental protection activities. For example, if the volunteer is interested in environmental protection, the collection unit collects information related to environmental protection activities. In this way, by filtering information based on the volunteer's living situation and areas of interest, more relevant information can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the volunteer's living situation and areas of interest into AI, which can then automatically filter the information.

[0076] The collection unit can estimate the emotions of the volunteers and determine the priority of information to be collected based on the estimated emotions of the volunteers. For example, if the volunteers are relaxed, the collection unit prioritizes collecting detailed information. For example, if the volunteers are relaxed, the collection unit prioritizes collecting detailed information. Furthermore, if the volunteers are stressed, the collection unit can prioritize collecting only basic information. For example, if the volunteers are stressed, the collection unit prioritizes collecting only basic information. Furthermore, if the volunteers are excited, the collection unit can prioritize collecting information related to their emotions. For example, if the volunteers are excited, the collection unit prioritizes collecting information related to their emotions. This enables efficient information collection by determining the priority of information based on the emotions of the volunteers. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input volunteers' emotions into an AI, which can then automatically determine the priority of the information.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of volunteers when collecting registration information. For example, if a volunteer lives in an urban area, the data collection unit will prioritize the collection of activity information in urban areas. Similarly, if a volunteer lives in a rural area, the data collection unit can prioritize the collection of activity information in rural areas. Furthermore, if a volunteer lives overseas, the data collection unit can prioritize the collection of activity information in international areas. This allows for the efficient collection of highly relevant information by considering the geographical location of volunteers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the volunteer's geographical location information into the AI, which can then automatically prioritize the collection of highly relevant information.

[0078] When collecting the registration information, the collection unit can analyze the social media activities of the volunteers and collect related information. For example, the collection unit collects related activity information based on content frequently shared by the volunteers on social media. For example, the collection unit collects related activity information based on content frequently shared by the volunteers on social media. The collection unit can also collect related information based on organizations and events that the volunteers follow on social media. For example, the collection unit collects related information based on organizations and events that the volunteers follow on social media. Furthermore, the collection unit can also collect related information based on groups and communities that the volunteers participate in on social media. For example, the collection unit collects related information based on groups and communities that the volunteers participate in on social media. This allows for efficient collection of related information by analyzing the social media activities of the volunteers. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the social media activities of the volunteers into AI, and the AI ​​can automatically collect related information.

[0079] The analysis unit can estimate the volunteer's emotion and adjust the presentation method of the analysis based on the estimated emotion of the volunteer. For example, if the volunteer is relaxed, the analysis unit provides a detailed analysis result. For example, if the volunteer is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the volunteer is stressed, the analysis unit can provide a concise analysis result. For example, if the volunteer is stressed, the analysis unit provides a concise analysis result. Furthermore, if the volunteer is excited, the analysis unit can provide a visually appealing analysis result. For example, if the volunteer is excited, the analysis unit provides a visually appealing analysis result. In this way, by adjusting the presentation method of the analysis based on the volunteer's emotion, it is possible to provide an analysis result that is easy for the volunteer to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the volunteer's emotions into the AI, which can then automatically adjust how the analysis is expressed.

[0080] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the volunteer's skills. For example, the analysis unit provides detailed analysis results to volunteers with advanced skills. For example, the analysis unit provides detailed analysis results to volunteers with advanced skills. The analysis unit can also provide concise analysis results to volunteers with basic skills. For example, the analysis unit provides concise analysis results to volunteers with basic skills. Furthermore, the analysis unit can also provide detailed analysis results related to a particular skill to volunteers who specialize in that skill. For example, the analysis unit provides detailed analysis results related to a particular skill to volunteers who specialize in that skill. In this way, by adjusting the level of detail of the analysis based on the importance of the volunteer's skills, appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the volunteer's skills into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0081] During analysis, the analysis unit can apply different analysis algorithms depending on the category of volunteer. For example, the analysis unit applies an analysis algorithm specialized for medical support activities to medical volunteers. For example, the analysis unit applies an analysis algorithm specialized for medical support activities to medical volunteers. The analysis unit can also apply an analysis algorithm specialized for environmental protection to environmental protection volunteers. For example, the analysis unit applies an analysis algorithm specialized for environmental protection to environmental protection volunteers. The analysis unit can also apply an analysis algorithm specialized for educational support to educational support volunteers. For example, the analysis unit applies an analysis algorithm specialized for educational support to educational support volunteers. This allows for the application of an appropriate analysis algorithm depending on the category of volunteer, thereby providing highly accurate analysis results. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the volunteer category into AI, and the AI ​​can automatically apply an appropriate analysis algorithm.

[0082] The analysis unit can estimate the volunteer's emotion and adjust the length of the analysis based on the estimated emotion of the volunteer. For example, if the volunteer is relaxed, the analysis unit provides a detailed analysis result. For example, if the volunteer is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the volunteer is stressed, the analysis unit can provide a concise analysis result. For example, if the volunteer is stressed, the analysis unit provides a concise analysis result. Furthermore, if the volunteer is excited, the analysis unit can provide a visually appealing analysis result. For example, if the volunteer is excited, the analysis unit provides a visually appealing analysis result. In this way, by adjusting the length of the analysis based on the volunteer's emotion, an analysis result of an appropriate length for the volunteer can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the volunteer's emotions into the AI, which can then automatically adjust the length of the analysis.

[0083] During analysis, the analysis unit can determine the priority of analysis based on the time of volunteer registration. For example, the analysis unit prioritizes analyzing information about recently registered volunteers. For example, the analysis unit prioritizes analyzing information about recently registered volunteers. The analysis unit can also prioritize analyzing information about volunteers who have been active for a long time. For example, the analysis unit prioritizes analyzing information about volunteers who have been active for a long time. Furthermore, the analysis unit can also prioritize analyzing information about volunteers related to a specific event or project. For example, the analysis unit prioritizes analyzing information about volunteers related to a specific event or project. This enables efficient analysis by determining the priority of analysis based on the time of volunteer registration. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the time of volunteer registration into AI, and the AI ​​can automatically determine the priority of analysis.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the volunteers. For example, the analysis unit prioritizes analyzing information about volunteers with specific skills. For example, the analysis unit prioritizes analyzing information about volunteers with specific skills. The analysis unit can also prioritize analyzing information about volunteers living in a specific area. For example, the analysis unit prioritizes analyzing information about volunteers living in a specific area. Furthermore, the analysis unit can also prioritize analyzing information about volunteers who are interested in a specific activity. For example, the analysis unit prioritizes analyzing information about volunteers who are interested in a specific activity. This enables efficient analysis by adjusting the order of analysis based on the relevance of the volunteers. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the volunteers into AI, and the AI ​​can automatically adjust the order of analysis.

[0085] The allocation unit can estimate the emotions of the volunteers and adjust the allocation method based on the estimated emotions of the volunteers. For example, if the volunteers are relaxed, the allocation unit applies a normal allocation method. For example, if the volunteers are relaxed, the allocation unit applies the normal allocation method. Furthermore, if the volunteers are feeling stressed, the allocation unit can also prioritize allocating less burdensome activities. For example, if the volunteers are feeling stressed, the allocation unit prioritizes allocating less burdensome activities. Furthermore, if the volunteers are excited, the allocation unit can also prioritize allocating more challenging activities. For example, if the volunteers are excited, the allocation unit prioritizes allocating more challenging activities. In this way, by adjusting the allocation method based on the emotions of the volunteers, the burden on the volunteers can be reduced. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit may input the emotions of volunteers into AI, which may then automatically adjust the allocation method.

[0086] The allocating unit can adjust the level of detail of the allocation based on the importance of the volunteer's skills when allocating. For example, the allocating unit may assign detailed tasks to volunteers with advanced skills. For example, the allocating unit may assign detailed tasks to volunteers with advanced skills. The allocating unit may also assign simple tasks to volunteers with basic skills. For example, the allocating unit may assign simple tasks to volunteers with basic skills. Furthermore, the allocating unit may assign detailed tasks related to a specific skill to a volunteer who specializes in that skill. For example, the allocating unit may assign detailed tasks related to a specific skill to a volunteer who specializes in that skill. In this way, by adjusting the level of detail of the allocation based on the importance of the volunteer's skills, appropriate activities can be allocated. Some or all of the above-described processing in the allocating unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocating unit may input the importance of the volunteer's skills into AI, and the AI ​​may automatically adjust the level of detail of the allocation.

[0087] The allocation unit can apply different allocation algorithms depending on the category of volunteers when allocating. For example, the allocation unit applies an allocation algorithm specialized for medical support activities to medical volunteers. For example, the allocation unit applies an allocation algorithm specialized for medical support activities to medical volunteers. The allocation unit can also apply an allocation algorithm specialized for environmental protection to environmental protection volunteers. For example, the allocation unit applies an allocation algorithm specialized for environmental protection to environmental protection volunteers. The allocation unit can also apply an allocation algorithm specialized for education support to educational support volunteers. For example, the allocation unit applies an allocation algorithm specialized for education support to educational support volunteers. This enables highly accurate allocation by applying an appropriate allocation algorithm depending on the category of volunteer. Some or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input the category of volunteers into AI, and the AI ​​can automatically apply an appropriate allocation algorithm.

[0088] The allocation unit can estimate the emotions of volunteers and determine allocation priorities based on the estimated emotions. For example, if a volunteer is relaxed, the allocation unit will allocate tasks according to normal priorities. The allocation unit can also prioritize assigning less demanding activities if a volunteer is stressed. Furthermore, if a volunteer is excited, the allocation unit can prioritize assigning challenging activities. This allows for efficient allocation by determining allocation priorities based on the emotions of volunteers. 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. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit may input the emotions of volunteers into AI, which may then automatically determine allocation priorities.

[0089] The allocation unit can optimally allocate volunteers by taking into account the geographical location information of the volunteers when allocating them. For example, the allocation unit preferentially allocates activities in urban areas to volunteers living in urban areas. For example, the allocation unit preferentially allocates activities in urban areas to volunteers living in urban areas. The allocation unit can also preferentially allocate activities in rural areas to volunteers living in rural areas. For example, the allocation unit preferentially allocates activities in rural areas to volunteers living in rural areas. Furthermore, the allocation unit can also preferentially allocate international activities to volunteers living overseas. For example, the allocation unit preferentially allocates international activities to volunteers living overseas. In this way, optimal activities can be allocated by taking into account the geographical location information of the volunteers. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit inputs the geographical location information of the volunteers into AI, and the AI ​​can automatically perform optimal allocation.

[0090] The allocating unit can analyze the social media activity of the volunteer and allocate relevant activities during allocation. The allocating unit, for example, allocates relevant activities based on content frequently shared on social media. For example, the allocating unit allocates relevant activities based on content frequently shared on social media. The allocating unit can also allocate relevant activities based on organizations or events followed on social media. For example, the allocating unit allocates relevant activities based on organizations or events followed on social media. The allocating unit can also allocate relevant activities based on groups or communities joined on social media. For example, the allocating unit allocates relevant activities based on groups or communities joined on social media. In this way, by analyzing the social media activity of the volunteer, relevant activities can be efficiently allocated. Some or all of the above-described processing in the allocating unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocating unit can input the social media activity of the volunteer into AI, and the AI ​​can automatically allocate relevant activities.

[0091] The monitoring unit can estimate the volunteer's emotion and adjust the progress display method based on the estimated emotion. For example, if the volunteer is relaxed, the monitoring unit displays a detailed progress status. For example, if the volunteer is relaxed, the monitoring unit displays a detailed progress status. Furthermore, if the volunteer is stressed, the monitoring unit can display a concise progress status. For example, if the volunteer is stressed, the monitoring unit displays a concise progress status. Furthermore, if the volunteer is excited, the monitoring unit can display a visually appealing progress status. For example, if the volunteer is excited, the monitoring unit displays a visually appealing progress status. In this way, by adjusting the progress display method based on the volunteer's emotion, it is possible to provide a progress status that is easy for the volunteer to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring department can input the volunteer's emotions into the AI, which can then automatically adjust how its progress is displayed.

[0092] During monitoring, the monitoring unit can predict current progress by referring to past progress data. For example, the monitoring unit predicts current progress status based on past progress data and notifies volunteers. For example, the monitoring unit predicts current progress status based on past progress data and notifies volunteers. The monitoring unit can also analyze past progress data and issue an alert if current progress is behind schedule. For example, the monitoring unit analyzes past progress data and issues an alert if current progress is behind schedule. Furthermore, the monitoring unit can also evaluate whether current progress is on track by referring to past progress data. For example, the monitoring unit evaluates whether current progress is on track by referring to past progress data. This allows current progress to be predicted by referring to past progress data, enabling efficient monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input past progress data into AI, which can then automatically predict current progress.

[0093] The monitoring unit can apply different monitoring methods to different categories of volunteers during monitoring. For example, the monitoring unit applies a monitoring method specialized for medical support activities to medical volunteers. For example, the monitoring unit applies a monitoring method specialized for medical support activities to medical volunteers. The monitoring unit can also apply a monitoring method specialized for environmental protection to volunteers working in environmental protection activities. For example, the monitoring unit applies a monitoring method specialized for environmental protection to volunteers working in environmental protection activities. The monitoring unit can also apply a monitoring method specialized for educational support to volunteers working in educational support activities. For example, the monitoring unit applies a monitoring method specialized for educational support to volunteers working in educational support activities. This enables highly accurate monitoring by applying an appropriate monitoring method according to the category of volunteer. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the volunteer category into AI, and the AI ​​can automatically apply an appropriate monitoring method.

[0094] The monitoring unit can estimate the volunteer's emotion and adjust the importance of the progress status based on the estimated emotion of the volunteer. For example, if the volunteer is relaxed, the monitoring unit displays a detailed progress status. For example, if the volunteer is relaxed, the monitoring unit displays a detailed progress status. Furthermore, if the volunteer is stressed, the monitoring unit can display a concise progress status. For example, if the volunteer is stressed, the monitoring unit displays a concise progress status. Furthermore, if the volunteer is excited, the monitoring unit can display a visually appealing progress status. For example, if the volunteer is excited, the monitoring unit displays a visually appealing progress status. In this way, by adjusting the importance of the progress status based on the volunteer's emotion, important information can be provided to the volunteer. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring department can input the volunteers' emotions into the AI, which can then automatically adjust the importance of their progress.

[0095] During monitoring, the monitoring unit can analyze changes in progress based on the time period during which the volunteer was active. The monitoring unit can analyze changes in progress based on, for example, when the volunteer started the activity. For example, the monitoring unit can analyze changes in progress based on when the volunteer started the activity. The monitoring unit can also analyze changes in progress based on when the volunteer participated in a particular event or project. For example, the monitoring unit can analyze changes in progress based on when the volunteer participated in a particular event or project. The monitoring unit can also analyze changes in progress based on when the volunteer finished the activity. For example, the monitoring unit can analyze changes in progress based on when the volunteer finished the activity. This enables efficient monitoring by analyzing changes in progress based on the time period during which the volunteer was active. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the time period during which the volunteer was active into AI, and the AI ​​can automatically analyze changes in progress.

[0096] During monitoring, the monitoring unit can analyze the progress by referring to market data related to the volunteers. For example, the monitoring unit analyzes the progress by referring to data on the market in which the volunteers are active. For example, the monitoring unit analyzes the progress by referring to data on the market in which the volunteers are active. The monitoring unit can also analyze the progress by referring to market data on the region in which the volunteers are active. For example, the monitoring unit analyzes the progress by referring to market data on the region in which the volunteers are active. Furthermore, the monitoring unit can also analyze the progress by referring to market data on the field in which the volunteers are active. For example, the monitoring unit analyzes the progress by referring to market data on the field in which the volunteers are active. In this way, progress can be efficiently analyzed by referring to the market data related to the volunteers. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input market data related to the volunteers into AI, and the AI ​​can automatically analyze the progress.

[0097] The reallocation unit can estimate the emotion of the volunteer and adjust the reallocation method based on the estimated emotion of the volunteer. For example, if the volunteer is relaxed, the reallocation unit applies a normal reallocation method. For example, if the volunteer is relaxed, the reallocation unit applies the normal reallocation method. Furthermore, if the volunteer is stressed, the reallocation unit can prioritize reallocating less burdensome activities. For example, if the volunteer is stressed, the reallocation unit prioritizes reallocating less burdensome activities. Furthermore, if the volunteer is excited, the reallocation unit can prioritize reallocating challenging activities. For example, if the volunteer is excited, the reallocation unit prioritizes reallocating challenging activities. In this way, by adjusting the reallocation method based on the emotion of the volunteer, the burden on the volunteer can be reduced. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation unit may input the emotions of volunteers into AI, which may then automatically adjust the reallocation method.

[0098] When reallocating tasks, the reallocation unit can analyze the volunteer's past activity history and select an optimal reallocation method. For example, the reallocation unit reallocates a similar activity to a volunteer with a history of successful activities. For example, the reallocation unit reallocates a similar activity to a volunteer with a history of successful activities. The reallocation unit can also reallocate a different activity to a volunteer with a history of unsuccessful activities. For example, the reallocation unit reallocates a different activity to a volunteer with a history of unsuccessful activities. Furthermore, the reallocation unit can also reallocate a volunteer who has demonstrated a particular skill in the past to an activity that can utilize that skill. For example, the reallocation unit reallocates a volunteer who has demonstrated a particular skill in the past to an activity that can utilize that skill. This enables efficient reallocation by selecting an optimal reallocation method based on the volunteer's past activity history. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation department can input the volunteer's past activity history into the AI, which can then automatically select the optimal reallocation method.

[0099] The reallocation unit can customize the reallocation method based on the volunteer's current living situation. For example, if a volunteer is currently a student, the reallocation unit can reallocate them to student-oriented activities. For example, if a volunteer is currently a student, the reallocation unit can reallocate them to student-oriented activities. For example, if a volunteer is currently working, the reallocation unit can reallocate them to activities that can be combined with their work. For example, if a volunteer is currently working, the reallocation unit can reallocate them to activities that can be combined with their family life. For example, if a volunteer is currently married, the reallocation unit can reallocate them to activities that can be combined with their family life. In this way, the burden on volunteers can be reduced by customizing the reallocation method based on their current living situation. Some or all of the above processing in the reallocation unit may be performed using AI, for example, or without using AI. For example, the reallocation unit can input the volunteer's current living situation into the AI, allowing the AI ​​to automatically customize the reallocation method.

[0100] The reallocation unit can estimate the emotions of volunteers and determine reallocation priorities based on the estimated emotions. For example, if a volunteer is relaxed, the reallocation unit will reallocate them according to normal priorities. The reallocation unit can also prioritize reallocating less demanding activities if a volunteer is stressed. Furthermore, if a volunteer is excited, the reallocation unit can prioritize reallocating challenging activities. This allows for efficient reallocation by determining reallocation priorities based on the emotions of volunteers. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation unit may input the emotions of volunteers into AI, and the AI ​​may automatically determine reallocation priorities.

[0101] The reallocation unit can select the optimal reallocation method by considering the geographical location information of the volunteers during reallocation. For example, the reallocation unit can prioritize reallocating volunteers living in urban areas to activities in urban areas. For example, the reallocation unit can prioritize reallocating volunteers living in rural areas to activities in rural areas. For example, the reallocation unit can prioritize reallocating volunteers living in rural areas to activities in rural areas. Furthermore, the reallocation unit can prioritize reallocating volunteers living overseas to international activities. For example, the reallocation unit can prioritize reallocating volunteers living overseas to international activities. In this way, the optimal activities can be reallocated by considering the geographical location information of the volunteers. Some or all of the above processing in the reallocation unit may be performed using AI, for example, or without AI. For example, the reallocation unit can input the geographical location information of the volunteers into the AI, and the AI ​​can automatically select the optimal reallocation method.

[0102] During reallocation, the reallocation unit can analyze the social media activity of the volunteer and suggest a reallocation method. The reallocation unit, for example, reallocates related activities based on content frequently shared on social media. For example, the reallocation unit reallocates related activities based on content frequently shared on social media. The reallocation unit can also reallocate related activities based on organizations or events followed on social media. For example, the reallocation unit reallocates related activities based on organizations or events followed on social media. The reallocation unit can also reallocate related activities based on groups or communities joined on social media. For example, the reallocation unit reallocates related activities based on groups or communities joined on social media. In this way, by analyzing the social media activity of the volunteer, related activities can be efficiently reallocated. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation unit can input the social media activity of the volunteer into AI, and the AI ​​can automatically suggest a reallocation method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, allocation unit, monitoring unit, and reallocation unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and the specific processing unit 290 of the data processing device 12 and collects volunteer registration information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The allocation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and allocates volunteer activities based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12 and monitors the progress of volunteer activities. The reallocation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and reallocates volunteer activities based on the progress. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, allocation unit, monitoring unit, and reallocation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12 and collects volunteer registration information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The allocation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and allocates volunteer activities based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12 and monitors the progress of volunteer activities. The reallocation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and reallocates based on the progress. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, allocation unit, monitoring unit, and reallocation unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset terminal 314 and the specific processing unit 290 of the data processing device 12 and collects volunteer registration information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The allocation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and allocates volunteer activities based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12 and monitors the progress of volunteer activities. The reallocation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and reallocates volunteer activities based on the progress. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, allocation unit, monitoring unit, and reallocation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414 and the specific processing unit 290 of the data processing device 12 and collects volunteer registration information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The allocation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and allocates volunteer activities based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12 and monitors the progress of volunteer activities. The reallocation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and reallocates volunteer activities based on the progress.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The analysis unit can estimate the emotions of the volunteers and determine the analysis priorities based on the estimated emotions of the volunteers. For example, if the volunteers are relaxed, the analysis can be performed with normal priority. Furthermore, if the volunteers are stressed, it can prioritize less burdensome analyses. Furthermore, if the volunteers are excited, it can prioritize more challenging analyses. This enables efficient analysis by determining the analysis priorities based on the emotions of the volunteers. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the emotions of the volunteers into an AI, which can then automatically determine the analysis priorities.

[0105] The collection unit can analyze the volunteer's past activity history and select the optimal collection method. For example, for a volunteer who has frequently registered via online form in the past, the online form can be provided preferentially. Furthermore, for a volunteer who has frequently registered via telephone in the past, telephone registration can be suggested. Furthermore, for a volunteer who has frequently registered in person in the past, in-person registration can be recommended. This enables efficient information collection by selecting the optimal collection method based on the volunteer's past activity history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the volunteer's past activity history into AI, which can then automatically suggest the optimal collection method.

[0106] The allocation unit can estimate the emotions of the volunteers and adjust the allocation method based on the estimated emotions of the volunteers. For example, if the volunteers are relaxed, a normal allocation method can be applied. Furthermore, if the volunteers are stressed, less demanding activities can be prioritized for allocation. Furthermore, if the volunteers are excited, challenging activities can be prioritized for allocation. This reduces the burden on the volunteers by adjusting the allocation method based on the emotions of the volunteers. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the allocation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the allocation unit can input the emotions of the volunteers into an AI, and the AI ​​can automatically adjust the allocation method.

[0107] The monitoring unit can estimate the volunteer's emotions and adjust the progress display method based on the estimated volunteer emotions. For example, if the volunteer is relaxed, a detailed progress status can be displayed. If the volunteer is stressed, a concise progress status can be displayed. Furthermore, if the volunteer is excited, a visually appealing progress status can be displayed. By adjusting the progress display method based on the volunteer's emotions, it is possible to provide progress status that is easy for the volunteer to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using AI, for example, or without AI. For example, the monitoring unit can input the volunteer's emotions into AI, and the AI ​​can automatically adjust the progress display method.

[0108] The reallocation unit can estimate the emotions of the volunteers and adjust the reallocation method based on the estimated emotions of the volunteers. For example, if the volunteers are relaxed, a normal reallocation method can be applied. Furthermore, if the volunteers are stressed, less demanding activities can be prioritized for reallocation. Furthermore, if the volunteers are excited, more demanding activities can be prioritized for reallocation. Thus, by adjusting the reallocation method based on the emotions of the volunteers, the burden on the volunteers can be reduced. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reallocation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reallocation unit can input the emotions of the volunteers into an AI, and the AI ​​can automatically adjust the reallocation method.

[0109] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the volunteer. For example, if the volunteer lives in an urban area, activity information in the urban area can be prioritized. Also, if the volunteer lives in a rural area, activity information in the rural area can be prioritized. Furthermore, if the volunteer lives overseas, international activity information can be prioritized. In this way, highly relevant information can be efficiently collected by taking into account the geographical location information of the volunteer. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the volunteer into AI, and the AI ​​can automatically prioritize collecting highly relevant information.

[0110] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the volunteer's skills. For example, detailed analysis results can be provided to volunteers with advanced skills. Brief analysis results can also be provided to volunteers with basic skills. Furthermore, detailed analysis results related to a particular skill can be provided to volunteers who specialize in that skill. In this way, by adjusting the level of detail of the analysis based on the importance of the volunteer's skills, appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the volunteer's skills into AI, which then automatically adjusts the level of detail of the analysis.

[0111] The allocation unit can analyze the social media activity of the volunteer and allocate relevant activities during allocation. For example, the allocation unit can allocate relevant activities based on the content frequently shared on social media. The allocation unit can also allocate relevant activities based on organizations and events followed on social media. Furthermore, the allocation unit can allocate relevant activities based on groups and communities participated in on social media. In this way, by analyzing the social media activity of the volunteer, relevant activities can be allocated efficiently. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input the social media activity of the volunteer into AI, which can then automatically allocate relevant activities.

[0112] During monitoring, the monitoring unit can predict current progress by referring to past progress data. For example, it can predict current progress based on past progress data and notify the volunteer. It can also analyze past progress data and issue an alert if current progress is behind schedule. It can also evaluate whether current progress is on track by referring to past progress data. This allows current progress to be predicted by referring to past progress data, enabling efficient monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input past progress data into AI, which can then automatically predict current progress.

[0113] When reallocating tasks, the reallocation unit can analyze the volunteer's past activity history and select the optimal reallocation method. For example, a volunteer with a history of successful activities can be reallocated to a similar activity. A volunteer with a history of unsuccessful activities can also be reallocated to a different activity. Furthermore, a volunteer who has demonstrated specific skills in the past can be reallocated to an activity that utilizes those skills. This enables efficient reallocation by selecting the optimal reallocation method based on the volunteer's past activity history. Some or all of the above-described processing in the reallocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reallocation unit can input the volunteer's past activity history into AI, which can then automatically select the optimal reallocation method.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The collection department collects the volunteer registration information. The volunteer registration information includes skills, experience, desired activities, past activity history, and qualification information. The collection department collects the information entered by the volunteer through an online form and can also retrieve the history of past activities from a database. The collection department also verifies the qualification information provided by the volunteer and registers it in the database. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the volunteers' skills, experience, and desired activities, and assigns the most appropriate volunteer activities. For example, it uses AI to analyze skill sets and suggests appropriate activities. It also takes into account past activity history and schedules to make efficient assignments. Step 3: The allocation unit assigns volunteer activities based on the information analyzed by the analysis unit. For example, volunteers with medical skills are assigned medical support activities. The allocation is also efficient, taking into account the volunteers' desired activities and schedules. Step 4: The monitoring unit monitors the progress of the volunteer activities allocated by the allocation unit. For example, the monitoring unit can monitor the progress of the volunteers in real time from the time they start their activities to the time they finish them, and allocate the next activity to them when they finish their current activity. Step 5: The Reallocation Department reallocates volunteers based on the progress monitored by the Monitoring Department. For example, if a volunteer finishes an activity earlier than planned, the reallocation department will assign the next activity. The Reallocation Department also periodically evaluates volunteer satisfaction and uses it to improve the system.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0118] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0160] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] 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.

[0179] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects volunteer registration information; an analysis unit that analyzes the information collected by the collection unit; an allocation unit that allocates appropriate volunteer activities based on the information analyzed by the analysis unit; a monitoring unit that monitors the progress of the volunteer activities allocated by the allocation unit; a reallocation unit that reallocates the resources based on the progress monitored by the monitoring unit. A system characterized by:

2. The collecting unit Collect detailed data on volunteers' skills, experience, desired activities, past activities, and qualifications The system of claim 1 .

3. The analysis unit Analyze the collected information and assign appropriate volunteer activities The system of claim 1 .

4. The allocation unit Volunteers with medical skills will be assigned to medical support activities. The system of claim 1 .

5. The monitoring unit Monitor volunteer progress in real time The system of claim 1 .

6. The reallocation unit If a volunteer completes an activity earlier than planned, assign them to the next activity. The system of claim 1 .

7. The reallocation unit Regularly assess volunteer satisfaction to help improve the system The system of claim 1 .

8. The collecting unit Adjusting the timing of collecting registration information based on estimating volunteers' emotions The system of claim 1 .

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